Package org.drip.product.definition
Class Component
- java.lang.Object
-
- org.drip.product.definition.Component
-
- All Implemented Interfaces:
ComponentMarketParamRef
- Direct Known Subclasses:
CalibratableComponent
,TreasuryFutures
public abstract class Component extends java.lang.Object implements ComponentMarketParamRef
Component abstract class extends the ComponentMarketParamRef and provides the following methods:
- Get the product's initial notional, notional, and coupon.
- Get the Effective date, Maturity date, First Coupon Date.
- List the coupon periods.
- Set the market curves - discount, TSY, forward, and Credit curves.
- Retrieve the product's settlement parameters.
- Value the product's using standard/custom market parameters.
- Retrieve the product's named measures and named measure values.
- Module = Product Core Module
- Library = Fixed Income Analytics
- Project = Product Components/Baskets for Credit, FRA, FX, Govvie, Rates, and Option AssetClasses
- Package = Fixed Income Components/Baskets Definitions
- Author:
- Lakshmi Krishnamurthy
-
-
Constructor Summary
Constructors Constructor Description Component()
-
Method Summary
Modifier and Type Method Description abstract CashSettleParams
cashSettleParams()
Get the Product's cash settlement parametersabstract CompositePeriodCouponMetrics
couponMetrics(int iAccrualEndDate, ValuationParams valParams, CurveSurfaceQuoteContainer csqs)
Get the Product's coupon Metrics at the specified accrual dateabstract java.util.List<CompositePeriod>
couponPeriods()
Get the Product's Cash Flow PeriodsCaseInsensitiveTreeMap<java.lang.Double>
customScenarioMeasures(ValuationParams valParams, CreditPricerParams pricerParams, ScenarioMarketParams mpc, java.lang.String strCustomScenName, ValuationCustomizationParams vcp, CaseInsensitiveTreeMap<java.lang.Double> mapBaseMeasures)
Generate a full list of custom measures for the set of scenario market parameters present in the org.drip.param.definition.MarketParamsabstract JulianDate
effectiveDate()
Get the Effective Dateabstract JulianDate
firstCouponDate()
Get the First Coupon Dateabstract int
freq()
Retrieve the Coupon Frequencyabstract double
initialNotional()
Get the Initial Notional for the Productabstract JulianDate
maturityDate()
Get the Maturity DateJulianDate
maturityPayDate()
Get the Maturity Pay Dateabstract java.util.Set<java.lang.String>
measureNames()
Retrieve the ordered set of the measure names whose values will be calculatedComponentMeasures
measures(ValuationParams valParams, CreditPricerParams pricerParams, ScenarioMarketParams mpc, ValuationCustomizationParams vcp)
Generate a full list of the Product's measures for the set of scenario market parameters present in the org.drip.param.definition.MarketParamsdouble
measureValue(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqs, ValuationCustomizationParams vcp, java.lang.String strMeasure)
Calculate the value of the given Product's measureabstract double
notional(int iDate)
Get the Notional for the Product at the given dateabstract double
notional(int iDate1, int iDate2)
Get the time-weighted Notional for the Product between 2 datesabstract double
pv(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqc, ValuationCustomizationParams vcp)
Compute the PV for the specified Market Parametersjava.lang.String
tenor()
Retrieve the Instrument's Imputed Tenorabstract CaseInsensitiveTreeMap<java.lang.Double>
value(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqs, ValuationCustomizationParams vcp)
Generate a full list of the Product measures for the full input set of market parameters-
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
-
Methods inherited from interface org.drip.product.definition.ComponentMarketParamRef
couponCurrency, creditLabel, forwardLabel, fundingLabel, fxLabel, govvieLabel, name, otcFixFloatLabel, payCurrency, principalCurrency, volatilityLabel
-
-
-
-
Method Detail
-
initialNotional
public abstract double initialNotional() throws java.lang.Exception
Get the Initial Notional for the Product- Returns:
- Initial Notional
- Throws:
java.lang.Exception
- Thrown if Initial Notional cannot be computed
-
notional
public abstract double notional(int iDate) throws java.lang.Exception
Get the Notional for the Product at the given date- Parameters:
iDate
- Date- Returns:
- Product Notional
- Throws:
java.lang.Exception
- Thrown if Notional cannot be computed
-
notional
public abstract double notional(int iDate1, int iDate2) throws java.lang.Exception
Get the time-weighted Notional for the Product between 2 dates- Parameters:
iDate1
- Date #1iDate2
- Date #2- Returns:
- The Product Notional
- Throws:
java.lang.Exception
- Thrown if Notional cannot be computed
-
effectiveDate
public abstract JulianDate effectiveDate()
Get the Effective Date- Returns:
- Effective Date
-
maturityDate
public abstract JulianDate maturityDate()
Get the Maturity Date- Returns:
- Maturity Date
-
firstCouponDate
public abstract JulianDate firstCouponDate()
Get the First Coupon Date- Returns:
- First Coupon Date
-
freq
public abstract int freq()
Retrieve the Coupon Frequency- Returns:
- The Coupon Frequency
-
couponPeriods
public abstract java.util.List<CompositePeriod> couponPeriods()
Get the Product's Cash Flow Periods- Returns:
- List of the Product's Cash Flow Periods
-
couponMetrics
public abstract CompositePeriodCouponMetrics couponMetrics(int iAccrualEndDate, ValuationParams valParams, CurveSurfaceQuoteContainer csqs)
Get the Product's coupon Metrics at the specified accrual date- Parameters:
iAccrualEndDate
- Accrual End DatevalParams
- The Valuation Parameterscsqs
- Component Market Parameters- Returns:
- The Product's coupon Nominal/Adjusted Coupon Measures
-
cashSettleParams
public abstract CashSettleParams cashSettleParams()
Get the Product's cash settlement parameters- Returns:
- Cash settlement Parameters
-
value
public abstract CaseInsensitiveTreeMap<java.lang.Double> value(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqs, ValuationCustomizationParams vcp)
Generate a full list of the Product measures for the full input set of market parameters- Parameters:
valParams
- ValuationParamspricerParams
- PricerParamscsqs
- Market Parametersvcp
- Valuation Customization Parameters- Returns:
- Map of measure name and value
-
measureNames
public abstract java.util.Set<java.lang.String> measureNames()
Retrieve the ordered set of the measure names whose values will be calculated- Returns:
- Set of Measure Names
-
pv
public abstract double pv(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqc, ValuationCustomizationParams vcp) throws java.lang.Exception
Compute the PV for the specified Market Parameters- Parameters:
valParams
- ValuationParamspricerParams
- PricerParamscsqc
- Market Parametersvcp
- Valuation Customization Parameters- Returns:
- The PV
- Throws:
java.lang.Exception
- Thrown if the PV cannot be computed
-
maturityPayDate
public JulianDate maturityPayDate()
Get the Maturity Pay Date- Returns:
- Maturity Pay Date
-
measureValue
public double measureValue(ValuationParams valParams, CreditPricerParams pricerParams, CurveSurfaceQuoteContainer csqs, ValuationCustomizationParams vcp, java.lang.String strMeasure) throws java.lang.Exception
Calculate the value of the given Product's measure- Parameters:
valParams
- ValuationParamspricerParams
- PricerParamscsqs
- ComponentMarketParamsstrMeasure
- Measure Stringvcp
- Valuation Customization Parameters- Returns:
- Double measure value
- Throws:
java.lang.Exception
- Thrown if the measure cannot be calculated
-
tenor
public java.lang.String tenor()
Retrieve the Instrument's Imputed Tenor- Returns:
- The Instrument's Imputed Tenor
-
measures
public ComponentMeasures measures(ValuationParams valParams, CreditPricerParams pricerParams, ScenarioMarketParams mpc, ValuationCustomizationParams vcp)
Generate a full list of the Product's measures for the set of scenario market parameters present in the org.drip.param.definition.MarketParams- Parameters:
valParams
- ValuationParamspricerParams
- PricerParamsmpc
- org.drip.param.definition.MarketParamsvcp
- Valuation Customization Parameters- Returns:
- ComponentOutput object
-
customScenarioMeasures
public CaseInsensitiveTreeMap<java.lang.Double> customScenarioMeasures(ValuationParams valParams, CreditPricerParams pricerParams, ScenarioMarketParams mpc, java.lang.String strCustomScenName, ValuationCustomizationParams vcp, CaseInsensitiveTreeMap<java.lang.Double> mapBaseMeasures)
Generate a full list of custom measures for the set of scenario market parameters present in the org.drip.param.definition.MarketParams- Parameters:
valParams
- ValuationParamspricerParams
- PricerParamsmpc
- org.drip.param.definition.MarketParamsstrCustomScenName
- Custom Scenario Namevcp
- Valuation Customization ParametersmapBaseMeasures
- Base Measures from used to calculate the desired delta measure. If null, the base measures will be generated.- Returns:
- Custom Scenarios Measures output set
-
-


Altair Preview¶
Samples¶
Here are some examples of the types of charts that can be produced using Altair. You can see the source code for these in the First two examples notebook.
(Source code) Adding a tooltip, so that when the mouse is over a point, more information is displayed:
(Source code) A bar chart, with artist names along the \(x\)-axis:
(Source code) An interactive chart, where only the points in the selected region have color:
Where to read more¶
The syntax for Altair can be intimidating the first time you see it (and the fifth time you see it).
alt.Chart(spotify_df).mark_circle().encode(
x = "Energy",
y = "Loudness",
color = alt.Color('Energy', scale=alt.Scale(scheme='turbo')),
tooltip = ["Song Name", "Artist"],
)
There are endless possibilities for customization. Here are some places to read about options, but we recommend that you instead begin going through the First two examples notebook, and return to these links when there is something specific you want to customize.
Options for what to draw: Marks
Different “channels”, like
color
,opacity
, andsize
. You can see a list of channels here: Documentation.Here is a list of color schemes: Vega color schemes and an example of how to use a color scheme in Altair.
Sometimes it helps to explicitly tell Altair what type of data it is: encoding types
I often find browsing examples easier than reading documentation. If you’re the same way, check out the Altair example gallery.


This is a paragraph using the bold and italic tags.
Quote for the day
A designer knows he has achieved perfection not when there is nothing left to add, but when there is nothing left to take away.


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| This inspection reports common JavaScript DOM patterns which may present problems with XHTML documents. In particular, the patterns detected will behave completely differently depending on whether the document is loaded as XML or HTML. This can result in subtle bugs where script behaviour is dependent on the MIME-type of the document, rather than it's content. Patterns detected include document.body, document.images, document.applets, document.links, document.forms, and document.anchors. |
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A mini handmade spectroscope is prepared by students to check air pollution
To demonstrate the principle
klambi Thumb Piano


{% extends "solution.html" %}
{% block puzzle-author %}
Galactic Trendsetters
{% endblock %}
{% block solution-body %}
Each digit stands for a different letter. After decrypting, the numbers say, “we are, we are, we are, we are, we are the engineers,” the first line of the Engineers’ Drinking Song. The answer is the boxed word, ENGINEERS.
{% endblock %}


We here at Salmon Cookies INC we have always been about family and providing the best salmon cookies to the pacific north west for nearly a week. With our family friendly environments and customer first additude we hope to be your sole salmon coolkie provider for many years to come.
Besides delivering the best salmon based cookies in the northwest we also have stylish salmon t-shirts and a salmon cookie cutter so even your cookies at home can share the salmon spirit.


More...
|
| | Builder (TActionItem component) |
| |
| virtual TBuilder | Icon (Icon icon) |
| | An icon to apply to the icon image. To determine the class dynamically, configure the item with a getClass function. More...
|
| |
| virtual TBuilder | IconCls (string iconCls) |
| | A CSS class to apply to the icon image. To determine the class dynamically, configure the item with a getClass function. More...
|
| |
| virtual TBuilder | IconUrl (string iconUrl) |
| | The url of an image to display as the clickable element in the column. More...
|
| |
| virtual TBuilder | GetClass (Action< JFunction > action) |
| | A function which returns the CSS class to apply to the icon image. More...
|
| |
| virtual TBuilder | GetTip (Action< JFunction > action) |
| | A function which returns the tooltip string for any row. More...
|
| |
| virtual TBuilder | IsDisabled (Action< JFunction > action) |
| | A function which determines whether the action item for any row is disabled and returns true or false. More...
|
| |
| virtual TBuilder | Handler (string handler) |
| | A function called when the icon is clicked. More...
|
| |
| virtual TBuilder | Scope (string scope) |
| | The scope (this reference) in which the handler, getClass, isDisabled and getTip fuctions are executed. Defaults to this Column. More...
|
| |
| virtual TBuilder | Tooltip (string tooltip) |
| | A tooltip message to be displayed on hover. Ext.tip.QuickTipManager must have been initialized. More...
|
| |
| virtual TBuilder | Disabled (bool disabled) |
| | If true, the action will not respond to click events, and will be displayed semi-opaque.  More...
|
| |
| virtual TBuilder | CustomConfig (Action< ConfigItemCollection > action) |
| | Collection of custom js config More...
|
| |
◆ Builder() [1/4]
◆ Builder() [2/4]
◆ Builder() [3/4]
◆ Builder() [4/4]
◆ Disabled()
If true, the action will not respond to click events, and will be displayed semi-opaque.
◆ GetClass()
A function which returns the CSS class to apply to the icon image.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ GetTip()
A function which returns the tooltip string for any row.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ Handler()
A function called when the icon is clicked.
◆ Icon()
An icon to apply to the icon image. To determine the class dynamically, configure the item with a getClass function.
◆ IconCls()
A CSS class to apply to the icon image. To determine the class dynamically, configure the item with a getClass function.
◆ IconUrl()
The url of an image to display as the clickable element in the column.
◆ IsDisabled()
A function which determines whether the action item for any row is disabled and returns true or false.
- Parameters
-
| action | The action delegate |
- Returns
- An instance of TBuilder
◆ operator Builder()
◆ Scope()
The scope (this reference) in which the handler, getClass, isDisabled and getTip fuctions are executed. Defaults to this Column.
◆ Tooltip()
A tooltip message to be displayed on hover. Ext.tip.QuickTipManager must have been initialized.
The documentation for this class was generated from the following file:


Image URL:
(try to change to another google image photo url, find an image url to paste in above, then click the button again)
If some images doesn't work, try:
- run ohmy.py, go to command line, "cd" to folder containing this file, do "
python ohmy.py
"
- open http://<IP_ADDRESS>:8000/
- try again in opened window


---
layout: page-fullwidth
permalink: /Prediction/
title: Prediction That Works
---
Any efforts in developing technology require regular assessment of where technical opportunities lie,
what uses are important and which trends are changing. Knowing the fundamental drivers in a technology field
(and nearby fields that may impact the value proposition of a given field) is very important.
And making decisions that will lead to correct choices for new technology development
is essential for success, especially for organizational leaders.
Challenge:
As important as making predictions is, it is very hard to do, especially when you are new in a field.
Also, good technology decisions are not based solely on technical issues; human behavior and social systems
are major elements in making good predictions,
as is being the right person in the right place at the right time with the right resources.
Approach:
A universally valuable approach for accurate prediction is to develop
models of the entities and relationships
that are elements in your decision-making process. Models help you to identify and reason about the essentials
and eliminate aspects that are secondary or irrelevant.
Resources:
Slides and Exercises:
The Art of Prediction, by Michael A. Heroux
Assignments:
- Watch the Michael Crichton talk above (52 minutes).
- Write a brief (few paragraphs, compact writing) reflection on this talk. Submit by email.


Last week I implemented multiple variants of highly parallelizable cryptographic hash function BLAKE3 using SYCL and today I'd like to present my collective understanding, which I gained while implementing/ benchmarking BLAKE3, targeting heterogeneous accelerator platform(s). BLAKE3 cryptographic hash function easily lends itself well to data parallel execution environments like SYCL/ OpenCL. Speaking from high level design point of view, it consists of following two steps.
The root of tree ( 32 -bytes wide ) is desired cryptographic hash of input byte array. Both of these steps are good candidates for data parallelism. Note, step-1 produces
N -many leaf nodes of Binary Merkle Tree, which are used for finding root of Merkle Tree in step-2 i.e. step-2 is data dependent on step-1.
In this document, I'll be working with input byte array of length M -bytes such that M = N * 1024, where N = 2i, i = {1, 2, 3 ...}. That means
after execution of step-1 of BLAKE3, I should have power of 2 -many leaf nodes ( = N ), which will be used for computing root of fully balanced Binary Merkle Tree.
This will simplify both explanation & implementation. I'll walk you through following two techniques of implementing BLAKE3.
Let me start with first approach which is simpler.
Let us assume, I've 8KB input which I take as byte array ( say const sycl::uchar * ) and split it into 8 equal sized chunks.
Now each of these 1024 -bytes wide chunks can be compressed in parallel. For doing so, I'll dispatch 8 work-items, with work-group size W ( <= 8 && 8 % W == 0 ),
where each work-item executes compress( ... ) function, consuming 1024 -bytes input message into hash state.
Once all these 8 work-items complete their execution, each of them output 64 -bytes chaining value ( which is actually BLAKE3 hash state matrix of that chunk ),
from which first 32 -bytes to be taken
as output chaining value of that chunk. These output chaining values are used as leaf nodes of Binary Merkle Tree, which I'm about to construct.
In final step of computation, I construct a Binary Merkle Tree from N ( = 8 ) output chaining values.
As Binary Merkle Tree is a hierarchical structure, I need to dispatch multiple rounds of kernels, respecting data dependency.
To be more specific, in this case 3 ( = log2(N), where N = 8 ) rounds will be required.
In first dispatch round, I'll dispatch 4 work-items, who will read ( total ) 4 consecutive pairs of output chaining values and interpret each pair of chaining values as left and
right child of ( to be computed ) parent node, placed right next to each other ( in ltr order ), as depicted below.
Computing parent node involves compressing a pair of chaining values, while setting some flags denoting that parent chaining value will be output of compress( ... ), where each chaining value is of 32 -bytes, making total of 64 -bytes input to compress( ... ). After completion of this dispatch round, we should have 4 parent nodes, who live just above leaf nodes. In next dispatch round, I've to ask for 2 work-items, each will compress two consecutive chaining values ( which were computed during last round ) and produce total 2 parent nodes, who live just below root of the tree ( to be computed in next dispatch round ).
In final round, it suffices to dispatch just a single task which takes
64 -bytes input ( read two chaining values, which are two immediate children of root of tree; these were computed during last round --- thus data dependency ) and produces 32 -bytes
output chaining value which is root of Merkle Tree. This root is our desired BLAKE3 hash. Also note, before root of tree can be computed, flag denoting
that output of compress( ... ) function invocation is root chaining value of BLAKE3 Merkle Tree, need to be set.
A pictorial demonstration might be helpful at this moment.
Empowered with this high level knowledge of algorithmic construction of BLAKE3, it's good time to dive into often mentioned compress( ... ) function. Simply speaking compression starts with 32 -bytes input chaining value and 64 -bytes input message, it consumes whole message into BLAKE3 hash state ( in multiple rounds, while also employing message permutation, using predefined indexing tricks ) and produces output of 64 -bytes, which is nothing but hash state after consuming whole input message inside it. First 32 -bytes of output is taken as chaining value which is either used as input to next stage of computation or as final root chaining value i.e. BLAKE3 digest. Let's emphasize on BLAKE3 hash state.
BLAKE3 hash state is 64 -bytes wide; as BLAKE3 word size is 32 -bit, hash state can be represented using an array of 16 elements where each element is 32 -bit wide unsigned integer i.e. sycl::uint. When compressing 64 -bytes message, BLAKE3 consumes input message in 7 rounds, while at end of each round ( except last one ) permutes 64 -bytes message in a predefined way. At end of applying 7 rounds, it takes first 32 -bytes of hash state, which has now consumed permuted variants of 64 -bytes input message, as output chaining value. Each round of BLAKE3 compression consists of bit wise manipulation of 32 -bit wide hash state words.
Note indices passed as argument to g( ... ) function from blake3_round( ... ), which clearly shows in first four g( ... ) function invocations, it's column-wise mixing first eight message words with hash state. And last four g( ... ) function invocations are diagonally mixing remaining eight message words with hash state. It's possible to reduce four vertical mixing invocations into single function call, where all four columns are mixed parallelly, if I represent hash state as an array of 4 vectors ( SYCL intrinsic ), where each vector is of type sycl::uint4, as shown below. With this new representation of hash state, diagonal mixing also enjoys boost, where all four diagonals of hash state matrix can be mixed parallelly.
With this new representation of hash state column-wise mixing looks like below.
But keeping hash state as 4x4 matrix comes with its own requirement, where it needs to be diagonalised such that each diagonal of 4x4 matrix is now in same column, before
diagonal mixing can be applied. After diagonal mixing, hash state needs to get back to its original form, which calls for undoing the diagonalisation previously performed.
Diagonalisation involves rotating each of four vectors leftwards by row index of respective vector i.e. {0, 1, 2, 3} in 4x4 state matrix. Note, vector lane rotation doesn't rotate
each lane content ( sycl::uint ), instead it rotates whole vector by N ( < 4, because each row has 4 lanes ) places.
I make use of vector swizzle operators provided by SYCL vector intrinsic API for rotating vector.
A pictorial depiction looks like below.
Note the color coding, which shows how diagonalisation helps in bringing each of 4 diagonals of 4x4 hash state matrix in same column. This makes applying diagonal mixing much easier ( and faster ) on 128 -bit vectors i.e. sycl::uint4.
Following code snippet can perform diagonal mixing on four 128 -bit vectors i.e. sycl::uint4[4]. This will consume last 8 message words of total 64 -bytes input message into hash state. Note, other than which message words are consumed, diagonal mixing is just same as column-wise mixing, because we've arranged columns to be so. This means, in implementation both of these mixings can be replaced using preprocessor directives or other compile-time code generation means.
After diagonal mixing diagonalisation will be undone, rotating 4x4 state matrix vectors rightwards by row index of respective vector i.e. {0, 1, 2, 3} in 4x4 state matrix. Following code snippet should bring back hash state is desired form, preparing it for next round.
During compression of 64 -bytes message input, after application of each round of mixing, sixteen message words ( 16 x 4 = 64 -bytes total message ) are permuted in following manner and permuted output is used as input message words during next round.
Now let us go back to chunk compression, where we had 1024 -bytes input ( as sycl::uchar * ); each compress( ... ) function invocation takes 64 contiguous bytes to mix with 4x4 hash state matrix, that means we've to iterate 16 times for processing whole chunk. Each of these 64 -bytes are called blocks, 16 of them make a chunk. For first block in a chunk, a predefined constant input chaining value is used, but all subsequent 15 blocks use previous block's 32 -bytes output chaining value as its input chaining value. Note, BLAKE3 hash state is 64 -bytes, input chaining value is 32 -bytes, so remaining 32 -bytes of hash state ( i.e. last two rows of 4x4 hash state matrix ) comes from predefined constants & other parameters passed to compress( ... ), which includes flags denoting whether this block is first/ last of this chunk or if output chaining value will be parent/ root node of BLAKE3 Merkle Tree, block length, chunk index etc.. After applying compress( ... ) first 32 -bytes of hash state is taken as output chaining value, to be used as input chaining value of next block in same chunk. Below is a pictorial demonstration for ease of understanding.
Last block of each chunk produces 32 -bytes output chaining value, which is considered to be leaf node of Binary Merkle Tree. After all chunks are compressed & we've N -many output chaining values, interpreted as leaf nodes of Binary Merkle Tree. Usual parallel Binary Merkle Tree construction algorithm can be applied. Note, for merging two consecutive chaining values ( at leaf level ) into single parent chaining value, compress( ... ) function is invoked with a predefined constant input chaining value, along with two consecutive chaining values being interpreted as 64 -bytes input message. Some input flags are passed to denote that its output chaining value will be a parent node. While computing root chaining value ( read target digest of input byte array ), two immediate child nodes just below root are compressed into single chaining value, while passing some flags to denote this is root node being computed.
It's good time to give second approach a go, where each SYCL work-item compresses more than one chunk.
Remember in first approach of parallel BLAKE3, I used a 4x4 matrix of 64 -bytes for representing hash state, when compressing each block of total 1024 -bytes wide chunk.
But this time, hash state is represented using a 16 x N matrix, where N = {2, 4, 8, 16} and ith row of state matrix holds N -many different chunk's hash state
word at index i; so N -many different chunk's hash states are represented in N -many columns of 16 x N shared state matrix. That means, with N = 4, sixteen 128 -bit
vectors will be used for representing whole hash state of 4 chunks. Programmatically I'd like to represent it using following syntax.
With N ( = 4 ) chunks being compressed together, each SYCL work-item mixes total 4096 -bytes of input message into hash state,
each 1024 -bytes chunk splitted in 16 blocks, each of width 64 -bytes. There'll be sixteen rounds required for compressing 4096 -bytes.
In each round, ith block of all N chunks are compressed together. Note color coding used in following demonstration, where
I attempt to show you how message words ( 32 -bit wide ) from each block are chosen to construct 128 -bit vectors ( using sycl::uint4 )
which are using during column-wise and diagonal mixing. I'd also like you to note that, there's no diagonalisation and undiagonalisation steps
required in this SIMD style mixing, because each chunk's hash state is actually a 16 word vector, which is a column of 16 x N state matrix.
After first block is processed, which consumes 64 -bytes message from each of four chunks ( i.e. first block of each chunk ), output chaining value
of four chunks are prepared by taking 8 x N state matrix, where lower 8 x N portion of matrix ( read last 8 rows ) is dropped. This should produce
32 -bytes output chaining value for each chunk, which will be used as input chaining values for those respective chunks when processing blocki+1
from all N ( = 4 ) chunks.
After all sixteen blocks from all chunks are compressed into hash state, 16 x N state matrix is truncated to 8 x N matrix ( by dropping last 8 rows ),
which holds N -many output chaining values of N -many chunks. These N -many output chaining values are considered as N -many leaf nodes of BLAKE3 Merkle Tree,
which will be constructed once all work-items complete compressing N -many chunks each.
Binary Merklization algorithm doesn't anyhow change in second approach.
Note, when N = 2, sixteen 64 -bit wide SIMD registers are used for representing hash state of two chunks, which are compressed in parallel. Similarly, for N = {4, 8, 16}
sixteen {128, 256, 512} -bit registers ( respectively ) will be used for representing hash state of N chunks. On modern CPUs which support avx512* instructions
512 -bit vectors can help boosting this style of leveraging arbitrary many SIMD lanes.
For understanding opportunities of using SIMD for parallelizing BLAKE3 on relatively large input byte arrays, I suggest you take a look at BLAKE3
specification's
section 5.3.
As you've now better understanding of aforementioned two approaches for computing BLAKE3 hash, I'd like to present you with benchmark results. In following tables, you'll see I'm taking random input of N -bytes; transferring whole input to accelerator's accompanying memory; invoking BLAKE3 kernel with on-device data pointer; waiting for all computation steps to complete and finally transferring 32 -bytes digest ( which is output chaining value of root node of Binary Merkle Tree in BLAKE3 hash ) to preallocated memory on host. Note, all these numbers represent mean value obtained after executing same kernel with same input size/ arguments K ( = 8 ) -many times.
| BLAKE3 Hash using approach_1 | ||||
|---|---|---|---|---|
| Input Size | Accelerator | Kernel Execution Time | Host -> Device Tx Time | Host <- Device Tx Time |
| 64 MB | ||||
| Tesla V100-SXM2-16GB | 844.598250 us | 6.166145 ms | 6.973250 us | |
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | 6.239875 ms | 9.797500 ms | 2.525625 us | |
| Intel(R) Iris(R) Xe MAX Graphics | 4.974242 ms | 17.749401 ms | 1.319500 us | |
| 128 MB | ||||
| Tesla V100-SXM2-16GB | 1.800964 ms | 12.269974 ms | 7.080000 us | |
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | 8.187520 ms | 20.664062 ms | 1.242000 us | |
| Intel(R) Iris(R) Xe MAX Graphics | 9.812348 ms | 35.475108 ms | 1.319500 us | |
| 256 MB | ||||
| Tesla V100-SXM2-16GB | 3.267731 ms | 24.462952 ms | 6.805500 us | |
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | 8.853032 ms | 32.455801 ms | 1.047125 us | |
| Intel(R) Iris(R) Xe MAX Graphics | 19.465823 ms | 70.886068 ms | 1.293500 us | |
| 512 MB | ||||
| Tesla V100-SXM2-16GB | 5.998047 ms | 48.833740 ms | 6.713750 us | |
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | 14.807205 ms | 48.242437 ms | 1.063000 us | |
| Intel(R) Iris(R) Xe MAX Graphics | 39.271700 ms | 141.716997 ms | 1.313000 us | |
| 1024 MB | ||||
| Tesla V100-SXM2-16GB | 11.915527 ms | 97.573730 ms | 8.423000 us | |
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | 22.864140 ms | 79.047688 ms | 1.088500 us | |
| Intel(R) Iris(R) Xe MAX Graphics | 77.556440 ms | 283.341799 ms | 1.534000 us |
In above table, you should see three accelerators I targeted for benchmarking BLAKE3 SIMD approach_1, where two of them are GPUs
from two different vendors and one is 64 -core CPU from Intel. You'll notice, Nvidia's Tesla V100 GPU performs best on all input sizes.
Being a 64 -core CPU, in majority of cases it performs better compared to Intel's Irix Xe Max GPU. Note, when input size is 64 MB, Intel GPU
perform's little better than 64 -core CPU. But as input size increases kernel execution time on Intel GPU starts to quickly increase, though
on Intel CPU execution time for 64 MB, 128 MB and 256 MB input sizes are pretty close to each other.
As output size is constant ( read 32 -bytes ), device to host data transfer cost is not much of concern. But input data size is variable,
host to device input data transfer cost can help us in answering is it worth transferring large byte array to accelerator for computing BLAKE3 hash ?
Comparing between multiple accelerators ( with same input size ), it shows as input data size increases host to device data transfer cost
increases quickly for GPU ( even surpasses input data transfer cost on CPU for same size ), which makes sense because those accelerators
are connected to host over PCIe bus. When comparing input data transfer cost of Nvidia's GPU and Intel's CPU, I see until 512 MB input size,
cost was lesser for GPU, but at 512 MB input size both of them take around same time. For both GPUs from two different vendors, I see
their host to device data transfer cost increases linearly as input size is doubled, because both of them are connected to host CPU using PCIe,
which doesn't have high bandwidth. Due to these relatively high input data transfer costs, it may not always benefit using this accelerated
BLAKE3 implementation, where explicitly data needs to be transferred to accelerator's local DRAM, and it may end up defeating whole purpose
of speeding up. Just to make it more evident, notice in above table, for 1 GB input size on Nvidia Telsa V100 GPU, input transfer is ~8x costlier than actual
computation of BLAKE3 hash.
Lastly I'd like to draw your attention to device to host data transfer cost ( transferring 32 -bytes digest back to host ), where
you should notice, on Nvidia's Tesla V100 GPU it's ~(6 - 7)x more expensive to transfer 32 -bytes ( over PCIe ) to host, when compared to
Intel's GPU.
In approach_2 of computing BLAKE3, I'm compressing {2, 4, 8, 16} chunks together & in following table I present kernel execution time and host <-> device data transfer cost, by timing SYCL events obtained as result of submitting respective commands on SYCL queue, which has profiling enabled.
| BLAKE3 Hash using approach_2, compressing {2, 4, 8, 16} chunks together | |||||
|---|---|---|---|---|---|
| Input Size | Accelerator | SIMD Width | Kernel Execution Time | Host -> Device Tx Time | Host <- Device Tx Time |
| 64 MB | |||||
| Tesla V100-SXM2-16GB | |||||
| 64 -bit | 1.016358 ms | 6.172363 ms | 7.568375 us | ||
| 128 -bit | 923.828375 us | 6.168457 ms | 7.323875 us | ||
| 256 -bit | 1.318848 ms | 6.168945 ms | 7.812625 us | ||
| 512 -bit | 2.055176 ms | 6.176270 ms | 10.254000 us | ||
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | |||||
| 64 -bit | 7.193866 ms | 13.689200 ms | 4.531500 us | ||
| 128 -bit | 6.739462 ms | 14.008103 ms | 2.967625 us | ||
| 256 -bit | 7.261953 ms | 14.829467 ms | 2.978000 us | ||
| 512 -bit | 11.546031 ms | 13.229008 ms | 1.385125 us | ||
| Intel(R) Iris(R) Xe MAX Graphics | |||||
| 64 -bit | 3.106389 ms | 17.748458 ms | 1.365000 us | ||
| 128 -bit | 28.628951 ms | 17.749823 ms | 1.332500 us | ||
| 256 -bit | 56.188691 ms | 17.748861 ms | 1.326000 us | ||
| 512 -bit | 105.559818 ms | 17.749823 ms | 1.365000 us | ||
| 256 MB | |||||
| Tesla V100-SXM2-16GB | |||||
| 64 -bit | 3.539550 ms | 24.455078 ms | 7.080250 us | ||
| 128 -bit | 4.190674 ms | 24.442871 ms | 7.080000 us | ||
| 256 -bit | 5.203370 ms | 24.459961 ms | 7.568250 us | ||
| 512 -bit | 13.925293 ms | 24.453369 ms | 7.568375 us | ||
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | |||||
| 64 -bit | 10.928828 ms | 33.915237 ms | 967.625000 ns | ||
| 128 -bit | 8.854166 ms | 32.901272 ms | 976.500000 ns | ||
| 256 -bit | 10.290331 ms | 33.643110 ms | 1.030125 us | ||
| 512 -bit | 18.790299 ms | 33.722702 ms | 966.125000 ns | ||
| Intel(R) Iris(R) Xe MAX Graphics | |||||
| 64 -bit | 11.941254 ms | 70.892191 ms | 1.326000 us | ||
| 128 -bit | 110.007846 ms | 70.894857 ms | 1.339000 us | ||
| 256 -bit | 245.655891 ms | 70.883748 ms | 1.391000 us | ||
| 512 -bit | 475.246200 ms | 70.886621 ms | 1.332500 us | ||
| 1024 MB | |||||
| Tesla V100-SXM2-16GB | |||||
| 64 -bit | 11.715087 ms | 97.482910 ms | 9.765625 us | ||
| 128 -bit | 12.184326 ms | 97.552734 ms | 8.300750 us | ||
| 256 -bit | 18.732911 ms | 97.577148 ms | 7.812250 us | ||
| 512 -bit | 52.898436 ms | 97.524170 ms | 8.056625 us | ||
| Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz | |||||
| 64 -bit | 35.084335 ms | 76.414034 ms | 903.625000 ns | ||
| 128 -bit | 25.805447 ms | 79.800968 ms | 1.052625 us | ||
| 256 -bit | 28.765474 ms | 80.076494 ms | 1.017875 us | ||
| 512 -bit | 61.698307 ms | 75.633475 ms | 1.107500 us | ||
| Intel(R) Iris(R) Xe MAX Graphics | |||||
| 64 -bit | 47.515533 ms | 283.342299 ms | 1.319500 us | ||
| 128 -bit | 431.324205 ms | 283.344665 ms | 1.306500 us | ||
| 256 -bit | 938.669251 ms | 283.345946 ms | 2.164500 us | ||
| 512 -bit | 1.843786 s | 283.342267 ms | 2.164500 us |
In above table, host <-> device data transfer cost is not something I'm interested in, but instead I want you to notice how changing SIMD lane count from 2 to 16 ( by doubling ),
affects kernel execution time on some accelerator with some specific input size. For example, let us zoom into row where input size is 256 MB; on Tesla V100 GPU
as number of SIMD lanes used are doubled ( i.e. # -of chunks being compressed together by each SYCL work-item is doubled ), kernel execution time is doubled.
Now notice, how kernel execution performs on Intel Iris Xe Max GPU, for input size 256 MB, using both approach_{1, 2} ( with SIMD lanes {2, 4, 8, 16} ). See with
approach_1, kernel execution takes ~19 ms, but when 2 chunks are compressed together ( using approach_2 ), resources are better utilized, results in improved kernel
execution time of ~12 ms. But kernel execution becomes ~9x costlier as soon as 4 chunks are compressed together, because now each work-item compresses 4 chunks in parallel,
while representing hash state using sixteen 128 -bit wide vectors. Thus each work-item ends up using too many resources ( read register files for keeping hash state ), which
results in register spilling --- portions of hash state are now placed on far distant and high latency global memory. Accessing global memory involves going through
multiple memory hierarchies i.e. global memory and two levels of caches etc., each of which adds its own latency. After compressing 4 chunks together, as I keep increasing
# -of chunks to compress together ( say 8/ 16 ), execution time also keeps getting doubled up. Notice, for 1024 MB input size, on Intel Iris Xe Max GPU, when 2 chunks are
compressed together, kernel execution time is ~47 ms, but when 4 chunks are compressed together execution time suffers heavily --- increasing ~9x ! This trend follows,
as kernel execution time is doubled and quadrupled ( compared to compressing two chunks together ) when 8 and 16 chunks are compressed in parallel, respectively.
Now let us explore whether similar kind of behaviour is visible when compressing multiple chunks together on Intel CPU. Let me begin with input size 64 MB, when only single chunk
is compressed by each SYCL work-item ( read approach_1 ), kernel execution time is ~6 ms. As I increase # -of chunks being compressed together execution time stays almost same until we reach
SIMD_LANE_COUNT = 16. Due to presence of limited number of 512 -bit registers, not many work-items can execute at a time, which is reflected when sixteen 512 -bit registers
are used for representing hash state, compressing 16 chunks in parallel. When I look at input size both 256 MB & 1024 MB, I see similar pattern, where compressing 4 chunks together
performs better than compressing {2, 8, 16} chunks together. When 4 chunks are compressed together sixteen 128 -bit vectors are used by each SYCL work-item
for representing hash state. Now there are 32 of these 512 -bit register files on each core of this machine, which means on each core 8 SYCL work-items should proceed in parallel,
without any register spilling.
With these benchmark results in mind, I'll say, approach_1 of BLAKE3, where each SYCL work-item compresses single chunk performs much better ( almost always ) compared to approach_2, where it's possible to compress 2/ 4/ 8/ 16 chunks in parallel, by each SYCL work-item.
For sake of reproducibility and future reference, I keep blake3's SYCL implementation here.
Here I've presented benchmark results of only three accelerators, but you can find more of them for different input sizes here.
You may be interested in BLAKE3 implementation approach_{1, 2}, which you can find in this file.
Note, this implementation might be helpful when hashing large input sizes, but does it really benefit using this implementation when host -> device data transfer
cost is much higher compared to kernel execution time ? is an important question.
If you happen to be interested in using BLAKE3 for constructing Merkle Tree, you may
check out my other project. Binary
Merklization using BLAKE3 is much easier as it requires me to hash only 64 -bytes input and produce 32 -bytes digest, which is an intermediate node of Merkle Tree. This kind of
hashing is called 2-to-1 hashing, where two BLAKE3 digests are concatenated & hashed. You may notice, 2-to-1 hashing is simpler ( and cheaper ) because I need to compress
only one chunk, which has only one block in itself. And that chunk is root node of BLAKE3's internal Merkle Tree, meaning BLAKE3's internal Merklization requirement is not a
requirement anymore.
I need to just make a single call to compress( ... )
function with proper flags to denote that this is the only node in BLAKE3's internal Merkle Tree --- compute-wise cheap ! So for generating all intermediate nodes of Binary Merkle Tree
when N -many ( read N is power of 2 ) leaf nodes are provided ( read each leaf node is a BLAKE3 digest ), using BLAKE3 hash, above linked implementation can come handy. Benchmark details
of Binary Merklization implementation using BLAKE3 for different input sizes on different accelerator platforms can be found here.
While we're at it, it can be interesting to take a look at ( and compare ) Binary Merklization implementation
using Rescue Prime hash, I wrote few weeks ago. It's clearly visible how performant BLAKE3 is compared to Rescue Prime, when Binary Merklization benchmarks are compared !
Have a great time !


To instaciate new BetslipCalculator, write the next code:
const betslipCalc = new BetslipCalculator();
const bets = [{
banker: false,
oddValue: 1.85
},
{
banker: false,
oddValue: 2.00
},
{
banker: true,
oddValue: 1.40
},
{
banker: false,
oddValue: 2.10
},
{
banker: true,
oddValue: 1.65
}];
In the code example above, we specified banker property on each bet. Property banker is only used when calculating system tickets. So if you need to calculate single or combo ticket you can omit banker property.
To add these bets to betslip, write next code:
betslipCalc.setBets(bets);
In code above, we have 5 bets, 2 of them are bankers. That means we have only 3 system available. Before we can calculate system, we need to select one of them or more. To select system, we can use toggleSystem method on BetslipCalculator class.
betslipCalc.toggleSystem(1);
Now, we have selected system 1/3, you can also select other systems if you want
betslipCalc.toggleSystem(2);
betslipCalc.toggleSystem(3);
...and finally calculate method
betslipCalc.calculateSystemWinnings();
betslipCalc.calculateSingleWinnings();
betslipCalc.calculateComboWinnings();


Visit any case at /cases/caseId?export=true, paste the JSON here to import into the case into this Quepid instance, you will then be taken to the new case.


Nainital
The beautiful lake resort nestles amidst seven hills in a lush valley at an altitude of 1938 meters. In 1841, the British first discovered Nainital as a holiday resort. Today, the town has emerged as one of the most important hill resorts of India
Nainital is a modem hill resort of northern India (336 kms from Delhi) connected by best-aligned and maintained roads from the railhead of Kathgodam. Railway line was laid up to Kathgodam in 1882. The road passes through forests where occasionally wild animals can also be seen. "Naini" is derived from the temple of goddess Naini which was built on the bank of the lake but later got destroyed due to land slides in the year 1880, The place is mentioned in Skand Puran as 'Trishul Sarover'. Tri Rishi (three sages Atri, Pulastiya and Pulha) had come there for pilgrimage while they were at Gagar range (now called China Peak), they felt thirsty and could not find any water.
The lake then formed was called Tri-Rishi Tal (The lake of three sages). Today it has an impressive lake surrounded by the hills from three sides with highest peak Cheena (2610 m) and the valley in between running from east to west. The lake is a little more than 3 kms in circumference.
Nainital remains packed from April to November with tourist for sight seeing as well as for seeking escape from the heat of the plains. Naini lake has facilities for boating and yatching. Facilities for horse riding, skating, golf and rock climbing are also available for the visitors. Fishing is also permitted with the permission of local authorities. Talli Tal and Malli Tal are the two main trading centre at Nainital
What to See Around
Hanumangarhi :
(1, 951 m) 3 km. A popular place for worship in the temple of lord Hanuman and its Sun-set view.
Khurpatal :
(1,635 m) 5 to 10 kms. This is an anglerís paradise on Nainital-Kaladhungi-Ramnagar Road. It is here that the conifers belt merges and gives way to the Sal trees. Fishing permits are available from Deputy Commissioner, Nainital.
Kilbury:
(2,194 m) 10 kms. A wooded spot, ideal for a quiet and peaceful holidays, which is connected by road.
Lariakanta :
(2, 481 m) 6 kms. This is the second highest peak of Nainital and commands a glorious view of the lake region.
Land's End:
(2,1l8 m) 4.08 kms. One can have magnificent view of Khurpatal and the terraced fields on the hill side.
Naini Peak:
(2,611 m) (Cheena Peak) 6 kms. Most favored picnic spot of the town. One can view the distant plains and snow clad Himalayas from the peak. A bird's eye view of the lake is also obtainable from here. One can either trek or go on a horse back up to Naina Peak.
Naini Lake:
The boat club overlooking the lake offers sailing facilities and also boating. Fishing is allowed with the permission of Executive Officer, Municipal Board, Nainital.
Snow-View:
(2270 m). 3 km. This is the nearest easily accessible peak on Sher-ka-Danda hill connected by Ropeway. A powerful telescope is fixed here which enables one to view the Himalayan ranges.
Nainital Ropeway :
Kumaon Mandal Vikas Nigam has since installed the most sophisticated ropeway at Nainital which is the first of its kind in India. The ropeway covers a distance of 705 meters with an altitude gain of 300 meters. Carrying a maximum load of 825 kgs or 12 persons in each of its two cars, the ropeway travels at a maximum speed of 6 mts/ sec.
Bhimtal :
(1,371 m) 22 kms away from Nainital. Bhimtal is named after Bhim, one of the five Pandavas of 'Mahabharata'. The lake at Bhim Tal is larger than Naini lake at Nainital. Boating, swimming and fishing are the main attractions. An island in the centre of the lake is an excellent spot for picnic. Fishing permits can be obtained from the fishing Inspector of Bhim Tal. Tourist Bungalow and private lodges are available here.
Naukuchia Tal: (1,219 m) 27 kms. This is a lake known for its nine comers. Area around the lake is an ideal place for bird watching. Fishing permits can be obtained from Fishing Inspector of Bhimtal.
Sat Tal:
(1371 m) 21 kms. away from Nainital. This is a charming spot and the venue of the famous Kohinoor Camp in summer. There is an Ashram run by Dr. E. Stanley Jones of United States Missionaries, where religious, educational and social youth camps are held every year. Nearby, there are several small lakes, numbering seven, from where the name Sat Tal is derived. The lakes include Panna, Nal Damyanti and Sita. Sukha Tal is located in the middle of thick pine and oak trees. There are excellent facilities for swimming, boating and fishing. A small World Life sanctuary is another spot of tourist attraction.


Established in Brunei Darussalam in 1965, Sin Guan Brothers Co. (SGBC) has proudly become one of the major wholesalers in agricultural products, food and beverages, confectionery and cleaning products all across Brunei. It has now become a leader in the wholesale market.
From humble origins, of a small shop in Jalan Sultan, the founder single-handedly built this business success story/venture. We appreciate and understand the value of the hard work and perseverance.
SGBC now imports products from ASEAN as well as other countries internationally such as China, New Zealand, Denmark, Holland, Canada, Turkey and a few other European countries. We aim to serve and provide our customers with the best products and premier services. Customer satisfaction is our top priority.


--- title: Beneficial Bugs layout: default ---
The Good, the Bad, and the Ugly
By Michelle Cook, former Red Butte Garden Greenhouse Coordinator
From the Autumn 2013 Newsletter
“Some big insect flew in and began walking on the table. I don’t know what insect it was, but it was brown, shining, and rich in structures. In the city the big universal chain of insects gets thin, but where there’s a leaf or two it’ll be represented.” ― Saul Bellow, The Adventures of Augie March
Insects are an integral part of any ecosystem, and while they sometimes “bug” us, many are valuable. Even yellow jackets! Insects are valuable because many prey on other insects for food, regulating their populations. Predatory insects can control many insect pests that damage our squash, roses or houseplants. The good news is that several of these predatory or beneficial insects are already in your flowers! Nature is already at work helping you garden.
The bad news is that many beneficial insect populations are too low in numbers to provide immediate, effective control of emerging infestations of non-beneficial insects. To get control of these serious infestations, you can add beneficial insects to your garden. Several releases about a month apart are usually needed.
Purchased beneficial insects often come in little jars and, many can be found at local nurseries. Releasing them is as easy as taking off the lid and sprinkling a few into the most pest infested areas throughout your home or garden. A cool morning is usually the recommended time of dispersal. At Red Butte Garden, we release a wide variety of beneficial insects. Following are a few of our favorites:
Convergent Lady Beetle or ladybug, Hippodamia convergens, is very well known and found naturally throughout much of North America. While adults eat pests, the main workhorse is the larvae.
PROS: Easy and affordable to purchase most of the year, eat a lot of pests of all different kinds, with good perennial plants, like yarrow, long term populations can establish, and they are cute!
CONS: Many released adults will not stay around as they often disperse almost immediately.
Green Lacewings, Chrysoperla sp., are little green-bodied insects that often land on your shoulder or can be seen flitting around your porch light. Their wings are clear and look quite delicate upon close inspection. They are usually purchased as small eggs attached to an index card, and are easy to release.
PROS: Easy to release, available most of the year, larvae eat a variety of pests, adults are pollinators.
CONS: Eggs take 1-2 weeks to hatch and start feeding, some adult dispersal.
Chinese or Praying Mantid, Tenodera sinensis, was originally brought to the U.S. just to eat other insects. It has naturalized into many ecosystems. From egg hatch at ½ inch nymphs, to 3-4 inch adults, mantis are voracious and adept hunters.
PROS: Stay around the release sight, actively search out prey, can be kept over the winter as a pet, nymphs start eating right away.
CONS: Purchased egg sacs can take several weeks to hatch and they eat anything they can catch, including other beneficial insects.
 Classified as a true bug, they fold their wings flat on their back forming a triangle at the top of the wings. The black and white pattern on the wing has a similarity to the skull and crossbones on a pirate flag. They are small insects, only about 1/16 of an inch.
PROS: Small, eat many different pests.
CONS: Not available locally, check our suppliers listed below.
Parasitic Wasps, like, Aphidius colemani, provides excellent control in greenhouses and on house plants. This very tiny black wasp feeds exclusively on aphids. We use them extensively.
PROS: Small, effective.
CONS: Not available locally, check our suppliers listed below.
Recommended Suppliers
ARBICO
www.arbico.com
1-800-827-2847
Rincon-Vitova Insectaries, Inc.
www.rinconvitova.com
1-800-248-2847


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Points of Distribution and Food Locations
Legend
Evacuation Route
10 distribution points or less
10 -20 distribution points
More than 20 distribution points
Shelter
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Evacuation Route
10 distribution points or less
10 -20 distribution points
More than 20 distribution points
Shelter


Document System Tutorial Part 16 - Schema List Modifier¶
In this part, we will discuss about schema list modifier based on the document list filter that checks the admin status of the user. If it is not an admin, what are the models that user can access to.
First of all, run your application using admin account. In the Document System Dashboard, click DOCUMENT GROUPS.
Click the existing record that you have.
Change the Document from Computer to To Do List.
Result
Logout your account then login johndoe account.
Click “DOCUMENTS”.
If you notice, the document record created by admin is still visible even if we disabled all permission levels that we have discussed in Part 14 tutorial of this application.
There is one more step that we have to take to make it record wise and that is Schema List Modifier. List Modifier allows you to modify the schema when rendering a list. It will pass to you the a pointer to the schema so you could modify it and the user access it to be able to customize per user (or per user group).
func(*uadmin.ModelSchema, *uadmin.User) (string, []interface{})
uadmin.ModelSchema has the following fields and their definitions:
Name - The name of the Model
DisplayName - A human readable version of the name of the Model
ModelName - The same as the Name but in small letters.
ModelID - (Data) A place holder to store the primary key of a single row for form processing
Inlines - A list of associated inlines to this model
InlinesData - (Data) A place holder to store the data of the inlines
Fields - A list of uadmin.F type representing the fields of the model
IncludeFormJS - A list of string where you could add URLs to javascript files that uAdmin will run when a form view of this model is rendered
IncludeListJS - A list of string where you could add URLs to javascript files that uAdmin will run when a list view of this model is rendered
FormModifier - A function that you could pass that will allow you to modify the schema when rendering a form. It will pass to you the a pointer to the schema so you could modify it and a copy of the Model that is being rendered and the user access it to be able to customize per user (or per user group).
ListModifier - A function that you could pass that will allow you to modify the schema when rendering a list. It will pass to you the a pointer to the schema so you could modify it and the user access it to be able to customize per user (or per user group).
uadmin.User has the following fields and their definitions:
Username - The username that you can use in login process and CreatedBy which is a reserved word in uAdmin
FirstName - The given name of the user
LastName - The surname of the user
Password - A secret word or phrase that must be used to gain admission to something. This field is automatically hashed for security protection.
Email - A method of exchanging messages between people using electronic devices.
Active - Checks whether the user is logged in
Admin - Checks whether the user is authorized to access all features in the system
RemoteAccess - Checks whether the user has access to remote devices
UserGroup - Returns the GroupName
UserGroupID - An ID to access the UserGroup
Photo - Profile picture of the user
LastLogin - The date when the user last logged in his account
ExpiresOn - The date when the user account expires
OTPRequired - Checks whether the OTP is Active
OTPSeed - Private field for OTP
Exit your application. Go to the main.go. Inside the main function, create a Schema List Modifier that calls the Document model. Place it after the docs.FormModifier declaration.
// ListModifier is based on the user ID where the admin status is active
// or not. If the user is not an admin, he has limited access to the
// models and its records.
docS.ListModifier = func(s *uadmin.ModelSchema, u *uadmin.User) (string, []interface{}) {
// Checks whether the user is not an admin
if !u.Admin {
// Returns the user ID
return "user_id = ?", []interface{}{u.ID}
}
// Returns nothing
return "", []interface{}{}
}
Now run your application using johndoe account.
Click DOCUMENTS.
If you notice, the Computer record created by admin is no longer visible because johndoe is not an admin and has no permission to read that record.
Click on To Do List. If you notice, there is no save button on the bottom right corner of the screen because johndoe is part of a Developer group and has no Edit access into it.
Click here to view our progress so far.
In the last part of this tutorial, we will talk about customizing your dashboard and dockerizing your application for the world to see.


100uM of PCA, PYO and PCN were reduced in PBS 50 (50mL) in the survival assay chambers. Electrolysis was performed overnight and the expected color changes were observed. Reduced phenazines were transferred into stoppered serum bottles. Serum bottles were transferred to the anaerobic chamber housing the plate reader.
Pre electrolysis (left) & post electrolysis (right)
Clockwise from the bottom right the chambers are PYO, PCA and PCN.
Electrolysis curves (left) & serum bottles (right)
Electrolysis seemed to go to completion and the color changes remained in the serum bottles upon transfer to the other chamber.
Black walled, clear bottomed fluorescence 96 well plates were transferred 48 hrs in advance. Oxidized phenazines (100uM in PBS 50), and buffer (PBS 50), were transferred into the chamber 24 hours in advance and were shaken repeatedly to remove oxygen.
Wells were measured in two fluorescence channels 360/40ex,460/40 em and 360/40ex, 528/40 em and absorbance scans were taken from 300nm - 700nm in increments of 10nm. Wells were scanned with oxidized phenazines before mixing, and then repeatedly scanned in a timecourse following mixing.
Mixing 75uL of reduced phz (or buffer) with 75uL of oxidized phz (or buffer)
1. Buffer
2. \(PCA_{red}\)
3. \(PCN_{red}\)
4. \(PYO_{red}\)
In this way we acquired spectra in the measured channels for all of the oxidized and reduced phenazines in isolation and in combination. Well conditions were repeated in triplicate.
library(tidyverse)
library(cowplot)
library(viridis)
library(knitr)
library(kableExtra)
knitr::opts_chunk$set(tidy.opts=list(width.cutoff=60),tidy=TRUE, echo = TRUE, message=FALSE, warning=FALSE, fig.align="center")
source("../../IDA/tools/plotting_tools.R")
theme_set(theme_1())
df_endpoints <- read_csv("data/2019_09_17_solution_ET_endpoints.csv")
df_bg_sub <- df_endpoints %>% gather(key = "wavelength", value = "intensity",
em460, em528, abs310, abs360, abs690, abs500) %>% group_by(wavelength) %>%
mutate(min = min(intensity)) %>% mutate(bg_sub = intensity -
min)
ggplot(df_endpoints, aes(x = red_ox, y = em460)) + geom_point() +
facet_wrap(~ox, drop = T, scales = "free")
ggplot(df_endpoints, aes(x = red_ox, y = em528)) + geom_point() +
facet_wrap(~ox, drop = T, scales = "free")
ggplot(df_endpoints, aes(x = red_ox, y = em528)) + geom_point()
ggplot(df_bg_sub, aes(x = red_ox, y = bg_sub)) + geom_point() +
facet_wrap(~wavelength, scales = "free")
predicted_values <- df_bg_sub %>% filter(red == "PBS" | ox ==
"PBS") %>% filter(red_ox != "PBS_PBS")
ggplot(predicted_values %>% filter(red == "PBS"), aes(x = red_ox,
y = intensity)) + geom_point() + facet_wrap(~wavelength,
scales = "free")
ggplot(predicted_values %>% filter(ox == "PBS"), aes(x = red_ox,
y = intensity)) + geom_point() + facet_wrap(~wavelength,
scales = "free")
metadata <- df_endpoints %>% select(well, red, ox, red_ox, rep)
df_spectra <- read_csv("data/2019_09_17_solution_ET_abs_spectra.csv") %>%
gather(key = "well", value = "intensity", -Wavelength)
df_spectra_meta <- left_join(df_spectra, metadata, by = c("well")) %>%
mutate(phz = ifelse(red == "PYO" | ox == "PYO", "PYO", NA)) %>%
mutate(phz = ifelse(red == "PCA" | ox == "PCA", "PCA", phz)) %>%
mutate(phz = ifelse(red == "PCN" | ox == "PCN", "PCN", phz)) %>%
mutate(phz_redox = ifelse(red == "PBS", "phz_ox", "phz_red"))
ggplot(df_spectra_meta %>% filter(red == "PBS" | ox == "PBS"),
aes(x = Wavelength, y = intensity, color = phz_redox)) +
geom_path() + facet_wrap(~phz, scales = "free")
ggplot(df_spectra_meta %>% filter(red == "PBS" | ox == "PBS"),
aes(x = Wavelength, y = intensity, color = phz)) + geom_path() +
facet_wrap(~phz_redox)
ggplot(df_spectra_meta, aes(x = Wavelength, y = intensity, color = red)) +
geom_point() + facet_wrap(~ox)
ggplot(df_spectra_meta, aes(x = Wavelength, y = intensity, color = ox)) +
geom_point() + facet_wrap(~red)
pred_red <- left_join(df_bg_sub %>% filter(red != "PBS" & ox !=
"PBS"), predicted_values, by = c("red", "wavelength"), suffix = c("",
"_from_red"))
pred_red_ox <- left_join(pred_red, predicted_values, by = c("ox",
"wavelength"), suffix = c("", "_from_ox")) %>% mutate(pred = bg_sub_from_red +
bg_sub_from_ox)
ggplot(pred_red_ox %>% filter(ox == "PYO"), aes(x = red_ox, y = pred)) +
geom_point(shape = 21, size = 3) + geom_point(aes(y = bg_sub),
color = "red") + facet_wrap(~wavelength, scales = "free")
ggplot(pred_red_ox %>% filter(ox == "PCA"), aes(x = red_ox, y = pred)) +
geom_point(shape = 21, size = 3) + geom_point(aes(y = bg_sub),
color = "red") + facet_wrap(~wavelength, scales = "free")
ggplot(pred_red_ox %>% filter(ox == "PCN"), aes(x = red_ox, y = pred)) +
geom_point(shape = 21, size = 3) + geom_point(aes(y = bg_sub),
color = "red") + facet_wrap(~wavelength, scales = "free")
Maybe we can get even better bg_sub by averaging the three PBS only measurements…?
ggplot(pred_red_ox %>% filter(ox == "PYO"), aes(x = red_ox, y = intensity)) +
geom_point() + geom_point(aes(y = intensity_from_red), shape = 21,
size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox),
shape = 3, size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox +
intensity_from_red), shape = 5, size = 2, color = "gray") +
geom_point(aes(y = min), shape = 6, size = 2, color = "gray") +
facet_wrap(~wavelength, scales = "free") + ylim(0, NA)
ggplot(pred_red_ox %>% filter(ox == "PCA"), aes(x = red_ox, y = intensity)) +
geom_point() + geom_point(aes(y = intensity_from_red), shape = 21,
size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox),
shape = 3, size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox +
intensity_from_red), shape = 5, size = 2, color = "gray") +
facet_wrap(~wavelength, scales = "free") + ylim(0, NA)
ggplot(pred_red_ox %>% filter(ox == "PCN"), aes(x = red_ox, y = intensity)) +
geom_point() + geom_point(aes(y = intensity_from_red), shape = 21,
size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox),
shape = 3, size = 2, color = "gray") + geom_point(aes(y = intensity_from_ox +
intensity_from_red), shape = 5, size = 2, color = "gray") +
facet_wrap(~wavelength, scales = "free") + ylim(0, NA)


N A Prasanna Venkatesh
Student
Computer Science Engineer,Vellore Institute of Technology
+91<PHONE_NUMBER>
Job objective
I am looking for a job right now in the sector of Internet and Web Programming .I have all the skills required for Creating the webpages and other stuffs.
Research Interest
My research and interest leie in internet and web programming,I also have the skills required for a team leader,I manage people very well.
I have interest in Machine Learning and Artificial Intelligence.
Key Skills
- I am good at HTML,CSS and JavaScript.
- I do competitive coding using Python and C++
- Creating android applications is one of my biggest passion.I have created many good android applications
- I also know and learning ML and AI.
- I play chess very well :D
Education
B.Tech CSE |
2023 |
Vellore Institute of Technology |
12th Standard |
2019 |
Chennai Public School |
10th Standard |
2017 |
Chennai Public School |
Work Experience : 5 years


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" and turned it into "
".


A frontend-focused web developer with a deep understanding of JavaScript, CSS, and HTML backed by a background in design, and illustration. I excel in frontend web architecture, componentisation, and design systems keeping teams streamlined and focused.
My desire for perfection drives me to sweat the details: keeping designs tight, interactions fluid, and code efficient and maintainable. Then my experience, pragmatism, and research allows me to budget for time, scope, and the future of the product.
I got my first web development role after teaching myself web development on my commute and, until now, I haven’t lost my passion for all things internet.
TradeGecko
Software Engineer
I championed large parts of UX architecture, componentisation, and frontend architecture, taking into consideration speed-to-production, developer enjoyment, and user accessibility.
I also built and maintained the marketing website on HubSpot, ensuring creating and updating content was quick and easy.
Win Technologies
Software Engineer
Perform Group
Web developer
Responsible for leading the creation and maintenance of live streaming websites, CMS integrations, technical documentation, and curated data feeds.
Key Clients included Toppfotball, Tennis TV, and Chelsea FC.
Junior web developer
After three months of teaching myself web development Perform Group gave me a shot as a junior developer, excited by my passion for the role. In less than a year I was leading teams for the Norwegian Tippeligaen (now Eliteserien) and Adeccoligaen (now OBOS-ligaen).
Research Now
Graphic design intern
This is where I gained my passion for web development. After impressing the team by teaching myself Flash on-the-job and still hitting deadlines I was asked to create an email. I was hooked! I bought books on web development and started learning on my daily 2-hour commute.
BA Illustration with Animation
Foundation in Art
A revelation, as before leaving the UK around 5 year ago, I hardly left the house. Moving to Singapore has opened my eyes to travelling and I have been trying to get to new countries at least every couple of months. Activities while abroad include diving, hiking, and just meeting new people.
I still enjoy life-drawing and try to go to classes to keep my drawing sharp. During breaks you could find me sketching or folding a paper animal.
Most of my spare time is spent with computers, and electronics: learning new things and immersing myself in their world.
The progressive problem-solving nature of real-time strategy games makes them my ‘go-to’, but I also enjoy a few casual action and first-person shooters to relax with.
Having taught myself guitar while at University, I enjoyed recording sessions in my bedroom... all parts played by yours-truly. However, these days I tend to keep my playing more to myself.
I am familiar with modern DSLR cameras, but as I generally take photos while out-and-about I stick mostly with point-and-shoot camera and my Phone. As I started diving I’m currently enjoying the Olympus TG-5.


labeled
by None at 2019-11-25 13:17:27.labeled
by None at 2019-11-25 13:20:11.labeled
by None at 2019-11-25 13:20:11.milestoned
by None at 2019-11-25 13:20:11.labeled
by None at 2019-11-25 13:28:32.labeled
by None at 2019-11-25 13:30:11.labeled
by None at 2019-11-25 13:31:28.milestoned
by None at 2019-11-25 13:31:28.Look at the recording for Jeremy to see what happened.
labeled
by None at 2019-11-25 13:23:03.milestoned
by None at 2019-11-25 13:23:03.renamed
by None at 2019-11-25 14:23:04.labeled
by None at 2019-11-25 13:18:35.milestoned
by None at 2019-11-25 13:18:35.assigned
by Axa Mei Liauw at 2019-11-25 14:36:19.closed
by None at 2019-11-25 14:36:20.milestoned
by None at 2019-11-25 14:36:41.labeled
by None at 2019-11-25 14:36:47.labeled
by None at 2019-11-25 14:42:38.labeled
by None at 2019-11-25 14:42:42.milestoned
by None at 2019-11-25 14:43:16.labeled
by None at 2019-11-25 14:43:59.milestoned
by None at 2019-11-25 14:44:03.labeled
by None at 2019-11-25 14:49:15.labeled
by None at 2019-11-25 15:00:42.This could be related to #317
labeled
by None at 2019-11-25 13:15:27.milestoned
by None at 2019-11-25 13:15:27.assigned
by None at 2019-11-25 15:15:12.It looks like something might be off with the icon mapping. devtools shows that we are getting "lux-icon-pending" when it has been approved.
This seems to be an issue for requests with the status "pending further approval" and "changes requested".
assigned
by Shaun Ellis at 2019-11-25 21:35:52.labeled
by None at 2019-11-25 14:41:35.milestoned
by None at 2019-11-25 14:41:35.assigned
by Axa Mei Liauw at 2019-11-25 17:13:13.closed
by Shaun Ellis at 2019-11-25 21:45:20.review_requested
by Axa Mei Liauw at 2019-11-25 17:21:57.referenced
by Shaun Ellis at 2019-11-25 21:45:20.merged
by Shaun Ellis at 2019-11-25 21:45:20.closed
by Shaun Ellis at 2019-11-25 21:45:20.head_ref_deleted
by Shaun Ellis at 2019-11-25 21:45:22.assigned
by Axa Mei Liauw at 2019-11-25 17:34:40.closed
by Shaun Ellis at 2019-11-25 21:46:59.review_requested
by Axa Mei Liauw at 2019-11-25 17:34:15.referenced
by Shaun Ellis at 2019-11-25 21:46:59.merged
by Shaun Ellis at 2019-11-25 21:46:59.closed
by Shaun Ellis at 2019-11-25 21:46:59.head_ref_deleted
by Shaun Ellis at 2019-11-25 21:47:02.assigned
by Axa Mei Liauw at 2019-11-25 17:40:40.closed
by Shaun Ellis at 2019-11-25 21:48:41.review_requested
by Axa Mei Liauw at 2019-11-25 17:44:49.referenced
by Shaun Ellis at 2019-11-25 21:48:41.merged
by Shaun Ellis at 2019-11-25 21:48:41.closed
by Shaun Ellis at 2019-11-25 21:48:41.head_ref_deleted
by Shaun Ellis at 2019-11-25 21:48:43.assigned
by Axa Mei Liauw at 2019-11-25 17:35:23.closed
by Shaun Ellis at 2019-11-25 21:50:12.review_requested
by Axa Mei Liauw at 2019-11-25 17:40:05.referenced
by Shaun Ellis at 2019-11-25 21:50:12.merged
by Shaun Ellis at 2019-11-25 21:50:12.closed
by Shaun Ellis at 2019-11-25 21:50:12.head_ref_deleted
by Shaun Ellis at 2019-11-25 21:50:14.assigned
by Axa Mei Liauw at 2019-11-25 17:54:39.closed
by Shaun Ellis at 2019-11-25 21:51:54.review_requested
by Axa Mei Liauw at 2019-11-25 18:03:52.referenced
by Shaun Ellis at 2019-11-25 21:51:54.merged
by Shaun Ellis at 2019-11-25 21:51:54.closed
by Shaun Ellis at 2019-11-25 21:51:54.head_ref_deleted
by Shaun Ellis at 2019-11-25 21:51:56.referenced
by James R. Griffin III at 2019-11-25 15:48:56.merged
by James R. Griffin III at 2019-11-25 15:48:56.closed
by James R. Griffin III at 2019-11-25 15:48:56.head_ref_deleted
by James R. Griffin III at 2019-11-25 15:49:02.head_ref_force_pushed
by James R. Griffin III at 2019-11-25 21:38:15.@lightgivener My understanding of this is that we have two editions that are the same catalogo entry and so different DCL numbers, yes? These are https://figgy.princeton.edu/catalog/parent/c15a5a17-48b1-42a9-bfc4-8bb8bb85bffd/ac6854b9-fcbc-4042-adf4-72a6e5487eb5 and https://figgy.princeton.edu/catalog/parent/c15a5a17-48b1-42a9-bfc4-8bb8bb85bffd/9736795c-4049-4fae-a990-86ce6a491356.
I'm a little fuzzy on the data model here - does this mean we should have two bib-IDs?
assigned
by Trey Pendragon at 2019-11-25 18:45:39.mentioned
by None at 2019-11-25 21:13:10.subscribed
by None at 2019-11-25 21:13:11.renamed
by Nikitas Tampakis at 2019-11-25 18:38:33.labeled
by Trey Pendragon at 2019-11-25 18:38:54.referenced
by None at 2019-11-25 20:03:20.merged
by None at 2019-11-25 20:03:20.closed
by None at 2019-11-25 20:03:20.referenced
by Trey Pendragon at 2019-11-25 18:48:55.closed
by None at 2019-11-25 20:03:20.labeled
by Trey Pendragon at 2019-11-25 18:40:09.deployed
by Trey Pendragon at 2019-11-25 14:37:34.head_ref_force_pushed
by None at 2019-11-25 20:49:59.head_ref_force_pushed
by None at 2019-11-25 21:24:50.labeled
by Nikitas Tampakis at 2019-11-25 18:51:43.@kelea99 Was someone specifically concerned about this, or was somebody getting a bunch of images that needed rotating? When we've thought about this in the past it turned out it was really rarely a problem. Do you think we should close this until it happens?
hi, @tpendragon ! This has come up a couple of times. Most recently it involved an external scholar who provided us with photography that she took in the field for her research. Post-figgy ingest, she noted that many of her images needed to be rotated. It would be helpful if this could be done by the scholar in figgy, so it would not mean that someone on PUL's end who can read the language (me, in this example) would be responsible for rotating and then a re-ingesting the images in figgy. Does this make sense? Of note: I do not see this as a priority :)
@kelea99 How was it handled this time? The studio just flipped them in photoshop or something?
@tpendragon, I rotated them in photoshop and cropped (if needed) there as well. they were saved and ingested again.
mentioned
by Kim Leaman at 2019-11-25 18:55:50.subscribed
by Kim Leaman at 2019-11-25 18:55:50.mentioned
by Trey Pendragon at 2019-11-25 19:35:46.subscribed
by Trey Pendragon at 2019-11-25 19:35:47.mentioned
by Kim Leaman at 2019-11-25 19:59:03.subscribed
by Kim Leaman at 2019-11-25 19:59:03.renamed
by Trey Pendragon at 2019-11-25 20:00:46.labeled
by Trey Pendragon at 2019-11-25 20:00:51.unlabeled
by Trey Pendragon at 2019-11-25 20:00:55.mentioned
by Trey Pendragon at 2019-11-25 22:20:24.subscribed
by Trey Pendragon at 2019-11-25 22:20:24.labeled
by None at 2019-11-25 14:50:48.labeled
by None at 2019-11-25 14:51:56.labeled
by Nikitas Tampakis at 2019-11-25 19:19:17.This is just in smaller screens: https://catalog.princeton.edu/catalog/4601808
labeled
by Nikitas Tampakis at 2019-11-25 19:17:51.labeled
by Nikitas Tampakis at 2019-11-25 19:21:43.closed
by Nikitas Tampakis at 2019-11-25 19:25:20.closed
by Nikitas Tampakis at 2019-11-25 19:26:20.closed
by Nikitas Tampakis at 2019-11-25 19:28:16.closed
by Nikitas Tampakis at 2019-11-25 19:30:03.closed
by Nikitas Tampakis at 2019-11-25 19:30:39.closed
by Nikitas Tampakis at 2019-11-25 19:30:48.This issue is relevant for a music-specific search page.
Proposed to remove from main search too.
closed
by Nikitas Tampakis at 2019-11-25 19:35:12.labeled
by Nikitas Tampakis at 2019-11-25 19:35:51.unlabeled
by Nikitas Tampakis at 2019-11-25 19:36:01.labeled
by Nikitas Tampakis at 2019-11-25 19:37:10.labeled
by Nikitas Tampakis at 2019-11-25 19:37:10.closed
by Nikitas Tampakis at 2019-11-25 19:37:19.closed
by Nikitas Tampakis at 2019-11-25 19:38:29.closed
by Nikitas Tampakis at 2019-11-25 19:39:22.closed
by Nikitas Tampakis at 2019-11-25 19:39:34.closed
by Nikitas Tampakis at 2019-11-25 19:41:02.labeled
by Nikitas Tampakis at 2019-11-25 19:41:49.unlabeled
by Nikitas Tampakis at 2019-11-25 19:42:03.labeled
by Nikitas Tampakis at 2019-11-25 19:42:04.closed
by Nikitas Tampakis at 2019-11-25 19:44:01.closed
by Nikitas Tampakis at 2019-11-25 19:46:31.closed
by Nikitas Tampakis at 2019-11-25 19:46:46.closed
by Nikitas Tampakis at 2019-11-25 19:46:59.Should ask DSSG if this should happen. If not, close it.
labeled
by Trey Pendragon at 2019-11-25 19:45:54.This bug
closed
by Nikitas Tampakis at 2019-11-25 19:47:48.closed
by Nikitas Tampakis at 2019-11-25 19:48:36.closed
by Nikitas Tampakis at 2019-11-25 19:48:07.closed
by Nikitas Tampakis at 2019-11-25 19:49:46.closed
by Nikitas Tampakis at 2019-11-25 19:50:55.Stackmap service has been refactored. Firestone locator is a mess.
closed
by Nikitas Tampakis at 2019-11-25 19:51:46.closed
by Nikitas Tampakis at 2019-11-25 19:52:10.closed
by Nikitas Tampakis at 2019-11-25 19:52:20.renamed
by Nikitas Tampakis at 2019-11-25 19:55:54.closed
by Nikitas Tampakis at 2019-11-25 19:56:12.referenced
by Francis Kayiwa at 2019-11-25 20:01:15.merged
by Francis Kayiwa at 2019-11-25 20:01:15.closed
by Francis Kayiwa at 2019-11-25 20:01:15.head_ref_deleted
by Francis Kayiwa at 2019-11-25 20:01:18.closed
by Francis Kayiwa at 2019-11-25 20:30:15.referenced
by Francis Kayiwa at 2019-11-25 20:30:15.merged
by Francis Kayiwa at 2019-11-25 20:30:15.closed
by Francis Kayiwa at 2019-11-25 20:30:16.closed
by Francis Kayiwa at 2019-11-25 20:30:32.Ended up being the mount was missing
closed
by None at 2019-11-25 20:31:35.


quests of the round table
Name
Card
Rank
Shield
name2
Card2
Rank2
Shield2
name3
Card3
Rank3
Shield3
p1 play zone
p2 play zone
p3 play zone
Name
Card
Rank
Shield
Self Card Play Zone
Self Card Display Area
Quest Information
stage 1
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Deconstructing Harry (1997)
Seen on 26 December 1997 for $8 with Wendy and Tony G. at the Paramus Picture Show, of all places.
Well, I should say right off that bat that despite two major objections, I really enjoyed this movie. I should also add that there is so much going on that I almost feel like I ought to see it a second time.
*Deconstructing Harry* blurs the lines between the real lives of writer Harry Block (Woody Allen) and the people in his life, and the characters in his stories that are based on them. So, the pill-popping, sex-obsessed Harry Block is also glimpsed in Bob, Ken (Richard Benjamin), Harvey (Tobey Maguire), Paul (Stanley Tucci), and Mel (Robin Williams).
Harry has a few ex-wives and ex-girlfriends who cannot deal with him and his obsession with sex. His latest book details how he cheated on his wife with her sister, and the latter shows up at his house ready to shoot him for his thinly disguised portrayal of his betrayals. He has also been invited to his alma mater, and--getting desperate--he winds up inviting Cookie (Hazelle Goodman) to accompany him, just as she is.  Along the way, he brings an old friend, and his son. Throughout this diegesis are flashes from his works of fiction, and flashbacks to Harry's real life, and his imagination.
All of this could be a frightening mish-mash, but Woody Allen has managed to pull it all together rather well. This format also allows him to bring out some hilarious and fantastic moments. Robin Williams is the actor who is literally out of focus (a wonderful cinematic effect) and expects his family to adjust to the distortion he has become. Joan, the psychiatrist, has a hilarious scene in which she constantly leaves a patient so she can yell at Harry in the next room for cheating on her. The visit to Ha Harry's orthodox Jewish sister and brother-in-law. Two older women who share a "dark secret" at a 1976 Bar Mitzvah reception with a Star Wars theme. There are many funny moments in *Deconstructing Harry* that make it worth seeing.
The performances are also very memorable, especially the womens'. Actresses as disparate as Judy Davis and Kirstie Alley and Demi Moore really stand out. The women suffer the brunt of Harry's behavior, and that makes them a lot more interesting than the men.
The denouement of *Deconstructing Harry* does not measure up to the movie, but that's okay, since the journey is more illuminating than the destination. Also, the use of really hateful, foul language (the "C word" is used more than once) is really beneath someone as smart as Woody Allen. While it amplifies the misogyny of Harry, that misogyny is evident without it. Some people would say it's brave of him to use this sort of language, but I think it's an offensive easy way out.
My other main objection is the heralding of the wonderful event--that Woody has finally given a major role to a black actor. Hazelle Goodman is absolutely hilarious, but she is playing the prostitute, Cookie! That the "first major role for an black actor" in a Woody Allen movie is that of a prostitute is insulting and a stereotype. A black actor could just as easily have played one of Harry's friends in this movie, and been just as important. Allen is often criticized that there are no black people in his movies. The defense has been that the social class he depicts don't have any interaction with blacks (which is hard to believe); so the only one that gets this new status is a hooker?
Overall, *Deconstructing Harry* is the strongest movie Allen has put out in years. The familiar themes of relationships, self-discovery, self-hating Jews, atheism, are all here and are very entertaining. It's hard not to disconnect Allen's movies from his real life, but either way, it's an enjoyable, intelligent romp.
Written and directed by Woody Allen cinematography by Carlo Di Palma, and production design by Santo Loquasto.
More movie reviews by Seth Bookey, with graphics, can be found at http://www.geocities.com/Athens/2679/kino.html
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


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THE RUGRATS MOVIE A film review by Matt Williams
RATING: * * out of * * * *
The Rugrats have made their way from Nickelodeon to the big screen, and the result is, well, alarmingly similar to the television cartoon.
For those unfamiliar with the animated series, it is centered on the point of view of several very young children (ranging in age from 1-2). The leader of the pack is Tommy Pickles (E.G. Daily), a bald, but fiesty, one year-old. His best friend is Chuckie Finster (Christine Cavanaugh). Also along for the ride are twins Phil and Lil DeVille (Kath Soucie), and obnoxiously precocious cousin Angelica (Cheryl Chase).
The film features the arrival of a new member in the Pickles household. Tommy gets a little brother, Dylan (aka Dill Pickles). This, of course, sets off a round of sibling rivalry, as Tommy must share the affections of his parents with this loud newcomer.
In an attempt to solve the problem, Tommy's friends kidnap Dylan and (with Tommy in tow), try to return him to the hospital. Of course, the five babies soon get lost in the wilderness (despite a high-tech dragon wagon which provides their ride), and the film follows their attempts to get back home.
Despite a few desperate pop-culture references, The Rugrats Movie is strictly kids stuff. From its incessant diaper humor to a series of mispronunciations that only a Family Circus devotee could love, the film is too obnoxious for younger kids, yet too juvenile for those much older.
The animation style boasts a few computer-generated backgrounds, but overall isn't that much more impressive than television quality. In fact, there's very little about this film that calls for seeing it on the big screen.
The Rugrats Movie has the look and feel of a direct-to-video film. Unless you're a hard-core Rugrats devotee, you can wait till then.
Copyright 1998 Matt Williams
- Matt Williams<EMAIL_ADDRESS>Reviewer for Cinematter: http://www.cinematter.com Home of over 650 reviews, and information on nearly 700 upcoming releases
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
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Related links: index of all rec.arts.movies.reviews reviews


com.thinkaurelius.faunus.formats.edgelist.EdgeListOutputFormat
There are two standard types of serialized graph representations: adjacency list and edge list. The adjacency list is the standard Faunus format - a vertex and all of its incident edges. An edge list, on the other hand, is simply a list of all the edges. The edge list format does not support element properties. The format is outVertexId\tinVertexId\tedgeLabel\n
.
The Graph of the Gods data set, when represented as an edge list, looks as as follows.
1 4 lives
1 3 brother
1 2 brother
1 0 father
2 5 lives
2 3 brother
2 1 brother
3 11 pet
3 6 lives
3 1 brother
3 2 brother
7 8 mother
7 9 battled
7 10 battled
7 11 battled
7 1 father
11 6 lives


Fork me on GitHub!
Jackie
Lindsey
Projects
- Sea otter (Enhydra lutris nereis) use of the estuarine habitat of Elkhorn Slough, CA. (2013-present)
- Masters thesis work with UCSC, USGS, and the Monterey Bay Aquarium.
- Foraging ecology of gray whales (Eschrichius robustus) in Washington state. (2011-2012)
- Capstone research project with the University of Washington and NMFS.
Work Experience
- UW Armbrust Lab: January 2011-March 2012
- Biological Oceanography work with flow-cytometry and R programming
- NOAA’s National Marine Mammal Laboratory: Summer 2011
- Intern with wildlife biologist Pat Gearin on gray whale projects
- Undergraduate TA for Honors Biology: September-December 2011
- Lead small group discussion of current scientific work
- UW Applied Physics Laboratory: January-June 2012
- EndNote database creation and management
- Seattle Aquarium: March-August 2012
- Interpretation supervisor: managing volunteer shifts, giving presentations to the public
- MLML Stranding Network Coordinator: September 2012-present
- Responding to calls from citizens and experts to record data on stranded marine mammals


- initConstraints() - Method in class com.wlu.ca.jkhalil.ai.Sudoku
-
Initialize binary constraints of all variables.
- initializeArcs() - Method in class com.wlu.ca.jkhalil.ai.AC3
-
Initialize arcs between all variables.
- isComplete(Variable[][]) - Method in class com.wlu.ca.jkhalil.ai.Sudoku
-
Check if assignment is complete
Assignment is complete if all squares have assigned values that are consistent with
original gird.
- isConsistent(Variable, Variable[][]) - Method in class com.wlu.ca.jkhalil.ai.Sudoku
-
Check if variable's value is consistent with assignment.
- isFound() - Method in class com.wlu.ca.jkhalil.ai.Variable
-
a variable's value is found if value != 0 and domain has
only one possibility.
- isSolved() - Method in class com.wlu.ca.jkhalil.ai.Sudoku
-
A csp sudoku puzzle is solved if for each Xi, Di == 1
and value != 0


About Us
Welcome to all about us and our company! Here we have a list of our main employees and their information so that you can get to know them better! We hope this will help you feel more secure about your purchases made here at Project Case. We are here to help and serve you!
Project Case started as just an idea and a couple design tools. We were frustrated not getting what we asked for when we bought a case, having our phone case fail to protect a phone, or not look like how we wanted it to. Thats why we created this website for everyone to use. No longer will you be wondering if the phone case you bought will look how you want it or protect your phone. We have you covered! Currently we have only one shipping center and headquaters in Portland, Oregon, but we are trying to exand as much as we can to help everyone feel secure about their cases.
These are some of our employees who have helped us from go from zero to hero!
Our Main Faculty
| Name and Job Title | About Them |
|---|---|
Chandler DodsonWebsite Desiner |
I am Chandler Dodson and I am from Sugar City, Idaho. I have worked with Poject Case for a couple years now as their website designer. I love it here and I am very grateful for my job. I am 26 years old and I have 2 beautiful children. I have been designing websites for about 2 years now, ever since I graduated from Brigham Young University. This company has helped me grow in experience and knowledge of my field. No-where are you going to find better people who want whats best for you! |
Spencer BensonSenior Manager |
My name is Spencer Benson and I have worked with Project Case since it started up. I helped Daniel set up and plan this whole company for your benefit. I am from Portland, Oregon and have lived here my whole life. In my spare time I am a personal trainer for my friends and family. I have a beautiful wife and 4 kids who I love with everything I am. Project Case is here to help and serve you! We garuntee we will do our best to help you. |
Daniel HyerOwner/Website Designer |
I am Daniel Hyer and I first had the original idea to create Project Case for those who were frustrated with their phone case or wanted to design their own. I am from Portland, Oregon and I have been building this company since 2018. I have a wife and 5 kids, with one grandchild. I can garuntee that we will serve you the best that we can and help you to find your personal case! |
Jessica MartezSenior Executive Assistant Manager |
Hello! I am Jess Martez. I am from San Diego, California and I have worked with Project Case for about a year now. I love it here and I am so blessed to have such wonderful co-workers. I love to bike and go on runs in my freetime. I occasionally compete in local bike races and I love to help people get into the sport. I have always been frustrated with phone cases I have recieved in the past, but I promise you that you will not be disappointed. |
Maddy FarrDesign Specialist |
I am Maddy Farr and I am the Design Specialist here at Project Case. I have worked here for about 2 years helping this company come up with designs for their cases for you to chose from. I am from Bellingham, Washington but have lived in Oregon ever since I started work here. I have loved art my entire life and have developed that love into a desire to design something useful to someone. I do my best to implement my art into the cases that you can create. |


#include <avr/io.h>
#include <avr/delay.h>
#include <util/delay.h>
Defines | |
| #define | FCPU 11059200ul |
| set the cpu frequency | |
| #define | RS 0 |
| set command input | |
| #define | RW 1 |
| writing to LCD | |
| #define | EN 2 |
| set Enable pin | |
| #define | lcd_port PORTC |
| #define | sbit(reg, bit) reg |= (1<<bit) |
| #define | cbit(reg, bit) reg &= ~(1<<bit) |
Functions | |
| void | init_ports () |
| void | lcd_reset_4bit () |
| void | lcd_init () |
| void | lcd_wr_command (unsigned char) |
| void | lcd_wr_char (char) |
| void | lcd_home () |
| void | lcd_cursor (char, char) |
| void | lcd_print (char, char, unsigned int, int) |
| void | lcd_string (char *) |
| void | lcd_set_4bit () |
Variables | |
| unsigned int | temp |
| unsigned int | unit |
| unsigned int | tens |
| unsigned int | hundred |
| unsigned int | thousand |
| unsigned int | million |
| int | i |
Program for printing data on LCD.
Copyright (c) 2010. ERTS Lab IIT Bombay All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holders nor the names of contributors may be used to endorse or promote products derived from this software without specific prior written permission.
Source code can be used for academic purpose. For commercial use permission form the author needs to be taken.
| void lcd_cursor | ( | char | row, | |
| char | column | |||
| ) |
Position the LCD cursor at "row", "column".
| row | no. of rows | |
| column | no. of columns |
| void lcd_init | ( | ) |
Function to Initialize LCD
| void lcd_print | ( | char | row, | |
| char | coloumn, | |||
| unsigned int | value, | |||
| int | digits | |||
| ) |
Function To Print Any input value upto the desired digit on LCD
| void lcd_set_4bit | ( | ) |
Function to Reset LCD
| void lcd_string | ( | char * | str | ) |
Function to Print String on LCD
| void lcd_wr_char | ( | char | letter | ) |
Function to Write Data on LCD
| void lcd_wr_command | ( | unsigned char | cmd | ) |
Function to Write Command on LCD


Start!
30
0
The Amazing Mouse Mase!
移动鼠标开始游戏
别碰到
Game Start!
Mission Complete!
Loser..
YOU'RE CHEATING!!!
S
E


Next: -, Previous: inspect, Up: Environment Dictionary
dribble
&optional pathname ⇒ implementation-dependent
pathname—a pathname designator.
Either binds *standard-input* and *standard-output* or takes other appropriate action, so as to send a record of the input/output interaction to a file named by pathname. dribble is intended to create a readable record of an interactive session.
If pathname is a logical pathname, it is translated into a physical pathname as if by calling translate-logical-pathname.
(dribble) terminates the recording of input and output and closes the dribble file.
If dribble is called while a stream to a "dribble file" is still open from a previous call to dribble, the effect is implementation-defined. For example, the already-open stream might be closed, or dribbling might occur both to the old stream and to a new one, or the old stream might stay open but not receive any further output, or the new request might be ignored, or some other action might be taken.
The implementation.
If a failure occurs when performing some operation on the file system while creating the dribble file, an error of type file-error is signaled.
An error of type file-error might be signaled if pathname is a designator for a wild pathname.
dribble can return before subsequent forms are executed. It also can enter a recursive interaction loop, returning only when (dribble) is done.
dribble is intended primarily for interactive debugging; its effect cannot be relied upon when used in a program.
Next: -, Previous: inspect, Up: Environment Dictionary


IIIT Bhubaneswar owes its origins to the initiative of the Government Odisha. It is a result of the desire of the Government to establish a world class institute of Information Technology in the state. The Institute has been registered as a society in Nov 2006. In January 2014, the Institute is converted to a University by the Government of Odisha. There is misconception that IIIT Bhubaneswar is a Institute promoted in PPP mode by the Government. The Institute is fully owned by the Government of Odisha. The Information Technology Department is the controlling Department. The Government provides funds for the Infrastructure creation. However, the Institute has to earn its Revenue Expenditure. This is the model in all State Government promoted IIITs in Bangalore, Hyderabad and Delhi. The Institute enjoys academic, financial and administrative autonomy by the powers invested through the IIIT Bhubaneswar act. IIIT aspires to be a national Institute in line with its Peer Institutes. The Governance structure of the Institute is modelled after those of the IITs. The Governor of the state is the Chancellor of the Institute. The Board of Governors has members from Odisha Legislative Assembly, Government of Odisha, Leaders from the IT industry and Eminent educationists. Currently, the Chairman of the Institute is the Chief Secretary of the Government of Odisha. The Director is the Executive Head of the Institute. The Director is assisted by the Registrar, the Deans, the Controller of examinations and the Comptroller of finance in day-to-day management of the Institute The Institute has its focus on quality and rigorous education, quality resource, academic infrastructure, technology and innovation. These initiatives have helped IIIT-Bhubaneswar achieve pre-eminence in India and beyond.