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Limiting background apps

The most common way Adaptive Battery saves minutes to hours of battery life is by restricting how apps run in the background. As mentioned earlier, some apps can consume a lot of power without you knowing it. When Adaptive Battery is turned on and an app is running too heavily, you will get a notification with the option to put it to sleep.

Over time, Adaptive Battery will learn which apps take up the most background usage and limit some of their functions. This doesn’t really affect your phone performance, but it means the battery won’t drain drastically when your phone is idle.

Learning your habits

Another way Adaptive Battery lives up to its name is by learning the patterns of how you use your phone. After having Adaptive Battery enabled for some time, your phone will keep track of what apps you use the most, how long you use them, and how quickly your battery drains when not optimized.

Eventually, your Android phone will utilize this data to fine-tune how it expends a full battery so that it can last throughout your daily usage. A crucial piece of information in this equation is learning your charging habits. Once the time is regular enough, Android will start stretching your battery life to when it anticipates you will plug in your phone to charge.

Reducing performance

One of the subtler ways Adaptive Battery improves battery life is by slightly reducing performance. Chips take a lot of power, and your battery can last much longer if that power is reduced to match your current needs.

On Samsung Galaxy phones, a complimentary feature to Adaptive Battery in the settings lets you change your device’s processing speed. This tool can save you more battery life than Android’s Adaptive Battery does. The Exynos processor in the flagship S-series devices barely lags in the “optimized” state, either. However, the difference in performance can be more noticeable in other Android devices, but if you are out and about without a charger, that might not be a concern.

How to turn on Android Adaptive Battery

Here’s how to turn on Adaptive Battery on a Google Pixel or Samsung Galaxy phone.

Google Pixel

Navigate to the settings by swiping down from the top screen and tapping the Settings cog. Then tap Battery. Select Adaptive Preferences, and lastly, hit the toggle on Adaptive Battery.

Samsung Galaxy

Navigate to the settings by swiping down on the screen and tapping the Settings cog. Select Battery and device care. You can optimize your battery usage here. Tap the Battery readout near the top, then scroll down to select More battery settings. There, you’ll find the Adaptive battery toggle.

Adaptive Battery will treat all apps the same, but you can manually give certain apps exceptions or stricter limitations. Navigate to your Settings and select Apps. From there, select the app you want to manage, scroll down to choose Battery, and select the desired battery usage for that app.

Overall, Adaptive Battery is a great feature to extend the life of your Android device. If you find your battery isn’t lasting long enough, try turning it on or limiting the usage of energy-hungry apps. Remember that Adaptive Battery needs time to learn your usage habits and may not work immediately, but you should notice results soon enough.

FAQs

If you find your battery life isn’t lasting long enough, then you should consider turning on Adaptive Battery to meet your usage habits better.

Fast charging won’t damage your battery. However, if you leave your phone plugged in for an extended time, you might consider turning on Protect battery in More battery settings. This will limit the maximum charge to 85% to help extend the lifespan of your battery.

Adaptive charging can help extend the lifespan of your battery and keep your device from running hot while charging. Learn more in our guide.

Quite the opposite. Adaptive Battery reduces the amount that apps and background processes drain your battery, extending your device’s battery life.

You should typically keep the adaptive battery feature on. It learns your usage patterns and optimizes apps’ battery usage based on that, which can help extend battery life. However, if you find that important apps are being prematurely shut down or are not working correctly, you may want to consider turning it off.

No, the adaptive battery feature does not slow charging. Its purpose is to manage how apps use the battery, not how the battery charges. However, some phones have an adaptive (or smart) charging feature, separate from the adaptive battery, that can slow charging to prevent battery aging.

Yes, adaptive charging can improve battery lifespan over time. This feature is designed to control the charging speed and avoid keeping the battery at 100% for extended periods, which can reduce the battery’s overall lifespan. It usually works by learning your daily charging patterns and holding the charge at 80%, only to fill to 100% just before you typically unplug it.

You're reading Android Adaptive Battery: Everything You Need To Know

Android 12 Features: Everything You Need To Know

Color extraction

Color extraction is the base coat of paint in the new redesign and is one of the best things about Android 12. Basically, the OS natively extracts the colors from your wallpaper and uses them to theme your OS. You can see how it looks in the screenshot above. In short, you set a wallpaper, Android 12 extracts the colors, and then your OS has hints of color from it.

The color changes take place all over the OS, including the Settings, Quick Settings tiles, and any app with Material You support. On Pixel devices, examples include the dialer, contacts app, and calculator. It even works with widgets as well, as shown in the video at the top of the article. We will see more as developers begin adding support.

Users can override the color extraction and choose a color if they want to. It looks quite nice, even if the OS tends to favor light, pastel colors over vibrant colors. Of course, the Styles & Wallpapers app on Pixel devices was revamped to house all of these new changes.

Quick Settings tiles

Joe Hindy / Android Authority

The Quick Settings received a rather large overhaul as well. The smaller toggles are gone, replaced by larger, rounded rectangle buttons. These new tiles work like previous toggles where you tap to turn them on and off. There are also new tiles for camera and microphone access (more on that later), Google Pay, alerts, and smart home stuff.

A few of the tiles work differently. For instance, the Internet tile always opens a prompt now and houses both mobile and Wi-Fi data information. By and large, it is just an aesthetic change. On the plus side, the larger tiles are much easier to read and house more information. Of course, that comes at the cost of space. Users who use toggles heavily will no doubt need multiple pages.

Settings

Joe Hindy / Android Authority

The Settings menu not only saw a redesign but also a few minor changes. For the redesign, it borrows heavily from Samsung’s One UI with large headers that take up a ton of space. It makes the top settings on any given page easier to reach on taller phones. The new design, codenamed Silky Home, required ADB to access in early developer previews but comes stock on Android 12.

The only other noteworthy change is the addition of Safety & Emergency to the main Settings page. It lets you define stuff like an emergency contact and some other stuff without the need to dive into the settings further.

Scrolling screenshots

Improved auto-rotate

Joe Hindy / Android Authority

For years, auto-rotate was decided by your phone’s accelerometer. It detects when your phone is flipped on its side and then the screen does the same. Android 12 introduces face detection for auto-rotate. Basically, your phone looks at your face and only rotates if it notices your phone has changed orientation. Thus, you can do things like lay in bed on your side and the phone won’t rotate.

Easier Wi-Fi sharing

C. Scott Brown / Android Authority

This one is a pretty minor new feature. The QR code method is still available like previous versions of Android. However, there is now a small button below that lets you use Nearby Share to send your Wi-Fi credentials to someone else’s phone. It’s faster when sharing with multiple people and doesn’t require you to hold your phone up for everyone to scan.

Rich content insertion

Joe Hindy / Android Authority

This minor but fun feature is new to Android 12. It’s an API for developers that lets their apps move media between apps. You’ll be able to do something like copy and paste an image from one app to another. It should also work with marked-up text (bold, italic, etc.), videos, audio files, and more. It would help cut out the middle man of sharing images from one app to another.

Other minor additions

C. Scott Brown / Android Authority

There was a slew of minor additions, including a bunch of new emoji , a screen dimming mode in the Accessibility settings, tweaked gesture controls, and a quick tap function for select Pixel devices that lets you use the back of your device to control your phone. Hit the links above to learn more about those extra features.

Haptic feedback coupled with audio

You probably recognize this feature mostly from gaming consoles. You feel it when your controller shakes in response to an explosion on the screen. Android 12 comes with this ability natively. Game developers can make your phone vibrate when things happen on screen. There are some non-gaming uses for it as well, such as with movies or syncing vibration with custom ringtones.

HEVC media transcoding: Apps without HEVC support can have Android 12 transcode it into AVC — a more available video compression format.

AVIF image support: AVIF is an image codec like PNG or JPEG. It promises the same quality as JPEG but won’t claim as much storage space on your device. It uses the AV1 codec, first introduced in Android 10.

Multi-channel audio: Android 12 now supports MPEG-H in passthrough and offload modes. Audio mixers, resamplers, and effects now support up to 24 channels overall.

Optimizations for large and funky displays: Basically, Android 12 includes better support for things like televisions, foldable, and tablets. Notably, Google is also working on another version of the OS focusing on this dubbed Android 12L. This version should be launched later this year.

Universal splash screens: Material You added a lot of cohesion to the Android experience. Part of that includes a splash screen for every app automatically whether the developer supported it or not. Devs have the ability to tweak the splash screen if they want.

Optimizations for apps: Some other optimizations for apps include Google blocking background apps from launching foreground services. There are also latency and workload distribution improvements that should also improve performance.

Picture-in-picture improvements: Some improvements to picture-in-picture mode include smoother transitions, improved controls depending on what content is in the picture.

Project mainline improvements: Google announced that ART would make its way to Project Mainline and be available for update through the Play Store. There are other modules being added as well.

The list is even longer than this, believe it or not. You can learn more on the Android 12 developer page here.

Security and privacy improvements

Joe Hindy / Android Authority

Google has calmed down a bit with security and privacy improvements in Android 12. Usually, Google has a ton of little tweaks. However, in recent years, Google has opted for larger and more impactful changes.

Privacy Dashboard

Microphone and camera indicators

Joe Hindy / Android Authority

Android 12 also includes indicators for camera and microphone use. Basically, a little microphone or camera indicator appears in the top right corner of your phone screen whenever an app accesses one of those things. A green dot persists after the initial indicator goes away to let you know that an app is still using it. It’s a quick and easy way to tell the user when the microphone or camera is active.

For added security, you can disable both the camera and microphone. Google added new toggles in the Quick Settings specifically for this purpose. You simply toggle one of them off to completely disable the camera or microphone. We tested it with the camera app and there is even a prompt that shows up if the camera permission is turned off.

Smaller security updates

Joe Hindy / Android Authority

There were some smaller security updates as well. We’ll list them out here.

Restricted Netlink MAC: In Android 11, only privileged apps could access a device’s Netlink MAC address. In Android 12, Google now restricts all apps from reading it.

Bluetooth permission changes: Apps can now scan for Bluetooth devices without needing to ask for your location.

Device sensor restrictions: Device sensors that sense motion or movement now have refresh restrictions. For example, an app can only ping your accelerometer so often before Android 12 tells it to wait a minute.

Android Compute Core: This is a new security feature in Android 12. We don’t know exactly how it works, but Google says it separates the cloud from on-device information when using things like Live Caption.

You can view more security and privacy changes from our roundup here or on the Android developer website here.

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

Xiaomi 12S: Everything You Need To Know

Just as importantly, the trio of 12S phones unveiled in China – the 12S, 12S Pro, and 12S Ultra – are the first to bear a Leica logo, marking the two companies’ new long-term camera collaboration.

Here’s everything we know so far about the three phones, including their prospects of launching in the West.

When will the Xiaomi 12S launch worldwide?

Xiaomi revealed the three 12S phones in China on 4 July, but what about the rest of the world?

The Xiaomi 12 and 12 Pro took a couple of months to get a global launch, so on that timetable we’d expect to see the 12S arrive globally some time in September.

That’s assuming they’ll launch at all. Last year’s Mi 11 Ultra never officially reached the West at all, and Xiaomi has already confirmed to Tech Advisor that “The series will not be coming to the UK.”

There’s still a chance it hits some other markets outside China of course, though right now we’d consider it a long shot. Hopefully we’ll find out more soon.

How much do the 12S phones cost?

With a launch only in China, we only have Chinese prices for the phones so far, but here’s how they convert worldwide:

Xiaomi 12S – From ¥3,999 (around $600/£490/€570)

Xiaomi 12S Pro – From ¥4,699 (around $700/£580/€670)

Xiaomi 12S Ultra – From ¥5,999 (around $900/£740/€860)

What are the Xiaomi 12S specs?

Now that Xiaomi has released the 12S series in China, we know the specs for all three phones. Here’s how they stack up:

Xiaomi 12S specs

The regular 12S will be the smallest and cheapest of the phones in the series, but don’t hold that against it. It’s available in black, purple, green, and a new white finish.

Xiaomi

It uses the same size 6.28in display as the Xiaomi 12, keeping things nice and compact. As you’d expect, it will be a 120Hz AMOLED at a Full HD+ resolution – exactly the same as its predecessor.

There are still some major performance upgrades though. The most obvious it that, like all the 12S phones, it will use the Snapdragon 8+ Gen 1 chipset. That should result in some modest performance gains, and perhaps better battery life – though the actual cell is the same 4500mAh capacity, with 67W wired and 50W wireless charging.

The camera has also been upgraded. The main rear lens is still 50Mp, but it now uses the improved Sony IMX707 sensor – the same used in the 12 Pro earlier this year – with OIS. Like all three of the new phones, it also features Leica branding on the rear to reflect the camera company’s work helping Xiaomi to optimise image quality.

As with all phones in the series, the Xiaomi 12S launches with Android 12 and MIUI 13 installed.

Here are the full Xiaomi 12S specs:

6.28in, 120Hz, FHD+ AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

128/256GB storage

50Mp, f/1.9 OIS main camera

13Mp, f/2.4 ultrawide camera

5Mp telemacro camera

32Mp selfie camera

4500mAh battery

67W wired charging

50W wireless charging

Gorilla Glass Victus

152.7 x 69.9 x 8.2 mm

180g

Xiaomi 12S Pro specs

As with the regular Xiaomi 12 line, the 12S Pro sits only a little above the 12S in the hierarchy, and is differentiated by the fact that it’s larger, and has better specced cameras and display.

Xiaomi

Like the 12S you’ll get the Snapdragon 8+ Gen 1, but with slightly improved performance thanks to a larger cooling area. The chipset choice is essentially the only key upgrade from the 12 Pro.

The bigger 6.73in E5 AMOLED display will likely be a big draw to many, backed up by a dynamic 1-120Hz refresh rate and high 2K resolution – though this is essentially unchanged from the 12 Pro.

You also get the same fast charging as the 12 Pro (120W wired, 50W wireless) and the same rear camera setup.

Here are the full specs:

6.73in, 1-120Hz LTPO, 2K AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

256GB storage

50Mp, f/1.9 OIS main camera

50Mp, f/2.2 ultrawide camera

50Mp, f/1.9 2x zoom camera

32Mp selfie camera

4600mAh battery

120 wired charging

50W wireless charging

Gorilla Glass Victus

163.6 x 74.6 x 8.2 mm

205g

Xiaomi 12S Ultra specs

The 12S Ultra is a more different phone. That’s obvious immediately from the design – not only is the 12S Ultra larger, it also has a totally different circular rear camera module, surrounded by a 23K gold ring.

Xiaomi

Unlike last year’s Mi 11 Ultra there’s no space carved out for a mini rear display, and instead this bank of space is all about the actual camera – though we’ll get to that in a minute.

The phone comes in a vegan leather finish, available in two colours: Classic Black and Verdant Green. It’s also IP68-rated for water-resistance.

As for the display, you get a 6.73in 120Hz LTPO 2.0 AMOLED in a 2K resolution. As with the other two phones, the 12S Ultra uses the Snapdragon 8+ Gen 1 chipset.

The Ultra is also set to impress on battery. It features a 4860mAh battery with 67W wired charging and 50W wireless – so it actually has slower wired charging than the 12 Pro, but with a slightly larger battery. It also includes a proprietary Surge G1 battery management chipset.

Xiaomi

Now let’s get to the camera. While early reports had suggested the Ultra might be the debut of one of Samsung’s 200Mp camera sensors, in fact the phone uses a brand-new Sony sensor for its main camera, at aperture f/1.9. The new IMX989 is notable for being a 1in sensor – similar to the one equipped by Sony’s own Xperia Pro-I.

It’s paired with 48Mp ultrawide and 5x periscope lenses in essentially the same camera setup as the Mi 11 Ultra, outside of the upgraded main lens. The fourth rear lens is simply a ToF sensor for depth detection, while the selfie camera is be a 20Mp shooter.

6.73in, 120Hz LTPO 2.0, 2K AMOLED display

Qualcomm Snapdragon 8+ Gen 1

8/12GB RAM

256/512GB storage

50Mp, f/1.9 OIS main camera

48Mp, f/2.2 ultrawide camera

48Mp, f/4.1 OIS 5x periscope camera

32Mp selfie camera

4860mAh battery

67W wired charging

50W wireless charging

IP68 rating

Gorilla Glass Victus

Mafia: Definitive Edition – Everything You Need To Know

Mafia: Definitive Edition – Everything you need to know

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It is a remake of the game that shares the same name and premise, and it pays tribute to the games of the early 2000s.

It is part of the Planned Mafia: Trilogy and 2 more games are to follow in the near future.

An excellent remake of a classic.

Experience a breathtaking cityscape.

Realistic recreation of Prohibition-era USA.

Plenty of Trophies and easter eggs to collect.

You need a beefy PC to run it

Check price

What’s new in Mafia: Definitive Edition?

The short answer would be that pretty much everything and nothing is new at the same time.

Following the trend of many gaming hits of the past that got remakes, Mafia: Definitive Edition is not just Mafia with new textures and better lighting, but rather a new game built from the ground up using the latest technologies.

The overall purpose of the game was to bring back what made the original so iconic and expand upon it. This includes recreating the 1930s cityscape, improving the musical score, and more.

You can see this for yourself from the announcement trailer below:

However, there are a few things that you can try in the game that weren’t in the 2002 original, and that is called Free Ride Mode.

This interesting new feature lets gamers explore Lost Heaven in a free-form sandbox mode where they can test drive the vehicles in their garage.

More so, easter eggs and collectibles have been hinted as well.

What consoles can I play Mafia: Definitive Edition on?

Mafia: Definitive Edition can run on Xbox, PS4, and Windows PC via Steam and the Epic Games Store, but thanks to the backward compatibility it will also be playable on the Xbox Series X and Xbox Series S, as well as the PlayStation 5.

Do I need a powerful PC to run Mafia: Definitive Edition?

Since for all intents and purposes Mafia: Definitive Edition can be considered a brand-new game, don’t expect a PC that ran the 2002 version smoothly to be anywhere near powerful enough to run this game.

That being said, here are the minimum specs for Mafia: Definitive Edition:

CPU: Intel Core-i5 2550K 3.4GHz / AMD FX 8120 3.1 GHz

RAM: 6 GB

OS: Windows 10 64-bit

Video card: NVIDIA GeForce GTX 660 / AMD Radeon HD 7870

Pixel Shader: 5.0

Vertex Shader: 5.0

Sound Card: DirectX Compatible

Free disk space: 50 GB

Dedicated Video RAM: 2048 MB

And here are the recommended system requirements, as per the System Requirements Lab:

CPU: Intel Core-i7 3770 3.4GHz / AMD FX-8350 4.2GHz

RAM: 16 GB

OS: Windows 10 64-bit

Video card: NVIDIA GeForce GTX 1080 / AMD Radeon RX 5700

Pixel Shader: 5.1

Vertex Shader: 5.1

Free disk space: 50 MB

Dedicated Video RAM: 8192 MB

Of course, there’s always room for improvement when it comes to gaming, and a PC booster will help you play Mafia: Definitive Edition even better.

That is why we recommend you give Game Fire a try, as it will make your PC give it’s all during intense gaming sessions.

Game Fire

Play Mafia at maximum performance with the help of this dedicated game booster. Get it now at a special price for a limited time!

Get it free Visit website

When will the game become available?

The game is set to be released tonight at around 4 PM PDT for US users, and players can then start downloading and playing the game immediately.

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Vivo Nex Faq: Everything You Need To Know

Vivo NEX has officially arrived in India and it is the redefining the meaning of a bezel-less display. The smartphone not only brings an innovative design, it’s also packing the top of the line hardware and is challenging all the flagship devices out there. Since Vivo NEX has so many things going on, many prospective buyers have asked us a ton of questions regarding the smartphone. While we have answered most questions in our Vivo NEX review, this article is an attempt to all your questions regarding the latest flagship from Vivo. So, if you have any doubts related to the smartphone, stay with us as we answer the most frequently asked questions about the Vivo NEX:

Vivo NEX FAQ: Tech Specs and Hardware

What are the tech specs of the Vivo NEX?

You can find all the tech specs of Vivo NEX in the table below:

NameVivo NEX

Dimensions162 x 77 x 8 mm

Weight199 g

Display6.59 inch Suoer AMOLED panel (1080 x 2316 pixels) ~388 ppi

ProcessorQualcomm Snapdragon 845

GPUAdreno 630

RAM8GB

Storage128GB/256GB

Camera12 MP (f/1.8) + 5 MP (f/2.4)

Camera8 MP (f/2.0)

Battery4000 mAhg

SystemFuntouch OS based on Android 8.1 (Oreo)

SensorsFingerprint (under display), accelerometer, gyro, proximity, compass

ConnectivityWi-Fi 802.11 a/b/g/n/ac, WiFi Direct

Vivo NEX vs Oppo Find X Specs

Since Vivo NEX is competing directly with Oppo Find X to become your next bezel-less smartphone, it’s natural to compare both these phones with each other. Well, you can see how both the phones stack up against each other in the table below:

NameVivo NEXOppo Find X

Dimensions162 x 77 x 8 mm156.7 x 74.2 x 9.4 mm

Weight199 g186 g

Display6.59 inch Super AMOLED panel (1080 x 2316 pixels) ~388 ppi6.42 inch, AMOLED , 1080 x 2340 pixels, (~401 ppi density)

ProcessorQualcomm Snapdragon 845Qualcomm Snapdragon 845

GPUAdreno 630Adreno 630

RAM8GB8GB

Storage128GB/256GB128GB/256GB

Camera12 MP (f/1.8) + 5 MP (f/2.4)16 MP (f/2.0) + 20 MP (f/2.0)

Camera8 MP (f/2.0)25 MP (f/2.0)

Battery4000 mAh3730 mAh

SystemFuntouch OS based on Android 8.1 (Oreo)ColorOS based on Android Oreo 8.1

SensorsFingerprint (under display), accelerometer, gyro, proximity, compass Face ID, accelerometer, gyrometer, proximity, compass

ConnectivityWi-Fi 802.11 a/b/g/n/ac, WiFi DirectWi-Fi 802.11 a/b/g/n/ac with Wi-Fi Direct, Bluetooth 5.0

PriceRs. 59,990

Does Vivo NEX have a headphone jack?

Yes, Vivo NEX has a headphone jack.

Does Vivo NEX have a USB-C Port?

Is Vivo NEX Waterproof?

No, Vivo NEX is not waterproof and doesn’t come with any IP rating.

Does Vivo NEX have a notification LED?

No, Vivo NEX doesn’t come with a notification LED.

Does it have a dedicated earpiece?

No, Vivo NEX doesn’t have a dedicated earpiece. In an attempt to remove the notch, Vivo has used a Screen SoundCasting technology which uses a micro-vibration unit to generate screen vibrations which are conducted as sound.

Vivo NEX FAQ: Display

Does it have an LCD or an AMOLED display?

The Vivo NEX brings a 6.59 inch Super AMOLED panel with a resolution of 1080 x 2316 pixels giving it a pixel density of 388 PPI.

Does Vivo NEX has a notch?

What is the screen-to-body ratio?

The Vivo NEX brings an impressive 91.24% screen to body ratio. In comparison, iPhone X has only 82% screen to body ratio.

How Bright is the display?

Maximum brightness under default settings is around 460 nits.

Vivo NEX FAQ: Biometric Authentication

Does Vivo NEX come with a fingerprint sensor?

Yes, Vivo NEX utilizes Vivo’s innovative under-display fingerprint technology.

Does Vivo NEX come with face recognition and face unlock?

No, Vivo NEX doesn’t come with any kind of face recognition or face unlocking.

How good is the under-display fingerprint sensor?

Will it work with a tempered glass applied on screen?

Yes, the under-display fingerprint on the Vivo NEX works flawlessly even with a tempered glass applied to the device.

Does it work with oily or dusty fingers?

Yes, the Vivo NEX had no problem in unlocking with oily or dusty fingers. We tested it in different scenarios (after eating lunch, a pack of chips, and more) and the phone was able to unlock every time.

Does the sensor work with wet/moist fingers?

No, just like it’s with a regular fingerprint sensor, the Vivo NEX doesn’t unlock with a wet/moist finger.

Vivo NEX FAQ: Performance

What processor is being used in Vivo NEX?

Vivo NEX is using the top of the line Snapdragon 845 processor along with Adreno 630 GPU.

How good is the overall performance?

Can the Vivo NEX run PUBG Mobile?

Yes, the Vivo NEX can easily run PUBG Mobile.

How is the day to day performance?

Just like any other flagship device, the Vivo NEX flies through in any day-to-day performance scenarios.

Vivo NEX FAQ: Camera

Where is the front camera?

The front camera is neatly hidden under the body and only comes up when required.

Is the secondary lens a telephoto lens or a wide-angle one?

The secondary lens is neither telephoto nor a wide-angle lens. It’s just a normal lens which is being used for providing depth effect in portrait mode shots.

Does it record 4K footage?

Does it have portrait mode?

Yes, Vivo NEX can take portrait shots.

Does the front camera have portrait mode?

Yes, the front camera has portrait mode capabilities. It even features different lighting mode such as monochrome and studio mode just like it is in iPhone X.

Does it bring EIS or OIS?

The Vivo NEX comes with 4-axis optical image stabilization or OIS. However, there’s no mention of EIS.

Does it have AI Camera?

Yes, the camera brings AI features such as automatic scene detection, automatic color calibration, and more.

Does it support slow-motion video recording?

Does it have a Pro mode?

No, there is no Pro mode in the camera.

Does it have animated emoji support?

No, Vivo NEX doesn’t bring animated emoji support. You do get AR stickers though.

Vivo NEX FAQ: Software

What’s the Android Version?

The Vivo NEX is running on Android 8.1 Oreo with FunTouch OS 4.0 on top.

Does it support Project Treble?

Yes, Vivo NEX supports Project Treble.

Will it get Android P?

Does it have an App Lock?

Yes, Vivo NEX brings app locking feature.

Does it support gestures?

Yes, Vivo NEX brings full-screen gesture support.

Vivo NEX FAQ: Connectivity

Does Vivo NEX support dual-SIM cards?

Yes, Vivo NEX supports dual-SIM cards.

Does Vivo NEX support dual-band WiFi?

Yes, Vivo NEX supports both 2.4 GHz and 5 GHz WiFi bands.

What version of Bluetooth Vivo NEX is using?

Does Vivo NEX supports 4G VoLTE?

Yes, Vivo NEX supports 4G VoLTE.

Does it have IR Blaster?

No.

Vivo NEX FAQ: Battery

What’s the battery size?

Vivo NEX packs a huge 4000 mAh battery.

Does it support fast charging?

Yes, it supports fast charging.

Does Vivo NEX supports Wireless Charging?

How long it takes to charge the phone from 0 to 100%?

The device charges fairly quickly using the included charging brick, taking about 45 minutes to go from 10 to 80 percent charge

How long does the battery last?

SEE ALSO: Vivo NEX Review: Packs One Surprise After Another

Get to Know the Vivo NEX

Update the detailed information about Android Adaptive Battery: Everything You Need To Know on the Hatcungthantuong.com website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!