Trending March 2024 # Career Insight: Know Everything About 3D Artists At A Glance # Suggested April 2024 # Top 6 Popular

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Roles and responsibilities: 3D Artists, also known as Computer-Generated Imagery (CGI) Artists create still and moving images and visual effects using computers. They are responsible for designing three-dimensional models, animations, and visual effects that are used in TV programs, movies, games, etc. As a mixture of technology and creativity, 3D Artists use their hand-drawing techniques and computer software to create animations and graphics. They have to start by storyboarding, that is visualizing the story and then, move on to sketching out and mapping the sequels of panels. As a major step to bring life to the work, 3D Artists use virtual rigs to model the character and backgrounds.

Average Salary (per annum): US$69,851

Qualifications: 

Ability to coordinate with animators and concept artists regarding the requirement of the project.

Work from scratch to create characters, objects, layouts, backgrounds, etc.

Talent to adapt to software upgrades and stay up to date with new technologies.

Good at integrating sounds, syncing dialogue, and adding sound effects to visuals.

Capability to visualize 3D iterations of two-dimensional sketches and artwork.

Training in animation, computer graphics, graphic design, or fine art. 

Working knowledge on the laws of physics such as gravity, acceleration, and the laws of energy.

Top 3 Online Courses:

The Complete 3D Artist: Learn 3D Art by Creating 3 Scenes by Udemy: The Complete 3D Artist course, created by Stephen Woods, is specifically focusing on teaching the fundamentals of 3D art. It is a project-based course designed to teach students the basics they need to learn to create 3D art in practical 3D software. 

Introduction to 3D Modeling by CG Spectrum: By signing in for the online course, students will learn how to create realistic props and assets using Maya and Substance Painter. Students will be mentored by an industry expert who has many years of experience working on blockbuster films and best-selling video games. It helps students build practical job skills that serve throughout their careers. 

Top Institutes Offering the Program:

Diploma in 3D Modeling for Animation & Games: LaSalle College, Vancouver

Game Art & 3D Animation Diploma: SAE Institute, Germany

BA (Hons) 3D Games Art & Design: University of Hertfordshire

Top Recruiters for This Job:

Toonz Animation India: Toonz Animation, a part of Toonz Media Group, is one of Asia’s most active animation production studios. The company is a 360-degree media powerhouse with over two decades of unparalleled experience. Toonz was established at a time when commercial studios for animation were a formidable name in the kinds and family entertainment segment, with studios and offices across the world.

Zynga: Founded in 2007, Zynga is changing the gaming industry forever with broadly popular games like FarmVilla, Zynga Poker, Draw Something, and Words with Friends, which are played by hundreds of millions of players every month. Now, the company is trying to orchestrate a turnaround hinge on delivering hints in the mobile games space.

Wargaming: Wargaming is a unique gaming service that unites all of Wargaming’s MMO games into a singular experience. The Belarusian video game company operates across more than 20 offices globally and development studios, the largest of which is located in Minsk. Wargaming develops trailblazing games including military-themed team-based games, the later world of warships, and the world of warplanes. 

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Everything You Need To Know About Scikit

Introduction

Scikit-learn is one Python library we all inevitably turn to when we’re building machine learning models. I’ve built countless models using this wonderful library and I’m sure all of you must have as well.

There’s no question – scikit-learn provides handy tools with easy-to-read syntax. Among the pantheon of popular Python libraries, scikit-learn ranks in the top echelon along with Pandas and NumPy. These three Python libraries provide a complete solution to various steps of the machine learning pipeline.

I love the clean, uniform code and functions that scikit-learn provides. It makes it really easy to use other techniques once we have mastered one. The excellent documentation is the icing on the cake as it makes a lot of beginners self-sufficient with building machine learning models.

The developers behind scikit-learn have come up with a new version (v0.22) that packs in some major updates. I’ll unpack these features for you in this article and showcase what’s under the hood through Python code.

Note: Looking to learn Python from scratch? This free course is the perfect starting point!

Table of Contents

Getting to Know Scikit-Learn

A Brief History of Scikit-Learn

Scikit-Learn v0.22 Updates (with Python implementation)

Stacking Classifier and Regressor

Permutation-Based Feature Importance

Multi-class Support for ROC-AUC

kNN-Based Imputation

Tree Pruning

Getting to Know Scikit-Learn

This library is built upon the SciPy (Scientific Python) library that you need to install before you can use scikit-learn. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.

Overall, scikit-learn uses the following libraries behind the scenes:

NumPy: n-dimensional array package

SciPy: Scientific computing Library

Matplotlib:  Plotting Library

iPython: Interactive python (for Jupyter Notebook support)

SymPy: Symbolic mathematics

Pandas: Data structures, analysis, and manipulation

Lately, scikit-learn has reorganized and restructured its functions & packages into six main modules:

Classification: Identifying which category an object belongs to

Regression: Predicting a continuous-valued attribute associated with an object

Clustering: For grouping unlabeled data

Dimensionality Reduction: Reducing the number of random variables to consider

Model Selection: Comparing, validating and choosing parameters and models

Preprocessing: Feature extraction and normalization

scikit-learn provides the functionality to perform all the steps from preprocessing, model building, selecting the right model, hyperparameter tuning, to frameworks for interpreting machine learning models.

Scikit-learn Modules (Source: Scikit-learn Homepage)

A Brief History of Scikit-learn

Scikit-learn has come a long way from when it started back in 2007 as scikits.learn. Here’s a cool trivia for you – scikit-learn was a Google Summer of Code project by David Cournapeau!

This was taken over and rewritten by Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel, all from the French Institute for Research in Computer Science and Automation and its first public release took place in 2010.

Since then, it has added a lot of features and survived the test of time as the most popular open-source machine learning library across languages and frameworks. The below infographic, prepared by our team, illustrates a brief timeline of all the scikit-learn features along with their version number:

The above infographics show the release of features since its inception as a public library for implementing Machine Learning Algorithms from 2010 to 2023

Today, Scikit-learn is being used by organizations across the globe, including the likes of Spotify, JP Morgan, chúng tôi Evernote, and many more. You can find the complete list here with testimonials I believe this is just the tip of the iceberg when it comes to this library’s popularity as there will a lot of small and big companies using scikit-learn at some stage of prototyping models.

The latest version of scikit-learn, v0.22, has more than 20 active contributors today. v0.22 has added some excellent features to its arsenal that provide resolutions for some major existing pain points along with some fresh features which were available in other libraries but often caused package conflicts.

We will cover them in detail here and also dive into how to implement them in Python.

Scikit-Learn v0.22 Updates

Along with bug fixes and performance improvements, here are some new features that are included in scikit-learn’s latest version.

Stacking Classifier & Regressor

Stacking is an ensemble learning technique that uses predictions from multiple models (for example, decision tree, KNN or SVM) to build a new model.

This model is used for making predictions on the test set. Below is a step-wise explanation I’ve taken from this excellent article on ensemble learning for a simple stacked ensemble:

The base model (in this case, decision tree) is then fitted on the whole train dataset

This model is used to make final predictions on the test prediction set

The mlxtend library provides an API to implement Stacking in Python. Now, sklearn, with its familiar API can do the same and it’s pretty intuitive as you will see in the demo below. You can either import StackingRegressor & StackingClassifier depending on your use case:

from

sklearn.linear_model

import

LogisticRegression

from sklearn.ensemble import RandomForestClassifier from chúng tôi import DecisionTreeClassifier

from

sklearn.ensemble

import

StackingClassifier

from

sklearn.model_selection

import

train_test_split

X

,

y

=

load_iris

(

return_X_y

=

True

)

estimators

=

[

(

'rf'

,

RandomForestClassifier

(

n_estimators

=

10

,

random_state

=

42

)),

(

'dt'

,

DecisionTreeClassifier

(

random_state

=

42

)

)

]

clf

=

StackingClassifier

(

estimators

=

estimators

,

final_estimator

=

LogisticRegression

()

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

stratify

=

y

,

random_state

=

42

)

clf

.

fit

(

X_train

,

y_train

)

.

score

(

X_test

,

y_test

)

Permutation-Based Feature Importance

As the name suggests, this technique provides a way to assign importance to each feature by permuting each feature and capturing the drop in performance.

But what does permuting mean here? Let us understand this using an example.

Let’s say we are trying to predict house prices and have only 2 features to work with:

LotArea – (Sq Feet area of the house)

YrSold (Year when it was sold)

The test data has just 10 rows as shown below:

Next, we fit a simple decision tree model and get an R-Squared value of 0.78. We pick a feature, say LotArea, and shuffle it keeping all the other columns as they were:

Next, we calculate the R-Squared once more and it comes out to be 0.74. We take the difference or ratio between the 2 (0.78/0.74 or 0.78-0.74), repeat the above steps, and take the average to represent the importance of the LotArea feature.

We can perform similar steps for all the other features to get the relative importance of each feature. Since we are using the test set here to evaluate the importance values, only the features that help the model generalize better will fare better.

Earlier, we had to implement this from scratch or import packages such as ELI5. Now, Sklearn has an inbuilt facility to do permutation-based feature importance. Let’s get into the code to see how we can visualize this:



As you can see in the above box plot, there are 3 features that are relatively more important than the other 4. You can try this with any model, which makes it a model agnostic interpretability technique. You can read more about this machine learning interpretability concept here.

Multiclass Support for ROC-AUC

The ROC-AUC score for binary classification is super useful especially when it comes to imbalanced datasets. However, there was no support for Multi-Class classification till now and we had to manually code to do this. In order to use the ROC-AUC score for multi-class/multi-label classification, we would need to binarize the target first.

Currently, sklearn has support for two strategies in order to achieve this:

from sklearn.datasets import load_iris  from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score X, y = load_iris(return_X_y=True) rf = RandomForestClassifier(random_state=44, max_depth=2) rf.fit(X,y) print(roc_auc_score(y, rf.predict_proba(X), multi_class='ovo'))

Also, there is a new plotting API that makes it super easy to plot and compare ROC-AUC curves from different machine learning models. Let’s see a quick demo:

from

sklearn.model_selection

import

train_test_split

from

sklearn.svm

import

SVC

from

sklearn.metrics

import

plot_roc_curve

from

sklearn.ensemble

import

RandomForestClassifier

from

sklearn.datasets

import

make_classification

import

matplotlib.pyplot

as

plt

X

,

y

=

make_classification

(

random_state

=5

)

X_train

,

X_test

,

y_train

,

y_test

=

train_test_split

(

X

,

y

,

random_state

=

42

)

svc

=

SVC

(

random_state

=

42

)

svc

.

fit

(

X_train

,

y_train

)

rfc

=

RandomForestClassifier

(

random_state

=

42

)

rfc

.

fit

(

X_train

,

y_train

)

svc_disp

=

plot_roc_curve

(

svc

,

X_test

,

y_test

)

rfc_disp

=

plot_roc_curve

(

rfc

,

X_test

,

y_test

,

ax

=

svc_disp

.

ax_

)

rfc_disp

.

figure_

.

suptitle

(

"ROC curve comparison"

)

plt

.

show

()

In the above figure, we have a comparison of two different machine learning models, namely Support Vector Classifier & Random Forest. Similarly, you can plot the AUC-ROC curve for more machine learning models and compare their performance.

kNN-Based Imputation

In kNN based imputation method, the missing values of an attribute are imputed using the attributes that are most similar to the attribute whose values are missing. The assumption behind using kNN for missing values is that a point value can be approximated by the values of the points that are closest to it, based on other variables.

The k-nearest neighbor can predict both qualitative & quantitative attributes

Creation of predictive machine learning model for each attribute with missing data is not required

Correlation structure of the data is taken into consideration

Scikit-learn supports kNN-based imputation using the Euclidean distance method. Let’s see a quick demo:

import

numpy

as

np

from

sklearn.impute

import

KNNImputer

X

=

[[4

,

6

,

np

.

nan

],

[

3

,

4

,

3

],

[

np

.

nan

,

6

,

5

],

[

8

,

8

,

9

]]

imputer

=

KNNImputer

(

n_neighbors

=

2

)

print

(

imputer

.

fit_transform

(

X

))

You can read about how kNN works in comprehensive detail here.

Tree Pruning

In basic terms, pruning is a technique we use to reduce the size of decision trees thereby avoiding overfitting. This also extends to other tree-based algorithms such as Random Forests and Gradient Boosting. These tree-based machine learning methods provide parameters such as min_samples_leaf and max_depth to prevent a tree from overfitting.

Pruning provides another option to control the size of a tree. XGBoost & LightGBM have pruning integrated into their implementation. However, a feature to manually prune trees has been long overdue in Scikit-learn (R already provides a similar facility as a part of the rpart package).

In its latest version, Scikit-learn provides this pruning functionality making it possible to control overfitting in most tree-based estimators once the trees are built. For details on how and why pruning is done, you can go through this excellent tutorial on tree-based methods by Sunil. Let’s look at a quick demo now:

from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification X

,

y

=

make_classification

(

random_state

=

0

)

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes without pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

rf

=

RandomForestClassifier

(

random_state

=

0

,

ccp_alpha

=

0.1

)

.

fit

(

X

,

y

)

print

(

"Average number of nodes with pruning

{:.1f}

"

.

format

(

np

.

mean

([

e

.

tree_

.

node_count

for

e

in

rf

.

estimators_

])))

End Notes

The scikit-learn package is the ultimate go-to library for building machine learning models. It is the first machine learning-focused library all newcomers lean on to guide them through their initial learning process. And even as a veteran, I often find myself using it to quickly test out a hypothesis or solution I have in mind.

The latest release definitely has some significant upgrades as we just saw. It’s definitely worth exploring on your own and experimenting using the base I have provided in this article.

Related

Everything We Think We Know About Iphone 5C

It’s now widely accepted Apple will, for the first time in iPhone history, launch not one but two new iPhones this coming Tuesday. One, a flagship iPhone 5S, and the other a long-rumored budget iPhone that should help the company tap emerging markets where telcos rarely subsidize devices.

Critics assume the so-called iPhone 5C will somehow flop because it’ll have a plastic shell. In reality, coupled with Apple’s marketing prowess and brand power, the iPhone 5C will widen Apple’s price umbrella and replace the $450 off-contract iPhone 4S as Apple’s most affordable iPhone yet – without offering a two-year old hardware.

Basically an iPhone 5 redesigned around the polycarbonate plastic casing offered in a variety of bright colors, the iPhone 5C is meant to improve Apple’s standing in emerging markets like China, India and Russia while appealing to those who are looking for a more affordable alternative to Apple’s flagship iPhone.

Here’s what we think we know so far about Apple’s controversial plastic iPhone, based on the widely-accepted rumors, leaks and reports by reliable bloggers and credulous publications.

The name: just don’t call it cheap

At first, journalists dubbed it the budget iPhone, with big media such as WSJ, Reuters and Bloomberg opting for the politically correct nicknames to refer to a ‘less-pricey’, ‘low-cost’ or ‘inexpensive iPhone’.

It wasn’t until chúng tôi published a set of photos showing a bunch of boxes with the label ‘iPhone 5C’ on the side that the blogosphere adopted the moniker.

Since then, a number of analysts and outlets have confirmed the name, including WSJ, Bloomberg and China Telecom. So, what does the ‘C’ in the iPhone 5C denomination stand for? A number of theories are floating around: some say the ‘C’ means Colors.

One thing is pretty certain: the ‘C’ does not stand for cheap. Apple’s never added the letter ‘C’ to an iPhone model before, leading some watchers to conjure the company might perhaps market the handset as an ‘iPhone C’. Which brings me to my question of the day.

iPhone 5C packaging render.

iPhone 5C packaging render.

Assuming Apple sticks to its mid-cycle S-upgrades, what is a 2014 plastic iPhone going to be called? An iPhone 5CS? An iPhone CS? An iPhone 6C? What about 2024? There should be an iPhone 6S in 2024 so what’ll they call its budget variant, an iPhone 6CS?

Perhaps Apple should think about rethinking its iPhone naming convention?

Design: scratch-resistant plastic shell

Borrowing vague design cues from the iPhone 3G/3GS, the iPhone 5C ditches the two-tone aluminum iPhone 5 design for a rounded polycarbonate plastic casing that’s much easier to shape than aluminum. Even the lens cover appears to be plastic (the iPhone 5 uses sapphire crystal for added protection). The all-plastic casing obviously reduces parts and assembly costs, allowing for a more affordable device.

The backplate will be colorized and the front panel should be all black, though that’s inconclusive. Taking a closer look at the inside of the backpanel, the iPhone 5C uses both metal and plastic parts to support the internal components.

A leaked manual clearly depicts the Lightning connector and standard hardware buttons, including the mute switch and Home, power/sleep and volume up/down buttons.

The volume up and down buttons (below) are more of a pill shape, somewhat resembling the buttons found on the fifth-generation iPod touch. Now, even though it’s made from plastic, the handset’s casing is described as pretty thick and substantial, making the iPhone 5C slightly thicker, wider and heavier than its counterpart, as seen below.

According to a scratch-resistance video by Taiwan’s Apple Daily, the iPhone 5C has a surface hardness of 8H on the pencil hardness test, or three times stronger than the regular PET film used to protect the iPhone’s display from scratches.

KGI Securities analyst Ming-Chi Kuo pegs the plastic body between 0.4 and 0.6 millimeters thick versus the average plastic casing at between 0.7 and 1 millimeter. The mix of glass fiber and plastic allows for a stronger, thinner and lighter appearance compared to other plasticky casings.

Along the bottom (from left to right) there’s the headphone jack, the mic hole, space for a screw, the Lightning connector, another hole for a screw and the cutouts for the speaker – again, resembling more the fifth-generation iPod touch than the iPhone 5.

On the back, there’s an iSight camera, noise-canceling microphone and standard LED flash (leaving the rumored dual-LED flash exclusive to the iPhone 5S).

The expected cutout for the SIM tray on the left appears to be sized appropriately for the Nano SIM cards, like the iPhone 5. Like all prior handset models, the branding on the back simply says ‘iPhone’, with no model-specific moniker.

The display: four-inch Retina screen

There’s no question the iPhone 5C will pack in a four-inch display, even if the less-informed analysts have conjured up that the screen won’t be Retina for cost-saving reasons. That couldn’t be further from truth.

Make no mistake about it, Apple’s 2013 iPhone lineup is going to be all-Retina. The same goes for unfounded talk of a 3.5-inch display, which can be easily disputed by a myriad of leaked back shells.

Will it be plastic with white or black front?

Will it be plastic with white or black front?

Bottom line: worst case, Apple could save a few bucks by outfitting the device with a non-IPS LCD technology, meaning poor man’s viewing angles like on the iPod touch.

Cameras: no surprises here

By all accounts, the iPhone 5C features the same front-facing FaceTime camera with a rather paltry 1.2-megapixel photos and bearable 720p HD video with up to 30 frames per second. The back-facing iSight camera is thought to be of an eight-megapixel variety with full HD 1080p video capture at up to 30 frames per second – essentially the same as on the iPhone 5.

iPhone 5C protective case rendering.

Internals: similar to the iPhone 5

The rumor-mill seemingly agrees that many of the internals inside the iPhone 5C will match up with the currently available iPhone 5. This totally makes sense: Apple’s been building the iPhone 5 for a year and they must have optimized manufacturing and parts costs by now.

I imagine Apple’s supply chain maestro Tim Cook opted to re-use the iPhone 5 components to keep bill of materials at a minimum as opposed to engineering brand new parts. Here is reliable analyst Ming-Chi Kuo’s view of iPhone 5C internals.

The iPhone 5 logic board (left) lines up nicely with the iPhone 5C screw holes (right).

Networking: no Gigabit Wi-Fi, NFC and LTE+

The iPhone 5 logic board (left) lines up nicely with the iPhone 5C screw holes (right).

At a minimum, the iPhone 5C should support all flavors of fourth-generation Long-Term Evolution (LTE) technology the iPhone 5 does. We’re not expecting support for up to three times faster LTE Advanced. LTE Advanced (also known as LTE-A or LTE+) allows for theoretical simultaneous download and upload speeds of 300 megabits per second, or up to three times faster than current LTE theoretical speeds.

While LTE Advanced networking is conceivable on the iPhone 5S, cost considerations and the currently very low penetration rate of this latest standard make the ultra-fast 150MBit LTE Advanced a no-go on the iPhone 5C.

On the other hand, if China Mobile will sell the iPhone 5C, the device must support the telco’s nascent TD-LTE 4G network. There could also be a special China Mobile version of the iPhone 5C, even if that runs contrast to Apple’s penchant for keeping things simple.

Whether or not the iPhone 5C supports 802.11ac – the latest in Wi-Fi networking – is up for the debate. Also known as Gigabit Wi-Fi, the standard promises three times the data throughput of the conventional Wi-Fi standard.

Now, only the 2013 editions of the MacBook Air family and the AirPort Express/Time Capsule wireless appliances sport 802.11ac networking . The upcoming Mac Pro will have it and it’s fairly safe to speculate that Gigabit Wi-Fi will be part of the iPad 5 and iPad mini 2, as well as Haswell-focused iMac, Mac mini and MacBook Pro refreshes.

AirDrop, one of the headline new iOS 7 features, is hardware-dependent. Even though AirDrop does not specifically require 802.11ac chips, the feature is currently supported only on the iPhone 5, iPad 4, iPad mini and fifth-generation iPod touch.

That said, we imagine the same Wi-Fi networking capabilities from the iPhone 5 will be supported on the iPhone 5C, at a minimum. Chances of Apple giving the iPhone 5C LTE Advanced and 802.11ac capabilities are slim – it’s perfectly plausible these features are exclusive to the flagship iPhone 5S.

No, we’re not expecting NFC either.

Summing up, we think the iPhone 5C will feature Bluetooth 4.0, 802.11n Wi-Fi on 2.4 and 5GHz bands, aGPS and GLONASS, as well as DC-HSPA+ and LTE.

Colors: five bright colorways

The iPhone 5C is expected to come in various colorways, some of them revealed by the frequent part-spotter Sonny Dickson. In total, the handset should be offered in five different colors: blue, green, yellow, white and red – the latter appearing to be more of a pinkish hue.

Sketchy reports have also called for a completely black iPhone 5C, though the leaked shots were later debunked as fake.

The fake iPhone 5C backplate.

The fake iPhone 5C backplate.

Here is a potential green variant without the sticker.

And the following image apparently represents a bunch of iPhone 5C units being warmed up at Pegatron’s Shanghai plant for quality assurance purposes.

Pegatron is Apple’s primary assembler of the iPhone 5C as Apple seeks to diversify risk after last year Foxconn manufacturing glitches with the iPhone 5 (hint: scratches, nicks and Scuffgate), according to WSJ.

The contract manufacturer, named after the mythical flying horse Pegasus, became a minor producer of iPhones in 2011 and started assembling iPad minis last year.

Packaging: plastic box ala iPod touch

Apple’s never made a plastic iPhone box before so folks were taken aback when chúng tôi published photos depicting boxes with the label ‘iPhone 5C’ on the side.

Though we can’t vouch for their authenticity, a number of photos from various sources have corroborated that the device will come inside a transparent plastic box that looks a lot like the iPod touch packaging.

Here’s iPhone 5C packaging shared by chúng tôi in late-July.

And below is the current iPod touch plastic box.

And here’s apparently a blue iPhone 5C, still in its retail packaging.

Watchful readers could point out Apple since 2012 has been color-matching the external coloring of the iPod nanos with the wallpaper, as can be seen below.

Summing up, as the iPhone 5C is all about building a more affordable device for cash-strapped buyers. Therefore, it’s not entirely inconceivable that cost saving measures would extend to the product’s retail packaging. Another benefit of transparent plastic boxes: folks get to see the actual device right on store shelves.

Pricing: think mid-range, not cheap

The iPhone 5C should be sold for a significantly lower price than the flagship iPhone 5S when purchased off-contract, with a full retail price pegged at $350-$450 or $400-$500, depending on whom you ask. At any rate, That’s considerably more affordable than the $649 price of the unsubsidized iPhone 5.

While Apple is believed to offer the iPhone 5C both on and off-contract from day one – unlike past iPhone releases where a contract-free variant arrived a few months following the launch – carrier subsidies should bring the upfront payment down to something like $99 for customers willing to sign on the dotted line.

Assuming the off-contract iPhone 5C starts out at $450 for the basic model with sixteen gigabytes of storage (or perhaps even 8GB?), the 32/64GB tiers should translate to $550 and $650, respectively. Sorry, we’re not expecting a 128GB iPhone 5C variant – yes, another iPhone 5S exclusive.

One caveat: should Apple drop the iPhone 5/4S/4 from the lineup come September 20, the on-contract iPhone 5C could just as well become Apple’s new “free” iPhone.

I’m only speculating here, but the aforementioned price matrix would be consistent with Apple’s strategy of discounting previous-generation iPhones. Oh, we also don’t expect the iPhone 4S to be offered shortly as its smaller 3.5-inch screen would feel oddly out of place in the iPhone 5/5S/5C four-inch Retina lineup.

Last but not least, it’ll be interesting what ad tactics Apple’s marketing wizards cook up to pitch a plastic iPhone to consumers without sounding like a lesser-buy.

Availability: September 20, coming later to China Mobile and NTT DoCoMo

We’re expecting both the iPhone 5C and iPhone 5S to be available across Apple’s key markets beginning Friday, September 20, with iOS 7 preloaded. This should give Apple a period of ten days to collect pre-orders between the September 10 announcement and wider September 20 availability.

As evidenced by employee vacation blackouts – not only by Apple, but major U.S. carriers as well – the device is expected to launch simultaneously across AT&T, Verizon and T-Mobile networks. Sprint hasn’t cancelled their retail employee vacations yet so we’re guessing the telco could land the new iPhones a bit later.

The iPhone 5 launched last September in the United States, United Kingdom, Australia, Canada, France, Germany, Hong Kong, Japan and Singapore. By the end of 2012, the handset launched on 240 different carriers in a hundred countries.

WSJ all but confirmed that Apple has finally inked a landmark distribution agreement with China Mobile, the world’s largest carrier. Two days ago, Reuters confirmed that Japan’s NTT DoCoMo will start carrying iPhones this Fall. Both are huge developments for Apple as the iPhone isn’t yet available on some of the world’s largest wireless carriers.

China Mobile has 700+ million subscribers, more than AT&T and Verizon combined! NTT DoCoMo is 60 million mobile users strong. Just adding these two carriers to the fold should add up significantly to iPhone sales and Apple’s bottom line.

Sales predictions

According to one estimate, expanded distribution will help the company push 13 million iPhone 5S/5C units in the first ten days of sale – or more than a million units a day – per Pacific Crest analyst Andy Hargreaves. Those would be record numbers, in accordance with the ‘most successful launch in Apple’s history’ prediction.

All told, Hargreaves expects up to 13 million iPhone sales before the September quarter closes and some 31 million units during the Christmas quarter.

iPhone 5C videos

If picture is worth a thousand words, a video may be worth a million.

• iPhone 5C mystery case by Macotakara

You’re going to love these hands-on clips.

Signing off…

About the flagship iPhone 5S: two-tone iPhone 5 design, colorized backplate choices – including the controversial gold/champagne and graphite variants – at least a third-faster A7 chip and improved camera with much better low light shooting, 120FPS slow motion video capture, a 12 or 13-megapixel sensor and dual-LED flash.

And of course, the central feature: a redesigned Home button with an AuthenTec fingerprint sensor for user authentication (Slide To Identify?) and more. It should be the handset’s killer feature. If it means anything, insiders liken it to the seismic shift Siri was two years ago.

Apple recently began training its AppleCare support staff and retail employees on iOS 7 and iTunes Radio so we’re expecting both devices to hit Apple Stores and carriers simultaneously. Those of you looking to dump your old device, Apple will pay up to $280 credit for used iPhones.

So what do you guys think?

Did I miss anything?

Everything You Need To Know About Twitter Advanced Search

And while many still view Twitter as a mere social media platform for following friends and being followed, a significant number are exploiting its immense potential as a business marketing platform, specifically for lead generation.

In fact, 82% of B2B marketers preferred using Twitter in 2023, which makes it second only to Linkedin for that audience.

Who Will Marketers Find With Twitter Search?

Contrary to earlier expectations – that Twitter growth would decline or flatten out – statistics show that Twitter growth is anything but stagnant.

Based on Twitter’s most recent quarterly filing, usage data shows that the platform grew by 24% relative to the preceding quarter, representing an additional 14 million users.

In the US, as of September 2023, 52% of Twitter users reported accessing (and probably using) the social media platform every, single, day.

And the Twitter Search function— specifically its Advanced Search feature— has huge business potential, through lead generation, for changing the marketing landscape.

What Exactly Is the Twitter Advanced Search Feature?

There are two Search features: the general Search feature (that you’ve most likely used) and the Advanced Search feature.

Let’s first explain the general Search feature.

The general Search feature is on the right-hand side of your laptop.

If you’re using a smartphone, however, the general Search is represented by the magnifying icon at the bottom of your mobile phone screen.

You can’t glean much from these general searches because your search range is limited.

That’s where the Advanced Search feature comes in.

How to Access Twitter’s Advanced Search Feature

You can access the Twitter Advanced Search by first using the general Search feature referenced above.

On your laptop, key in your search phrase. Let’s choose [Tesla], the company associated with Elon Musk.

After typing [Tesla] in the general search field, you’ll spot some three straight dots on the right-hand side of the screen.

If you point your cursor at the three dots, the word “more” will pop up.

By now, you’re probably saying to yourself, “That’s great for desktop users. But what about mobile?”

Advanced Search Fields

You’re going to get a pretty long list of search fields to choose from, which can be a little overwhelming. For example, under the heading “Words,” this list appears:

All of these words.

This exact phrase.

Any of these words.

None of these words.

These hashtags.

Written in (language).

Other headings include:

People.

Places.

Dates.

Other.

What’s cool about these fields is that you aren’t limited to choosing just one. So, for example, you could perform a search that combined an exact phrase, from specific accounts, during a specific date range.

Before moving on to what happens after you hit Search, let’s explore some of the common search queries and how you can use them to refine your searches.

How to Use the “Words” Field in Advanced Search

Perhaps the most commonly used search feature, there are six fields under the “Words” Search category on Twitter’s Advanced Search.

Let’s take a look at each so you understand what they mean.

All of These Words

Use this field to find tweets that include the words or phrase you type. To ensure the best search results, use quotation marks.

If you’re searching for tweets and you aren’t sure of the exact phrase, you can query some relevant and specific words — whatever you think the original tweet or tweets must have contained.

This Exact Phrase

Use this field to enter a very specific phrase.

So if you were searching for tweets about Heisman Trophy winners that attended USC, for example, you would enter [USC Heisman Trophy Winners] instead of just [Heisman Trophy Winners].

One cool thing is that quotes are automatically added in this field.

Any of These Words

Use this field to search for a group of related words.

The search field would add the word “or” between each word or phrase you typed to trigger the most specific results.

None of These Words

If you want to search Twitter but omit certain words or phrases, this is your search option. Using this field will eliminate any tweets that contain words or phrases you don’t want to be included in a search result.

For example, you may want results for [USC] but exclude any mention of [USC football].

This is an effective way for businesses to exclude search results for the names of other companies in the same industry.

These Hashtags

If you want to search for a hashtag, whether it’s a hashtag of your brand or the hashtag of a competitor, this is the relevant search option. For example, if you’re a sneaker company and you want to check out what people are tweeting about regarding a big brand, you could type [#Nike] to get results.

Any Language

Use this field if you want to get information about tweets written in one of the more than 50 foreign languages listed.

This may help if you are analyzing the foreign market in your industry or have a brand that operates in multiple countries.

How to Use The “Account” Field When Doing Advanced Search

Formerly named “People,” under “Accounts,” there are three different fields that can alter your search results depending on what you type.

Just type the username of a person with a Twitter account, or several usernames separated by a comma.

Then choose whether you want results exclusively sent by that account, to that account, or that mentioned that account.

Just remember to use proper Twitter accounts that begin with @.

How to Use The “Engagement” Field When Doing Advanced Twitter Search

The depth of engagement is measured by the number of retweets, replies, and likes a tweet receives. A tweet that generates thousands of retweets is doubtless an engaging tweet.

The way to do this is to specify the minimum number of retweets, likes, or replies in the search field or any combination of those three.

By doing this, you will absolutely weed out posts that add more noise and don’t add value to your search.

How to Use The “Dates” Field When Doing Advanced Twitter Search

If you remember a tweet but you can’t remember the exact date it was tweeted, the “Date” option has you covered, that is if you can remember the period within which the tweet was sent out.

Under the heading ‘Dates,’ you can select a specific date range and only receive search results from that time.

You’ll simply enter the date ranges, and the relevant tweet will be displayed, regardless of how long ago the phrase was tweeted.

One thing to note is that the first tweet was sent on March 21, 2006, so the system would default to that date if you entered a date earlier than that one.

Don’t Forget the Search Operators

These will help you weed out the useful tweets from the pics of what users had for dinner.

How to Understand Advanced Twitter Search Results

Twitter has an algorithm that will determine how you get the search results and in which order.

While what drives the nature and order of Twitter’s Advanced Search results isn’t known with certainty, several metrics may play a role.

These may include tweets that have elicited a certain level of reaction, relevance, time-lapse of the tweet, and location, among others.

After you have typed in all the information to generate a search, the results page will include the headings:

Top: This is a list of what Twitter considers the tweets that are triggering the most reaction.

People: These are the accounts of users based on your search criteria.

Videos: These are tweets with links to videos on other websites.

After reviewing the results, you may realize that you need to refine or broaden your search. You can do that by simply going back to the Advanced Search page.

Depending on the level of analysis needed, there are also paid programs that can email alerts based on hashtags, business name, etc. An example of this is Twilert, which is basically a Google Alerts for Twitter.

There are several ways to use this Twitter search feature to drive revenue through lead generation.

One way to do this is to assess what customers complain about regarding your competitors’ products. Then you can use this information to improve your product.

The Advanced Search feature can provide your business with valuable demographic information, information about prospects in your local area, and lead generation opportunities.

When your goal is lead generation, you will want to focus on these areas:

People.

Places.

Dates.

Other.

1. Competitor mentions

2. Product or service mentions

Type in the name of a product or service you offer under Words. If you sell computer security software, you would type that phrase + a “?”.

You can use the results to create a database of people who have made inquiries about products or services you sell and reach out to them about what you can offer.

But they are effective because they identify people who are unhappy with products or services they purchased or people who have expressed interest in those products and services but may have not yet engaged with a seller.

But there are other ways.

Check out these 8 Terrific Tips to Optimize a Twitter Business or Brand Profile.

Another Tool in the Your Marketing Tool Chest

Twitter Advanced Search helps you to analyze your market, judge how your competitors are doing based on positive or negative sentiment, and improve your geotargeting based on the number of tweets in specific locations.

It may take a few searches before you understand all the ways you can manipulate the results.

But once you do, you can hone in on the information you need to help boost your Twitter marketing efforts.

More Resources:

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Everything You Never Wanted To Know About Your Genes

Everything You Never Wanted to Know About Your Genes George Annas on the promise and shortcomings of the pending genetics privacy law

Bioethicist George Annas says the current legislation is a watered-down version of a bill drafted at BU in the 1990s.

A bill that would bar insurers and employers from discriminating against people whose genetic tests show they may be prone to cancer, Alzheimer’s, or other diseases has been stalled in Congress for years. Last week, the Genetic Information Nondiscrimination Act finally made it through the House and now must win Senate approval. President Bush has said that he will sign the bill, which prohibits insurers from denying coverage or raising rates for people whose genes suggest a predisposition to a disease or medical condition and prevents employers from basing hiring decisions on that information. 

To learn about the history and the promise of the act, BU Today spoke with George Annas, the Edward R. Utley Professor of Health Law, Bioethics, and Human Rights at the School of Public Health and chair of the SPH health law, bioethics, and human rights department.

The insurance companies weren’t that happy with this. This was back in the mid 1990s, when most people involved in the genome project felt that once we had the genome, we were going to be able to predict lots of stuff. And maybe someday we will. But we’re not there yet. But the insurance companies felt, oh my god, if you can predict, here’s what’s going to happen: people are going to go and get their genome done, find out they only have 10 years to live, and then go and buy a billion-dollar policy. Well, of course the way to get around that was to limit the amount of money you could buy a policy for without a genetic test, for example, or answering questions about family history. And employers were worried that we were going to find out some stuff about your genetics that was going to show that you’re susceptible to x, y, z disease, and therefore if you work for us, you have a higher probability of getting that disease.

As a result of that, they backed off of genetic privacy, and said, let’s assume that everybody’s going to find this information out anyway, and let’s just do genetic discrimination. Let’s tell people that they shouldn’t worry about being tested because we are going to prohibit their insurance companies and their employers from discriminating against them on the basis of a genetic test. And that’s what this is. So this is the ultimate legislation. The bill has been so watered down. It doesn’t really do very much.

That’s the kind of thing researchers worry about. Now, companies worry about some of the same issues. They worry about, what if they find a correlation in these genes and make a product out of it — do they own it or can the people who provided the tissue claim some ownership in it?

I don’t know if you read about Jim Watson. This is too perfect. James Watson discovered the structure of DNA and is a giant booster of all things genetic. He wants genetic engineering; he wants to make designer babies, if we can ever do it. So a company told Watson that they’d do his genome for $1,000, and he said great, and then he called them back and said, wait a minute, just don’t do the section of the genome that codes for Alzheimer’s disease. I don’t know if it’s because he didn’t want to know or if he didn’t want anybody else to know. Genetic privacy is two things: one, things you don’t want to know yourself, and two, things you want to know but you want to make sure nobody else knows. That’s the best ad for genetic privacy. Mr. Genetics says no, there are some things better left unknown.

Chris Berdik can be reached at [email protected]. Art Jahnke can be reached at [email protected].

Explore Related Topics:

Looking For Liquidity? Here’s Everything To Know About Nft Lending

The last 24 months have been a whirlwind for NFT enthusiasts, with unprecedented demand for digital ownership creating a new and exciting asset class right before our eyes. But eventually, all new toys lose their shine. And after a crazy period of buying, selling, and trading NFTs, investors seek new ways to leverage their assets.

Enter the rise of fractionalized ownership, staking, and NFT’s hottest new sector: lending. 

You read that right. People are lending their relatively-illiquid JPEGs for instant payouts in crypto and cash. And it’s become a massive sector of the market. 

It’s finally time to break down the basics of NFT lending — how it really works, and the different types of lending models.

But first, a definition.

What is NFT lending?

NFT lending is the act of collateralizing your NFT as a loan in exchange for immediate crypto payment. And it solves the asset class’ most significant problem: liquidity. Relative to other asset classes, NFTs are relatively illiquid — meaning it’s not easy to quickly sell your NFT for its designated market value in cash (or cryptocurrency). In other words, it can take months for someone to buy your JPEG. Additionally, for investors with sizable investment allocations tied up in NFTs, quick access to liquid capital can sometimes be a tall order. Loans also provide NFT owners with a means to generate non-taxable income, as opposed to the tax implications of a sale.

Here’s how it works: The borrower needs a loan and puts up an asset as collateral (NFT). The lender supplies the loan in exchange for interest. But if the borrower can’t repay the loan on the agreed terms, the lender will receive the collateral. In most cases, this process is autonomously executed by smart contracts on the blockchain.

But in all cases, NFT lending is executed via one of four main models, each with its own benefits and drawbacks.

Peer-to-Peer: NFT lending platforms made simple

The simplest form of NFT lending is peer-to-peer, since it closely resembles the relationship between a borrower and a lender you can find at your local bank. 

Most transactions take place on peer-to-peer NFT lending platforms like NFTfi, and follow a similar process. But unlike borrowing against an asset with a stable price, NFTs are a bit more tricky. The market is incredibly volatile, which means the market value of an NFT today may be significantly different than its value down the line. So how do you appraise its current value? 

The truth is, it depends. Most peer-to-peer lending platforms use a simple offer system to allow anyone to make loans and set terms without a centralized or third-party intermediary. 

A user will list their NFT on the platform and receive loan offers based on the lender’s perceived collateral value of the NFT. If the borrower accepts the offer, they will immediately receive a wrapper ETH or DAI from the lender’s wallet. Simultaneously, the platform will automatically transfer the borrower’s NFT into a digital escrow vault (read: smart contract) until the loan is either repaid or expires. If the borrower defaults on the loan, the smart contract automatically transfers the NFT into the lender’s wallet.

Consolidating multiple NFTs with Arcade, and more

The bottom line? Peer-to-peer lending has emerged as the most favorable option for both borrowers and lenders, mainly due to its ease of use and security. The flexibility for both parties to set terms helps to account for rare NFT traits, and the smart contract logic within the escrow process is fairly straightforward. However, it’s important to note that peer-to-peer lending may not be the quickest model, since it relies on a borrower finding a lender willing to agree to set terms mutually.

According to Richard Chen, General Partner at cryptocurrency-focused investment firm 1confirmation, peer-to-peer lending is not only the safest model, but also the most liquid and competitive on the lending side.

“If you list a CryptoPunk on NFTfi, you’ll get a dozen offers pretty quickly,” said Chen in an interview with nft now. As DeFi yields have fallen, DeFi lenders have shifted toward NFT lending, since that’s where the highest yields in crypto are right now.”

Peer-to-Pool NFT lending

As the name suggests, peer-to-pool lending allows users to borrow directly from a liquidity pool, rather than wait to find a suitable lender match. To assign value to the collateralized NFTs, peer-to-pool platforms like BendDAO use blockchain bridges (Chainlink oracles, to be specific) to obtain floor price information from OpenSea and then allow users to instantly access a set percentage of their NFTs floor price as an NFT-backed loan. The NFT is then simultaneously locked within the protocol. 

When liquidation happens, it’s not based on the time of repayment. Instead, it occurs when the health factor of the loan — which is a numeric representation of safety comprised of the collateralized market value and the outstanding loan amount — falls below a certain threshold. However, the borrower has 48 hours to repay the loan and reclaim their collateral. 

Meanwhile, lenders who supplied liquidity to the liquidity pool receive interest-bearing bendETH tokens, where the price is pegged one-to-one with the initial deposit. 

“Given the illiquidity of NFTs, The price oracles used in peer-to-pool can be manipulated much more easily compared to other tokens,” said Chen. To him, good NFT appraisal tools like Deep NFT Value exist, but there’s “no oracle infrastructure yet, so teams are running their own centralized oracles which are prone to infrastructure hacking risk.”

Non-fungible debt positions

A spin-off of MakerDAO’s collateralized debt position structure, where borrowers over-collateralize ETH (a risky asset) in exchange for DAI (a less-risky stablecoin), non-fungible debt positions offer a similar deal. But on NFDP platforms like JPEG’d, instead of depositing ETH in exchange for a DAI, borrowers deposit select blue-chip NFTs and receive $PUSd, a synthetic stablecoin pegged to USD, in return. 

Like peer-to-pool lending, JPEG’d uses custom chainlink oracles to fetch and maintain on-chain pricing data. The goal? To combine floor prices and sales data to price collateral in real-time with high accuracy. 

Non-fungible debt positions are still very new, and will need to mature more before it’s considered a reputable lending model. Collateralized debt positions on MakerDAO are over-collateralized by 150 percent (or 1.5 times), to mitigate the volatility of ETH. NFTs are even more volatile, and the lack of need for over-collateralization raises some concern about the unpredictability of the NFT market and future liquidations. Additionally. JPEG’d is currently the only platform offering this structure, and is limited solely to CryptoPunks, so the available market is tiny, and the platform risk is quite high. All things considered, non-fungible debt positions should command close scrutiny as it unfolds.

NFT rentals and leasing via capital

Breaking rank with the other three structures, NFT renting allows NFT holders to lease out their NFTs in exchange for upfront capital. Platforms like ReNFT operate similar to peer-to-peer marketplaces, enabling renters and tenants to transact with varying rental terms and agreements without waiting for permission.

Like exchanges on NFTfi, all rental transactions are facilitated by smart contracts. But instead of a borrower sacrificing an NFT as collateral and locking it into a digital vault, the NFT is transferred to another person’s wallet for a specified period. In exchange, the “borrower” receives a lump sum of cryptocurrency. At the end of the predetermined period, the NFT is automatically returned to its owner. This is the simple form of “lending,” since there are no repayment terms, interest, or worry of liquidation.

Unlike other forms of lending where lenders are rewarded by earning interest, NFT rentals generally give lenders access and credibility. The NFT space thrives on social proof, and owning an expensive NFT can increase attention and recognition in the space. Some communities are also token-gated, where renting an NFT helps users gain exposure to people and experiences they may not otherwise acquire. Similar to renting clothing, cars, or other items of status-laden material, the emerging sector of NFT rentals is poised to become one of the most enduring ones.

Ultimately, whether NFT lending is the right decision for you specifically boils down to your time horizon and risk tolerance. Like all crypto protocols, it’s essential to do your own research and not over-leverage or invest money you’re not comfortable losing.

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