RecSys'17 Highlights
In this post I will give my personal thoughts on some articles from 2017 ACM Conference on Recommender Systems, that I chose out of my own interest, with no specific order, divided by recent trends in the area. If you believe I misunderstood any of the papers please correct me. The main topics covered are:
- Explanations and Model Interpretability
- Deep Learning and Session-Based RS
- Gap between academia and industry
Explanations and Model Interpretability
User Preferences for Hybrid Explanations: Authors compare different types of explanations - obtained from different sources in a hybrid recommender such as contextual, social, item-based or user-based - and different visualizations - natural language, rule-based and graphical visualizations such as Venn diagrams - in order to understand which presentation styles improve users experience. Their results show that Venn diagrams outperforms other types of explanations, but are hard to expand for three or more sources of information. Other dimensions did not show a statistically significant difference for user experience in their experiments.
Using Explainability for Constrained Matrix Factorization: Adbollahi proposes a Matrix Factorization model that has explainability constraints, meaning that it is able to generate accurate predictions while still being able to make explanations using no additional data source (only the rating matrix). This is obtained by regularizing the loss function with the addition of a term that represents how explainable predictions are (amount of user-based neighbors or item-based neighbors). Experiments show that EMF (Explainable-Matrix Factorization) is able to generate more explainable recommendations while maintaining the accuracy of MF.
Folding: Why Good Models Sometimes Make Spurious Recommendations: This paper is a really interesting read, it studies why sometimes matrix factorization methods make really bad recommendations in top-K results. It introduces a new metric to measure the effect of folding, which authors claim to be a major contributing factor to such spurious recommendations. Authors define folding as the phenomenon in which disparate groups are collapsed onto the same region in the embedding space. For example, users who are children having projections in embedding space (from matrix factorization ) really close to horror films, even though there is no data supporting such closeness.
Xin et. al also argue that folding happens when the model assigns high affinity to unrelated missing data. This is related to the fact that a model has to be able to discern why a user-item-rating is missing from the observations. The Missing Not At Random (MNAR) says that not all missing data have an equal likelihood of being unobserved (there is a set of items that some users will most likely never consume, and another set of items that users might eventually consume). Considering that the density of ratings in datasets is really small, the MNAR principle has to be taken into account to avoid such spurious recommendations.
Evaluating Decision-Aware Recommender Systems: This paper explores recommender systems that decide whether they should make a recommendation or not, based on their confidence of their recommendations. They propose two ways of measuring such confidence: support (number of neighbors that participated in the computation) in the case of nearest-neighbors algorithms and uncertainty (sd of the predictions) in the case of Probabilistic Matrix Factorization. Experimental results show that their proposed decision-aware recommenders improve accuracy at the expense of item and user coverage. This is another step into better understanding recommender systems errors and assumptions when making spurious recommendations.
Deep Learning and Session-Based RS
First I will talk about DLRS, which is the second workshop on DL (Deep Learning) for recommender systems, and then I will dive into some papers regarding session-based recommenders. The first set of slides from @balazshidasi is an introduction to deep learning, explaining what it is/is not able to do, and why it is receiving so much attention now. The main contributing factors to its success can be divided into three factors: computational power (GPU), more data and research breakthroughs.
Even though deep learning has had impressive results recently, it is not the answer to every problem in machine learning as it has its limitations. So how could we use such concepts in recommender systems? Balázs argues that DL has a potential for feature extraction directly from content (from images or text for example), modeling dynamic behavior with RNNs and generating more accurate representations of users and items.
He points here to the state of the art when using neural networks in recommender systems: embeddings & 2vec models, extracting features from different data domains, deep models for collaborative filtering, autoencoders for collaborative filtering and session-based recommendations with RNNs. Exploring different approaches and architectures of deep models in the context of recommender systems is a promising research area.
Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks:Authors in this paper extend related work by adding an extra component on RNN Session-based Recommenders that captures users profiles and information between sessions. The idea is to use a hierarchical architecture that model cross-sessions iterations by adding another RNN between user sessions, which is capable of capturing the evolution of the user across sessions and providing personalization capabilities. Their results show that the proposed approach is better than baselines and state-of-the-art RNNs that are not sensitive to cross sessions interactions and the ones where user sessions are concatenated.
Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture: Loyola et al proposed an encoder-decoder architecture for the problem of predicting which item will be seen next for a user during a session and whether he has the intention to purchase an item or not. Inspired by recent encoder-decoder approaches in machine translation, the proposed model that is a bidirectional RNN as the encoder and a normal RNN as the decoder, and authors added alignment (passing labels as well as predictions to the next time state) and attention (encapsulating expressive portions of the source sequence in a separate block) mechanisms as well.
When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation: This paper makes shows how the state-of-the-art is still not well defined for session-based recommenders. Authors point for example that GRU4REC was evaluated on a proprietary protocol, making it hard to reproduce its results. They compare the proposed heuristic-based algorithm, that uses the most similar sessions to provide recommendations using a similarity measure to predict next items on the session, with GRU4REC and a hybrid of both. Jannach et al found that the nearest-neighbor scheme performed better in multiple datasets, and the hybridization of both recommenders could improve results, even more, capturing both co-occurrence and sequential signals in the data.
Gap between academia and industry
Rethinking Collaborative Filtering: A Practical Perspective on State-of-the-art Research Based on Real World Insights: Koenigstein refers to his previous paper “The list recommendation problem”, where a more appropriate task - which better relates to real-world recommenders goal - is proposed, the list recommendation problem: given a specific user u, the goal is to produce an ordered personalized list l of K items that maximize the probability that u will click on an item from l. He argues that using such formulation, there is no trade-off between accuracy and diversity, as the model will automatically learn the right amount of diversity needed.
Another point that he makes is that the trade-off between accuracy and novelty is a product of traditional CF models trying to predict the next item the user will buy, instead of optimizing for the real goal of most real-world systems, which is to influence the user to consume more items. As CF models output consists of many popular items that users would most likely consume regardless of the recommendations, current systems are failing to actually influence users behavior. He argues that a combination of CF models (which are great at learning tastes by learning embeddings of tastes) and Reinforcement Learning - which optimizes a reward function based on interactions with the system - will be the future of recommender systems.
Finally, he talks about the problem of evaluating RS, which in an offline setup has evolved from RMSE to other formulations such as rankings is very different from online experiments which are expensive and time-consuming (and sometimes impossible in academic research). This is definitively an open research area, and Koenigstein points to promising research “Learning from logged implicit exploration data” trying to mitigate this problem.
Déjà Vu: The Importance of Time and Causality in Recommender Systems: This was a talk given by @JustinBasilico on how important time is for recommenders at Netflix. He stresses that handling time in offline academic experiments is different from production systems such as Netflix. From slides we can see that Basilico explores the importance of time in various contexts: during data collection and evaluation procedures, recommender algorithms that use time in their model, models learning, and recommender systems that change over time (UX, recommender algorithms, feedback loops), and during the interaction with the system, so users need less time to find something great to watch.
This is a nice reminder that evaluation procedures and methodology in academia should take into account time, for example splitting data for training models and testing them using a fixed date cut instead of user-wise.


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Send to form field value¶
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We end with a solution to a problem of prediction based on more than one predictor.
Let $p$ (for predictors) be a positive integer. Suppose the random vector
$$ \begin{bmatrix} Y \\ X_1 \\ X_2 \\ \vdots \\ X_p \end{bmatrix} $$is multivariate normal, and suppose we are trying to predict $Y$ based on the $p$ predictor variables
$$ \mat{X} ~ = ~ \begin{bmatrix} X_1 \\ X_2 \\ \vdots \\ X_p \end{bmatrix} $$You will recognize the following as extensions of results you know well from predicting $Y$ based on a single $X$.
To complete the specification of the normal conditional distribution of $Y$ given $\mat{X}$, we have to identify the conditional mean and the conditional variance. We will not derive these quantities, but we will demonstrate how their formulas are clear analogs of the familiar calculations when there is only one predictor variable.
To keep our variables organized and our notation compact, we will partition the random vector and its mean vector.
$$ \begin{bmatrix} Y \\ X_1 \\ X_2 \\ \vdots \\ X_p \end{bmatrix} ~ = ~ \begin{bmatrix} Y \\ \mat{X} \end{bmatrix} ~~~~~~~~~~~~~~~ \begin{bmatrix} \mu_Y \\ \mu_{X_1} \\ \mu_{X_2} \\ \vdots \\ \mu_{X_p} \end{bmatrix} ~ = ~ \begin{bmatrix} \mu_Y \\ \mat{\mu_X} \end{bmatrix} $$We can partition the covariance matrix as well, according to the demarcating lines shown below.
We will start by recalling how to predict $Y$ based on one predictor variable $X$. As in previous sections, let $a^*$ be the slope of the regression line. Our first step is to write $a^*$ in terms of elements of elements of the covariance matrix of $X$ and $Y$.
$$ a^* ~ = ~ \rho \frac{\sigma_Y}{\sigma_X} ~ = ~ \frac{\sigma_{X,Y}}{\sigma_X \sigma_Y} \cdot \frac{\sigma_Y}{\sigma_X} ~ = ~ \sigma_{Y,X}({\sigma_X^2})^{-1} $$The equation of the regression line is
\begin{align*} E(Y \mid X = x) ~ &= ~ a^*x + (\mu_Y - a^*\mu_X) \\ &= ~ a^*(x - \mu_X) + \mu_Y \\ &= ~ \sigma_{Y,X}({\sigma_X^2})^{-1}(x - \mu_X) + \mu_Y \end{align*}To extend this result to the case where $\mat{X}$ has $p$ components, replace each of the quantities in the formula by its matrix counterpart.
The result should be quite believable though we have proved it only in the case of one predictor variable. There are several ways to prove it in the general case. One way is to use the division rule to find the conditional density of $Y$ given $X$. A more insightful way is to write multivariate normal variables in terms of independent normal variables and then make use of the independence just as we did when we had one predictor.
Whether or not you go through the proof in the future, keep in mind that order matters when you are multiplying matrices: you can only multiply matrices if their shapes allow it. In the formula for $E(Y \mid \mat{X} = \mat{x})$, the mean $\mu_Y$ is a scalar. So $\bsymb{\Sigma_{Y,X}}\bsymb{\Sigma_{X,X}}^{-1} \big{(} \mat{x} - \mat{\mu_X} \big{)}$ had better be a scalar as well. It is, because:
Return to predicting $Y$ based on just one variable $X$, and this time write $\rho^2$ in terms of elements of the covariance matrix of $X$ and $Y$.
$$ \rho^2 ~ = ~ \frac{\sigma_{X,Y}^2}{\sigma_X^2 \sigma_Y^2} ~ = ~ \sigma_{Y,X}({\sigma_X^2})^{-1}\sigma_{X,Y}({\sigma_Y^2})^{-1} $$Thus for every $x$, $$ Var(Y \mid X = x) ~ = ~ (1 - \rho^2)\sigma_Y^2 ~ = ~ \sigma_Y^2 - \rho^2\sigma_Y^2 ~ = ~ \sigma_Y^2 - \sigma_{Y,X}({\sigma_X^2})^{-1}\sigma_{X,Y} $$
As before, replace each of the quantities above by its matrix counterpart to extend the result to the case of $p$ predictors.


gai_strerror — print suitable error string
#include <lwres/netdb.h>
char *
gai_strerror(
int ecode)
;
lwres_gai_strerror()
returns an error message corresponding to an error code returned by
getaddrinfo()
.
The following error codes and their meaning are defined in
include/lwres/netdb.h
.
address family for hostname not supported
temporary failure in name resolution
invalid value for
ai_flags
non-recoverable failure in name resolution
ai_family
not supported
memory allocation failure
no address associated with hostname
hostname or servname not provided, or not known
servname not supported for ai_socktype
ai_socktype
not supported
system error returned in errno
The message invalid error code is returned if
ecode
is out of range.
ai_flags
,
ai_family
and
ai_socktype
are elements of the
struct addrinfo
used by
lwres_getaddrinfo()
.


@js1568 | github.com/jstrangfeld |<EMAIL_ADDRESS>|<PHONE_NUMBER>
Implement web application design strategies; standardize user interface design, public data APIs, and information architecture.
Maintain www.dvrpc.org and secondary websites, develop new web applications for internal use, design and create high quality emails, assist with server side data exchange for web mapping applications, provide information resources to staff.
Designed and developed prototype social network web application for healthy individuals and groups.
Updated (fixed) corporate Drupal website: added external and original RSS feeds to homepage, simplified design consistently, ensured XHTML standard compliance.


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FinishFastCGIRequest()
语法
FinishFastCGIRequest()概要
Finish the current FastCGI request and free all resources associated to it. It's not mandatory to use this command, as the request will be automatically finished when WaitFastCGIRequest()() is called again, or when the thread ends. It can still be useful in some special case where resources are light before doing other processing.
参数
无.
返回值
Returns non-zero if a new request has been processed.
示例
If Not InitCGI() End EndIf If Not InitFastCGI(5600) ; Create the FastCGI program on port 5600 End EndIf While WaitFastCGIRequest() If ReadCGI() WriteCGIHeader(#PB_CGI_HeaderContentType, "text/html", #PB_CGI_LastHeader) ; Write the headers to inform the browser of the content format WriteCGIString("<title>PureBasic - FastCGI</title>" + "Hello from PureBasic FastCGI !
" + "Actual time: "+FormatDate("%hh:%ii", Date()) + "" + "") FinishFastCGIRequest() ; Do some processing ; Delay(1000) ; Simulate large processing EndIf Wend
参阅
InitCGI(), InitFastCGI(), WaitFastCGIRequest()
已支持操作系统
所有


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adp
Businessmodel of ADP
Customer Segments
ADP provides a full range of human resources and human capital management services to a broad client portfolio. The Company’s strategic software-as-a-service offerings serve approximately 530,000 worldwide, principally comprising corporate and institutional employers of all sizes and across all business sectors. The Company’s professional employer organisation specifically targets small and medium-sized businesses, providing human resources outsourcing solutions through a co-employment model. ADP serves approximately 439,000 worksite employees across all 50 US states in this way.
ADP’s clients span a number of business sectors, including the pharmaceutical, manufacturing, financial services, and retail industries, and include numerous high-profile companies, including JP Morgan Chase, Sharp, BT, Marriot, Fujifilm, and Innocent Drinks.
ADP serves an international client base. Its principal market is the US, where its professional employer organisation services are provided exclusively. The Company offers wage and tax collection and remittance services in the US, Canada, the UK, the Netherlands, France, Australia, India, and China. The Company also provides its ore services more broadly across the Americas, Asia Pacific, and Europe, as well as in the Middle East and Africa.
Value Propositions
ADP provides value to its clients in the following ways:
- Its industry standing and reputation, with the Company established as one of the most well-known and well-regarded companies in its field, with a track record of providing innovative, effective, and reliable solutions;
- Its broad portfolio of services, with the Company offering a broad range of solutions and services to employers of all sizes across multiple industries;
- Its accessibility and flexibility, with the Company offering technology-based solutions that enable customers to access tools and resources across multiple platforms, quickly and easily;
- Its research and insights, with the Company basing its various solutions and services on in-depth industry insights and research, enabling the Company to provide more effective and targeted solutions; and
- Its international reach, with the Company providing services to an international client base spanning North America, Europe, the middle East, Africa, and Asia Pacific, as well as a large domestic client base in the US. ### Channels
ADP operates a website at www.adp.com, through which it provides information on its various services, solutions, and activities. The Company provides a range of online platforms and portals through which customers can access a range of services and tools, including its ADP Portal, ADP iPayStatements, and ezLaborManager. The Company however, does not appear to sell products directly to customers online.
The bulk of ADP’s sales are made through its in-house direct sales organisations, which itself is organised by geographic region. These sales personnel operate out of the Company’s offices across the Americas, Asia Pacific, and Europe, Middle East, and Africa, including in Mexico, Colombia, Algeria, Portugal, the UK, Australia, and Japan.
Additionally, ADP markets its employer services solutions through various indirect sales channels, including marketing relationships with banks and certified public accountants, among others.
Customer Relationships
ADP provides a broad range of services and tools to its customers on a self-service basis through its online portals and its software-as-a-service solutions. These channels enable clients to manage their accounts, control payroll operations, make payments, manage time and attendance, without interacting with members of the Company’s sales and support personnel.
ADP provides tailored services through its direct sales force, consulting closely with clients in order to establish the most appropriate solutions and products for each individual client. Larger clients have their own relationship management teams that are available to provide personalised assistance at all times. ADP’s client contracts are typically fixed term and span multiple years.
ADP provides ongoing services to its clients, and is available to respond to queries and enquires from potential clients, through its sales and service teams. Clients can access personalised technical support by contacting the Company over the phone or online. Alternatively, the Company provides a range of online resources, including insights and research papers, user guides, and FAQs, and provides information on upcoming events and webinars.
Additionally, ADP operates a number of social media accounts – including with LinkedIn, Twitter, Google+, and Facebook – through which it is able to interact directly with clients.
Key Activities
ADP is a provider of technology-backed human capital management and business process outsourcing solutions.
The Company organises its operations into two reportable business segments: Employer Services, which offers a range of human resources, business process outsourcing, and technology-enabled human capital management solutions, including payroll services, benefits administration, talent management, and insurance services; and Professional Employer Organisation, which offers small and medium-sized businesses a software-as-a-service human resources outsourcing solution that operates under a co-employment business model.
ADP provides services to an international client base, with operations across the Americas, Europe, Asia Pacific, the Middle East, and Africa, serving employers of all sizes across multiple business sectors.
Key Partners
ADP works in conjunction with a network of partner companies and organisations, which support the development and distribution of the Company’s core human capital management solutions. These partnerships include:
- Supplier Partners, comprising suppliers of various services, tools, and equipment that support the Company’s activities across both operating segments, as well as companies to which certain functions can be outsourced;
- Accounting Partners, comprising various accounting firms and professionals which provide payroll, human resources, and compliance services on behalf of the Company;
- Affiliates and Franchise Partners, comprising various professional service providers and business to which the Company provides services in return for certain marketing and branding services;
- Brokers and Insurance Partners, comprising various brokers and insurance providers that work with the Company to assist in providing insurance and other human resources services;
- Financial Partners, comprising a range of banking institutions and financial service providers through which the Company markets and distributes certain services; and
- Technology Partners, comprising technology companies, software providers, and IT services providers that assist in developing and maintaining the Company’s software platforms and IT infrastructure. ADP has launched a number of partnerships in recent years. This includes a partnership with Workday to develop a unified global payroll experience, and a tie-up with Intacct to streamline back-office processes of mid-sized companies.
Key Resources
ADP’s key resources are its intellectual properties and technologies, its online platforms and portals, its IT and communications infrastructure, its research and development facilities, its sales and marketing channels, its partnerships, and its personnel.
ADP own a number of patents, and is the licensee under a number of agreements for computer programs and databases, that are key to its operations. Searches of records published by the US Patent and Trademark Office identified a number of patent applications filed in the name of ADP or its subsidiaries, in particular ADP LLC, including applications entitled ‘Payroll data entry and management’, ‘Display screen with an animated graphical user interface’ and ‘Display screen or portion thereof with graphic user interface’.
ADP owns and or leases a number of physical properties around the world. This notably includes a network of processing and print centres, operational offices, sales offices, and its corporate headquarters in Roseland, New Jersey.
Cost Structure
ADP incurs costs in relation to the development of its technologies and online platforms, the maintenance of its IT and communications infrastructure, the operation of its sales and distribution channels, the procurement of professional services, the implementation of marketing and advertising campaigns, the management of its partnerships, and the retention of its personnel.
For the year ending 30th June 2016 ADP recorded total cost of revenues in the amount of $6.84 billion. This included operating expenses of $6.03 billion, and systems development and programming costs totaling $603.7 million. The Company also recorded selling, general, and administrative expenses in the amount of $2.64 billion.
Revenue Streams
ADP primarily generates revenues through the collection of fees for providing various services, such as employer services payroll processing fees. It also derives a portion of its revenue from investment income on payroll funds, payroll tax filing funds, and fees charged to implement clients on the Company’s solutions.
For the year ending 30th June 2016 ADP generated annual revenue of $11.67 billion, up on the $10.94 billion recorded by the Company the previous year. The Company attributed this increase in revenue to new business and new business bookings growth.


{% extends "header.html" %} {% block body %}
Thank you very much for your donation!
All donations are logged automatically, though they are currently processed manually. I will write an automated process if donations become frequent enough. If you do not see your donation as having affected the support amounts within 24-48 hours, feel free to email using the contact below to make sure it wasn't missed.
-Harrison


from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(data[..., 0], 4, 4)
We can now do a first segmentation of the data using the Colored Fractional
Anisotropy (or cfa). It encodes in a 3D volume the orientation of the diffusion.
threshold = (0.7, 1, 0, 0.1, 0, 0.1)
CC_box = np.zeros_like(data[..., 0])
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff


{% extends 'base.html' %} {% import 'macros.html' as macro %} {% block styles%} {{ super() }} {% endblock %} {% block content %}
Category: {{pitch.category}}
Pitch: {{pitch.pitch}}
Author: {{pitch.author}}
{% for comment in pitch.comment %}comment:{{comment.description}}
{%endfor%}


Project for fun by Noel Delgado. Color Palettes powered by ColourLovers. Entypo pictograms by Daniel Bruce — www.entypo.com.


abstract fun onReceived(@NonNull purchaserInfo: PurchaserInfo): Unit
Will be called after the call has completed.
purchaserInfo - PurchaserInfo: [PurchaserInfo] class sent back when the call has completed
purchaserInfo


IGrades
Information about student grades.
Fields
semestersCount: number - how many semesters are there
evaluationType: number - evaluation type
isGpaWeighted: boolean - do grades have weight?
selectedSemester: number - which semester is selected (this field does nothing if there are 2 semesters)
subjects: IGrades.ISubject[] - array of subjects


To do
Can I email is based on contributions. Want to help? Here are a few tasks you can help with, even if you know nothing about email development.
Add links
-
{% for feature in site.features %}
{% if feature.links.size == nil or feature.links.size == 0 %}
- {{ feature.title | escape_once }} {% endif %} {% endfor %}


---
layout: default
title: Tom Osman
description: Techno DJ & Producer
image: /images/logo.jpg?
tags: [DJ, Music Producer, Techno, Melodic Techno, Hard Techno, Berlin, Berlin DJ]
call-to-action: booking
call-to-action-url<EMAIL_ADDRESS>---
Techno DJ & Producer
Tom Osman (Thomas Ascioglu) is a Techno DJ & Producer living in Berlin.
During the lockdown in 2020 Tom Osman started mixing and played for small groups.
He's still in the early days of his DJ career but has received great feedback at different gigs around Berlin.
Tom Osman is actively looking for more opportunities to play in front of techno lovers.
Tom Osman's music is influenced by Berlin's diverse techno scene, where he has been a regular guest at different clubs and events.
As a DJ he likes to combine melodic with dark and hard elements to take his audience on a fast and energetic journey.


We hope you find this site comfortable. I would like to make an introduction about the project before you begin to test the platform. Tweet Event Application , born as an idea for a project in a subject of my university. The main idea was to create a platform to obtain information through the Twitter API and through data mining techniques and specialized algorithms to provide the user with an approach on the issues that have been a "trend" for users Twitter. This would be a brief introduction , but I invite you to try the tool, analyze results, and share your experience with us.


Consider the following Jison grammar:
%lex %% \s+ /* skip whitespace */ tokens<<EOF>> return "EOF" . return "INVALID" /lex %start ss %% ss : s "EOF" ; productions
Now consider the following five strings:
How many of these strings are parsed successfully according to the grammar above?
2
To do


TCGBPurchasable Protocol Reference
| Declared in | TCGBPurchasable.h |
|---|
Overview
The protocol TCGBPurchasable is for developing IAP Adapter.
This protocol contains several methods such as to request item list, to puchasing item and retrying incompleted purchasing.
Initilaize IAP Adapter
– initializePurchaseWithAppID:store:userID:enableDebugMode:
This method ininialize the IAP Adapter class.
- (void)initializePurchaseWithAppID:(NSString *)appID store:(NSString *)store userID:(NSString *)userID enableDebugMode:(BOOL)isDebugMode
Parameters
appID |
appID is ToastCloud IAP’s appID, not ToastCloud projectID. |
|---|---|
store |
store should be appstore. Others will be ignored. |
userID |
userID is obtained after logged in. This must be unique cause of tracing purchasing. |
isDebugMode |
isDebugMode is for setting debug mode of ToastCloud IAP module. |
Declared In
TCGBPurchasable.h
Request Item List
– requestItemListPurchasableWithCompletion:
required method
This is the primary method for obtaining ItemList which is registered at ToastCloud IAP Console and Apple Itunes Connect.
- (void)requestItemListPurchasableWithCompletion:(void ( ^ ) ( NSArray<TCGBPurchasableItem*> *purchasableItemArray , TCGBError *error ))completion
Parameters
completion |
completion may return the NSArray of TCGBPurchasableItem. |
|---|
Discussion
Request a item list which is purchasable. This list has items which are registered in both Market(AppStore) and ToastCloud IAP Console.
Warning: You should call this method after logged in
, otherwise you will get TCGB_ERROR_NOT_LOGGED_IN error in the completion.
Declared In
TCGBPurchasable.h
– requestItemListAtIAPConsoleWithCompletion:
required method
This is the method for obtaining ItemList which is registered at ToastCloud IAP Console.
- (void)requestItemListAtIAPConsoleWithCompletion:(void ( ^ ) ( NSArray<TCGBPurchasableItem*> *purchasableItemArray , TCGBError *error ))completion
Parameters
completion |
completion may return the NSArray of TCGBPurchasableItem. |
|---|
Discussion
Request a item list which is purchasable. This list has items which are only registered in ToastCloud IAP Console, not Market(AppStore)
Warning: You should call this method after logged in
, otherwise you will get TCGB_ERROR_NOT_LOGGED_IN error in the completion.
Declared In
TCGBPurchasable.h
Request Purchasing Item
– requestPurchaseWithItemSeq:viewController:completion:
required method
This is the method to request purchasing item which identifier is itemSeq. There is a viewController parameter and you may put your top most viewController. If you don’t, this method will find out top most view controller and put it in the parameter.
- (void)requestPurchaseWithItemSeq:(long)itemSeq viewController:(UIViewController *)viewController completion:(void ( ^ ) ( TCGBPurchasableReceipt *purchasableReceipt , TCGBError *error ))completion
Parameters
itemSeq |
itemID which you want to purchase. |
|---|---|
viewController |
represent to current viewcontroller. |
completion |
completion may return the TCGBPurchasableReceipt instance. |
Discussion
Request Purchasing Item that has itemId.
Warning: You should call this method after logged in
, otherwise you will get TCGB_ERROR_NOT_LOGGED_IN error in the completion.
Declared In
TCGBPurchasable.h
Request non-consumed Item List
– requestItemListOfNotConsumedWithCompletion:
required method
This method provides an item list that have non-consumed.
- (void)requestItemListOfNotConsumedWithCompletion:(void ( ^ ) ( NSArray<TCGBPurchasableReceipt*> *purchasableReceiptArray , TCGBError *error ))completion
Parameters
completion |
completion may return the NSArray of TCGBPurchasableReceipt instances. |
|---|
Discussion
Request a Item List which is not consumed. You should deliver this itemReceipt to your game server to consume it or request consuming API to ToastCloud IAP Server. You may call this method after logged in and deal with these non-consumed items.
Warning: You should call this method after logged in
, otherwise you will get TCGB_ERROR_NOT_LOGGED_IN error in the completion.
Declared In
TCGBPurchasable.h
Request retry purchasing processes
– requestRetryTransactionWithCompletion:
required method
This method is for retrying failed purchasing proccesses.
- (void)requestRetryTransactionWithCompletion:(void ( ^ ) ( TCGBPurchasableRetryTransactionResult *transactionResult , TCGBError *error ))completion
Parameters
completion |
completion may return the TCGBPurchasableRetryTransactionResult which has two member variables which are named ‘successList’ and ‘failList’. |
|---|
Discussion
Request a retrying transaction which is not completed to IAP Server
Declared In
TCGBPurchasable.h


Frequently Asked Questions
"What are your hours?"
"Can you tell me more about your business?"
"What
services do you offer?"
"Do you get jiggy with it?"
"Can you tell me more about your love for caterpillar?"
"What do you use to start fires?"
"Do you like green
eggs and ham?"
"Can you tell me more about your old chevy?"
"Can you help me change my spark plugs?"


This action will mark all test specifications for this version as obsolete and will mark this version as obsolete as well. You can make version "non-obsolete" again by clicking on Edit button and unchecking "obsolete" checkbox. Please note that this will not mark test specifications as non-obsolete though.
Do you want to continue?


Meet Arcadia
Arcadia promotes community health in Middle Tennessee. We support projects focused on creative land sharing, cutting-edge perennial farming, and ecological land stewardship.
Our mission is to reduce the substantial barriers to these activities. We envision a future in which anyone can obtain secure access to land doing exciting projects that matter to all of us, for the long term.
- Fosters cooperation.
-
Arcadia enables cooperative land use with creative & secure land sharing agreements.
- Designs & plans projects.
-
We design and coordinate community-focused, community-led land projects, emphasizing cutting-edge agroecology practices.
- Connects partners.
-
Arcadia links community organizations, local businesses, and non-profits with viable land projects to grow community resilience.


This is header
Website Name
header of first news
Old News
Manchester United lost to Barcelona!
header of second news
Latest News
Manchester United won Barcelona!!!


Under Construction
Sorry.... We are improving and fixing problems of our website.
We will be back very soon....


Gerrard
This page contains an index of all the individuals in the database with the surname of Gerrard. Selecting the person’s name will take you to that person’s individual page.
| Given Name | Birth | Death | Partner | Parents |
|---|---|---|---|---|
| Beatrice Jones | 1882-02-25 | 1911-04-19 | Harris, Lewis John | Gerrard, John Jones Boumphrey, Elizabeth |
| Betty Jones | Patterson, ??? | Jones Gerrard, Robert Patterson Brown, Lily Miriam | ||
| David Jones | 1853-04-18 | Gerrard, Edward Jones, Margaret | ||
| Edith Jones | 1858-10-13 | 1st Qtr 1862 ???? | Gerrard, Edward Jones, Margaret | |
| Edward | about 1829 | 4 Qtr 1862 ?? | Jones, Margaret | |
| Edward Jones | 1884-11-29 | 2nd Qrt 1928 | Gerrard, John Jones Boumphrey, Elizabeth | |
| Elizabeth Jones | 2nd Qtr 1891 | 1st Qtr 1892 | Gerrard, John Jones Boumphrey, Elizabeth | |
| Humphrey Jones | 1904-08-31 | 1966-11-25 | Williams, Winifred | Gerrard, John Jones Boumphrey, Elizabeth |
| Isabella Jones | 1st Qtr 1889 | 2nd Qtr 1898 | Gerrard, John Jones Boumphrey, Elizabeth | |
| John Jones | about 1851 | 1904-11-13 | Boumphrey, Elizabeth | Gerrard, Edward Jones, Margaret |
| John Jones | 1892-12-12 | Thompson, Irene Ruby | Gerrard, John Jones Boumphrey, Elizabeth | |
| Rebecca Jones | 3rd Qtr 1900 | 1972-05-20 | Richards, Trevor | Gerrard, John Jones Boumphrey, Elizabeth |
| Reginald Jones | 1856-01-17 | Gerrard, Edward Jones, Margaret | ||
| Samuel Jones | 3rd Qtr 1895 | 3rd Qtr 1898 | Gerrard, John Jones Boumphrey, Elizabeth | |
| William Jones | 1st Qtr 1887 | 1955-08-18 | Gerrard, John Jones Boumphrey, Elizabeth | |
| [Living] | Gosling, [Living] | Gerrard, [Living] Cullen, [Living] | ||
| [Living] | Corneluis, [Living] | Gerrard, Humphrey Jones Williams, Winifred | ||
| [Living] | Cullen, [Living] | Gerrard, Humphrey Jones Williams, Winifred | ||
| [Living] | King, [Living] | Gerrard, [Living] Cullen, [Living] | ||
| [Living] | Yorath, [Living] | Gerrard, [Living] Cullen, [Living] | ||
| [Living] | Gerrard, [Living] , [Living] | |||
| [Living] | Gerrard, [Living] King, [Living] | |||
| [Living] | Gerrard, [Living] Yorath, [Living] | |||
| [Living] | Gerrard, [Living] , [Living] | |||
| [Living] | , [Living] | Gerrard, [Living] Cullen, [Living] | ||
| [Living] | Wilson, [Living] | Gerrard, [Living] Symonds, [Living] | ||
| [Living] | Symonds, [Living] | Gerrard, John Jones Thompson, Irene Ruby | ||
| [Living] | unknown, [Living] | Gerrard, [Living] Symonds, [Living] |


This project stated out with an article in Make back in 2011. The article in question was one overveiwing the construction of [Ben Krasnow’s DIY scanning electron microscope](https://www.youtube.com/watch?v=VdjYVF4a6iU) (SEM). At the time, I was in aw of the thing, and also instantly wanted on for myself.
This was however impractical for me at the time. I was far to young and inexperienced to even come remotely close to having the skills to operate, much less even build or repair on of these things. So that was that, until one day a few years latter, when searching craigslist for an optical microscope, I came across a JEOL JSM 35CF. The unit was in relatively good shape, and more importantly was located close by in Seattle. The only issue was of course the cost. Coming in at around 2000$, it was of course a good deal for what it was, but well out of my price range.
The unit came and went on and off of craigslist, and that was that. There was however one crucial development that came from this, I set up and email alert on eBay and Craigslist.
This was of course meet with much disappointment as excellent units surplussed from NASA or various semiconductor applications went up for sale, and were all either to far away, to expensive, but mostly some combination of the two. And that brings us to the present.
In March 2017, I got an alert for a unit on eBay that was only 400$ and located just north of Seattle, well withing my range for price and distance. Armed with a pickup, trailer, and moving supplies, I headed up north.
The unit in qestion is a ISI Super IIIA, manufactured in Japan in 1978, and surplussed by Boeing in 2002. The unit worked when I was decommissioned in 2002, and it came with the manual, full schematics, and some spare parts, something that would be hard if not impossible to find for this unit.
After the hour long drive, I was greeted with a surprise. The eBay listing had shown one unit intact what I assumed to be parts of the same unit disassembled. As it turns out, and was not noted in the listing, I had bought nearly two complete unites. The only things missing from unit two was the high voltage oil tank and the scan column. I had everything else to unit 2 though, the the control and imaging units, the main power supply, a housing, and the vacuum valve assembly and controller. I was ecstatic about this, one of my largest concerns with this project was where was I going to find spare parts, and as it turns out, I nearly had a complete set.
Additionally, the seller also gave me a Princeton Gama Tech x-ray spectroscopy unit, amplifier and SEM interface included (no pulse processor though, I am going to look into using [PRA 15](http://www.physics.usyd.edu.au/~marek/pra/) from the University of Sydney for data analysis (I have seen it used for neutron spectroscopy spectroscopy, and from looking at the setup used, it should be possibly to adapt it for x-ray).
Getting the unit home was actually relatively easy, the seller had already completely disassembled it, making it easy to load all of the components into the back of the pickup and then strap down the frames to the trailers. It was a tight fit to get everything into the back of the pickup, but there was no way that I was going to say no to any of the additional equipment that the seller was offering me.


Smartsheet C# SDK Documentation v2.126.0
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We try to create and offer an online, community maintained index of all things realted to quidditch.
All Games, Teams, Players and Referees get listed on one single place.


Born and raised outside DC. I spent the first part of my life playing every sport that was offered. I was extremely active and enjoyed being outside and around people. Before 6th grade, I moved to Austin where I spent three incredibly eye opening years before returning to DC. During my short stint in Austin, I fell in love with barbeque, water sports, and football.
Back in DC, everything felt strange as life kicked into high gear again. Being used to the east-coast pace made it easy to coast all the way through high school. I found myself graduating and getting ready to move to Philly and become a Quaker. From day one as a quaker my life revloved around Lacrosse. That remains true even to today. I spent 4 wonderful years at UPenn only to realize that what I was building wasn't for me. What would I do? Say goodbye to Philly and hello to Boulder. Since I've lived in Boulder, I have become deeply invested in every part of my life. My journey is just beginning as today is the beginning of the most exciting chapter yet.
gSchool
I'm an outdoor junkie with a passion for challenges. After gSchool, I hope to continue learning the trade and using my newfound skills to build scalable products that help everyday life. My goal is to become a proficient coder and designer with a focus on simple effective yet moving products.


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