text:
Christie Nicholson
Founder New York, NY, USA
Rocket Steel
The decision to design Starship—the rocket that will carry us to the moon and Mars and back—using steel is perhaps Elon Musk's best idea ever.
By Christie Nicholson - December 6, 2019
Why We Don't See the Same Reality
What two people see is truly different and tied at a neural level to their own personal experiences.
By Christie Nicholson - September 10, 2019
How the Customer Interview Illuminates Innovation
Few companies set up their customer conversations in ways that lead to accurate and useful data. The key is to ask better questions.
By Christie Nicholson - August 20, 2019
What is the Optimal Amount of Choice Designers Should Provide?
Understanding the psychology of both clients and consumers
By Christie Nicholson - June 19, 2019
Design Business
People Don't Want Your Product Design. They Want the Outcome It Provides
Understanding the Jobs-to-be-Done methodology's role in the human-centered design process
By Christie Nicholson - May 20, 2019
How You Can More Accurately Predict the Future of Design, Using Steve Jobs' "Lost Speech" from 1983 as an Example
A 28-year-old Jobs was eerily prescient. The key is to focus on certain fundamental nodes of progress
By Christie Nicholson - April 22, 2019
What Makes Sugru Stick, Both Socially and Materially?
Just over a decade ago came a great innovation in a staple design material....
A Look at Materials for Additive Manufacturing (a.k.a. 3D Printing)
If you are a fresh industrial design student, you'll most likely have your first...
The ID Student's Friend: Foamcore, Blue Foam, and Others
If you're an industrial design student, now that school is back in swing you've...
Epoxy Resin 101
For those of you about to enter Industrial Design programs, you'll find epoxy resins...
By Christie Nicholson - September 3, 2014
New Technique for 3D Printing Epoxy-Based Resin Yields Super Strong, Lightweight Parts
Photo credit: Brett G. Compton & Lori Sanders)Wind energy is gaining support in the...
Wrap Your Head Around This One: Potentially Immortal Bacteria That Eat Electricity and Could Become Living Batteries
One of the stranger (and little known) facts of nature is that our living...
Can 3D Printers Save Libraries?
With text becoming digital we'd think that libraries are suffering, if not dead already....
How Sandstone Arches Are Formed
Sometimes inspiration for how to use a material can come from the natural world....
By Christie Nicholson - August 7, 2014
A New Wearable Battery: More Flexible and Durable than Anything Else
There are at least two big challenges with creating wearable technology. The first is...
By Christie Nicholson - July 30, 2014
If Researchers are Right, This Rock Will Make Solar Power Cheaper than Coal
In 2007, a student at the University of Tokyo brought a lump of a...
Morphable Drag-Reducing Surfaces, Inspired by Golf Balls
If I had to guess, I'd say that a smooth surface is better than...
Poison, Dragon's Blood and Mummies: How Artists Created Colors Prior to Pantone
What's the most expensive color?Prior to the 19th Century, Lapis Lazuli blue was a...
By Christie Nicholson - July 9, 2014
Hearing Aids as Phones, For Everyone
Phones are continually getting smaller—the paper-clip sized phone that Derek uses in Zoolander was...
Our Brains are Built to Like Curved Screens
There's a good reason we are experiencing the rise of the so-called "visual web."...
Startup Seeks to Produce Metals Cleanly
The creation of metals is an often forgotten but critical business involved in most...
A Ford Made with Tomatoes
Would you like ketchup with that Ford Focus? No joke. Ford announced yesterday that...
Trying to Make White T-Shirts Environmentally Friendly (Because They Aren't)
Think that pure white, organic cotton tee shirt is environmentally friendly? Hm, most likely...
By Christie Nicholson - June 4, 2014
Superhydrophobia: Breakthroughs in Next-Level Waterproofing
Water might be good for our bodies, but it's terrible for our stuff. Our...
What Smartwatches Need: Physical, Clickable Interactivity, Not Swiping
As we reported earlier this year, there is no shortage of smartwatches approaching the...
New Light Panel Technology: Imagine Having a Ceiling Skylight on the 1st Floor of a Multistory Building
Summer sunlight can change my mood, and I don't suffer from seasonal affective disorder....
The First Stain on Super Material Graphene: It May Be Toxic
In the world of material science graphene might be considered its Superman, Einstein and...
By Christie Nicholson - May 7, 2014
A Soft-Materials 3D Printer from Carnegie Mellon and Disney
When I think of 3D printing, I think of objects that are hard, made...
For Minority-Report-like Interface, Just Add Water. Or Actually, Fog
I'm definitely among those who have been waiting for Minority Report-like gesturing to become...
A Material that Can Be a Mirror... Then a Window
A group of MIT scientists have created a new material that can be both...
Wearable Technology: Smart Band-Aids Can Do a Lot More than Cover Up Cuts
One of the most popular wearable medical inventions so far might be the Band-Aid....
By Christie Nicholson - April 9, 2014
Reducing the Heat In Computing
Graphene + Copper (not to scale, obviously)About a year ago, I traveled to Cornell...
Researchers Discover How to Design Non-Living, Living Material
There's an entirely new direction for materials coming to life—specifically, a hybrid that combines...
By Christie Nicholson - March 26, 2014
Materials Science is About To Get Crazy
There is an exciting development in the works regarding materials science, one that will...
Do You Use, or Design, Plastic Products? New Research Means You May Want to Think Twice About the Material
Update: A response from Eastman Chemical Company, with whom Core77 has had a working...


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Q: "Syntax error: operand expected ..." in script that was working right I had a script that was doing well until now, I don't now what has changed, but the script is the same.
I had this variables:
SIZEKB=$[[(SIZE+1023)/1024]]
REQUIREDKB=$[[SIZEKB+MINFREEKB]]

Which I repeat were working ok, but now I got this error:
line 12: [(SIZE+1023)/1024]: syntax error: operand expected (error token is "[(SIZE+1023)/1024]")
line 13: [SIZEKB+MINFREEKB]: syntax error: operand expected (error token is "[SIZEKB+MINFREEKB]")

What should I change to correct the syntax, thanks!

A: The easiest way to write those assignment is to providean arithmetic context:
((SIZEKB=(SIZE+1023)/1024))
((REQUIREDKB=SIZEKB+MINFREEKB))

You can use also
SIZEKB=$[(SIZE+1023)/1024]
REQUIREDKB=$[SIZEKB+MINFREEKB]

but this is deprecated and provided for compatibility with old scripts (perhaps those written for bash version 1?).

A: You need to place the equations in two curly brackets and so:
REQUIREDKB=$(( $SIZEKB+$MINFREEKB ))

Alternatively, you can use single square brackets and so:
REQUIREDKB=$[ $SIZEKB+$MINFREEKB ]

redpajama_set_name: RedPajamaStackExchange


From Zeus to Atlas to Prometheus, the mysteries of the ancient Greek mythological titans and deities have lived on for centuries. Now, the secrets of the ancient Greek gods are revealed in the new thrilling adventure game Secrets of Olympus.
The fantastical and magical game offers you hours of brain-tingling fun. You can also discover the secrets of the Greek gods by winning stars and playing the shape shifter mini-game to score additional points and unlock powerful spells to help you win. Do you have the brains to outwit the ancients? Find out in the Secrets of Olympus.
✓ Powerful spells help you win!


text:
La pointe de la Beccaz est un sommet de France situé en Haute-Savoie.

Géographie

Le sommet qui s'élève à  d'altitude marque le point culminant d'un petit chaînon montagneux qui s'étire au sud de la Tournette, au-dessus de Faverges. Les autres sommets de la montagne sont le crêt des Mouches à  d'altitude, la pointe de Banc Fleury à  d'altitude, le Bonverday à  d'altitude et la pointe de Chauriande à  d'altitude, tous situés au sud. Ces sommets marquent les rebords surélevés d'un synclinal perché reposant sur des terrains du Hauterivien, cerné de falaises de calcaire urgonien dont le rocher d'Arclosan au sud-est et orienté selon un axe nord-sud. Entre les parois rocheuses s'étagent des pelouses alpines dans des terrains du Sénonien dont l'une autour des chalets d'Arclosan a servi d'alpage.

La pointe de la Beccaz n'est pas accessible via un sentier de randonnée. En revanche, un sentier accessible depuis Montmin ou Saint-Ferréol arrive à l'extrémité méridionale de la montagne et permet de gagner le crêt des Mouches et la pointe de Banc Fleury par les chalets d'Arclosan.

Références

Beccaz
Beccaz
Beccaz
Beccaz

redpajama_set_name: RedPajamaWikipedia


text:
Q: Making a single character immortal in a minecraft server I am setting up a Minecraft server which will feature a single player who is very averse to dying. Does anyone know of a way to have a forge-modded survival server where a single character is immortal, but the rest functions as normal? Extra points if that character can also be setup to do 0 damage to others.

A: I am answering my own question here with a somewhat OK solution, but i would still very much like to hear better/alternative solutions.
It is possible to use the /effect command to give resistance 5 to a player. Resistance reduces damage by 20% pr level, so it SHOULD make characters invulnerable at level 5. The effect can be made semi-permanent as far as I can see, by giving a long duration. A /kill command will still kill the player, as will void damage.
https://minecraft.fandom.com/wiki/Resistance
https://www.digminecraft.com/game_commands/effect_command.php

A: You can run the command /effect [Username] resistance 255 600 to make the player take no damage for 10 minutes, and the command /effect [Username] weakness 255 600 for the player to do no damage for 10 minutes


Main office: A main office is a site where everyone is connected via a network and where the bulk of corporate information is located. A main office can have hundreds or even thousands of people who depend on network access to do their jobs. A main office might use several connected networks, which can span many floors in an office building or cover a campus that contains several buildings.
Remote locations: A variety of remote access locations use networks to connect to the main office or to each other.
branch offices have local network resources, such as printers, but must access information directly from the main office.
Home offices: When individuals work from home, the location is called a home office. Home office workers often require on-demand connections to the main or branch offices to access information or to use network resources such as file servers.
Mobile users: Mobile users connect to the main office network while at the main office, at the branch office, or traveling. The network access needs of mobile users are based on where the mobile users are located.
Figure 1-1 shows some of the common locations of networks that can be used to connect users to business applications.
Many different types and locations of networks exist. You might use a network in your home or home office to communicate via the Internet, to locate information, to place orders for merchandise, and to send messages to friends. You might have work in a small office that is set up with a network that connects other computers and printers in the office. You might work in a large enterprise in which many computers, printers, storage devices, and servers communicate and store information from many departments over large geographic areas. All of these networks share many common components.
The physical components are the hardware devices that are interconnected to form a computer network. Depending on the size of the network, the number and size of these components varies, but most computer networks consist of the basic components shown in Figure 1-2.
Personal computers (PCs): The PCs serve as endpoints in the network, sending and receiving data.
and intelligent switching of the data within the local network.
Routers: Routers interconnect networks and choose the best paths between networks.
When designing and describing a computer network, you use a drawing or diagram to describe the physical components and how they are interconnected.
A cloud represents the Internet or WAN connection.
A cylinder with arrows represents a router.
A rectangular box with arrows represents a workgroup switch.
A tower PC represents a server.
A laptop or computer and monitor represent an end user PC.
A straight line represents an Ethernet link.
A Z-shaped line represents a serial link.
Other information can be included as space allows. For example, it is sometimes desirable to identify the interface on a device in the format of s0/0/0 for a serial interface or fa0/0 for a Fast Ethernet interface. It is also common to include the network address of the segment in the format such as 10.1.1.0/24, where 10.1.1.0 indicates the network address and /24 indicates the subnet mask.
Data and applications: When users are connected through a network, they can share files and even software application programs, making data more easily available and promoting more efficient collaboration on work projects.
Physical resources: The resources that can be shared include both input devices, such as cameras, and output devices, such as printers.
Network storage: Today the network makes storage available to users in several ways. Direct attached storage (DAS) directly connects physical storage to a PC or a shared server. Network attached storage (NAS) makes storage available through a special network appliance. Finally, storage area networks (SAN) provide a network of storage devices.
Backup devices: A network can also include backup devices, such as tape drives, that provide a central means to save files from multiple computers. Network storage is also used to provide archive capability, business continuance, and disaster recovery. Figure 1-4 shows some common shared resources.
The overall benefit to users who are connected by a network is an efficiency of operation through commonly available components used in everyday tasks, sharing files, printing, and storing data. This efficiency results in reduced expenditures and increased productivity.
therefore, the need for network security has to be balanced with the need for connectivity.
E-mail: E-mail is a valuable application for most network users. Users can communicate information (messages and files) electronically in a timely manner, to not only other users in the same network but also other users outside the network (suppliers, information resources, and customers, for example). Examples of e-mail programs include Microsoft Outlook and Eudora by Qualcomm.
Web browser: A web browser enables access to the Internet through a common interface. The Internet provides a wealth of information and has become vital to the productivity of both home and business users. Communicating with suppliers and customers, handling orders and fulfillment, and locating information are now routinely done electronically over the Internet, which saves time and increases overall productivity. The most commonly used browsers are Microsoft Internet Explorer, Netscape Navigator, Mozilla, and Firefox.
Instant messaging: Instant messaging started in the personal user-to-user space; however, it soon provided considerable benefit in the corporate world. Now many instant messaging applications, such as those provided by AOL and Yahoo!, provide data encryption and logging, features essential for corporate use.
Collaboration: Working together as individuals or groups is greatly facilitated when the collaborators are on a network. Individuals creating separate parts of an annual report or a business plan, for example, can either transmit their data files to a central resource for compilation or use a workgroup software application to create and modify the entire document, without any exchange of paper. One of the best-known traditional collaboration software programs is Lotus Notes. A more modern web-based collaboration application is a wiki.
Database: This type of application enables users on a network to store information in central locations (such as storage devices) so that others on the network can easily retrieve selected information in the formats that are most useful to them. Some of the most common databases used in enterprises today are Oracle and Microsoft SQL Server.
The key to user applications is that they enable users to be connected to one another through the various types of software. As a business begins to rely on these applications as part of the day-to-day business process, the network that the applications operate in becomes a critical part of the business. A special relationship exists between these applications and the network. The applications can affect network performance, and network performance can affect applications. Therefore, you need to understand some common interactions between user applications and the network. Figure 1-5 characterizes some of the interactions for different types of applications.
further direct human interaction. Bandwidth was important but not critical because little human interaction occurred. As long as the time the application took to complete did not become excessive, no one really cared.
Interactive applications, such as Enterprise Resource Planning (ERP) software, perform tasks, such as inventory inquiries and database updates, that require more human interaction. The user requests some type of information from the server and then waits for a reply. With these types of applications, bandwidth becomes more important because users are intolerant of slow responses. However, application response is not solely dependant on the bandwidth of the network; the server and storage devices also play a part. However, in cases where the network becomes a problem, other features such as quality of service (QoS) can alleviate some bandwidth limitations by giving the traffic from interactive applications preference over batch applications.
bandwidth mandatory, but QoS is also mandatory. VoIP and video applications must be given the highest priority.
In today's environment, the end user is bombarded with ads indicating how much money can be saved by converting to VoIP and how installation is as easy as dropping a VoIP router into the network. Although this is often true in the home network, it can result in disaster in a small office network. Applications that used to work start to run so slowly that they are unusable, for example, when someone is on the phone, and voice quality is poor. This type of implementation does not provide enough bandwidth to the Internet, nor does it provide a proper QoS scheme.
Both issues can be overcome with proper network design.
Speed: Speed is a measure of how fast data is transmitted over the network. A more precise term would be data rate.
Cost: Cost indicates the general cost of components, installation, and maintenance of the network.
Scalability: Scalability indicates how well the network can accommodate more users and data transmission requirements. If a network is designed and optimized for just the current requirements, it can be very expensive and difficult to meet new needs when the network grows.
Reliability: Reliability indicates the dependability of the components (routers, switches, PCs, and so on) that make up the network. Reliability is often measured as a probability of failure, or mean time between failures (MTBF).
Topology: Networks have two types of topologies: the physical topology, which is the arrangement of the cable, network devices, and end systems (PCs and servers), and the logical topology, which is the path that the data signals take through the physical topology.
These characteristics and attributes provide a means to compare different networking solutions. Increasingly, features such as security, availability, scalability, and reliability have become the focus of many network designs because of the importance of the network to the business process.
Building a reliable and scalable network depends on the physical and logical topology. Topology defines the interconnection method used between devices including the layout of the cabling and the primary and backup paths used in data transmissions. As previously mentioned, each type of network has both a physical and a logical topology.
Bus: Computers and other network devices are cabled together in a line.
Ring: Computers and other network devices are cabled together with the last device connected to the first to form a circle, or ring. This category includes both ring and dual-ring topologies.
Star: A central cabling device connects the computers and other network devices. This category includes both star and extended-star topologies.
Figure 1-6 shows some common physical topologies used in networking.
The logical topology of a network refers to the logical paths that the signals use to travel from one point on the network to another—that is, the way in which data accesses the network media and transmits packets across it.
The physical and logical topologies of a network can be the same. For example, in anetwork physically shaped as a linear bus, the data travels along the length of the cable.Therefore, the network has both a physical bus topology and a logical bus topology.
On the other hand, a network can have quite different physical and logical topologies. For example, a physical topology in the shape of a star, in which cable segments connect all computers to a central hub, can have a logical ring topology. Remember that in a ring, the data travels from one computer to the next, and inside the hub, the wiring connections are such that the signal actually travels around in a circle from one port to the next, creating a logical ring. Therefore, you cannot always predict how data travels in a network simply by observing its physical layout.
Star topology is by far the most common implementation of LANs today. Ethernet uses a logical bus topology in either a physical bus or a physical star. An Ethernet hub is an example of a physical star topology with a logical bus topology.
Figure 1-7 shows some common logical topologies used in networking.
The bus topology is commonly referred to as a linear bus; all of the devices on a bus topology are effectively connected by one single cable.
in the network. An example of a physical bus topology is a Thicknet Ethernet cable running through the length of a building with devices taped into it, though this is an antiquated connection method that is no longer used. An example of a logical bus topology is an Ethernet hub.
The star topology is the most common physical topology in Ethernet LANs. When a star network is expanded to include an additional network device that is connected to the main network devices, the topology is referred to as an extended-star topology. The following sections describe both the star and extended-star topologies.
When installed, the star topology resembles spokes in a bicycle wheel. It is made up of a central connection point that is a device, such as a hub, switch, or router, where all the cabling segments actually meet. Each device on the network is connected to the central device with its own cable.
important and is the reason why almost every newly designed Ethernet LAN has a physical star topology. Figure 1-9 depicts a star topology with all transmissions going through a single point.
A common deployment of an extended-star topology is in a hierarchical design such as a WAN or an Enterprise or a Campus LAN. Figure 1-10 shows the topology of an extended star.
The problem with the pure extended-star topology is that if the central node point fails, large portions of the network can become isolated. For this reason, most extended-star topologies employ a redundant connection to a separate set of connection devices to prevent isolation in the event of a device failure.
In a single-ring topology, all the devices on the network share a single cable, and the data travels in one direction only. Each device waits its turn to send data over the network. The single ring, however, is susceptible to a single failure, stopping the entire ring from functioning. Figure 1-11 shows the traffic flow in a single-ring topology.
In a dual-ring topology, two rings allow data to be sent in both directions. This setup creates redundancy (fault tolerance), meaning that if one ring fails, data can be transmitted on the other ring. Figure 1-12 shows the traffic flow in a typical dual-ring topology.
Another type of topology that is similar to the star topology is mesh topology. Mesh topology provides redundancy between devices in a star topology. A network can be fully meshed or partially meshed depending on the level of redundancy needed. This type of topology helps improve network availability and reliability. However, it increases cost and can limit scalability, so you need to exercise care when meshing.
The full-mesh topology connects all devices (or nodes) to one another for redundancy and fault tolerance. Implementing a full mesh topology is expensive and difficult. This method is the most resistant to failures because the failure of any single link does not affect reachability in the network.
Figure 1-13 shows the connections in a full-mesh topology.
In a partial-mesh topology, at least one device maintains multiple connections to all other devices, without having all other devices fully meshed. This method trades off the cost of meshing all devices by allowing the network designer to choose which nodes are the most critical and appropriately interconnect them.
Figure 1-14 shows an example of a partial-mesh topology.
Another key component for most business users today is a connection to the Internet. An Internet connection is a WAN connection, but small- to medium-sized computer networks can use various methods and topologies to interconnect to the Internet.
You have three common methods of connecting the small office to the Internet. Digital subscriber line (DSL) uses the existing telephone lines as the infrastructure to carry the signal. Cable uses the cable television (CATV) infrastructure. Serial uses the classic digital local loops.
In the case of DSL and cable, the incoming lines are terminated into a modem that converts the incoming digital encoding into a digital format for the router to process. In the case of serial this is done by channel service unit (CSU)/digital service unit (DSU). In all three cases (DSL, cable, and serial), the digital output is sent to a router that is part of the customer premises equipment (CPE). Figure 1-15 shows the equipment placement for these different connection methods.


Unless you're giving prints or files to someone who has paid for them in some way, you should always use watermarks on all your publicly displayed electronic images. This includes images on all your social media, your blog, and your online gallery (unless it's in a format that can't be right-clicked and saved or you've disabled the right-click feature altogether).
For as great as this technology-savvy society is for business, it can open you up for missed marketing opportunities (or even worse, theft). Most people are probably innocently unaware; they see an image they like, right click it, and save it to their desktop or repost it to their sites. Only a small minority are actually looking to steal professional images.
Nonetheless, if there's a chance people will repost your images, watermarking them ensures that anyone who sees the photo knows who took it. Bam. Marketing (and rights protection).
By watermarking, you not only take advantage of passive marketing opportunities but also protect your financial and brand well-being. Say a magazine editor sees one of your photos on someone's Facebook page that would be perfect to accompany one of the magazine's articles, but the photo has a big, fat watermark on it.
First of all, the person knows immediately that you produced the image, and second, she knows she can't use it in that condition, so she has to contact you for a nonwatermarked version. That's when you have the power to approve or decline the usage and set your terms.
Without such a safety net in place, your photo could end up anywhere without acknowledgement (or even your permission). These are your photos and your marketing opportunities. If you don't protect them, no one will.


Flat-out hatred can be a good thing in sports.
Especially at the professional level. You had a huge dose of hatred for several years when the Red Sox were a worthy challenger and got together with the Yankees. There was nothing manufactured about the hatred when Red Sox catcher Jason Varitek shoved his glove in the face of Alex Rodriguez.
There is a good dose of hatred between the Jets and the Patriots. In football, great teams always keep their eyes firmly focused on the big prize, and team rivalries don't mean as much today as they did in past generations. Rex Ryan badly wants to beat Bill Belichick and the Patriots, but he wants an AFC title and a spot in the Super Bowl a lot more.
Hockey leads to hatred. The Rangers have had no more hated rival over the last four decades than the Philadelphia Flyers.
There's a grudging respect given to the New Jersey Devils, while the New York Islanders are struggling to build a competitive team as they try to survive. But the Rangers and Flyers have hated each other since Dave Schultz, Bobby Clarke and Bernie Parent were lining up against Brad Park, Jean Ratelle and Eddie Giacomin in the early and mid-1970s.
Older Rangers fans will never forget the 1974 loss in the NHL semifinal round to the Flyers. That was the first time that any "original six" team lost a series to one of the expansion teams. The Rangers were excellent that season, but the Flyers were an unstoppable force, as the Broad Street Bullies used their muscle to get by the Rangers.
Two things stand out about that seventh-game loss to the Flyers. One is Schultz's vicious beating of Dale Rolfe, an action that did not see any other Rangers player come to the defense of his teammate. The Rangers' defenseman absorbed blow after blow, and that awful display emboldened the Flyers.
Late in the game — with the Rangers trailing 4-3 — the cameras panned the Rangers' bench. While most of the players were properly downcast as a result of their impending defeat, left wing Vic Hadfield was smiling as if his world was all good. He was traded to the Penguins in the offseason.
The hate between the two teams has never abated.
In recent years, the Rangers have had the edge on the Flyers. Last year, the Rangers swept the six games that the two teams played in the regular season, but that didn't mean that the Flyers had backed down and accepted their status.
As the playoffs got under way, the Flyers were dreaming of a chance to get revenge on the Rangers.
That seemed like it might come to fruition when the Rangers beat the Ottawa Senators in the opening round of the playoffs, while the Flyers upset the Penguins. However, the Flyers lost in the next round to the Devils and did not earn an opportunity to face the Rangers in the Eastern Conference Finals.
That has stuck in the craw of Philadelphia head coach Peter Laviolette, who was given a two-year extension by the Flyers to remain behind the bench.
Laviolette has two notable achievements on his resume. He led the undermanned Carolina Hurricanes to the only Stanley Cup in their history in 2006. Four years later, his Flyers trailed the Boston Bruins three games to none, and he led them back from that precipice to a seven-game series victory. It's only happened three times in NHL history, and it had not happened in 35 years.
Laviolette is a formidable coach, and when the cameras were on him during HBO's 24-7 series — showing preparations for the Winter Classic between the Flyers and the Rangers — it was obvious how much beating the Rangers meant to him.
Everything he did in the month of December was designed to get his team prepared for a peak effort against the Rangers, and things went well early for him, as Philadelphia built a 2-0 lead.
However, that was just temporary joy for the Flyers. The Rangers came back and won the game by a 3-2 margin.
The final installment of the series showed Laviolette off by himself, wiping away a tear or two as a result of the loss.
This from a grown man, at the peak of his profession after a regular-season defeat.
Hatred is a good thing in professional sports.
The first game between the Flyers and Rangers is Nov. 4 at Madison Square Garden. Get your popcorn ready.


text:
\section{Introduction}
The unprecedented adoption of Machine Learning (ML) in critical sectors such as finance, healthcare and education has made fairness-related bias detection and mitigation a crucial part of ML systems. It is important that ML models do not discriminate against individuals based on protected attributes such as race, gender or skin color when making predictions.
Fairness research has typically focused on detecting and mitigating bias for a single protected attribute. With such detection and mitigation, the fairness gap for \emph{intersectional subgroups} such as \texttt{Female} \underline{and} \texttt{Black} might still be high. This phenomenon is called Fairness Gerrymandering~\citep{ghosh2021characterizing, pmlr-v80-kearns18a, pmlr-v81-buolamwini18a}. Several works~\citep{pmlr-v80-kearns18a, overlapping_fairness, MultiFair, fairness_auditing} proposed techniques to address fairness gerrymandering, which require predetermining fairness violation metrics. Nevertheless, a recent survey~\citep{comp_persp} highlights the scarcity of research in detecting and mitigating intersectional bias.
One of the key challenges of this research is the limited access to protected attributes and their \emph{unknown correlations} with other attributes in the dataset. These limitations are common due to privacy or data restrictions.
Another key challenge of fairness research is reducing the bias across groups and \emph{intersectional subgroups} without a significant drop in accuracy.

In this paper we propose a generic and simple fairness-aware learning framework that addresses the intersectional subgroup bias problem without requiring specific fairness metrics to be predetermined. It learns latent representations without relying on protected attributes and their interactions with other attributes in the dataset.
Previous work~\citep{fairness_mi, song2019learning} shows that reducing the mutual information between a protected attribute and output variable plays a significant role in reducing the bias associated with that protected attribute. We propose a generic framework that reduces the mutual information not only between a single protected attribute and the output variable, but also between any subset of protected attributes and the output variable.  Experimentally, we show that this approach debiases the model significantly with little or no drop in accuracy.
Furthermore, we also show that the models trained with our framework become insensitive to the values of protected attributes. This shows that our models are causally fair according to the definitions of causal fairness by~\citep{causal_fair}.

\paragraph{Motivating example:}
\vspace{-7pt}
Table~\ref{tab:TPRs_Male_Female} illustrates accuracy and TPR gap between two subpopulations, $\texttt{Male} \cap \texttt{White}$  and $\texttt{Female} \cap  \texttt{Black}$ for the Law School Admissions Council (LSAC) dataset~\citep{wightman1998lsac}. TPR is an important metric in this specific example since it measures the advantaged outcome.
We compare our fairness-aware framework with three other baseline models: 1) the original model without mitigation, 2) a baseline model which is trained without \texttt{Gender} and \texttt{Race} attributes and 3) a baseline model which is trained after upsampling minority subgroups.
We observe a significant TPR gap between two populations in the original model. Two of the baseline models reduce the fairness gap. However, they are less effective compared to our approach. Our approach learns latent representations that do not rely on protected attributes and their relationship to output variable, and thus reduces the fairness gap significantly.
\begin{table}
  \caption{Accuracy and True Positive Rate (TPR) for two subpopulations, computed for the original model, our model trained using fairness-aware framework and two baseline models.
  }

  \label{tab:TPRs_Male_Female}
  \centering
  \def1.0{1.0}
  \begin{tabular}{cccc}
    \toprule
    & \multirow{2}{*}{Accuracy} & \multicolumn{2}{c}{TPR} \\
    \cmidrule{3-4}

     &  &  $\texttt{Male} \cap \texttt{White}$  & $\texttt{Female} \cap \texttt{Black}$ \\
    \midrule
    Original model & 0.87 & 0.96 & 0.77\\
    Removed protected attributes & 0.86 & 0.96 & 0.83 \\
    Upsampled minority groups & 0.86 & 0.88 & 0.82\\
    Our fairness-aware framework & 0.86 & 0.94 & 0.90\\
  \bottomrule
\end{tabular}
\end{table}
The main contributions of this paper are as follows:
\begin{itemize}
\vspace{-2pt}
    \item We introduce a novel bias mitigation framework that aims to reduce the mutual information between intersectional subgroups and the output variable.

    \item We show empirically that our approach reduces the equality and demographic parity gaps significantly and surpasses state-of-the-art approaches.

    \item We study the sensitivity of the debiased model to the presence of protected attributes and the effectiveness of our approach when non-protected attributes are correlated with the protected ones.
    \item We demonstrate that feature interactions between protected and non-protected attributes reduce significantly when the models are trained using our bias mitigation framework.
\end{itemize}

\section{Related Work}
Fairness literature offers numerous definitions of fairness~\citep{21_def_fairness}, its measurement and mitigation. We base our fairness definition and measurement on the work of ~\citep{Hardt2016EqualityOO} and three well-known metrics: Demographic Parity~\citep{dp, dp2, dp3}, Equalized Odds and Equality of Opportunity~\citep{Hardt2016EqualityOO}. Demographic Parity compares the average prediction score across different subgroups.
Equality of Opportunity, in addition to that, takes the label distribution into account and measures the TPR gap among different groups.
Equalized Odds~\citep{Hardt2016EqualityOO} measures both the TPR and False Positive Rate (FPR) gaps among different groups.
Specific metrics have been developed for intersectional subgroups such as the min-max ratio~\citep{ghosh2021characterizing} and differential fairness metric~\citep{intersectional_def_fairness}. In this paper we focus on measuring the commonly used Demographic Parity and Equalized Odds for intersectional subgroups, which facilitate comparisons with previous work.

Bias mitigation techniques reduce the disparities among the groups and intersectional subgroups measured by the aforementioned metrics.
Three types of mitigation techniques have been proposed to combat fairness bias~\citep{comp_persp}:

    \textbf{Postprocessing techniques}
    aim to reduce fairness bias during model inference. Those approaches enforce model predictions to follow the same distribution observed during training~\citep{zhao-etal-2017-men}, transforming model predictions to follow a specific fairness measure.

    These techniques, however, require access to protected attributes during inference, which is not always available due to data scarcity or privacy reasons.

    \textbf{Dataset preprocessing techniques} such as balancing the distribution of data labels, downsampling and sample re-weighing~\citep{Kamiran2011DataPT} alleviate modelling bias to a certain extent.

    However, \cite{balance_not_enough} shows that data preprocessing and balancing datasets often have limited effect, compared with training inherently unbiased models. Apart from data balancing, one can also delete protected attributes from the training set or mask them. However, this is not sufficient, since protected attributes are often correlated with other attributes in the data.

    \textbf{Train-time techniques} aim to combat fairness bias during model training. This can be accomplished using constraints based on adversarial loss~\citep{data_decisions, Zhang2018MitigatingUB, balance_not_enough}, feature importance~\citep{Liu2019IncorporatingPW, Du2019LearningCD, right_for_right_reasons}, fairness measurement~\citep{agarwal2018reductions}, decision boundary~\citep{fairness_constraints} or statistical dependence~\citep{prejudice}. Adversarial loss requires defining additional heads or constraints for a specific protected attribute. It maximizes the primary objective of a specific task while minimizing the model's ability to predict specific protected attributes. Constraints based on feature importance, on the other hand, heavily rely on the feature contribution scor
    ~\citep{Liu2019IncorporatingPW, Du2019LearningCD}, which is not always reliable~\citep{Hooker2019ABF}.

    ~\cite{overlapping_fairness} propose Bayes-optimal classification framework for intersectional group fairness which is also tied to fairness metric constraints.

    ~\cite{MultiFair} propose a framework based on mutual information minimization  for intersectional subgroups. This framework, however, requires two estimators and two additional predictors which makes the mitigation process complex.

In contrast, we propose a simple and generic training-time fairness-aware framework that doesn't rely on specific fairness metrics, or architectural modifications such as adversarial heads. It accounts for intersectional fairness of any subsets of input features and is straightforward to implement.

\vspace{-1pt}
\section{Preliminaries}
In this section we formalize the problem, introduce preliminary notations and concepts that are used later in the paper.
The goal is to learn a fair Neural Network (NN) model that mitigates the fairness gap for intersectional subgroups formed by multiple protected attributes. We seek to reach this goal with minimal impact on accuracy.
During inference, no information about the protected attributes is necessary.

\vspace{-1pt}
\subsection{Notation}
\label{notation}
We consider a typical ML model, $f:R^M \rightarrow R^C$, that is trained on a dataset $D = \{(x_1, y_1), ..., (x_N, y_N)\}$, where each sample $x_i$ consists of a set of $M$ features $x_i = \{x_i^{1}, x_i^{2}..., x_i^{M}\}$ where $x_i^j \in R$ represents the $j^\texttt{th}$ feature in the $i^\texttt{th}$ sample. $y_i \in [1,...,C]$ is the label corresponding to sample $x_i$. Let $x'^j \in R$ denote a shared baseline across all samples for input feature $j$. It indicates the absence of signal or feature value in the input. Traditionally, the zero value is used to indicate the absence of signal but for certain features zero value might represent a meaning. For example, $0$ and $1$ might indicate male and female for the gender feature in some datasets.

\paragraph{Protected Attribute Notation.} \vspace{-7pt} Let $A_k \subseteq \{1, 2,..., M\}$ be a subset of features that are known to be protected. For example, $A_k$ may correspond to $\{race\}$ or $\{gender, race\}$. Let also $\mathcal{A}$ denote a set of subsets of $A_k$. For example, $\mathcal{A}$ may correspond to $\mathcal{A}=\{\{gender\}, \{race\}, \{gender, race\}\}$.

Finally, let also $\mathcal{S}(x_i,x',A_k)$ denote a substitution function that replaces the features that aren't in $A_k$ with values from baseline $x'$.
\begin{equation}
\label{fig:mask_context}
    \mathcal{S}(x_i,x',A_k) = \left \{ \begin{array}{rcl}
    x_i^j, & j \in A_k \\
    x'^j, & \text{otherwise} \\
\end{array}\right. \forall j\in [M]
\end{equation}
We denote by $x^{A_k}$ the subset of features in $x$ corresponding to protected features $A_k$. Analogously, $x^{\setminus A_k}$ denotes a subset of features that excludes protected attributes $A_k$.

\vspace{-3pt}
\subsection{Information Theory}
\label{mutual_information}
We briefly revisit information theory in order to analyse the relationship between a subset of protected attributes $A_k$ and dependant output variable $y$. This analysis explains the reasoning behind our bias mitigation approach.
The entropy $H(X)$ measures the average uncertainty~\citep{10.5555/1146355} of a random variable $X$.
Mutual Information (MI) uses the entropy to measure the shared information between two random variables. In our case, the mutual information between an input feature $x^j$ and the output variable $y$ can be measured as $MI(x^j;y) = H(x^j) - H(x^j|y)$.
During bias mitigation, we aim to reduce the $MI$ between the protected attributes $x^{A_k}$ and $y$. Reducing $MI(x^{A_k};y)$ implies increasing the uncertainty $H(x^{A_k}|y)$. We increase that uncertainty by associating protected attributes with a uniformly distributed random output variable $Unif(1,C)$.
We propose a proxy metric that associates $x^{A_k}$ with random labels independent of the values in $x^{\setminus A_k}$. The new examples generated by $\mathcal{S}(x_i,x',A_k)$ reduce overall mutual information between $x^{A_k}$ and the protected attributes in the dataset.
The proxy measure aims to reduce the distance between $\mathcal{S}(x_i,x',A_k)$ and $Unif(1,C)$ which can be represented as a regularization term in the optimization objective.
Table~\ref{tab:mut_info} shows how the mutual information between protected attributes $\texttt{Gender}$ and $\texttt{Race}$ reduces as we augment the data with samples that associate intersectional subgroups of protected attributes with random guesses.
In the next section we describe how we incorporate this regularization term into the model's optimization objective.

\vspace{-5pt}
\begin{table}[hbt!]
  \def1.0{1.0}
  \caption{MI between protected attributes $\texttt{Gender}$ and $\texttt{Race}$ and output variable $\texttt{Passed Bar}$ before and after MI constraint-based data augmentation for the LSAC and Adult datasets.}
  \label{tab:mut_info}
  \centering
  \begin{tabular}{lcccc}
    \toprule
    & \multicolumn{2}{c}{Passed Bar (Before Mitigation)} & \multicolumn{2}{c}{Passed Bar (After Mitigation)}\\
     \cmidrule{2-5}
    & LSAC & Adult & LSAC & Adult \\
    \midrule
    Gender & 0.00101 & 0.043532 & 0.00029 & 0.011177 \\
    Race & 0.02265 & 0.010469 & 0.004876 & 0.0010130 \\
  \bottomrule
\end{tabular}
\end{table}
\vspace{-7pt}
\section{Fairness-Aware Learning Algorithm}
\label{sec: fairness-learning-algo}
The learning algorithm we propose is similar in spirit to the ones which incorporate predetermined constraints into optimization objective as regularization terms.
It is more generic in its nature and does not use EO, DP or adversarial heads as a proxy for the minimization of the mutual information~\citep{fairness_mi, song2019learning} between the protected attributes and the output variable.
We give a formal definition of the objective function starting from the definition of the proxy constraint for mutual information.

\begin{definition} Given a subset of protected attributes $A_k \in \mathcal{A}$, an input example $x_i$, a uniformly random chosen label $y_{rand}$ and a baseline $x'$, the proxy constraint for the mutual information is defined as follow:
\end{definition}
\vspace{-9pt}
\begin{equation}
\label{L_A}
L^\mathcal{A}(x_i, x', \mathcal{A}, y_{rand}) = -\sum_{A_k\in \mathcal{A}} \sum_{c\in C} \mathbbm{1} (y_{rand} = c) \cdot \log(f_c(\mathcal{S}(x_i,x',A_k)))
\end{equation}
\vspace{-1pt}
where $c \in C$ are possible prediction classes and $y_{rand} = Unif(1,C)$ is a label drawn uniformly random from $C$ and $f_c$ is the NN's output for class $c$. The first summation over the subsets of protected attributes $A_k$ helps us to
address bias mitigation for multiple subsets of protected attributes. The joint objective of a multiclass classification problem is the following.
\begin{equation}
\label{optim_loss}
L_{combined} = \sum_{(x_i, y_i) \in D} L(x_i,y_i) + \alpha \cdot \sum_{(x_i, x') \in D'} L^A(x_i, x', \mathcal{A}, Unif(1,C))
\end{equation}
\vspace{-1pt}
In our setup we use a classification loss but other loss definitions can be used instead. $L(x_i, y_i)$ represents the loss for the original model.
The hyperparameter
$\alpha$ is used to balance the amount of regularization that we incorporate into the loss.
$D'=\{(x_1, x'), ..., (x_N, x')\}$ represents the dataset with a set of baseline values $x'=\{x'^j\}_{j=1}^{j=M}$ for each feature $j$. $Unif(1, C)$ chooses a label uniformly at random from $[1,...,C]$.~\cite{sturmfels2020visualizing, baselines} discuss different strategies for baseline selection. In a general case we can also choose different baselines for a feature and aggregate their impact on the overall loss.
Algorithm~\ref{alg:listing} illustrates an example of how our proposed loss can be computed during training using Gradient Descent (GD) algorithm. Our method is not limited to GD and can be trained with other optimization algorithms as well. The proxy constraint plays a role of weighted data augmentation that helps the model to learn associations between protected attributes and randomly guessed labels.
\vspace{-8pt}
\SetKwInput{KwInput}{Input}
\SetKwInput{KwOutput}{Output}
\begin{algorithm}
\caption{Fairness-Aware Learning Algorithm}
\label{alg:listing}
\KwInput {Training Datasets $D=\{(x_i, y_i)\}_{i=1}^{i=N}$,
Validation dataset $D^{\text{valid}}=\{(x_i, y_i)\}_{i=1}^{i=F}$, Baseline $x'=\{x'^j\}_{j=1}^{j=M}$, A set of subsets of protected attributes $\mathcal{A}$,
hyperparameter $\alpha$, learning rate $\eta$, $max\_epochs$.\\
}
\KwOutput {$W_{\text{best}}$ for the best accuracy of the model $f$ on $D^{\text{valid}}$
}
Initialize the model parameters $W_0$, set epoch=0; \\

\While{$epoch < max\_epochs$}
{
    $L_{\text{initial}}$ = $\frac{1}{N} \cdot \sum_{i=1}^{i=N} \sum_{c=1}^{C} \mathbbm{-1} (y_i = c) \cdot log(f_c(x_i))$;  \\
    $y_{\text{rand}} = uniform\_rand(C)$ \tcp*[r]{Uniformly at random chooses class in $[1, C]$ for each example}
     $L^\mathcal{A}$ = $\frac{1}{N}\sum_{i=1}^{i=N} \sum_{k=1}^{|\mathcal{A}|} \sum_{c=1}^{C} \mathbbm{-1} (y_i = c) \cdot log(f_c(\mathcal{S}(x_i, x', A_k)))$ ; \\
    $L_{\text{combined}} = L_{\text{initial}} + \alpha \cdot L^\mathcal{A}$  \\
    $\text{epoch} = \text{epoch}+1$ ; \\
   $W_{\text{epoch}} = Optimizer(L_{\text{combined}}, \eta)$\tcp*[r]{Optimizer can be Adam, for example}
    Update $W_{\text{best}}$ based on the highest accuracy measures on $D^{\text{valid}}$
    so far.
}
\end{algorithm}
\vspace{-7pt}
\section{Experiments}
In this section we present experimental results of our fairness-aware learning framework and a number of state-of-the-art approaches.
We also describe experimental setup and discuss empirical results.
\vspace{-5pt}
\subsection{Experimental Setup}
\label{sec:setup}
The UCI Adult dataset~\citep{Dua:2019} and the Law School Admissions Council (LSAC) dataset~\citep{wightman1998lsac} are two highly unbalanced datasets used in our experiments. Appendix~\ref{dataset_details} provides additional details about those datasets. For these two datasets we built a 2-layer RELU-BatchNorm-Linear NN models similar to the one demonstrated in the literature~\citep{data_decisions}. The first linear layer contains 128, the second 64 and the last output layer only a single neuron. The models are trained using approximately 100 epochs with Adam optimizer, $0.001$ learning rate and binary cross entropy logit loss as the baseline model's loss, $L(x_i, y_i)$. As bias mitigation constraint, $L^\mathcal{A}$, we used squared distance between model's output and provided label instead of cross entropy loss since it is commonly used for these dataset. Cross entropy defined in equation~\ref{optim_loss} is more generic and provides similar results to squared distance. We chose baseline values $x'$ carefully, $-1$ and $-2$ to indicate missingness of the attributes in the dataset.
We compared our method with the well known GerryFair~\citep{pmlr-v80-kearns18a} and a mutual information reduction-based approach~\citep{fairness_mi} adopted for intersectional fairness. GerryFair performs a zero-sum optimization between a fairness auditor and a classifier subject to the auditor's constraints. On the other hand,~\citep{fairness_mi, song2019learning, Louppe2017LearningTP} show that mutual information reduction-based approaches can be formulated as generative adversarial optimization problems. Here the classifier plays the role of the generator and the discriminator aims to reduce the mutual information between the protected attributes and the output of the classifier. Inspired by~\citep{song2019learning, Louppe2017LearningTP} we implemented an adversarial network with two 2-layer RELU-BatchNorm-Linear NNs. One of those networks serves as a generator and the other one as a discriminator.

\vspace{-1pt}
\subsection{Evaluation metrics}
\label{sec:eval_metrics}
As fairness measurement metrics we adopted Equalized Odds (EO)~\citep{Hardt2016EqualityOO, data_decisions} and Demographic Parity (DP)~\citep{dp2, dp3} metrics to measure model's intersectional subgroup biases.
Inspired by~\citep{data_decisions} and ~\citep{ghosh2021characterizing} we measure EO as the differences between minimum and maximum TPR and FPR scores across all subgroups formed by a given subset of protected attributes. Let $G$ denote the  set of different combinatorial options formed by the subgroups of protected attributes in the set $A_k \in \mathcal{A}$. For example $G = \{(Male, White), (Male, Black), (Male, Asian), (Female, White), (Female, Black), \\ (Female, Asian), ...  \}$. The values in $G$ are then denoted by $G_i \in G$. EO based on TPR and FPR are then defined as  $EO_G^{TPR} = | max(TPR(G_i)) - min(TPR(G_j)) |$ and $EO_G^{FPR} = | max(FPR(G_i)) - min(FPR(G_j)) |, G_i \in G, G_j \in G$ accordingly.
Similarly, demographic parity is measured as $DP_G = | max(DP(G_i)) - min(DP(G_j))|, G_i \in G, G_j \in G$.

\subsection{Experimental Results}
In order to better understand the accuracy vs fairness gap relationship, we run multiple experiments by varying the weight of the fairness component from zero to a large number both for our approach and two baseline approaches. We compare our approach against GerryFair~\citep{pmlr-v80-kearns18a} and a mutual information-based approach~\citep{fairness_mi} in terms of Accuracy vs TPR, FPR and DP gaps. As described in section~\ref{sec:setup} mutual information based fairness mitigation is performed based on adversarial training. Our experimental results depicted on figure~\ref{fig:our_vs_gerry_lsac} reveal that our method results in higher accuracy when reducing TPR gap from 0.5 to 0.2 for LSAC and from 0.46 to 0.23 for Adult datasets.
All three methods exhibit similar accuracy when the TPR gap is further reduced from approximately 0.2 to 0.0. We examine similar patterns for Accuracy vs FPR and DP gap measurements as well.
We observe that the adversarially trained approach slightly underperforms two other approaches in terms of accuracy. We hypothesize that generator-discriminator based approaches are more effective for binary protected attributes as also shown in~\citep{Louppe2017LearningTP}. When dealing with multiple non-binary protected attributes, generator-discriminator based networks become less effective and might require further fine tuning.
We conclude that our method can be beneficial especially when the goal is to reduce fairness gaps substantially without hurting the accuracy too much. Appendix~\ref{additional_experiments} provides additional results for the COMPAS~\citep{compas_ds_cite} dataset.
\begin{figure}[h]
  \centering
  \includegraphics[width=\linewidth]{images/lsac_adult_gerryfair_adv_ours2.png}
  \caption{Our method, GerryFair and adversarially trained approaches compared on accuracy vs fairness metrics (TPR, FPR and DP gaps) for LSAC (top) and Adult (bottom) datasets. Best viewed in color.}
  \label{fig:our_vs_gerry_lsac}
\end{figure}
Furthermore, we compare our method with the original and two additional baseline models. One of the baselines represents a model trained without protected attributes. In this case we removed \texttt{Gender} \underline{and} \texttt{Race} attributes from the dataset. The second baseline model is trained on a dataset where examples for all underrepresented intersectional subgroups of \texttt{Gender} \underline{and} \texttt{Race} are upsampled. Figure ~\ref{fig:original_removed_upsamples_ours} summarizes Accuracy, TPR, FPR and DP gaps across all baselines and our approach for LSAC and Adult datasets. We observe that our method is comparable or better in reducing the fairness gap for almost the same accuracy trade-off.

\begin{figure}
  \centering
  \includegraphics[width=\linewidth]{images/original_removed_upsamples_ours5.png}
  \caption{Our method ($\alpha=0.5$) vs original model, removed \texttt{Gender} and \texttt{Race} attributes and upsampled for underrepresented subgroups. Best viewed in color.}
  \label{fig:original_removed_upsamples_ours}
\end{figure}

In order to validate the effectiveness of our approach we perform two additional studies. We aim to understand how inference time masking of protected attributes and the presence of attributes strongly correlated with protected attributes, change the accuracy, TPR, FPR and DP gaps.

\paragraph{Masking protected attributes:}
\vspace{-5pt}
We mask protected attributes in the test dataset and compare model accuracy and fairness metrics before and after masking. This validates the hypothesis that fairness gap changes will remain changed before and after protected attributes are masked in the test dataset, if our method is effective in mitigating bias and is causally fair~\citep{causal_fair}.
Table~\ref{tab:TPRs_FPRs_DPs_masked} showcases the high sensitivity of the original model's accuracy, TPR, FPR and DP gaps when protected attributes are masked. In contrast to the original model, our model is almost insensitive to the masking of protected attributes showing its robustness to the presence of protected attributes. This also shows that privacy-preserving inference is possible since protected attributes are not required to be present at inference time.
Appendix~\ref{additional_experiments} demonstrates the results of the same experiment for Adult dataset.
\begin{table}
\vspace{-2pt}
  \caption{Test Accuracy, TPR, FPR and DP Passed Bar gaps for the Original and Our Models ($\alpha=0.5$) with and without masking of protected attributes applied on LSAC dataset. }

  \label{tab:TPRs_FPRs_DPs_masked}
  \centering
  \begin{tabular}{lcccc}
    \toprule
   & Accuracy & TPR & FPR & DP passed \\

    \midrule
    Original model & 0.87 & 0.55 &	0.14 &	0.23 \\
    Original model w/masked $A^{(Gender,Race)}$ & 0.85 & 0.53 & 0.05 & 0.15 \\
    Our model & 0.86 & 0.28 & 0.06 & 0.11 \\
    Our model w/masked $A^{(Gender,Race)}$ & 0.86 & 0.28 & 0.06 & 0.10 \\
  \bottomrule
\end{tabular}
\end{table}
\begin{table}
\vspace{-10pt}
  \caption{Accuracy and TPR computed for the original model, a baseline model trained without $\texttt{Gender}$ and $\texttt{Race}$, and our approach. The dataset contains an additional attribute $\texttt{Race1}$ for which mitigation is intentionally not performed.

  }
  \label{tab:corr_race_race1}
  \centering
  \begin{tabular}{cccc}
    \toprule
    & \multirow{2}{*}{Accuracy} & \multicolumn{2}{c}{TPR} \\
    \cmidrule{3-4}

     &  &  $\texttt{Male} \cap \texttt{White}$  & $\texttt{Female} \cap \texttt{Black}$ \\
    \midrule
    Original model & 0.87 & 0.99 & 0.76\\
    Removed protected attributes & 0.86 & 0.96 & 0.77 \\
    Our model ($\alpha=0.5$) & 0.86 & 0.96 & 0.90\\
  \bottomrule
\end{tabular}
\end{table}

\paragraph{Studying the effects of strongly correlated features with protected attributes:}
\vspace{-10pt}
In order to understand the effectiveness of our approach in the presence of features strongly correlated with protected attributes we use two race related features \texttt{Race1} and \texttt{Race}. \texttt{Race1} is a coarse-grained representation of the \texttt{Race} attribute. We apply bias mitigation only to fields \texttt{Gender} and \texttt{Race}. No mitigation for field \texttt{Race1} is carried out. Table~\ref{tab:corr_race_race1} shows that our approach is still effective in mitigating the bias in underrepresented groups such as $\texttt{Female} \cap \texttt{Black}$. It is not as effective as in the absence of \texttt{Race1} field~\ref{tab:TPRs_Male_Female}, however, it is more effective than removing \texttt{Gender} and \texttt{Race} from the dataset.

\paragraph{Feature interaction effects:}
\vspace{-5pt}
In addition to accuracy, TPR, FPR and DP gaps, we also analyse pairwise feature interaction effects of protected attributes based on \cite{archattribute}. We validate the hypothesis that pairwise feature interaction of protected attributes with non-protected ones drops significantly for the unbiased model. Furthermore, our experiments reveal that the decline of feature interaction scores for protected attributes leads to the emergence of stronger interaction patterns between other attributes.
Figure~\ref{fig:finter_standard} visualizes aggregated pairwise feature interaction heatmaps for the original, biased model, at the top and unbiased model, based on our approach, at the bottom of the diagram. The results suggest that feature interaction effects for \texttt{Male} and \texttt{Female} look very similar. We also observe that the protected attribute \texttt{gender} has a relatively strong interaction pattern with the \texttt{age} attribute and \texttt{race} with the \texttt{fulltime} in the biased model. The unbiased model, however, exhibits no distinct and strong feature interaction patterns for \texttt{gender} and \texttt{race} with \texttt{age} and \texttt{fulltime} respectively. On the other hand, we discern stronger emerging interaction patterns between \texttt{parttime} and \texttt{zgpa}, \texttt{fulltime} and \texttt{zgpa}. This helps us better understand how feature interaction patterns are impacted when the models are trained with bias mitigation constraints. These findings can serve as sanity checks and facilitate better understanding of bias mitigation techniques.

\begin{figure}[h]
  \centering
  \includegraphics[width=10.0cm,
height=7.1cm]{images/merged_interact_before_after_mitigation2.png}
  \caption{Aggregated pairwise feature interaction heatmaps before (top) and after (bottom) bias mitigation for both \texttt{Male} and \texttt{Female} based on LSAC test dataset. Best viewed in color.}

  \label{fig:finter_standard}
\end{figure}

\section{Conclusion and Future Work}
In this paper, we propose a framework to mitigate modeling bias in intersectional subgroups independent of the type of the protected attributes. The framework incorporates a generic proxy constraint into the optimization objective which reduces the mutual information between protected attributes and the output variable. We study accuracy vs TPR, FPR and DP gap trade-offs both with respect to state of the art approaches as well as data pre-processing techniques such as upsampling and removing protected attributes. We show empirically, that our approach surpasses GerryFair and adversarially trained approaches when reducing the fairness gaps.
Furthermore, we show, empirically, that our approach is still effective when other features are correlated with protected attributes and protected attributes are not available during inference time. Lastly, our experiments reveal that bias removal reduces the interaction effects between protected attributes and other attributes in the dataset.

In the future, we plan to analyse the effects of different types of bias mitigation constraints on a specific ML task of interest. This type of research can help better understand what kind of constraints might work better for a specific problem space and why. In addition to that it is also valuable to investigate techniques for estimating the optimal hyperparameter value for the weight of bias mitigation term, instead of a grid search approach.

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


In a case described by Singapore prosecutors as "arguably one of the worst of its kind" in the city-state, the married couple — who were sentenced two years ago over the abuse of another maid — beat and kicked their helper and made her clean the house in her underwear.
"In the present case, the accused persons had systematically and persistently abused Moe Moe Than both physically and psychologically throughout the period of her employment," state prosecutors told the court.
Chia followed, scolded and slapped the maid, and instructed her to throw up into a plastic bag "and thereafter (made) to eat her own vomit," the charge added.
The same couple were in March 2017 sentenced to jail terms for abusing their Indonesian maid — the husband for two years and four months and the wife for two months. They have yet to serve those sentences.


text:
Q: Serilog to MongoDB Atlas I am trying to get Serilog to log messages to a collection in a MongoDB Atlas DB, in a .Net Core API.
I need to set the connection string in appsettings.json so as to make it configurable. I am doing so like so
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  }

The above does not log successfully. However, if I point it at a local instance of MongoDB, and use the same method/connection string format, then it logs.
I went through all of the steps to create the collection in MongoDB Atlas, created the collection, user and white listed the necessary IP.
Any ideas as to what I am doing wrong here?

A: Just in case someone else runs into this scenario, I will post the solution to the problem, which in the end was simple, but tricky to find.
It turns out that the Serilog MongoDB Sink uses the .Net Driver version 2.3.
So, on the connection string generator in Atlas, I selected that version for .NET and it generated a connection string without the "+srv" part.
Hope that helps someone, sometime!


text:
Rolo Sep 291
Cougar offense looking to rebound after subpar start
By Alex Crescenti
PULLMAN, Wash. — We're now a part of the way through the college football season, and the Washington State Cougars (1-3) are limping into the middle of the season with an offense that isn't putting up enough points and a defense that is being asked to hold on for as long as they can.
On Saturday against Utah, the offense was only able to put up one touchdown on the board and gave up eight sacks, but on the flip side, the defense was able to keep the team in the game forcing turnovers and limiting the Utes offense, something that gives them confidence moving forward.
"One of the things we said earlier, we're playing a football game, not a football half. You know so it's just that that conscience effort to play each quarter and finish each quarter out, and each play," said defensive lineman Ron Stone Jr.
"There's some time we didn't get enough push, there's times we unable to identify it. We could run the ball harder, there's a lot of things that come in, but I appreciate their work I appreciate what they're trying to do," added Head Coach Nick Rolovich.
There is some good news though for the offense. Coach Rolovich revealed running back Max Borghi who was injured against Utah and quarterback Jayden de Laura who was injured against USC both have good chances of playing on Saturday against Cal (1-3).
The kickoff is set for 2:30 p.m. from Berkeley, California.
RELATED: Cougar Chalk Talk: WSU's offense continues to hold back success early in the season
RELATED: Chalk Talk: The Cougars recent struggles to hold a big lead
COPYRIGHT 2022 BY KXLY. ALL RIGHTS RESERVED. THIS MATERIAL MAY NOT BE PUBLISHED, BROADCAST, REWRITTEN OR REDISTRIBUTED.
Cougs
Crescenti
Rolovich
Spokane NAACP weighs in on Tyre Nichols' death

redpajama_set_name: RedPajamaCommonCrawl


➭ For Army Wing of National Defence Academy : 12th Class pass of the 10+2 pattern of School Education or equivalent examination conducted by a State Education Board or a University.
➭ For Air Force and Naval Wings of National Defence Academy and for the 10+2 Cadet Entry Scheme at the Indian Naval Academy : 12th Class pass of the 10+2 pattern of School Education or equivalent with Physics and Mathematics conducted by a State Education Board or a University.
Note: Candidates who are appearing in the 12th Class under the 10+2 pattern of School Education or equivalent examination can also apply for this examination.
Selection Process: Selection will be based on Written Examination, Psychological Aptitude Test and Intelligence Test.
Application Fee: Candidates have to pay a fee of Rs. 100/- either by depositing the money in any branch of SBI by cash, or by using net banking facility of the SBI or by using visa/master credit/debit card. SC/ST candidates/Sons of JCOs/NCOs/ORs specified candidates who are exempted from payment of fee.
How to Apply UPSC Vacancy: Interested Candidates may apply online through UPSC website www.upsconline.nic.in on or before 30.06.2017 till 23.59 hrs thereafter website link will be disabled.


Who are the new licenses for?
The Financial Markets Conduct Act 2013 introduces six new types of financial market service licenses. These are for….
A license to provide crowd funding services is for businesses who want to act as an intermediary between companies making share offers and investors – by providing the facility through which offers can be made to investors. When your service is licensed, companies can offer shares through your service without having to supply product disclosure statements (PDS).
A license for peer-to-peer lending services is for businesses who want to provide an intermediary facility to connect borrowers and lenders. This is generally intended for situations where the loan is sought for personal, charitable or small business purposes. When your service is licensed, borrowers can issue debt securities through your service without having to supply product disclosure statements.
An MIS is a managed investment scheme. You'll need a license if you're the appointed or designated manager of a non-restricted and registered MIS. Specifically, you'll need a license to (a) make a regulated offer of managed investment products to retail investors; (b) register a non-restricted MIS you have been appointed the manager of. When FMA grants a MIS license, it will specify the class of MIS product(s) you offer and may also specify the product(s) if you only offer a very limited range of products.
The definition of an MIS under the FMC Act is very broad ; it includes collective investment schemes, and most schemes involving participatory securities under the Securities Act 1978; it does not include discretionary investment management services (DIMS), insurance contracts, or schemes that only involve managing separate and direct interests in underlying property. MIS schemes may be open-ended or closed-ended. Open-ended schemes are offered continuously, for example KiwiSaver, superannuation, workplace savings schemes, open-ended unit trusts, and other schemes that invest in relatively liquid assets. Closed-ended schemes are more equity-like, such as forestry partnerships and property syndicates that invest in a single asset class.
A DIMS is a discretionary investment management service – an investment arrangement where you make buy–sell decisions about your client's portfolio, rather than your client making those decisions. It includes arrangements where the client has the right to be consulted or can change a decision you have previously made. DIMS cover a wide range of services and can vary considerably. From 1 December 2015, anyone providing class DIMs to retail clients must have an FMC Act DIMS license.
If you're in the business of entering into derivatives, you are a derivatives issuer. You must be licensed to make a regulated offer of derivatives from 1 December 2014. A regulated offer includes any offer of derivatives when disclosure must be made to investors, for example, because they are a retail investor. The FMC Act has a broad definition of a derivative. It includes futures and forwards, options (except options to acquire an equity security, a debt security, or a managed investment product by way of issue), swaps and contracts for difference, margin contracts, rolling spot contracts, caps, collars, floors and spreads. The definition is also wide enough to catch new derivative products as they are developed.
This license is for someone who is, or will be, the independent trustee for a restricted managed investment scheme. For the definition of 'independent' please see section 131(3) of the FMC Act. A restricted scheme is identified as 'restricted' on the Register of Managed Investment Schemes. It includes KiwiSaver schemes, and workplace superannuation or legacy schemes.


text:
Karin Knapp (* 28. Juni 1987 in Bruneck, Südtirol) ist eine ehemalige italienische Tennisspielerin.

Karriere
2008 stand sie bei den Proximus Diamond Games im Endspiel, sie unterlag dort Justine Henin mit 3:6 und 3:6. Nach einer längeren Verletzungspause und mehreren Rückschlägen startete sie ihre Karriere im Spätsommer des Jahres 2010 neu. Sie bestritt verschiedene ITF-Turniere, von denen sie drei gewinnen konnte; bei einem weiteren stand sie im Finale. 2011/2012 kehrte Knapp auf die WTA Tour zurück. Sie erreichte beim Turnier in Bogotá im Einzel das Viertelfinale und im Doppel zusammen mit der Vorjahressiegerin Edina Gallovits-Hall das Halbfinale.

2013 konnte sie mit dem italienischen Team mit einem 4:0-Sieg im Endspiel über Russland den Fed-Cup-Gewinn feiern. Knapp steuerte an der Seite von Flavia Pennetta einen Punkt bei. Im selben Jahr erreichte sie in Wimbledon das einzige Mal in ihrer Karriere das Achtelfinale im Einzel bei einem Grand-Slam-Turnier.

2014 gewann sie erstmals ein WTA-Turnier. In Taschkent, wo sie im gesamten Turnier ohne Satzverlust blieb, besiegte sie im Endspiel Bojana Jovanovski in zwei Sätzen. 2015 folgte der zweite Turniersieg auf der WTA Tour. Beim Nürnberger Versicherungscup 2015 bezwang sie im Finale ihre Landsfrau Roberta Vinci, die spätere US-Open-Finalistin, in drei Sätzen. Knapp erreichte am 24. August 2015 mit Rang 33 ihre beste Platzierung in der Weltrangliste. Bei den Olympischen Spielen 2016 in Rio de Janeiro trat sie im Einzel an, wo sie in der ersten Runde gegen Lucie Šafářová ausschied.

Knapp wurde verletzungsbedingt ab Mitte Januar 2018 nicht mehr in den Weltranglisten geführt und beendete im Mai 2018 aufgrund einer anhaltenden Knieverletzung ihre Karriere.

Turniersiege

Einzel

Doppel

Abschneiden bei Grand-Slam-Turnieren

Einzel

Zeichenerklärung: S = Turniersieg; F, HF, VF, AF = Einzug ins Finale / Halbfinale / Viertelfinale / Achtelfinale; 1, 2, 3 = Ausscheiden in der 1. / 2. / 3. Hauptrunde; Q1, Q2, Q3 = Ausscheiden in der 1. / 2. / 3. Runde der Qualifikation; n. a. = nicht ausgetragen

Doppel

Weblinks

Einzelnachweise

Billie-Jean-King-Cup-Spielerin (Italien)
Olympiateilnehmer (Italien)
Teilnehmer der Olympischen Sommerspiele 2016
Sportler (Südtirol)
Italiener
Person (Ahrntal)
Geboren 1987
Frau


Importance of Commercial Transport Services There are a lot of companies over each country that provide commercial and fragile transport services that you can try to hire. You have to know that all of the manufacturers and international business distributors have this on their speed dial. But it is important that they have an internal salesperson that will be in need of transportation. There are a lot of companies that offer both fragile and commercial transport services for freight and money as well. Geographical boundaries will not be an issue for these transport companies. Each day, these commercial transport companies are transporting a lot of products successfully and that is why this kind of service is needed. This type of industry is also up to date with the latest equipment for shipping and other technological advances. These companies are able to provide commercial freight transfer services to different countries, this type of business can carry out international dealing with different clients. The clients are also impressed about how careful the workers are with offloading and stacking everything up safely, these workers are qualified and dedicated. By following the guide below, you will be able to understand more about the whole process. Before you go into this kind of project, you have to know that research is very essential, it will always come into mind, whenever you lack information or details about a certain thing or project, you will have to do some research and get as much information you can because information will help you get into the right path and have the greatest result.
It will not be an easy step but when you follow the tip in the above article, you will be able to see just easy it was to get the best commercial freight transport company to help with your international transaction and dealings with different companies, it is always better to think about the hiring a professional or choose the right company and with this tip, you will be guided thoroughly and properly towards a better the days to come. Next step is that you will have to avoid getting disappointed with the service or product that you will get from the professional or company that you hired or choose, you have to make sure that you have already considered the essential factors before you even think about hiring or choosing the company so that you will enjoy the advantages. You have to make sure that you have enough data or details that will not leave you in a state of shock when something bad happens or the risk that you thought about happening actually does happen, you do not have to worry because you already know that it would happen and you know exactly what to do.
Next PostNext Why not learn more about Businesses?


text:
package org.locationtech.geomesa.fs.tools.ingest

import com.typesafe.scalalogging.LazyLogging
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileUtil, Path}
import org.apache.hadoop.mapreduce.JobStatus
import org.apache.hadoop.tools.{DistCp, DistCpOptions}
import org.locationtech.geomesa.tools.Command
import org.locationtech.geomesa.tools.ingest.AbstractIngest.StatusCallback
import org.opengis.feature.simple.SimpleFeatureType

import scala.collection.JavaConversions._
import scala.collection.mutable

object StorageJobUtils extends LazyLogging {

  def distCopy(srcRoot: Path,
               dest: Path,
               sft: SimpleFeatureType,
               statusCallback: StatusCallback,
               stageId: Int,
               numStages: Int): Boolean = {
    val typeName = sft.getTypeName
    val typePath = new Path(srcRoot, typeName)
    val destTypePath = new Path(dest, typeName)

    statusCallback.reset()

    Command.user.info("Submitting DistCp job - please wait...")
    val opts = new DistCpOptions(List(typePath), destTypePath)
    opts.setAppend(false)
    opts.setOverwrite(true)
    opts.setCopyStrategy("dynamic")
    val job = new DistCp(new Configuration, opts).execute()

    Command.user.info(s"Tracking available at ${job.getStatus.getTrackingUrl}")

    // distCp has no reduce phase
    while (!job.isComplete) {
      if (job.getStatus.getState != JobStatus.State.PREP) {
        statusCallback(s"DistCp (stage $stageId/$numStages): ", job.mapProgress(), Seq.empty, done = false)
      }
      Thread.sleep(1000)
    }
    statusCallback(s"DistCp (stage $stageId/$numStages): ", job.mapProgress(), Seq.empty, done = true)

    val success = job.isSuccessful
    if (success) {
      Command.user.info(s"Successfully copied data to $dest")
    } else {
      Command.user.error(s"Failed to copy data to $dest")
    }
    success
  }

  // TODO parallelize if the filesystems are not the same
  def copyData(srcRoot: Path, destRoot: Path, sft: SimpleFeatureType, conf: Configuration): Boolean = {
    val typeName = sft.getTypeName
    Command.user.info(s"Job finished...copying data from $srcRoot to $destRoot for type $typeName")

    val srcFS = srcRoot.getFileSystem(conf)
    val destFS = destRoot.getFileSystem(conf)

    val typePath = new Path(srcRoot, typeName)
    val foundFiles = srcFS.listFiles(typePath, true)

    val storageFiles = mutable.ListBuffer.empty[Path]
    while (foundFiles.hasNext) {
      val f = foundFiles.next()
      if (!f.isDirectory) {
        storageFiles += f.getPath
      }
    }

    storageFiles.forall { f =>
      val child = f.toString.replace(srcRoot.toString, "")
      val target = new Path(destRoot, if (child.startsWith("/")) child.drop(1) else child)
      logger.info(s"Moving $f to $target")
      if (!destFS.exists(target.getParent)) {
        destFS.mkdirs(target.getParent)
      }
      FileUtil.copy(srcFS, f, destFS, target, true, true, conf)
    }
  }
}

redpajama_set_name: RedPajamaGithub


text:
Роутело (Mesitornis) — рід птахів родини роутелових (Mesitornithidae). Містить два види.

Поширення
Ендеміки Мадагаскару. Роутело бурий живе у вологих лісах вздовж східного узбережжя острова. У роутело білогрудого відомо 5 популяцій на півночі і заході країни та одна популяція на сході.

Опис
Птахи середнього розміру, завдовжки до 30 см. Ззовні схожі на пастушків. Оперення темно-коричневе. Хвіст довгий і широкий, заокруглений. Крила короткі. Дзьоб довгий, ледь зігнутий донизу.

Спосіб життя
Роутело мешкають у вологих або сухих лісах. Живуть парами або невеликими родиннми групами до 4 птахів. Наземні птахи. Шукають у лісовий підстилці комах та інших безхребетних. Літають неохоче.

Види
 Роутело бурий (Mesitornis unicolor)
 Роутело білогрудий (Mesitornis variegata)

Примітки

Роутелові
Роди птахів


text:
February 2, 2022 Milan
How to Recover Gravatar Account
Getting a Gravatar account back is crucial, as it is the primary way to add an avatar across various platforms. Though you could infer it has to do something with avatars from the name, you may not realize its importance. We'll explain that in a moment, but know the platform neither recycles accounts nor do they expire. In other words, once lost and never recovered means you can never reuse that account information, and may not find your collection of Gravatars again. We're not saying this to scare you, only motivate you into giving it your all. With that out the way, let's dive into how to recover a Gravatar account.
Check for an existing Gravatar account on the WordPress.com network
We'll first explain why you should do so. Then, we'll get to the steps and what you can conclude. Let's start.
Sign up for Gravatar, even if you have an account already. Here's why
Gravatar is directly tied to the WordPress.com network and utilizes it for authentication. Therefore, when you need to regain access to the former, you'll have to go through the latter, a link they call WordPress.com Connect. However, that's not the only service that takes advantage of WordPress.com—other Automattic products such as Crowdsignal and Akismet also do. This can lead to confusion, especially if you decide to call it quits and simply create a new Gravatar account, though we hope it won't come to that.
On the topic of starting from scratch, we should also inform you that WordPress.com doesn't reuse usernames. Thus, they won't rename it for you to create a new account with the old username. Additionally, even if you deleted an account in the past, the data tied to it will still hinge on the username in their system, but remain invisible, as an "empty profile" or "abandoned profile". You can link and unlink e-mail addresses, though.
In contrast, if you learn the username is already taken, you'll know it belongs to your account and might aid recovery. In all cases, you know which username is off-limits when starting anew, and knowing why can bring relief.
Steps to check if Gravatar account exists
Hopefully, we helped you understand the truth of the matter. With that acknowledged, we suggest you:
Visit the Gravatar Sign Up page.
Enter an e-mail address and username you feel are correct.
If you get an error stating, "Sorry, that email address is already used" or the identical one mentioning username, you're on the right path. Otherwise, keep trying.
Note. If you created an account before 2007, you did so before WordPress.com acquired it. If you never changed passwords or the e-mail address, which, frankly, is rare, your account is independent of the platform. In that case, we propose you reach out to WordPress.com Customer Service, described in method 3, and chose one of the offered ways to recover your account for Gravatar.
Recover Gravatar account by resetting the password
We'll likely dedicate an article to WordPress.com account recovery. However, for now, we'll show you a quick set of steps that will get you through. Let's start.
I'm still logged in
This is the best-case scenario. You're signed in on one or more devices and can access the account panel, despite the fact you can't log out because you don't remember the password. If that's the case, follow these steps to reset the password for Gravatar via WordPress.com:
Visit www.wordpress.com and make sure you're logged in. You'll recognize this from the top menu.
Go to the WordPress.com Password Settings page from the account dashboard.
Type your new password, then repeat it for confirmation.
Click the Save Changes button at the bottom.
Visit Gravatar's official website and click the Sign in link in the upper right corner.
Enter your e-mail address or username and click on Continue.
Enter your newly created password to access your Gravatar account.
I cannot sign in
Though seemingly terrifying, being unable to log in to Gravatar is not a massive problem, so long you have access to the e-mail address on file. As you can see above, you can sign in with a combination of an e-mail address and password. Even if you don't remember it, you can click the Email me a login link button to gain one-time access through an e-mail on the Sign in page. Further, you can reset the password like this:
Select the Lost your password? link.
Enter the username or e-mail address for the Gravatar account.
Click on Get New Password.
You'll get a password reset link to your e-mail. It will take you to the WordPress.com page, and let you configure a new one by entering it twice.
I used Google or Apple to create my account for Gravatar
Good on you! Doing so made things convenient and extra secure. However, it also means you have to rely on external services and their account rescue methods. Luckily, we went over Gmail account password recovery and methods to restore Apple ID. Consequently, you can regain access to your account on the Gravatar platform.
Contact WordPress.com Customer Service for help with Gravatar
If everything else fails or your case is specific, the only method to recover an account on the Gravatar platform that remains is this one. With that acknowledged, you can reach out to WordPress.com Customer Support using:
Live chat. If your WordPress.com account uses a Premium or Business plan, you can get 24/7 Live Chat Support from WordPress.com.
Phone number. Call the toll-free WordPress phone number 1-877 273-3049 and press 2.
Note. They also have a comprehensive Contact Us form and a team of Happiness Engineers that will provide basic help free of charge. However, you need to be logged in to your account already to gain access, rendering the procedure moot.
How to Recover Nokia Account
How to Recover Photos Deleted from iPhone