Uses of Interface
com.opengamma.strata.data.MarketData
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Packages that use MarketData Package Description com.opengamma.strata.calc Calculates risk measures on trades, applies scenarios and manages market data.com.opengamma.strata.calc.marketdata Provides the ability to obtain market data and perform calibrations and scenario perturbations.com.opengamma.strata.calc.runner The calculation runner.com.opengamma.strata.data Basic types to model market data.com.opengamma.strata.data.scenario Basic types to model market data across scenarios.com.opengamma.strata.market.curve Definitions of curves.com.opengamma.strata.market.curve.node Curve nodes.com.opengamma.strata.measure.bond Base package for calculation functions.com.opengamma.strata.measure.capfloor Calculation functions for Ibor cap/floor products.com.opengamma.strata.measure.credit Calculation functions for credit products.com.opengamma.strata.measure.fxopt Calculation functions for FX option products.com.opengamma.strata.measure.index Calculation functions for index products.com.opengamma.strata.measure.rate Base package for calculation functions.com.opengamma.strata.measure.swaption Calculation functions for swaption products.com.opengamma.strata.pricer.credit Calculators for credit instruments, such as Credit Default Swap (CDS).com.opengamma.strata.pricer.curve Provides the ability to calibrate curves. -
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Uses of MarketData in com.opengamma.strata.calc
Methods in com.opengamma.strata.calc with parameters of type MarketData Modifier and Type Method Description Results
CalculationRunner. calculate(CalculationRules calculationRules, List<? extends CalculationTarget> targets, List<Column> columns, MarketData marketData, ReferenceData refData)
Performs calculations for a single set of market data.void
CalculationRunner. calculateAsync(CalculationRules calculationRules, List<? extends CalculationTarget> targets, List<Column> columns, MarketData marketData, ReferenceData refData, CalculationListener listener)
Performs calculations asynchronously for a single set of market data, invoking a listener as each calculation completes. -
Uses of MarketData in com.opengamma.strata.calc.marketdata
Classes in com.opengamma.strata.calc.marketdata that implement MarketData Modifier and Type Class Description class
BuiltMarketData
Market data that has been built.Methods in com.opengamma.strata.calc.marketdata with parameters of type MarketData Modifier and Type Method Description BuiltMarketData
MarketDataFactory. create(MarketDataRequirements requirements, MarketDataConfig marketDataConfig, MarketData suppliedData, ReferenceData refData)
Builds a set of market data.BuiltScenarioMarketData
MarketDataFactory. createMultiScenario(MarketDataRequirements requirements, MarketDataConfig marketDataConfig, MarketData suppliedData, ReferenceData refData, ScenarioDefinition scenarioDefinition)
Builds the market data required for performing calculations for a set of scenarios. -
Uses of MarketData in com.opengamma.strata.calc.runner
Methods in com.opengamma.strata.calc.runner with parameters of type MarketData Modifier and Type Method Description Results
CalculationTaskRunner. calculate(CalculationTasks tasks, MarketData marketData, ReferenceData refData)
Performs calculations for a single set of market data.void
CalculationTaskRunner. calculateAsync(CalculationTasks tasks, MarketData marketData, ReferenceData refData, CalculationListener listener)
Performs calculations asynchronously for a single set of market data, invoking a listener as each calculation completes.FxRateProvider
FxRateLookup. fxRateProvider(MarketData marketData)
Obtains an FX rate provider based on the specified market data. -
Uses of MarketData in com.opengamma.strata.data
Classes in com.opengamma.strata.data that implement MarketData Modifier and Type Class Description class
ImmutableMarketData
An immutable set of market dataMethods in com.opengamma.strata.data that return MarketData Modifier and Type Method Description MarketData
ImmutableMarketData. combinedWith(MarketData other)
default MarketData
MarketData. combinedWith(MarketData other)
Combines this market data with another.static MarketData
MarketData. empty(LocalDate valuationDate)
Obtains an instance containing no market data.MarketData
MarketDataFxRateProvider. getMarketData()
Gets the market data that provides the FX rates.static MarketData
MarketData. of(LocalDate valuationDate, Map<? extends MarketDataId<?>,?> values)
Obtains an instance from a valuation date and map of values.static MarketData
MarketData. of(LocalDate valuationDate, Map<? extends MarketDataId<?>,?> values, Map<? extends ObservableId,LocalDateDoubleTimeSeries> timeSeries)
Obtains an instance from a valuation date, map of values and time-series.default <T> MarketData
MarketData. withValue(MarketDataId<T> id, T value)
Returns a copy of this market data with the specified value.Methods in com.opengamma.strata.data with parameters of type MarketData Modifier and Type Method Description ImmutableMarketDataBuilder
ImmutableMarketDataBuilder. add(MarketData other)
Adds all time series and values from another market data instance.MarketData
ImmutableMarketData. combinedWith(MarketData other)
default MarketData
MarketData. combinedWith(MarketData other)
Combines this market data with another.static MarketDataFxRateProvider
MarketDataFxRateProvider. of(MarketData marketData)
Obtains an instance which takes FX rates from the market data.static MarketDataFxRateProvider
MarketDataFxRateProvider. of(MarketData marketData, ObservableSource fxRatesSource)
Obtains an instance which takes FX rates from the market data, specifying the source of FX rates.static MarketDataFxRateProvider
MarketDataFxRateProvider. of(MarketData marketData, ObservableSource fxRatesSource, Currency triangulationCurrency)
Obtains an instance which takes FX rates from the market data, specifying the source of FX rates. -
Uses of MarketData in com.opengamma.strata.data.scenario
Methods in com.opengamma.strata.data.scenario that return MarketData Modifier and Type Method Description default MarketData
ScenarioMarketData. scenario(int scenarioIndex)
Returns market data for a single scenario.Methods in com.opengamma.strata.data.scenario that return types with arguments of type MarketData Modifier and Type Method Description default Stream<MarketData>
ScenarioMarketData. scenarios()
Returns a stream of market data, one for each scenario.Methods in com.opengamma.strata.data.scenario with parameters of type MarketData Modifier and Type Method Description static ScenarioMarketData
ScenarioMarketData. of(int scenarioCount, MarketData marketData)
Obtains an instance by wrapping a single set of market data. -
Uses of MarketData in com.opengamma.strata.market.curve
Methods in com.opengamma.strata.market.curve with parameters of type MarketData Modifier and Type Method Description ImmutableList<Double>
CurveDefinition. initialGuess(MarketData marketData)
Gets the list of all initial guesses.double
CurveNode. initialGuess(MarketData marketData, ValueType valueType)
Gets the initial guess used for calibrating the node.default ImmutableList<Double>
NodalCurveDefinition. initialGuess(MarketData marketData)
ImmutableList<Double>
ParameterizedFunctionalCurveDefinition. initialGuess(MarketData marketData)
ImmutableList<Double>
RatesCurveGroupDefinition. initialGuesses(MarketData marketData)
Gets the list of all initial guesses.ResolvedTrade
CurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
Creates a resolved trade representing the instrument at the node.ImmutableList<ResolvedTrade>
RatesCurveGroupDefinition. resolvedTrades(MarketData marketData, ReferenceData refData)
Creates a list of trades representing the instrument at each node.Trade
CurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
Creates a trade representing the instrument at the node. -
Uses of MarketData in com.opengamma.strata.market.curve.node
Methods in com.opengamma.strata.market.curve.node with parameters of type MarketData Modifier and Type Method Description double
FixedIborSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
FixedInflationSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
FixedOvernightSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
FraCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
FxSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
IborFixingDepositCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
IborFutureCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
IborIborSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
OvernightFutureCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
OvernightIborSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
TermDepositCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
ThreeLegBasisSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
double
XCcyIborIborSwapCurveNode. initialGuess(MarketData marketData, ValueType valueType)
ResolvedSwapTrade
FixedIborSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
FixedInflationSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
FixedOvernightSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedFraTrade
FraCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedFxSwapTrade
FxSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedIborFixingDepositTrade
IborFixingDepositCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedIborFutureTrade
IborFutureCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
IborIborSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedOvernightFutureTrade
OvernightFutureCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
OvernightIborSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedTermDepositTrade
TermDepositCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
ThreeLegBasisSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
ResolvedSwapTrade
XCcyIborIborSwapCurveNode. resolvedTrade(double quantity, MarketData marketData, ReferenceData refData)
CdsIndexCalibrationTrade
CdsIndexIsdaCreditCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
Creates a trade representing the CDS index at the node.CdsCalibrationTrade
CdsIsdaCreditCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
Creates a trade representing the CDS at the node.SwapTrade
FixedIborSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
FixedInflationSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
FixedOvernightSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
FraTrade
FraCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
FxSwapTrade
FxSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
IborFixingDepositTrade
IborFixingDepositCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
IborFutureTrade
IborFutureCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
IborIborSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
OvernightFutureTrade
OvernightFutureCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
OvernightIborSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
TermDepositTrade
TermDepositCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
ThreeLegBasisSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
SwapTrade
XCcyIborIborSwapCurveNode. trade(double quantity, MarketData marketData, ReferenceData refData)
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Uses of MarketData in com.opengamma.strata.measure.bond
Methods in com.opengamma.strata.measure.bond that return MarketData Modifier and Type Method Description MarketData
BondFutureOptionMarketData. getMarketData()
Gets the market data.MarketData
LegalEntityDiscountingMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.bond with parameters of type MarketData Modifier and Type Method Description LegalEntityDiscountingProvider
LegalEntityDiscountingMarketDataLookup. discountingProvider(MarketData marketData)
Obtains a discounting provider based on the specified market data.default BondFutureOptionMarketData
BondFutureOptionMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.default LegalEntityDiscountingMarketData
LegalEntityDiscountingMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.BondFutureVolatilities
BondFutureOptionMarketDataLookup. volatilities(SecurityId securityId, MarketData marketData)
Obtains bond future volatilities based on the specified market data.BondFutureOptionMarketData
BondFutureOptionMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data.LegalEntityDiscountingMarketData
LegalEntityDiscountingMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.capfloor
Methods in com.opengamma.strata.measure.capfloor that return MarketData Modifier and Type Method Description MarketData
IborCapFloorMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.capfloor with parameters of type MarketData Modifier and Type Method Description default IborCapFloorMarketData
IborCapFloorMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.IborCapletFloorletVolatilities
IborCapFloorMarketDataLookup. volatilities(IborIndex index, MarketData marketData)
Obtains cap/floor volatilities based on the specified market data.IborCapFloorMarketData
IborCapFloorMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.credit
Methods in com.opengamma.strata.measure.credit that return MarketData Modifier and Type Method Description MarketData
CreditRatesMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.credit with parameters of type MarketData Modifier and Type Method Description CreditRatesProvider
CreditRatesMarketDataLookup. creditRatesProvider(MarketData marketData)
Obtains credit rates provider based on the specified market data.default CreditRatesMarketData
CreditRatesMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.CreditRatesMarketData
CreditRatesMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.fxopt
Methods in com.opengamma.strata.measure.fxopt that return MarketData Modifier and Type Method Description MarketData
FxOptionMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.fxopt with parameters of type MarketData Modifier and Type Method Description default FxOptionMarketData
FxOptionMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.FxOptionVolatilities
FxOptionMarketDataLookup. volatilities(CurrencyPair currencyPair, MarketData marketData)
Obtains FX options volatilities based on the specified market data.FxOptionMarketData
FxOptionMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.index
Methods in com.opengamma.strata.measure.index that return MarketData Modifier and Type Method Description MarketData
IborFutureOptionMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.index with parameters of type MarketData Modifier and Type Method Description default IborFutureOptionMarketData
IborFutureOptionMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.IborFutureOptionVolatilities
IborFutureOptionMarketDataLookup. volatilities(IborIndex index, MarketData marketData)
Obtains Ibor future option volatilities based on the specified market data.IborFutureOptionMarketData
IborFutureOptionMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.rate
Methods in com.opengamma.strata.measure.rate that return MarketData Modifier and Type Method Description MarketData
RatesMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.rate with parameters of type MarketData Modifier and Type Method Description FxRateProvider
RatesMarketDataLookup. fxRateProvider(MarketData marketData)
Obtains an FX rate provider based on the specified market data.default RatesMarketData
RatesMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.RatesProvider
RatesMarketDataLookup. ratesProvider(MarketData marketData)
Obtains a rates provider based on the specified market data.RatesMarketData
RatesMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.measure.swaption
Methods in com.opengamma.strata.measure.swaption that return MarketData Modifier and Type Method Description MarketData
SwaptionMarketData. getMarketData()
Gets the market data.Methods in com.opengamma.strata.measure.swaption with parameters of type MarketData Modifier and Type Method Description default SwaptionMarketData
SwaptionMarketDataLookup. marketDataView(MarketData marketData)
Obtains a filtered view of the complete set of market data.SwaptionVolatilities
SwaptionMarketDataLookup. volatilities(RateIndex index, MarketData marketData)
Obtains swaption volatilities based on the specified market data.SwaptionMarketData
SwaptionMarketData. withMarketData(MarketData marketData)
Returns a copy of this instance with the specified market data. -
Uses of MarketData in com.opengamma.strata.pricer.credit
Methods in com.opengamma.strata.pricer.credit with parameters of type MarketData Modifier and Type Method Description LegalEntitySurvivalProbabilities
IsdaCompliantCreditCurveCalibrator. calibrate(IsdaCreditCurveDefinition curveDefinition, MarketData marketData, ImmutableCreditRatesProvider ratesProvider, ReferenceData refData)
Calibrates the ISDA compliant credit curve to the market data.IsdaCreditDiscountFactors
IsdaCompliantDiscountCurveCalibrator. calibrate(IsdaCreditCurveDefinition curveDefinition, MarketData marketData, ReferenceData refData)
Calibrates the ISDA compliant discount curve to the market data.LegalEntitySurvivalProbabilities
IsdaCompliantIndexCurveCalibrator. calibrate(IsdaCreditCurveDefinition curveDefinition, MarketData marketData, ImmutableCreditRatesProvider ratesProvider, ReferenceData refData)
Calibrates the index curve to the market data. -
Uses of MarketData in com.opengamma.strata.pricer.curve
Methods in com.opengamma.strata.pricer.curve with parameters of type MarketData Modifier and Type Method Description ImmutableRatesProvider
RatesCurveCalibrator. calibrate(RatesCurveGroupDefinition curveGroupDefn, MarketData marketData, ReferenceData refData)
Calibrates a single curve group, containing one or more curves.ImmutableRatesProvider
RatesCurveCalibrator. calibrate(List<RatesCurveGroupDefinition> allGroupDefns, ImmutableRatesProvider knownData, MarketData marketData, ReferenceData refData)
Calibrates a list of curve groups, each containing one or more curves.
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Missing xhtml:div
Explanation
If the value of "type" is "xhtml", the content of the Text construct MUST be a single XHTML div element, and SHOULD be suitable for handling as XHTML.
Note: the XHTML div element itself is not considered part of the content
Solution
If your content is intended to be XHTML, insert

before your XHTML content, and

after it.
If, however, your content is simple text or html, change the type attribute to match your content. Here are a few example titles, each intended to convey the simple string AT&T
as a title:
<title>AT&T</title>
<title type="text">AT&T</title>
<title type="html">AT&T</title>
<title type="html"><![CDATA[AT&T]]></title>
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This is a list of spacecraft in the Culture novels and short stories by Iain M. Banks. All important Culture ship classes are listed, with a select number of names belonging to individual ships within each class.
Most ships in this list are members of The Culture, the hybrid society featured in many of these novels. In this setting, each Culture ship is the "body", so to speak, of an artificial intelligence with a distinctive personality, called "Minds". Many of these ships are significant characters in the novels.
| Abbr. | Class | Description | Selected List of Names |
|---|---|---|---|
| GSV | General Systems Vehicle | Mobile habitats and factory ships, largest and most capable Culture ship type. Build and host other ships, including smaller Systems Vehicles. May be home to billions of people. Some militarised and used as giant warships during the Idiran War. They are described in the books as holograms of the whole Culture, each one containing all the knowledge and productive capacity needed to reconstruct the civilization in case of catastrophe. |
|
| MSV | Medium Systems Vehicle | Smaller versions of the above. Sometimes former GSVs downgraded as larger GSV classes were developed. |
|
| LSV | Limited Systems Vehicle | Smaller versions of the above. Sometimes former GSVs or MSVs downgraded as larger GSV classes were developed. |
|
| GCV | General Contact Vehicle | Contact craft intermediate in size and function between Systems Vehicles and Contact Units. Capabilities include building other ships. |
|
| GCU | General Contact Unit | Standard Contact craft, used for exploration and for studying and interacting with other societies. Considerable combat capability, but less than dedicated warships. |
|
| (D)GOU | Demilitarised GOU | GOUs with main armament removed, used as courier ships. | |
| LCU | Limited Contact Unit | Smaller, less capable versions of the above. |
|
| (D)LOU | Demilitarised LOU | LOUs with main armament removed, used as courier ships. | |
| ROU | Rapid Offensive Unit | Dedicated fast warships. |
|
| (D)ROU | Demilitarised ROU | ROUs with main armament removed, used as courier ships. | |
| VFP | Very Fast Picket | Another name for demilitarised ROUs. | |
| GOU | General Offensive Unit | Dedicated warships. |
|
| FP | Fast Picket | Another name for demilitarised GOUs or LOUs. | |
| LOU | Limited Offensive Unit | Dedicated warships, smaller and less capable than GOUs and ROUs. |
|
| SL | Superlifter | High-speed tugs, consisting largely of engine and used to accelerate or decelerate other ships. Some temporarily militarised as a stop-gap in the early stages of the Idiran War. |
|


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Chocolate Pizza
Posted on 15 DEC 2013 / DESSERTS
The best food blog on the web
Posted on 15 DEC 2013 / DESSERTS
For the fig-swirl: Melt butter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside and let cool completely.
Ice Cream: In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm. Whisk in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacturer instructions, i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts. Continue to process for +/- 10 minutes. Pour semi frozen mixture into a pyrex dish or glass Tupperware. Freeze for at least two hours before serving.
Vanessa Stevenson
Food enthusiast, photography fan. Add a pinch of raw foodism and that's pretty much who I am.


| Package | Description |
|---|---|
| unitControlModule.stateFactories | |
| unitControlModule.unitWrappers |
| Modifier and Type | Class and Description |
|---|---|
class |
StateFactoryDefault
SimpleStateFactory.java --- A basic StateFactory containing to all basic Unit
actions and states.
|
class |
StateFactoryTerran_Goliath
StateFactoryTerran_Goliath.java --- A StateFactory used for generating all
necessary Objects for the Terran_Goliath.
|
class |
StateFactoryTerran_Marine
StateFactoryTerran_Marine.java --- A StateFactory used for generating all
necessary Objects for the Terran_Marine.
|
class |
StateFactoryTerran_Medic
StateFactoryTerran_Medic.java --- A StateFactory used for generating all
necessary Objects for the Terran_Medic.
|
class |
StateFactoryTerran_ScienceVessel
StateFactoryTerran_ScienceVessel.java --- A StateFactory used for generating
all necessary Objects for the Terran_Science_Vessel.
|
class |
StateFactoryTerran_SCV
StateFactoryTerran_SCV.java --- A StateFactory used for generating all
necessary Objects for the Terran_SCV.
|
class |
StateFactoryTerran_SiegeTank
SiegeTankStateFactory.java --- A StateFactory used for generating all
necessary Objects for the Terran_SiegeTank.
|
class |
StateFactoryTerran_SiegeTank_SiegeMode
StateFactoryTerran_SiegeTank_SiegeMode.java --- StateFactory used for
generating all necessary Objects for the Terran_SiegeTank_SiegeMode.
|
class |
StateFactoryTerran_Vulture
StateFactoryTerran_Vulture.java --- A StateFactory used for generating all
necessary Objects for the Terran_Vulture.
|
class |
StateFactoryTerran_Wraith
StateFactoryTerran_Wraith.java --- A StateFactory used for generating all
necessary Objects for the Terran_Wraith.
|
class |
StateFactoryWorker
StateFactoryWorker.java --- A StateFactory for a general worker Unit.
|
| Modifier and Type | Field and Description |
|---|---|
private StateFactory |
PlayerUnit.stateFactory |
| Modifier and Type | Method and Description |
|---|---|
protected StateFactory |
PlayerUnitTerran_Wraith.createFactory() |
protected StateFactory |
PlayerUnitTerran_Vulture.createFactory() |
protected StateFactory |
PlayerUnitTerran_SiegeTank_SiegeMode.createFactory() |
protected StateFactory |
PlayerUnitTerran_SiegeTank.createFactory() |
protected StateFactory |
PlayerUnitTerran_SCV.createFactory() |
protected StateFactory |
PlayerUnitTerran_ScienceVessel.createFactory() |
protected StateFactory |
PlayerUnitTerran_Medic.createFactory() |
protected StateFactory |
PlayerUnitTerran_Marine.createFactory() |
protected StateFactory |
PlayerUnitTerran_Goliath.createFactory() |
protected abstract StateFactory |
PlayerUnit.createFactory()
Function for defining the StateFactory that will be used to determine the
available actions, the world-and goal-states as well as the associated
updaters.
|


Suffolk's biggest pub quiz is just days away - here's how to enter
by Brad JonesIt features local celebrity quizmasters and will be held simultaneously at 14 locations in Suffolk - four of which still have places available.
The Save our Suffolk Estuary Quiz is on Friday November 22, at 6.45pm, and will raise money for the Alde and Ore Estuary Trust.
The four pubs which still have quiz team spaces are: The Froize in Chillesford (with quizmaster, actress Aline Mowat), The Ship at Blaxhall (with Charlie Haylock and councillor Ray Herring), the Jolly Sailor in Orford (with Terry Hunt), and the Town Hall in Orford (with Iestyn Edwards - aka Madame Galina).
More than 600 people are expected to take part, with the questions written by actor Max Norman.
To book a place, visit the website - it is £5 a head, and the hope is the event will raise more than the £43,000 generated at the last AOET quiz in 2017.


sJc
I plan to put stuff here
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About
Theme Styles
Theme | BlackDoc
Currently v2.0.1
© 2021. All rights reserved.
Eberron Campaign Notes
Misc notes
The party
Thoradin
NPCs
Antor
Aril
Diorn
Ghost
Locations
Thrane
Aundair
Marketplace
Tower Vigilant
Factions
Arcanix Institute
Church of the Nightmother
House Orien
Misc notes
Owe 30gp to Joy/Thoradin/Sylvia/Azra/Miaulin
Diorn
gives us a
Clock of Arachnida
Thoradin
speaks to the soul in his sword
Says there’s another way into the pit
It is Dagon’s entry into this world
She tried to close the pit
Dagon can’t cross through yet?
“It does not do to change the past. But perhaps it could be shut again”
Reached
Marketplace
, seems to have a
Church of the Nightmother
presence
Miaulin spied, found a dwarf who resembles me
One of the dwarves there recognizes me from the war
Just left Thrane
Cursed sword belonged to a friend of Sorlir
Probably contains her soul or something
Dagon definitely up to something at the black pit
Attacked by ghosts inside the giant dead warforged
The party
Player
Character
Class
Sam
Thrace Thunderfist
Artificer (Armourer) 5 Ranger 1
Alex
Thoradin Woodwalker
Fighter (Battlemaster)
Josh
Azra Byle
Cleric (Life)
Chris
Miaulin
Rogue
Ellie
Joy
Warlock
Vanda
Sylvia Adrieth
Bard
???
Blink dog
Good boi
Thoradin
Has a cursed sword
May have been a bayonet from a gun of the giant warforged
Belonged to
Aril
Probably contains her soul or something
Might have been designed to capture the souls of those it kills
NPCs
Antor
Cunt
Researched healing magic as part of the
Arcanix Institute
Became obsessed with using magic to rewind time
Wants to rewind Khorvaire to before the war
Aril
Subject of
Antor
’s experimental healing magic using time reversal
Might have found some way to slow/pause time in the black pit
Or maybe just a way of preserving her soul…
“This moment is forever”
Diorn
Dwarf, member of
Church of the Nightmother
Thrace goofed by trying to accound a fictitious history with him
Outranked Thrace during their military service
Ghost
Friend of Sorlir
Knows Joy, fellow Tiefling
Locations
Thrane
Sucks
Threw us in prison under trumped up charges of necromancy
From the train incident with
House Orien
Aundair
“They’re keeping an eye on things going south”
Marketplace
Doesn’t actually seem to be a marketplace there anymore
In fact, not much more than
Church of the Nightmother
here.
Tower Vigilant
Hollowed out body of an ancient giant warforged
Factions
Arcanix Institute
Sucks
Based in
Aundair
Seems to have created some technorexes
Church of the Nightmother
Band of weirdos fresh from Rose Cross (no not
those ones
)
House Orien
“House Orien would like to take this opportunity to inform you that we suck”


ARA - Ansible Run Analysis
ARA records Ansible playbook runs seamlessly to make them easier to visualize, understand and troubleshoot.
{% if playbooks and playbooks > 0 %}
Here's the data that ARA is making available to help you:
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{{ playbooks }}
Playbook runs
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{{ tasks }}
Tasks
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{{ task_results }}
Task results
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Hosts
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Hosts with gathered facts
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Playbook, role and task files
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Records


TokenRequest implements ArrayCopyable, Arrayable, Jsonable Uses ArrayTrait, JsonTrait
Request to Authlete's /api/auth/token API.
An entity body of a token request may contain the client ID (client_id
)
and the client secret (client_secret
) along with other request parameters
as described in
2.3.1. Client Password
of RFC 6749. If client credentials
are contained both in the Authorization
header and in the entity body,
they must be identical. If they do not match, Authlete's /api/auth/token
API reports an error. It is not an error of your authorization server
implementation but an error of the client application.
Interfaces, Classes and Traits
- ArrayCopyable
- Interface to declare instances can be converted into/from arrays.
- Arrayable
- Interface to declare that instances can be converted into an array.
- Jsonable
- Interface to declare that instances can be converted into JSON strings.
Table of Contents
- copyFromArray() : mixed
- Copy the content of the given array into this object.
- copyToArray() : mixed
- Copy the content of this object into the given array.
- fromArray() : static
- Convert an array into an instance of this class.
- fromJson() : static
- Convert a JSON string into an instance of this class.
- getClientCertificate() : string
- Get the client certificate from the MTLS of the token request from the client application.
- getClientCertificatePath() : array<string|int, string>
- Get the certificate path presented by the client during client authentication.
- getClientId() : string
- Get the client ID extracted from the Authorization header of the token request from the client application.
- getClientSecret() : string
- Get the client secret extracted from the Authorization header of the token request from the client application.
- getDpop() : string
- Get the `DPoP` header presented by the client during the request to the token endpoint. This header contains a signed JWT which includes the public key that is paired with the private key used to sign it.
- getHtm() : string
- Get the HTTP method of the token request. This property is used to validate the `DPoP` header.
- getHtu() : string
- Get the URL of the token endpoint. This property is used to validate the `DPoP` header.
- getParameters() : string
- Get the token request parameters which the token endpoint implementation of your authorization server received from the client application.
- getProperties() : array<string|int, Property>
- Get the properties to be associated with an access token which may be issued as a result of the token request.
- setClientCertificate() : TokenRequest
- Set the client certificate from the MTLS of the token request from the client application.
- setClientCertificatePath() : TokenRequest
- Set the certificate path presented by the client during client authentication.
- setClientId() : TokenRequest
- Set the client ID extracted from the Authorization header of the token request from the client application.
- setClientSecret() : TokenRequest
- Set the client secret extracted from the Authorization header of the token request from the client application.
- setDpop() : TokenRequest
- Set the `DPoP` header presented by the client during the request to the token endpoint. This header contains a signed JWT which includes the public key that is paired with the private key used to sign it.
- setHtm() : TokenRequest
- Set the HTTP method of the token request. This property is used to validate the `DPoP` header.
- setHtu() : TokenRequest
- Set the URL of the token endpoint. This property is used to validate the `DPoP` header.
- setParameters() : TokenRequest
- Set the token request parameters which the token endpoint implementation of your authorization server received from the client application.
- setProperties() : TokenRequest
- Set the properties to be associated with an access token which may be issued as a result of the token request.
- toArray() : array<string|int, mixed>
- Convert this object into an array.
- toJson() : string
- Convert this object into a JSON string.
Methods
copyFromArray()
Copy the content of the given array into this object.
public
copyFromArray(array<string|int, mixed> &$array) : mixed
Parameters
- $array : array<string|int, mixed>
Return values
mixed —copyToArray()
Copy the content of this object into the given array.
public
copyToArray(array<string|int, mixed> &$array) : mixed
Parameters
- $array : array<string|int, mixed>
Return values
mixed —fromArray()
Convert an array into an instance of this class.
public
static fromArray([array<string|int, mixed> $array = null ]) : static
This static function returns a new instance of this class.
If $array
is null
, null
is returned.
Parameters
- $array : array<string|int, mixed> = null
-
An array
Return values
static —An instance of this class.
fromJson()
Convert a JSON string into an instance of this class.
public
static fromJson(string $json) : static
This static function returns a new instance of this class.
If $json
is null
or the type of $json
is not string
,
null
is returned.
Parameters
- $json : string
-
A JSON string.
Return values
static —An instance of this class.
getClientCertificate()
Get the client certificate from the MTLS of the token request from the client application.
public
getClientCertificate() : string
Return values
string —The client certificate.
getClientCertificatePath()
Get the certificate path presented by the client during client authentication.
public
getClientCertificatePath() : array<string|int, string>
Tags
Return values
array<string|int, string> —Certificates in PEM format.
getClientId()
Get the client ID extracted from the Authorization header of the token request from the client application.
public
getClientId() : string
Return values
string —The client ID extracted from the Authorization
header.
getClientSecret()
Get the client secret extracted from the Authorization header of the token request from the client application.
public
getClientSecret() : string
Return values
string —The client secret extracted from the Authorization
header.
getDpop()
Get the `DPoP` header presented by the client during the request to the token endpoint. This header contains a signed JWT which includes the public key that is paired with the private key used to sign it.
public
getDpop() : string
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Tags
Return values
string —The value of the DPoP
header.
getHtm()
Get the HTTP method of the token request. This property is used to validate the `DPoP` header.
public
getHtm() : string
In normal cases, the value is POST
. When this request parameter is
omitted, POST
is used as the default value.
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Tags
Return values
string —The HTTP method. For example, POST
.
getHtu()
Get the URL of the token endpoint. This property is used to validate the `DPoP` header.
public
getHtu() : string
If this request parameter is omitted, the tokenEndpoint
property of
the Service
is used as the default value.
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Tags
Return values
string —The URL of the token endpoint.
getParameters()
Get the token request parameters which the token endpoint implementation of your authorization server received from the client application.
public
getParameters() : string
Return values
string —The request parameters of the token request.
getProperties()
Get the properties to be associated with an access token which may be issued as a result of the token request.
public
getProperties() : array<string|int, Property>
Return values
array<string|int, Property> —Properties associated with the access token.
setClientCertificate()
Set the client certificate from the MTLS of the token request from the client application.
public
setClientCertificate(string $certificate) : TokenRequest
Parameters
- $certificate : string
-
The client certificate.
Return values
TokenRequest —$this
object.
setClientCertificatePath()
Set the certificate path presented by the client during client authentication.
public
setClientCertificatePath([array<string|int, string> $path = null ]) : TokenRequest
Parameters
- $path : array<string|int, string> = null
-
Certificates in PEM format.
Tags
Return values
TokenRequest —$this
object.
setClientId()
Set the client ID extracted from the Authorization header of the token request from the client application.
public
setClientId(string $clientId) : TokenRequest
If the token endpoint of the authorization server supports Basic
Authentication as a means of
client authentication,
and if the request from the client application contained its client ID
in the Authorization
header, the value should be extracted from there
and set as the value of this request parameter.
Parameters
- $clientId : string
-
The client ID extracted from the
Authorization
header.
Return values
TokenRequest —$this
object.
setClientSecret()
Set the client secret extracted from the Authorization header of the token request from the client application.
public
setClientSecret(string $clientSecret) : TokenRequest
If the token endpoint of the authorization server supports Basic
Authentication as a means of
client authentication,
and if the request from the client application contained its client
secret in the Authorization
header, the value should be extracted
from there and set as the value of this request parameter.
Parameters
- $clientSecret : string
-
The client secret extracted from the
Authorization
header.
Return values
TokenRequest —$this
object.
setDpop()
Set the `DPoP` header presented by the client during the request to the token endpoint. This header contains a signed JWT which includes the public key that is paired with the private key used to sign it.
public
setDpop(string $dpop) : TokenRequest
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Parameters
- $dpop : string
-
The value of the
DPoP
header.
Tags
Return values
TokenRequest —$this
object.
setHtm()
Set the HTTP method of the token request. This property is used to validate the `DPoP` header.
public
setHtm(string $htm) : TokenRequest
In normal cases, the value is POST
. When this request parameter is
omitted, POST
is used as the default value.
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Parameters
- $htm : string
-
The HTTP method. For example,
POST
.
Tags
Return values
TokenRequest —$this
object.
setHtu()
Set the URL of the token endpoint. This property is used to validate the `DPoP` header.
public
setHtu(string $htu) : TokenRequest
If this request parameter is omitted, the tokenEndpoint
property of
the Service
is used as the default value.
See "OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer (DPoP)" for details.
Parameters
- $htu : string
-
The URL of the token endpoint.
Tags
Return values
TokenRequest —$this
object.
setParameters()
Set the token request parameters which the token endpoint implementation of your authorization server received from the client application.
public
setParameters(string $parameters) : TokenRequest
The value of this request parameter is the entire entity body (which is
formatted in application/x-www-form-urlencoded
) of the request from
the client application. This request parameter is mandatory.
Parameters
- $parameters : string
-
The request parameters of the token request.
Return values
TokenRequest —$this
object.
setProperties()
Set the properties to be associated with an access token which may be issued as a result of the token request.
public
setProperties([array<string|int, Property> $properties = null ]) : TokenRequest
If the value of the grant_type
parameter contained in the token
request from the client application is authorization_code
, properties
set by this request parameter will be added as the extra properties of
a newly created access token. The extra properties specified when the
authorization code was issued (using
AuthorizationIssueRequest::setProperties()
) will also be used, but
their values will be overwritten if the extra properties set by this
request parameter have the same keys. In other words, extra properties
contained in this request will be merged into existing extra properties
which are associated with the authorization code.
Otherwise, if the value of the grant_type
parameter contained in the
token request from the client application is refresh_token
, properties
set by this request parameter will be added to the existing extra
properties of the corresponding access token. Extra properties having
the same keys will be overwritten in the same manner as the case of
grant_type=authorization_code
.
Otherwise, if the value of the grant_type
parameter contained in the
token request from the client application is client_credentials
,
properties set by this request parameter will be used simply as extra
properties of a newly created access token. Because
Client Credentials flow
does not have a preceding authorization request, merging extra
properties will not be performed. This is different from the cases of
grant_type=authorization_code
and grant_type=refresh_token
.
In other cases (grant_type=password
), properties set by this request
parameter will not be used. When you want to associate extra properties
with an access token which is issued by
Resource Owner Password Credentials flow,
use TokenIssueRequest::setProperties()
instead.
Keys of extra properties will be used as labels of top-level entries in
a JSON response containing an access token which is returned from an
authorization server. An example is example_parameter
, which you can
find in 5.1. Successful Response
of RFC 6749. The following code
snippet is an example to set one extra property having
example_parameter
as its key and example_value
as its value.
$properties = array(
new Property("example_parameter", "example_value")
);
$request.setProperties($properties);
Keys listed below should not be used and they would be ignored on Authlete side even if they were used. It's because they are reserved in RFC 6749 and OpenID Connect Core 1.0.
-
token_type
-
expires_in
-
refresh_token
-
scope
-
error
-
error_description
-
error_uri
-
id_token
Note that there is an upper limit on the total size of extra properties. On the server side, the properties will be (1) converted to a multidimensional string array, (2) converted to JSON, (3) encrypted by AES/CBC/PKCS5Padding, (4) encoded by base64url, and then stored into the database. The length of the resultant string must not exceed 65,535 in bytes. This is the upper limit, but we think it is big enough.
Parameters
- $properties : array<string|int, Property> = null
-
Properties to be associated with the access token.
Return values
TokenRequest —$this
object.
toArray()
Convert this object into an array.
public
toArray() : array<string|int, mixed>
Return values
array<string|int, mixed> —An array.
toJson()
Convert this object into a JSON string.
public
toJson(int $options) : string
Parameters
- $options : int
-
Options passed to
json_encode()
. This parameter is optional and its default value is 0.
Return values
string —A JSON string.


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Dr. Sivarama Krishnan Rajaraman holds the position of Deep Learning Research Scientist at the Lister Hill National Center for Biomedical Communications, U. S. National Institutes of Health. Dr. Rajaraman received his Ph.D. in Information and Communication engineering from Anna University, India. At the U. S. National Library of Medicine, he pursued his postdoctoral studies that involved R&D projects in medical image analysis toward developing robust, cost-effective solutions for clinical decision-making. These projects apply computational sciences and engineering methods toward advancing life science applications and aid healthcare professionals in low-cost, high-quality decision-making at the point of care screening/diagnostics. He is a versatile researcher with expertise in machine learning, particularly deep learning, biomedical image analysis/understanding, and computer vision. He has more than 15 years of experience in academia where he taught core and allied subjects in biomedical engineering. He has authored several national and international journal and conference publications in his area of expertise. Dr. Rajaraman is a Life Member of the Society of Photo-optical Instrumentation Engineers (SPIE), a regular member of the Institute of Electrical and Electronics Engineers (IEEE), IEEE Engineering in Medicine & Biology Society (EMBS), and the Biomedical Engineering Society (BMES).
| Programming Languages | Python |
| Scripting Languages | MATLAB |
| APIs and Libraries | OpenCV, Tensorflow, Keras, Scikit-learn |
| Operating Systems | Widows, Unix/Linux, and Google Android |
| Document Markup Language | LaTeX |
| Other Tools | Keras, Tensorflow, Weka, Microsoft Office, IBM SPSS |
Location: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, USA
Tools and resources: Matlab, Python, Keras DL framework with Theano/Tensorflow backend, Singularity and Docker containers for packaging scientific workflows, software, libraries, and data, IBM SPSS version 25.0 for statistical validation, Biowulf (95,000+ core/30 PB Linux cluster) NIH high performance computing (HPC) facility, NVIDIA DGX-1 workstation with Ubuntu Linux host OS and V100 GPUs for accelerated DL applications, Windows, and Linux desktop computers with multiple NVIDIA GEFORCE GTX 1080 GPUs.
Scientific Highlights: Novel Coronavirus disease 2019 (COVID-19) is caused by the new Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that originated in Wuhan in the Hubei province in China and has spread worldwide. The World Health Organization (WHO) declared the outbreak a pandemic on March 11, 2020. The disease is rapidly affecting worldwide population with statistics quickly falling out of date. As of April 12, 2020, there are over 1.8 million confirmed cases reported globally with over 100,000 reported deaths. Lung disease that causes difficulty in breathing has been reported as an early indicator along with hyperthermia in the COVID-19 infected population. The lung abnormalities caused by non-2019-nCOV viruses are observed as peripheral or hilar and visually similar to, yet often distinct from, viral pneumonia and other bacterial pathogens. In this project, we propose several measures to effectively detect COVID-19 disease manifestations in Chest X-rays.
Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK (2020) Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs. PLoS ONE 15(11): e0242301. https://doi.org/10.1371/journal.pone.0242301.
Rajaraman S, Sornapudi S, Alderson PO, Folio LR, Antani SK. Interpreting Deep Ensemble Learning through Radiologist Annotations for COVID-19 Detection in Chest Radiographs. medRxiv 2<IP_ADDRESS>54385; doi: https://doi.org/10.1101/2<IP_ADDRESS>54385.
Rajaraman S, Antani, S. Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays. Diagnostics 2020, 10, 358.
Rajaraman S, Siegelman J, Alderson PO, Folio LS, Folio LR and Antani SK, "Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays," in IEEE Access, vol. 8, pp.<PHONE_NUMBER>50, 2020, doi: 10.1109/ACCESS.2020.3003810.
According to the World Health Organization (WHO), tuberculosis (TB) remains the most deadly infectious disease in the world. Analysis of frontal chest X-rays (CXR) is one of the most popular methods for initial TB screening, however, the method is impacted by the lack of experts for screening chest radiographs. State-of-the-art CADx software typically is based on ML approaches that use hand-engineered features, demanding expertise in analyzing the input variances and accounting for the changes in size, background, angle, and position of the region of interest (ROI) on the underlying medical imagery. More automatic DL tools have demonstrated promising results in a wide range of ML applications. Convolutional Neural Networks (CNN), a class of DL models, have gained research prominence in image classification, detection, and localization tasks because they are highly scalable and deliver superior results with end-to-end feature extraction and classification. In this study, we evaluated the performance of CNN based DL models for population screening using frontal CXRs. We worked with the public and private CXR collections, archived at NLM. We observed that CNNs are a promising feature extracting tool for medical imagery including the automated diagnosis of TB from chest radiographs but emphasized the importance of large data sets for the most accurate classification. We also performed Ensemble learning (EL) to construct non-linear decision making functions and improve visual recognition by combining multiple models toward obtaining promising predictions. EL methods allow blending intelligence from different learning algorithms. We created a stacked ensemble of that worked with hand-engineered and deep CNN features toward the process of improving the detection accuracy to advance research in TB detection. The results obtained with the stacked model ensemble were found to be promising compared to the state-of-the-art in TB detection. Training metrics are not always reliable and standard visualization techniques like feature and activation visualization, saliency maps, partial occlusion measurements, and class activation maps (CAM) help in providing a sanity check on the learning strategy. Saliency maps provide intuition of attention and help to analyze the most influential features. Partial occlusion studies help in identifying whether the trained model is actually classifying based on the task-specific features and not the surrounding context and gaining a clear understanding of the learned behavior of the model for the underlying task. CAM helps to visualize discriminative image regions used by the trained model to identify an image category. We performed these visualizations to understand the learned parameters, model behavior and optimized the model architecture and hyper-parameters for improved learning.
Rajaraman S, Candemir S, Xue Z, Alderson P, Kohli M, Abuya J, Thoma GR, Antani SK. A novel stacked generalization of models for improved TB detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC 2018), Honolulu, Hawaii, 2018. pp. 718-721.
Rajaraman S, Antani SK, Candemir S, Xue Z, Abuya J, Kohli M, Alderson P, Thoma GR. Comparing deep learning models for population screening using chest radiography. Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751E (27 February 2018).
Rajaraman S, Antani SK, Xue Z, Candemir S, Jaeger S, Thoma GR. Visualizing abnormalities in chest radiographs through salient network activations in Deep Learning. Proc. IEEE Life Sciences Conference (LSC), Sydney, Australia, 2017. pp. 71-74, DOI:10.1109/LSC.2017.8268146.
Rajaraman S, Candemir S, Xue Z, Alderson P, Thoma G, Antani SK. In Santosh KC et al. (Eds.). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. (pp. 1-26). New York, NY: CRC Press, Taylor & Francis Group.
Pneumonia affects 7% of the global population, resulting in 2 million pediatric deaths every year. Chest X-ray (CXR) analysis is routinely performed to diagnose the disease. Computer-aided diagnostic (CADx) tools aim to supplement decision-making. These tools process the handcrafted and/or convolutional neural network (CNN) extracted image features for visual recognition. However, CNNs are perceived as black boxes since their performance lack explanations. This is a serious bottleneck in applications involving medical screening/diagnosis since poorly interpreted model behavior could adversely affect the clinical decision. In this study, we evaluate, visualize, and explain the performance of customized CNNs to detect pneumonia and further differentiate between bacterial and viral types in pediatric CXRs. We present a novel visualization strategy to localize the region of interest (ROI) that is considered relevant for model predictions across all the inputs that belong to an expected class. We statistically validate the models’ performance toward the underlying tasks. We observe that the customized VGG16 model achieved better performance in detecting the disease and distinguishing between bacterial and viral pneumonia respectively.
Rajaraman S, Candemir S, Kim I, Thoma GR, Antani SK. Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs. Appl. Sci. 2018, 8, 1715.
Rajaraman S, Candemir S, Thoma G, Antani S. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs, Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500S (13 March 2019); doi: 10.1117/12.2512752.
In this study, we investigated the usage of deep CNN models for automatic detection of cardiomegaly in digital CXRs. We fine-tuned several deep CNN architectures to detect the presence of cardiomegaly in CXRs. Next, we introduced an alternative fine-tuning approach where we first fully trained an architecture with a large-scale CXR collection and fine-tuned the model with cardiomegaly CXRs. As test sets, we used two publicly available datasets, the NLM-Indiana Collection, and the NIH-CXR datasets. Based on our preliminary results, we observed the following: (a) data-based approach produced better results than prior rule-based approaches developed for cardiomegaly detection; (b) our preliminary test with alternative fine-tuning approach was promising; (iii) there was a correlation between the Softmax probability and severity of the disease.
Candemir S, Rajaraman S, Thoma GR, Antani SK. Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation. Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.
In this work, we classified the spine x-ray images according to image characteristics that exhibited gender. We compared classification results of customized CNNs with pre-trained CNN models and fine-tuned with the spine images. We developed a method for extracting the ROI in the cervical spine images using a content-based image retrieval method and evaluated the effect of ROI cropping. We verified our method using the images in the NHANES II dataset hosted by NLM.
Xue Z, Rajaraman S, Long LR, Antani SK, Thoma GR. Gender Detection from Spine X-ray Images Using Deep Learning. Proc. IEEE International Symposium on Computer-Based Medical Systems (CBMS), Karlstad, Sweden, 2018. pp. 54-58, DOI:10.1109/CBMS.2018.00017.
In this study, we performed a multi-modality classification with deep CNN models. We worked with 12 different modality images including X-rays, CT, MRI, PET, photos, illustrations, light microscope, electron microscope, fluorescence microscope, endoscope, fundus, and ultrasound. We found the optimal model through cross-validation studies and visualized the weights, activations and CAM in the trained model toward understanding its learning strategy. We collected data from NLM OpenI ®, Cancer Imaging Archive and other open-source medical image libraries.
Kim I, Rajaraman, S, Antani S. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. MDPI Diagnostics 2019, 9, 38. Rajaraman S, Antani S. (2019) Visualizing Salient Network Activations in Convolutional Neural Networks for Medical Image Modality Classification. In: Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol. 1036. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-13-9184-2_4
Respiratory diseases account for the majority of deaths and disabilities across the world. Chest X-ray (CXR) analysis remains a common part of the protocol for confirming lung abnormalities. However, the ratio of expert radiologists to the radiograph volume is continuing to increase leading to severe backlogs and interpretation delays. These issues can be mitigated by a computer-aided diagnostic (CADx) system to supplement decision-making and improve throughput while preserving and possibly improving the standard-of-care. These systems reportedly use handcrafted features and/or data-driven algorithms like deep learning (DL) to learn underlying data distributions. The remarkable success of convolutional neural networks (CNN) toward image recognition tasks has made them a promising choice for automated medical image analyses. Ensemble learning combines the predictions of multiple learning algorithms to construct complex, non-linear functions and improve performance, robustness, and generalization. This study aims to construct and assess the performance of an ensemble of machine learning (ML) models applied to the challenge of classifying normal and abnormal CXRs and significantly reducing the diagnostic load of radiologists and primary-care physicians.
Rajaraman S, Sornapudi S, Kohli M, Antani SK. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 3689 – 3692. Rajaraman S, Antani S. A combination of multi-model knowledge transfer and ensemble learning for improved TB detection in chest radiographs. Submitted to SPIE Journal of Medical Imaging
Medical image analysis typically includes several visual tasks such as image enhancement, segmentation, and classification. These tasks are often implemented through separate deep learning models pretrained on large-scale natural images with visual characteristics different than medical images. This traditional approach for analysis is not efficient because it involves unnecessary training repetitions for each task and requires each task to have a relatively large amount of annotated data. Therefore, we believe new approaches need to be adopted for efficient medical image analysis. Recognizing the increased interest in developing approaches that achieve high performance with minimal resource utilization and amount of annotated data, in the first study, we propose a multi-task approach, called unified modality-specific representation (U-Rep), to enhance the analysis of medical image tasks with heterogeneous annotations. The proposed U-Rep is a modality-specific approach that learns, using unlabeled data, the features of a specific medical imaging modality. The trained modality-specific U-Rep is then shared to simultaneously learn different target tasks with limited amounts of heterogeneous annotations. Our experimental results using two medical image datasets show that the proposed approach improves the performance and generalization of the target tasks. They also show that the proposed approach prevents unnecessary repetitions of training task-specific models in isolation, which leads to a reduction in the training time and parameters. We believe the proposed approach for analysis would tread a path toward promising future research in medical image analysis, especially for tasks with limited amounts of heterogeneous annotations. Medical images are acquired at different resolutions based on clinical goals or available technology. In general, however, high-resolution images with ﬁne structural details are preferred for visual task analysis. Recognizing this signiﬁcance, several deep learning networks have been proposed to enhance medical images for reliable automated interpretation. These deep networks are often computationally complex and require a massive number of parameters, which restrict them to highly capable computing platforms with large memory banks. In the second study, we propose an efﬁcient deep learning approach, called Hydra, which simultaneously reduces computational complexity and improves performance. The Hydra consists of a trunk and several computing heads. The trunk is a super-resolution model that learns the mapping from low-resolution to high-resolution images. It has a simple architecture that is trained using multiple scales at once to minimize a proposed learning-loss function. We also propose to append multiple task-speciﬁc heads to the trained Hydra trunk for simultaneous learning of multiple visualtasks in medical images. The Hydra is evaluated on publicly available chest X-ray image collections to perform image enhancement, lung segmentation, and abnormality classiﬁcation. Our experimental results support our claims and demonstrate that the proposed approach can improve the performance of super-resolution and visual task analysis in medical images at a remarkably reduced computational cost.
Alzamzmi GA, Rajaraman S, Antani SK. Unified Representation Learning for Efficient Medical Image Analysis 2020, [Online] URL: https://arxiv.org/abs/2006.11223.
Alzamzmi GA, Rajaraman S, Antani SK. Accelerating Super-Resolution and Visual Task Analysis in Medical Images. Appl. Sci. 2020, 10, 4282.
I am a member of the team offering the Radiology Preprocessor course. The main objective of the publicly available multi-center radiology preprocessing (RP) course is to teach radiology preprocessors (RPs), the basic principles of radiology modalities and how they provide required quantification in clinical trials including target lesion selection and consistent measurement. RPs are sometimes referred to as "post-processors" that measure lesions after or separate from radiologists required for clinical trials, or "scribes" commonly assisting radiologists in private practice or centers without residents in measuring lesions, identifying incidental findings while acting as worklist triage. Literature studies reveal that RP augmented radiologist workflow can result in improved target lesion concordance with oncologists on patients in clinical trials, faster radiologist recognition of clinical findings, resulting in earlier referring clinician notification. We welcome feedback from core labs to optimize the educational value of the course. By request, we will be adding more information and chest CT to include infectious diseases based on need for consistency in labeling pathology for machine learning training. To help promote advances in Artificial Intelligence in radiology, this course additionally helps bridge the gap between coders, engineers and radiologists.
Medical imaging datasets are available in sparse due to privacy issues and the high cost of annotations. Data augmentation is a widely used practice in machine learning that helps in reduced bias, overfitting and improved model generalization. In this study, we evaluated the performance of traditional augmentation methods and Generative Adversarial Networks (GANs) toward synthesizing data to enrich training and observed for possible improvement in performance toward abnormality detection in chest radiographs. GANs provide an avenue to capture the biological variance in the underlying image data and synthesize realistic samples. We observed that the traditional data augmentation strategies including unsharp masking, Gaussian smoothing and minimum filtering helped in improved performance by mimicking the biological variance in the unseen test data and helped the model to optimally converge in comparison to GAN generated synthetic images.
Ganesan P, Rajaraman S, Long LR, Ghoraani B, Antani SK. Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 841 – 844.
Location: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, USA
Tools: Matlab, Python, Keras DL framework with Theano/Tensorflow backend, Singularity and Docker containers for packaging scientific workflows, software, libraries, and data, IBM SPSS version 25.0 for statistical validation.
Computation Resources: Matlab, Python, Keras DL framework with Theano/Tensorflow backend, Singularity and Docker containers for packaging scientific workflows, software, libraries, and data, IBM SPSS version 25.0 for statistical validation.
Scientific Highlights: Malaria is caused by parasites that are transmitted through the bites of infected Anopheles mosquitoes. With about 200 million cases worldwide and about 400,000 deaths per year, malaria is a major burden on global health. The current standard method for malaria diagnosis in the field is light microscopy of blood films. However, microscopic diagnostics is not standardized and depends heavily on the experience and skill of the microscopists. To improve malaria diagnostics, LHNCBC, in collaboration with NIH’s National Institute of Allergy and Infectious Diseases (NIAID) and Mahidol-Oxford University, is developing a mobile application that runs on an Android phone for parasite detection and counting in blood films. Giemsa-stained thin blood smear slides from 150 P. falciparum-infected and 50 healthy patients were collected and photographed at Chittagong Medical College Hospital, Bangladesh. The smartphone’s built-in camera acquired images of slides for each microscopic field of view. The images were manually annotated by an expert slide reader at the Mahidol-Oxford Tropical Medicine Research Unit in Bangkok, Thailand. The de-identified images and annotations are archived at NLM. We applied a level-set based algorithm to detect and segment the red blood cells. We proposed the advantages offered through visualizing the features and activations in customized CNNs applied to the challenge of malaria cell classification. We evaluated the performance of customized and pre-trained CNN based DL models as feature extractors toward classifying parasitized and uninfected cells to aid in improved disease screening. We experimentally determined the optimal model layers for feature extraction from the underlying data. Statistical validation of the results demonstrated the use of pre-trained CNNs as a promising tool for feature extraction for this purpose. CNN models are perceived as black boxes since there is a lack of understanding of the learned behavior from the underlying task of interest. This lack of transparency is a serious drawback, particularly in applications involving medical screening and diagnosis since poorly understood model behavior could adversely impact the process of decision-making. In this study, we highlighted the advantages offered through visualizing and understanding the weights, saliencies, CAM and ROI localizations in CNNs applied to the challenge of classifying parasitized and uninfected cells to aid in malaria screening. We provided an explanation on what made these models arrive at the classification decision. We evaluated the performance of different CNNs and statistically validated with a large-scale clinical dataset at the patient level. We observed that CNN models could serve as a promising classification tool for this purpose.
Rajaraman S, Jaeger S, Antani SK. (2019) Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977 https://doi.org/10.7717/peerj.6977.
Rajaraman S, Antani SK, Jaeger S. Visualizing Deep Learning Activations for Improved Malaria Cell Classification. Proceedings of the First Workshop in Medical Informatics and Healthcare (MIH 2017), Proceedings of Machine Learning Research (PMLR), v. 69, p. 40-47.
Rajaraman S, Antani SK, Poostchi Mohammadabadi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ6:e4568 https://doi.org/10.7717/peerj.4568.
Rajaraman S, Silamut K, Hossain MA, Ersoy I, Maude RJ, Jaeger S, Thoma GR, Antani SK. Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images. J. Med. Imag. 5(3), 034501 (2018), doi: 10.1117/1.JMI.5.3.034501.
Tools: Matlab, IBM SPSS version 23.0 for statistical validation
Computation Resources: Desktop with Windows OS.
Scientific Highlights: Skin tumor is the uncontrolled growth of skin cells which may be cancerous. We aimed to develop a CADx tool for diagnosing skin tumors. The dermal images of three types including benign tumor, malignant melanoma, and normal moles were collected and preprocessed to remove hair cells. A contour based level set technique was applied to segment the lesion. Morphological features were extracted and a random subset method was used to perform feature selection. The classification was performed using classifiers including a multi-layer perceptron and support vector machine (SVM). The classification accuracy was found to be 94% and 96% respectively. We statistically validated the performance of the classifiers using one way ANOVA and Tukey post-hoc analyses.
Thamizhvani TR, Lakshmanan S, Rajaraman S. Computer Aided Diagnosis of Skin Tumours from Dermal Images. Hemanth D., Smys S. (eds) Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. Thamizhvani TR, Lakshmanan S, Rajaraman S. Mobile application-based computer-aided diagnosis of skin tumours from dermal images, The Imaging Science Journal, 66:6, 382-391, 2018, DOI: 10.1080/13682199.2018.1492682.
Location: Anna University, Chennai, India
Tools: Matlab, IBM SPSS version 23.0 for statistical validation
Computation Resources: Desktop with Windows OS.
Scientific Highlights: Karyotyping is a specialized procedure that helps diagnosing the genetic abnormalities and is one of the fundamental techniques employed for studying the genetic information. Cytogeneticists examine the karyotypes to identify the presence of chromosomal pairs, and missing or rearranged genetic materials in the chromosomes. Automatic karyotyping packages are accounted to be 70% efficient, as suggested by the cytogenetic experts. However, these packages are found to have errors in counting due to the presence of unwanted components including stain, debris and interphase cells, chromosomal misplacement, chromosomal inversion and rejection that occurs due to occlusion of chromosomes. In this study, we attempted to resolve these limitations and improve the efficiency of automatic karyotyping systems. We proposed a novel, efficient method based on multi-directional block ranking to remove the interphase cells without affecting the metaphase spread of chromosomes. We also proposed a robust algorithm based on evolutionary computation methods to identify and resolve occlusion in an occluded chromosomal cluster. This reduced errors in an automatic karyotyping system due to incorrect identification of partially or totally hidden metaphase chromosomes. We also proposed a novel, efficient, similarity-based classifier that used only three features as used by the cytogeneticists in manual diagnosis of genetic disorders. The effectiveness of the work was evaluated with the standardized karyotype data obtained from Center for Medical Genetics, Kilpauk, Chennai, and BIOIMLAB, Laboratory of Medical Imaging, University of Padova, Italy
Rajaraman S, Vaidyanathan G, Chokkalingam A. Performance Evaluation of Bio-Inspired Optimization Algorithms in Resolving Chromosomal Occlusions. Journal of Medical Imaging and health Informatics, vol. 5, pp. 264-271, March 2015.
Rajaraman S, Chokkalingam A. Classification of Denver System of Chromosomes using Similarity Classifier guided by OWA Operators. Current Bioinformatics, vol. 9, no. 5, pp. 499-508, July 2014.
Rajaraman S, Chokkalingam A. Performance Evaluation of Bio-Inspired Optimization Algorithms in resolving Chromosomal Occlusions. Proceedings of the IEEE International Conference on Control Instrumentation Communication and Computational Technologies (ICCICCT 2014), pp. 57-64, Noorul Islam University, Kanyakumari, India, July 1– 11th 2014.
Rajaraman S, Vaidyanathan G, Chokkalingam A. Performance Evaluation of Nature – Inspired Optimization Techniques in Disentangling Text Pattern Overlaps. Journal of Multiple valued Logic and Soft Computing, vol. 23, no. 5-6, pp. 503-527, June 2014.
Rajaraman S, Chokkalingam A. Occlusion Resolving in Text Patterns. Proceedings of the International Conference on Emerging Trends in Science Engineering and Technology (ICETSET 2014), pp. 197-201, Jerusalem College of Engineering, India, April 18th, 2014.
Rajaraman S, Chokkalingam A. Lukaseiwicz logic based fuzzy similarity classifier for Denver group chromosomal classification. Biosci. J., Uberlandia, vol. 30, no. 3, pp. 843-852, March 2014.
Rajaraman S, Chokkalingam A. Chromosomal Edge Detection using Modified Bacterial Foraging Algorithm. International Journal of Bio-Science and Bio-Technology, vol. 6, no. 1, pp. 111-122, February 2014.
Rajaraman S, Vaidyanathan G, Chokkalingam A. Segmentation and Removal of Interphase Cells from Chromosome Images using Multidirectional Block Ranking. International Journal of Bio-Science and Bio-Technology, vol. 5, no. 3, pp. 79-92, June 2013.
Rajaraman S, Chokkalingam A. Connected Components Labeling and Extraction Based Interphase Removal from Chromosome Images. International Journal of Bio-Science and Bio-Technology, vol. 5, no. 1, pp. 81-90, February 2013.
| Year | Course | Offered by |
|---|---|---|
| 2021 | NIH Implicit Bias | NIH Learning Management System (LMS), NIH, Bethesda, Maryland, USA |
| 2021 | Amazon AWS Fundementals | Amazon LLC |
| 2020 | Excel 2016 formulas | Computer training program, Centre for information technology, National Institutes of Health (NIH), Bethesda,Maryland, USA |
| 2020 | Excel 2016 Pivot tables | -DO- |
| 2020 | Excel 2016 data analysis | -DO- |
| 2020 | Microsoft Outlook tips and tricks | -DO- |
| 2020 | Excel 2016 database features | -DO- |
| 2020 | NIH Anti-harassment training | NIH Office of Equity, Diversity, and Inclusion, NIH,Bethesda, Maryland, USA |
| 2020 | NIH Coronavirus Safety Guidance | NIH Learning Management System (LMS), NIH, Bethesda, Maryland, USA |
| 2019 | NIH Anti-harassment training | NIH Office of Equity, Diversity, and Inclusion, NIH,Bethesda, Maryland, USA |
| 2019 | NIH online technology transfer training | NIH Learning Management System (LMS), NIH, Bethesda, Maryland, USA |
| 2018 | NIH online technology transfer training | NIH Learning Management System (LMS), NIH, Bethesda,Maryland, USA |
| 2018 | NIH Continuity of operations training | NIH Office Of Management (OOM), NIH, Bethesda, Maryland, USA |
| 2018 | Introduction to Data Science with Python | DataCamp E-Learning, New York, USA |
| 2017 | CANcer Distributed Learning Environment (CANDLE) | National Cancer Institute (NCI) and the Department of Energy (DOE), NIH, Bethesda, Maryland, USA |
| 2017 | NIH online technology transfer training | NIH Learning Management System (LMS), NIH, Bethesda,Maryland, USA |
| 2001 | Postgraduate diploma in computer applications (PGDCA) | National Institute of Information Technology (NIIT), India |
Lifetime member of the international society for optics and photonics (SPIE)
Regular member of the Institute of Electrical and Electronics Engineers (IEEE) and the Engineers in Medicine and Biology Society (EMBS) for the year 2019-2020
Professional member of the Biomedical Engineering Society (BMES)
| Name of the Journal | Publisher |
|---|---|
| Electronics (Web of Science IF: 2.412) (Topics Board) | Multidisciplinary Digital Publishing Institute (MDPI), Switzerland |
| American Journal of Neural Networks and Applications | Science Publishing Group, USA |
| Artificial intelligence research journal | Bio-Byword Scientific Publishing, Australia |
| Name of the Journal | Publisher |
|---|---|
| The LANCET Digital Health | The LANCET |
| Scientific Reports | Nature journals |
| Digital Medicine | Nature journals |
| PLOS Neglected Tropical Diseases | PLOS |
| ACM Transactions on Multimedia Computing, Communications and Applications | ACM |
| ACM Transactions on Management Information Systems | ACM |
| International Journal of Urology | Wiley Online Library |
| Concurrency and Computation: Practice and Experience | Wiley Online Library |
| Expert Systems | Wiley Online Library |
| International Journal for Numerical Methods in Biomedical Engineering | Wiley Online Library |
| International Journal of Imaging Systems and Technology | Wiley Online Library |
| Journal of Clinical Laboratory Analysis | Wiley Online Library |
| Rapid Reviews: COVID-19 (RR: C19) | MIT Press and UC Berkeley |
| Nephrology | ASPN |
| Transactions on Fuzzy Systems | IEEE |
| EEE/ACM Transactions on Computational Biology and Bioinformatics | IEEE |
| IEEE Transactions on Radiation and Plasma Medical Sciences | IEEE |
| IEEE Transactions on Cognitive and Developmental Systems | IEEE |
| Journal of Electromagnetics, RF, and Microwaves in Medicine and Biology | IEEE |
| Journal of Translational Engineering in Health and Medicine | IEEE |
| Transactions on Medical Imaging | IEEE |
| Transactions on Network Science and Engineering | IEEE |
| Access | IEEE |
| Journal of Biomedical And Health Informatics | IEEE |
| Sensors | IEEE |
| IEEE Wireless Communications Magazine | IEEE |
| Systems | IEEE |
| Biomedical Physics & Engineering Express | IOP |
| SciNotes | IOP |
| Annals of Saudi Medicine | ASM |
| BMC Malaria Journal | BioMed Central |
| BMC Medical Informatics and Decision Making | BioMed Central |
| BMC Medical Imaging | BioMed Central |
| Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization | Taylor & Francis |
| Journal of Biomolecular Structure and Dynamics | Taylor & Francis |
| Journal of Experimental & Theoretical Artificial Intelligence | Taylor & Francis |
| Information Fusion | Elsevier |
| Industrial Crops & Products | Elsevier |
| Results in Physics | Elsevier |
| Applied Soft Computing | Elsevier |
| Biomedical Signal Processing and Control | Elsevier |
| Measurement | Elsevier |
| Pattern Recognition Journal | Elsevier |
| Translational Research | Elsevier |
| Computers and Electronics in Agriculture | Elsevier |
| Computers in Biology and Medicine | Elsevier |
| Neurocomputing | Elsevier |
| Computers and Electrical Engineering | Elsevier |
| SLAS Technology | SAGE |
| The Open Biomedical Engineering Journal | Bentham Science |
| Processes | MDPI |
| Symmetry | MDPI |
| Brain Sciences | MDPI |
| Healthcare | MDPI |
| Diagnosis | MDPI |
| Machine Learning and Knowledge Extraction | MDPI |
| Applied Sciences | MDPI |
| Algorithms | MDPI |
| Materials | MDPI |
| Children | MDPI |
| Electronics | MDPI |
| Processes | MDPI |
| Mathematics | MDPI |
| Journal of Intelligent Systems | De Gruyter |
| International Journal of Applied Mathematics and Computer Sciences | De Gruyter |
| International Journal of Machine Learning and Cybernetics | Springer |
| Multimedia Tools and Applications | Springer |
| Journal of Medical Systems | Springer |
| European Journal of Nuclear Medicine and Molecular Imaging. | Springer |
| IET Journal of Research. | IET |
| International Journal of Engineering Research and Technology | IRP House |
| ELECTRICA | İstanbul University-Cerrah paşa Faculty of Engineering |
Mentored students at the undergraduate and postgraduate levels in electronics, communication, and biomedical engineering discipline.
Guided several projects at the undergraduate and postgraduate levels. Organized several workshops, seminars, and faculty development programs, national and international conferences.
Served as a senior member in several institutional accreditation committees including National Board of Accreditation (NBA) and National Assessment and Accreditation Council (NAAC)
Rajaraman S, (Most recent) Rajaraman S, Candemir S, Xue Z, Alderson P, Thoma G, Antani SK. In Santosh KC et al. (Eds.). Medical Imaging: Artificial Intelligence, Image Recognition, and Machine Learning Techniques. (pp. 1-26). New York, NY: CRC Press, Taylor & Francis Group.
Rajaraman S, Jaeger S, Antani SK. (2019) Performance evaluation of deep neural ensembles toward malaria parasite detection in thin-blood smear images. PeerJ 7:e6977 https://doi.org/10.7717/peerj.6977.
Kim I, Rajaraman, S Antani S. Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities. MDPI Diagnostics 2019, 9, 38.
Rajaraman S, Candemir S, Kim I, Thoma GR, Antani SK. Visualization and Interpretation of Convolutional Neural Network Predictions in Detecting Pneumonia in Pediatric Chest Radiographs. Appl. Sci. 2018, 8, 1715.
Thamizhvani TR, Lakshmanan Rajaraman S, Mobile application-based computer-aided diagnosis of skin tumours from dermal images, The Imaging Science Journal, 66:6, 382-391, 2018, DOI: 10.1080/13682199.2018.1492682.
Rajaraman S, Silamut K, Hossain MA, Ersoy I, Maude RJ, Jaeger S, Thoma GR, Antani SK. Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images. J. Med. Imag. 5(3), 034501 (2018), doi: 10.1117/1.JMI.5.3.034501.
Rajaraman S, Antani SK, Poostchi Mohammadabadi M, Silamut K, Hossain MA, Maude RJ, Jaeger S, Thoma GR. (2018) Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images. PeerJ6:e4568 https://doi.org/10.7717/peerj.4568.
Ganesan P, Rajaraman S, S, Long LR, Ghoraani B, Antani SK. Assessment of Data Augmentation Strategies Toward Performance Improvement of Abnormality Classification in Chest Radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 841 – 844.
Rajaraman S, , Sornapudi S, Kohli M, Antani SK. Assessment of an ensemble of machine learning models toward abnormality detection in chest radiographs. Proc. IEEE Engineering in Medicine and Biology Conference (EMBC), Berlin, Germany, 23 – 27 July 2019. pp. 3689 – 3692.
Rajaraman, S Antani S. (2019) Visualizing Salient Network Activations in Convolutional Neural Networks for Medical Image Modality Classification. In: Santosh K., Hegadi R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol. 1036. Springer, Singapore. DOI: https://doi.org/10.1007/978-981-13-9184-2_4
Rajaraman S, , Candemir S, Thoma G, Antani S. Visualizing and explaining deep learning predictions for pneumonia detection in pediatric chest radiographs, Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109500S (13 March 2019); doi: 10.1117/12.2512752. Candemir S, Rajaraman S, Thoma GR, Antani SK. Deep Learning for Grading Cardiomegaly Severity in Chest X-rays: An Investigation. Proc. IEEE Life Sciences Conference (LSC 2018), Montreal, Quebec, Canada, 28 – 30 October 2018. pp. 109-113.


Reducing Greenhouse Gas Emissions
We are already experiencing the effects of climate change. Temperatures are rising, snow and rainfall patterns are shifting, and more extreme climate events—like heavy rainstorms and record high temperatures—are already taking place. These changes are linked to the climbing levels of carbon dioxide and other greenhouse gases in our atmosphere, caused by human activities such as burning fossil fuels for energy.
By taking action to reduce emissions of the greenhouse gas pollution that warms our planet, we can reduce the risks we will face from future climate change. EPA, businesses, and individuals all have an important role to play.
EPA is supporting the Presidents' Climate Action Plan and addressing climate change through standards that reduce carbon pollution and support a strong economy.
In 2014 alone, over 19,000 organizations and millions of Americans partnered with EPA to reduce greenhouse gas emissions, cut wasted energy, and save money.
Everyone can play a part in reducing greenhouse gas emissions. Learning about the issues is the first step in protecting our planet for future generations.
- Measure your emissions with our Household Carbon Footprint Calculator
- Take simple steps and learn What You Can Do about Climate Change
- Switch to Green Power
- Choose ENERGY STAR products


A coverage of a casual look with Yoav in the nature. Photo shoot and styling tips with the blue color, the must have color for men...


Font File Formats
- PostScript Type1
- Supplement (discussion of multiple master fonts & counter hints)
- Format
- PostScript unicode character names
- PostScript Language Reference Manual
- PostScript Multiple Master
- Type1 MM format specification (in the Type1 Supplement)
- Design considerations
- Naming requirements
- Type2 (In March of 2000, Adobe removed multiple master support from Type2 and CFF files)
- Type2 MM format specification (In OBSOLETE type2 spec)
- CFF MM format specification (In OBSOLETE CFF spec)
- CID keyed fonts
- PostSript Type2
- Compact Font Format Specification (CFF)
- For more information see under OpenType fonts
- PostScript Type3
- PostScript Language Reference Manual 3.0 (see section 5.7)
- PostScript Type14 (Chameleon)
- The PLRM (5.8.1) documents that this font format is undocumented.
- PostScript Type42
- Adobe Feature File (fea)
- (FontForge's implementationof this format is a superset of what Adobe accepts, and a superset of what Adobe documents. Neither can completely describe opentype. Adobe claims they will update the feat spec in late 2007).
- AFM
- PFM
- I can't find microsoft's docs for pfm files any more, I think the format may be obsolete having been replaced by ntf.
- Adobe's notes on PFM files for two byte fonts
- Third Party description
- NTF
- This format is supposed to replace the pfm files above in windows >2000. I can't find any docs on it.
- BDF
- X11 Long Font Descriptorspec defines standard X BDF Properties
- ABF -- Binary format
- Extensions to BDF for greymap support
- True Type Standard
(Sadly different sources have slightly different definitions of less important parts of the standard, be warned)- Apple (I find Apple's prose difficult, and sometimes misleading. I suggest using a different source when possible)
- Microsoft
- random useful site
- TTC -- True Type Font Collection
- Apple Advanced Typography extensions to TrueType
- Apple distortable font (variation tables) -- vaguely equivalent to Multiple Master fonts for TrueType
- OpenType (postscript embedded in a truetype wrapper, or advanced typography tables in a truetype wrapper)
- PostScript Type2
- CFF
- Adobe's version of file format
- SING Gaiji extention (more information is available in the documentation subdirectory of the Glyphlet GDK)
- Microsoft's version
- Possible source of script codes for scripts not specified by MS/Adobe: ISO 15924
- Microsoft's full list of locale/language IDs (not all are supported, some may never be)
- Open Font Format Specification (ISO/IEC 14496-22:2007)
(based on OpenType 1.4 but an international standard) - Apple's sfnt wrapper around a PS type1 font
- Various bitmap only sfnt formats
- WOFF -- Web Open Font Format, mozilla's compressed sfnt format
- PostScript Type42 (the opposite of opentype, it's truetype embedded in postscript)
- SVG 1.1 fonts
- Macintosh font formats
- Windows raster font formats
- X11 pcf format
- Sadly there is no real standard for this. There's the source code used by X11.
- So I wrote my own description...
- PC Screen Font (psf/psfu/psf2)
- TeX font formats
- pk packed bitmap format
- gf generic font (bitmap) format
- tfm metrics format
- To make these viewable you probably want to do something like:
$ weave pktype.web
$ pdftex pktype.tex
- SIL Graphite Fonts (smart font extension to TrueType. Additional tables containing rules for composing, reordering, spacing, etc. glyphs)
- Palm pilot fonts (pdb files)
- OpenDoc. Sadly Proprietary so I shan't support it.
- Acorn RISC OS font format (these fonts are often zipped up with a non-standard zip).
- Ikarus IK format is documented in Peter Karow's book Digital Formats for Typefaces, Appendices G&I. (copies may still be available from URW++)
Interestingly the exact format of a curve is up to the interpretation program. - sfd files (FontForge's internal spline font database format)
- cidmap files (Fontforge's format for mapping cids to unicode)
- XML formats
Other font links
- Adobe's downloadable font spec
- Adobe's technical notes
- Adobe's Font Policies document
- PostScript reference manual
- (oldreference manual)
- Microsoft's downloadable fonts
- Downloadable PS CID CJK fonts (this site also has cmap files)
others - Downloadable OTF CID CJK fonts (this site also has cmap files)
- Most recent cid2code tables that I'm aware of
- PANOSE
Unicode
- Unicode consortium
- Apple's corporate use extensions (0xF850-0xF8FE)
- Adobe's corporate use extensions (0xF634-0F7FF) (also includes some of Apple's codes above)
- FontForge's corporate use extensions(0xF500-0xF580)
- A registry of code points in the private area(does not include any of Adobe's or Apple's codepoints)
- American Mathematical Society's corporate use extensions (0xE000-0xF7D7)
- MicroSoft uses 0xF000-0xF0FF in their "Symbol" encoding (3,0) when they want to an uninterpretted encoding vector (ie. a mapping from byte to glyph with no meaning attached to the mapping)
- Unicode en français
- Pictures of the characters
- Unicode script assignments
- Unicode Bloopers
- PostScript Unicode names
- Glyph names for new fonts (these are the names FontForge automatically assigns to glyphs)
- Adobe Glyph Names provides further synonyms
- Glyph name limitations
- Linux issues
Other Encodings
- Microsoft's Codepages, and at the unicode site
- Mac Encodings
- MacRoman
- IPA
- GB 18030
- TeX latin encodings (possibly also on your local machine in
/usr/share/texmf/dvips/base
) - TeX cyrillic encodings
Books
FontForge
- Haralambous, Yannis, 2004, Fontes & Codages
- Haralambous, Yannis (translated: P Scott Horne), 2006, Fonts & Encodings
Typography
Font editor concepts
Karow, Peter, 1994, Font Technology, Description and Tools
Karow, Peter, 1987, Digital Formats for Typefaces
TeX
Hoenig, Alan TeX Unbound: LaTeX and TeX Strategies for Fonts, Graphics & More
Knuth, Donald, 1979, TeX and METAFONT, New Directions in Typesetting
Interview
I was interviewed by the Open Source Publishing people at LGM2. There's an mp3 file of the interview available on their site.