text:
Health Newsmaker: Carr has helped Saint Anne's achieve the highest caliber of care
Sep 3, 2016 at 4:34 PM Sep 3, 2016 at 4:34 PM
Chief Medical Officer of the Steward Health Care System (Steward), Dr. Justine Carr, recently announced her retirement, leaving behind a legacy of improvements in patient care across area community hospitals, including at Saint Anne's Hospital.
During Dr. Carr's eight-year tenure, Saint Anne's Hospital has seen improved patient outcomes in quality, safety, prevention and timely care, standardization of policies and procedures, improvement in behavioral health care access and quality, and reduction in Emergency Department wait times.
Dr. Carr was instrumental in the introduction of information technology and developed innovative enhancements to better serve patients. She oversaw the development of tele-ICU, ensuring continuous back-up coverage of all ICU patients. Under her direction, Steward hospitals received Leapfrog A Safety Scores 94% of the time, and Steward hospitals were consistently found in annual Leapfrog Top Hospitals. Dr. Carr also served on the Steward Health Care Network Board, overseeing the development of Accountable Care, including top performance in the Pioneer Accountable Care Program, which saved more than $30 million in its first three years while also improving quality and patient outcomes.
"Dr. Carr has helped Saint Anne's achieve the highest caliber of health care, benefiting countless Fall River area families," said Carole Billington, vice president of operations & chief nursing officer at Saint Anne's Hospital. "By increasing the quality of patient care and access for the community, Dr. Carr's imprint on our hospital will be a constant reminder of her contributions."
Carr has also held national health leadership positions.
In 2003, Dr. Carr was appointed by then-U.S. Health and Human Services Secretary Tommy Thompson under President George W. Bush to serve on the National Committee on Vital and Health Statistics, an 18-member public advisory board to the secretary on health data strategy and implementation of HIPAA privacy and administrative simplification. She was appointed chair of the Committee in 2012, and in 2014 served as the first chair of the Working Group on HHS Data Access and Use. She has also been invited to participate in initiatives such as the Agency for Health Research and Quality, and the Institute of Medicine Digital Learning Collaborative.
During Carr's tenure, Steward has invested in a quality infrastructure focused on best-in-class outcomes and experiences for patients while becoming a leading innovator and provider network in New England.


This one-of-30 Cadillac Series 75 Convertible Coupe featuring coachwork from Fleetwood is heading to Mecum's next sale.
The 1940s marked a return to prosperity for American car manufacturers, with the struggles of the Great Depression receding. Grand automobiles returned to the road as manufacturing goods for the allied war effort brought money back into the United States. This 1940 Cadillac Series 75 Convertible Coupe is a product of its time and, with just 30 believed to exist, this example will be sought after at Mecum's upcoming 2019 Phoenix sale.


text:
"You take Michael off that team, and Scottie moves down to fifth" – Larry Bird revealed his honest take on Scottie Pippen – Basketball Network
Larry Bird, Michael Jordan, Scottie Pippen
© RVR Photos-USA TODAY Sports
As a southern Indiana native, Larry Bird always rooted for the Pacers. But when it became clear that the Chicago Bulls were better than Indiana in the 90s, Larry Legend gave Michael Jordan and Scottie Pippen the props.
However, when it comes right down to it, Bird blatantly said it was MJ that made Pippen a great player.
"I believe he (Michael Jordan) had the second best player in the league right next to him in Scottie Pippen. You take Michael off that team, and Scottie moves down to fifth. But when Michael was out there with him, they were the best two in the league," Celtics legend wrote in his book "Bird Watching."
According to Bird, while it is true that Jordan and Pippen as a duo were unstoppable, it's a completely different story when you take one guy out of the picture.
Based on Bird's assessment, Pippen played at his best alongside Jordan, but that wasn't vice versa because Pip, without MJ, wouldn't have been that lethal.
As it turned out, many could say Bird was right with his analysis, as everybody knows Pippen underachieved without Jordan. However, another Boston Celtics legend begged to disagree, and he has an interesting point to back it up.
For Paul Pierce, Pippen was also at the pinnacle of his career when he won six NBA championships with Jordan and the Bulls. And for a talented duo to be successful, "The Truth" stressed the other guy has to sacrifice, and that's what Pippen did the entire time he co-piloted with MJ in Chicago.
"When you talk about dynamic duo, you both could probably be 'the' guy… the second guy has to sacrifice," Pierce pointed out. "One of the guys had to swallow that pill, to not be 'the' guy. Over time, you look, and you're like, 'he got it'… Think about when [Michael] Jordan left [Scottie] Pippen, and Pippen was like a MVP candidate. He was good enough to be the [No.] 1 guy."
Regardless of whoever you side with in this story, the fact remains that Jordan and Pippen were both legendary, be it individually or as a duo.
Horford believes that while everyone is enamored with the Warriors awesome offensive exploits, it's their defense that has allowed them to flourish recently
Vaughn said the Brooklyn Nets need more production from Simmons, and that involves staying on the court by not getting ejected or in foul trouble
Oakley added that Thomas is trying too hard to fix his reputation and broken relationships but unfortunately for him, Michael Jordan doesn't want to do anything about it.
Like MJ, Reggie Miller also hated one thing about Isiah Thomas.
Kevin Garnett recalled his playing days with the likes of Kobe Bryant, Shaquille O'Neal, and Chauncey Billups which he described as "hell."
It was a long road for GPII to get to where he is today as it took multiple rejections for him to finally find a home with the Warriors and win a championship
Matt Barnes recently revealed he risked it all for Rachel Nichols to speak her truth about her firing from ESPN
Today, Kevin Garnett's protege is no longer in the league.

redpajama_set_name: RedPajamaCommonCrawl


Why should you complete this assessment?
If you would like to know more about options that could help your housing situaton, then this is the tool for you.
It will ask a series of questions and then guide you through the different options you can explore to potentially improve your housing situation.
The assessment should only take about 15 minutes to complete.


JCW Acoustic Supplies has seen a 26% growth over the past 12 months. Laura Keegan, Commercial Director of the company, explains the strategy behind the unprecedented success. When Laura Keegan became Commercial Director of JCW Acoustic Supplies in 2013, she took the helm of a business in a stagnant market. To deliver a step change in that market, she knew she needed to make a step change in the business.
Most of us have to deal with constant noise in our homes on a constant basis. With most of it coming from our own homes and noisy neighbours. Some people have no idea how to deal with this issue of increasing levels of noise within the home.Now you may be wondering what exactly is soundproofing? Soundproofing is the process of trying to eliminate or remove unwanted noise within a room.


APUS Rocks ! I have never seen such a growth trajectory.Best wishes to continue your growth story .
I love the localization efforts around the world ! Congratulations on all the success thus far and wishing you all the best for the future !
My Phone has evoluted into a complicated machine,you have a perfect offering to help notice users like myself to use my phone more effectively way to go !


text:
Die Behaarte Segge (Carex hirta), auch Raue Segge genannt, ist eine Pflanzenart aus der Familie der Sauergrasgewächse (Cyperaceae). Sie zählt zu den häufigsten Sauergräsern und ist eine der wenigen deutlich behaarten Seggen-Arten.

Beschreibung
Die Behaarte Segge ist eine ausdauernde, krautige Pflanze, die Wuchshöhen zwischen etwa 5 bis 60 Zentimeter erreicht. Sie ist ein sommergrüner Geophyt, die in der Vegetationsperiode lange unterirdische Ausläufer bildet. Die Triebe sind aufrecht, stumpf dreikantig und bis oben beblättert. Am Sprossgrund besitzt sie einige spreitenlose, braun bis purpurrot gefärbte, beim Aufreißen schwach netzfaserige Blattscheiden. Die flachen, etwas rinnigen grünen bis graugrünen Blätter sind zerstreut bis wollig behaart, nur selten auch vollständig kahl und etwa 3 bis 6 Millimeter breit. Die Ränder der sich zur Spitze allmählich verschmälernden Blattspreiten sind rau. Das Blatthäutchen (Ligula) ist flachbogig.

Die Behaarte Segge ist eine Verschiedenährige Segge. Der Blütenstand besteht aus drei bis neun sitzenden bis lang gestielten, aufrechten Ähren. Die Hüllblätter des Blütenstandes sind laubblattartig und meist länger als dieser. Die Behaarte Segge besitzt zwei bis sechs eiförmig bis kurz zylindrische weibliche und darüber ein bis drei schlank zylindrische männliche Ähren. Die mehrblütigen weiblichen Ähren sind 1 bis 3 Zentimeter lang und etwa 7 Millimeter breit. Sie treten aus ihrem Tragblatt heraus und sind meist nicht auf ganzen Stängel verteilt. Die männlichen Ähren überragen die weiblichen und werden bis zu 3 Millimeter lang und nur 4 Millimeter breit. Die braunen, grün gekielten, etwa 4 bis 8 Millimeter langen Spelzen verschmälern sich in eine Stachelspitze. Männliche und weibliche Blüten mit je drei Staubblättern (Antheren) und dreinarbigen Fruchtknoten. Die gelbgrün gefärbten Fruchtschläuche stehen schräg ab und werden 5 bis 7 Millimeter lang und 2 Millimeter breit. Sie verschmälern sich in einen langen, tief zweizähnigen Schnabel. Die gelbbraune Frucht ist verkehrt eiförmig und dreikantig. Sie sind leicht aufgeblasen und häufig zerstreut bis dicht behaart.

Die Behaarte Segge blüht von April bis Juli.

Meistens ist die gesamte Pflanze behaart, häufig ist die Behaarung aber auf einzelne Stellen beschränkt, besonders auf die Umgebung der Mündung der Blattscheiden. Von manchen Autoren wird diese Form (fo. hirtiformis ) als die Varietät Carex hirta var. hirtiformis  aufgefasst.

Die Chromosomenzahl beträgt 2n = 112.

Verbreitung und Standort

Die Behaarte Segge kommt in ganz Europa mit Ausnahme des äußersten Nordens, in Nordafrika und Kleinasien bis zum Iran vor. In Nordamerika und Neuseeland wurde sie eingeschleppt (Neophyt).   In Mitteleuropa ist sie häufig, sie fehlt aber in den Mittelgebirgen und den Alpen meist oberhalb 1200 m. In den Allgäuer Alpen steigt sie auf der Lengenfeldalpe im Obertal östlich des Nebelhorn in Bayern bis zu 1400 m Meereshöhe auf.

Die Behaarte Segge bevorzugt Wegränder, Raine, und Uferböschungen, besiedelt aber auch Wiesen, Gärten und Waldschläge sowie lückige Gehölze. An Uferböschungen kommt sie auch auf periodisch überfluteten Stellen vor, weil sie sich hier mit ihren Ausläufern rasch ausbreiten kann. Sie braucht tiefen, lehmigen oder sandigen, basenreichen und zumindest zeitweise feuchten Boden.

Vergesellschaftung
Die Behaarte Segge ist nach Oberdorfer eine Kennart der Ordnung der Flut- und Feuchtpionierrasen (Agrostietalia stolonifera) sowie von Trittpflanzengesellschaften (Plantaginetalia majoris). Ferner wächst sie in halbruderalen Queckenrasen trockenwarmer Standorte, in Trocken- und Halbtrockenrasen und nährstoffreichen Stauden- und ausdauernde Unkrautfluren.

Ökologie
Ökologische Zeigerwerte nach Ellenberg: L - 7 T - 6 K - 3 F - 6~ R - X N - 5 S - 0

Die Pflanze ist eine Halblichtpflanze, das heißt, sie wächst bei vollem Licht, erträgt aber in Grenzen eine Beschattung. Sie gedeiht am besten auf frischen bis feuchten Böden, erträgt aber sehr gut stark wechselnde Feuchteverhältnisse an ihrem Standort. Ihr ökologischer Schwerpunkt liegt auf mäßig stickstoffversorgten Böden, auf stickstoffarmen und -reichen Standorten ist sie seltener. Sie ist nicht salzertragend und etwas wärmeliebend.

Als Tiefwurzler und Wurzelkriechpionier trägt die Behaarte Segge maßgeblich zur Bodenfestigung bei. Sie kann in Gärten durch ihre Ausläufer lästig werden.

Die Behaarte Segge ist eine ausdauernde, Ausläufer bildende Halbrosettenpflanze und ein Rhizom-Geophyt. Die Behaarung der Pflanze dient als Strahlenschutz.

Die Blüten der Behaarten Segge werden vom Wind bestäubt (Anemophilie). Die Samen werden entweder durch den Wind (Anemochorie), durch Wasser (Hydrochorie), durch Selbstausbreitung (Autochorie) oder durch die Klebwirkung der Samen (Epichorie), die an Tieren haften bleiben können, verbreitet.

Gefährdung und Schutz
Die Behaarte Segge gilt weltweit als nicht gefährdet und genießt keinen gesonderten gesetzlichen Schutz.

Detailfotos

Quellen

Literatur
 A. Petersen: Die Sauergräser. Akademie-Verlag, Berlin 1989, ISBN 3-05-500257-1.

Einzelnachweise

Weblinks

 Verbreitung auf der Nordhalbkugel aus: Eric Hultén, Magnus Fries: Atlas of North European vascular plants. 1986, ISBN 3-87429-263-0 bei Den virtuella floran. (schwedisch)
 Thomas Meyer:  Datenblatt mit Bestimmungsschlüssel und Fotos bei Flora-de: Flora von Deutschland (alter Name der Webseite: Blumen in Schwaben)
Bilder:
 http://www.guenther-blaich.de/pflseite.php?par=Carex+hirta
 http://www.gerhard.nitter.de/Steckbriefe/Carex-hirta.html
 https://nafoku.de/rff/e/carehirt.htm

Seggen

redpajama_set_name: RedPajamaWikipedia


text:
Регулярный сезон
В этом регулярном сезоне команды разбиты на 2 группы А и Б соответственно.

Группа А

Группа Б

Групповой этап

Группа В

Группа Г

Классификационный раунд

Матчи за 15-17 места

Матчи за 9-14 места

Плей-офф

Раунд 1/4 финала

Группа Д

Группа Е

Матчи за 5-8 места

Финал четырёх

Итоговое положение
  Планета-Университет Ухта
  АлтайБаскет (Барнаул)
  Новосибирск (Новосибирск)
 4 Согдиана-СКИФ Воронеж
 5 Динамо-Ставрополь Ставрополь
 6 Автодор (Саратов)
 7 Динамо (Москва)
 8 Динамо-МТГУ Майкоп
 9 КАМиТ-Университет Тверь
 10 Тамбов (Тамбовская область)
 11 Динамо-Политех (Курск)
 12 Старый Соболь Нижний Тагил
 13 Эльбрус Черкесск
 14 Родники Ижевск
 15 Нефтехимик Тобольск
 16 Десна Брянск
 17 Волжанин-ГЭС Волжский

Ссылки
 Сайт российской федерации баскетбола

Чемпионат России по баскетболу
2011 год в баскетболе
2012 год в баскетболе


When we make a raised bed to bring the soil closer to us for working, when we build a road not only for transportation but also for harvesting rainwater or when we dig, line and build a dam for holding water for irrigation, recreation and fire prevention, there are earthworks – earth that has been formed for increased functionality.
So why come to a course on earthworks?
maintenance of conventional outdoor swimming pools add beauty and function to our landscapes.
Those are a few of the earthworks we'll be covering and a few that we'll be physically putting in the ground during the course.
Please be aware as this is a hands on workshop, our maximum is 12 participants.
Gord explaining how to remove vegetation before an earthwork for maximum regeneration of ground cover.
We will be employing a mix of classroom, hands-on, experiential and social learning in this 2 and a half day workshop. We will teach you to read the landscape to recognize opportunities for harvesting water. A combination of classroom and hands-on exercises will give you the right tools you need to drought-proof your landscape.
We will be exploring the world of Earthworks and learning about such techniques as Swales, Keyline Design, Hugelkultur, Terracing and Earth Dams. We will delve deep into these concepts and discuss the correct application for each technique and why and where they work and don't work. Throughout the weekend we will be employing our new knowledge to survey, site, design and construct some of these incredibly productive and effective structures.
Gord listening to a student's question on using a levelling rod and directing machinery to build the keyway of an earthen dam.
Are you a Landowner concerned about drought proofing your property?
We teach you practical ways to hydrate your land and even keep a little water in the bank for peak summer, when reservoirs are typically dry.
Are you a Farmer, Orchardists or Production grower?
Learn how to make the most of your existing infrastructure to harvest rainwater and snowmelt to extend your season and irrigation needs.
Are you an Earthworks Professionals or Machine Operators?
Learn new and exciting techniques that conscious landowners are looking for.
Are you a Permaculture Designer or Residential Landscaper?
We teach you to see the potential in any landscape and show you how to carve out beautiful, productive, functioning landscapes that your clients will love!
Gordon Hiebert: Gord is the Owner, Chief Instructor and Project Manager at Element Eco-Design. Gord has in-depth experience in Forest Protection, Green Building and Permaculture Design. Gordon has learned personally from many of the top names in Permaculture including Rob Avis(Into to Perm., 2009), Geoff Lawton(PDC & Permaculture Earthworks, 2010), Daren Dorhety (Keyline Design, 2012), Anne Adams (Holistic Management, 2012), Dave Jacke (Edible Forest Gardening, 2012), Eric Toensmeier (Perennial Agro-Forestry, 2012) Mark Shepherd (Restoration Agriculture, 2013) and more. Gord is a passionate, dynamic speaker and holds a deep understanding of natural systems.
Javan offers a unique balance of content-focused education with student-centered learning. Javan has spent extensive time learning and installing Earthworks with Sepp Holzer, including time studying with him in Austria in 2013, Geoff Lawton, Darren Doherty and Mark Shepard. He's recently back from Cuba where he lead a diverse crew of Cuban and International students installing over 220m of terraces with nothing but picks and shovels. Javan is a wealth of knowledge and has a fun upbeat teaching style that is bound to leave you grinning from ear to ear.
Still not sure if this workshop is a good fit for you?
Check out some of the videos below of previous projects….
Join Element Eco-Design and learn how to use Permaculture Earthworks to drought proof your landscape and move your property from average to abundant.


Borough Plumbing and Heating, provides help with your drain cleaning needs. We are a locally owned and operated family company. You will receive personalized service and care. No sub contractors, no salesman just solutions and advice based on your unique needs. Call us you'll notice the difference.
Plugged drains are a real inconvenience and need to be handled in a timely way. Call and we will be happy to answer any questions you may have. Drain lines if installed properly are designed to not back up. So if you are experiencing multiple backups there is a problem and simply cleaning will not provide a long term solution. The problem may need further investigation or even a camera in the line to provide a visual inspection. Borough Plumbing and Heating provide personalized client care. And utilize high quality General and Rigid power equipment.


text:
'use strict';

var assert = require('assert'),
  akamai = require('../akamai');

describe('Module API', function () {
  it('akamai.purge should return a function', function () {
    assert.equal(typeof akamai.purge, 'function');
  });

  it('akamai.invalidate should return a function and set the action', function () {
    var testPurge = akamai.invalidate;
    assert.equal(typeof testPurge, 'function');
    assert.deepEqual(testPurge.options, {action: 'invalidate'});
  });

  it('akamai.remove should return a function and set the action', function () {
    var testPurge = akamai.remove;
    assert.equal(typeof testPurge, 'function');
    assert.deepEqual(testPurge.options, {action: 'remove'});
  });

  it('akamai.purge.staging should return a function and set the action', function () {
    var testPurge = akamai.purge.staging;
    assert.equal(typeof testPurge, 'function');
    assert.deepEqual(testPurge.options, {domain: 'staging', action: 'remove'});
  });

  it('akamai.purge.production.cpcode should return a function and set the action', function () {
    var testPurge = akamai.purge.production.cpcode;
    assert.equal(typeof testPurge, 'function');
    assert.deepEqual(testPurge.options, {domain: 'production', action: 'remove', type: 'cpcode'});
  });

  it('akamai.invalidate.production.cpcode should return a function and set the action', function () {
    var testPurge = akamai.invalidate.production.cpcode;
    assert.equal(typeof testPurge, 'function');
    assert.deepEqual(testPurge.options, {domain: 'production', action: 'invalidate', type: 'cpcode'});
  });
});

redpajama_set_name: RedPajamaGithub


text:
The Very Hungry Caterpillar at Flushing Town Hall
Sunday March 15th 2:14pm and 4:15pm
Flushing Town Hall
On Sunday, March 15, a very hungry caterpillar will eat its way through Queens, a chameleon will visit a zoo, and a brown bear will discover colors, in a heartwarming performance by Mermaid Theatre of Nova Scotia. The innovative theatre—traveling from Canada to Flushing—will present two shows that day—one in English and one in Mandarin.
Mermaid Theatre's lively compilation captures the magic of not only one, but three Eric Carle favorites retold on stage through the magic of black light and fanciful puppets. The hour-long triple-bill will feature:
● The Mixed-Up Chameleon, who is bored with his life as he sits about changing color all day until he takes a trip to the zoo. His hilarious attempts to emulate the beautiful animals he sees conclude with his discovery that there is value in his own unique self.
● The Very Hungry Caterpillar, who eats his way through an amazing variety of foods on his path to becoming a beautiful butterfly. (Eric Carle's international bestseller The Very Hungry Caterpillar sold over 45 million copies and has been translated into 65 languages.)
● Brown Bear, Brown Bear, What Do You See?, which has helped hundreds of thousands of toddlers to identify colors and creatures.
Capturing the rhythmic text and beautiful tissue-paper collage illustrations of the classic picture book, Mermaid Theatre's adaptation generates fresh appreciation of an endearing cast of characters for audiences ages three to eight years old and their families.
Visit HERE for a preview of the performance.
Founded in 1972, Mermaid Theatre of Nova Scotia is best known for its unique stage adaptations of children's beloved literary classics. Its innovative puppetry, striking scenic effects, evocative original music, and gentle storytelling have earned Mermaid Theatre multiple awards and international recognition as their stories have been shared with over six million spectators in fifteen countries. The group has received the Citation of Excellence for Puppetry by UNIMA-USA—an honor that is equivalent to the Oscars in the puppetry world—multiple times, among others for The Very Hungry Caterpillar and Other Eric Carle Classics, which was adapted, designed and directed by Jim Morrow, with music by Steven Naylor and narration by Gordon Pinsent.
The show will be performed in English at 2:15 PM and in Mandarin (中文) at 4:15 PM. Tickets are available for $14/$10 Members/$8 Children/$6 Member Children/FREE for Teens at www.flushingtownhall.org or by calling the box office at (718) 463-7700 x222.
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text:
\section{Introduction}

Along with the blooming of Artificial Intelligence (AI) and the accompanying increase in model complexity, there has been a surge of interest in explainable AI (XAI henceforth), namely AI that allows humans to understand its inner workings.\cite{doshi-velez2017, linardatos2020, Gilpin.2018, biran2017, doran2017} This interest is particularly keen in safety-critical domains such as healthcare, where it is perceived that XAI can engender trust, help monitoring bias, and facilitate AI development.\cite{doshi-velez2017, lipton2018} XAI has already shown to improve clinicians' ability to diagnose and assess prognoses of diseases as well as assist with planning and resource allocation. For example, Letham and colleagues developed a stroke prediction model that matches the performance of the most accurate machine learning algorithms, while remaining as interpretable as conventional scoring methods used in clinical practice.\cite{letham2015}

Despite the enthusiasm, and a growing community of researchers devoting energy to XAI, there is currently no consensus on the reliability of XAI techniques and several researchers have cast serious doubts on whether XAI solutions should be incorporated into guidelines and standards, or even deployed at all.\cite{mccoy2021, Ghassemi.2021}

Undoubtedly, there is an inherent tension between the desire for machines performing better than humans, and the desire for machines providing human-understandable explanations. Together with their super-human capacities -- such as the ability to juggle dozens or hundreds of factors -- the sub-symbolic character of statistical learning techniques contributes to rendering machine learning models opaque to humans.

There is a wide variety of techniques for XAI, and many categorizations have been proposed in the literature. Techniques can roughly be grouped into local vs. global, and model-specific vs. model-agnostic approaches. Local methods aim to explain model outputs for individual samples, while global methods focus on making models more explainable at an aggregate level. Model-specific methods are tailored to explain a specific type of model, while model-agnostic methods can be applied to a range of different models. Many of the well-known techniques yield post-hoc explanations, meaning that they generate explanations for already trained, so-called 'black-box', models. Alternatively, there exist approaches that are inherently explainable, also known as white-box models, such as decision trees and linear regression models. A detailed taxonomy is beyond the scope of this paper; for an extensive overview we refer the reader to existing reviews.\cite{carvalho2019, molnar_book2022, Ras.2022}

What is important to stress is that, in order to overcome the opaqueness of black-box models, XAI techniques often bring in additional layers of complexity, such as:
\begin{itemize}
    \item explanations themselves can be inaccurate or deceiving,\cite{rudin_stop2019, Ghassemi.2021}
    \item since an objective ground truth is lacking, it is hard to evaluate XAI techniques,\cite{zhou2021, yang2019}
    \item trade-offs exist between explainability and other desiderata such as high performance.\cite{adadi2018, weld2019}
\end{itemize}

What role is left then for XAI in the medical realm, where adoption and trust of new technologies are notoriously complex? Given the wealth and heterogeneity of XAI techniques, as well as the doubts and limitations mentioned in the literature, the answer to this question is not straightforward.

In this Viewpoint we collect, review and appraise the criticisms that are leveled against XAI. We conclude that, despite several critical points of attention, there is no decisive argument to abandon the research for better XAI techniques, especially in the medical field, where a higher level of oversight is preferable.

\section{Criticisms to Explainable AI}

We begin by recollecting the main doubts raised about the usefulness and efficacy of XAI, in no particular order; each point is then expanded and discussed. The criticisms, along with the main counterpoints discussed below, are visually represented in Figure 1.
\begin{enumerate}
    \item XAI techniques may be incorrect or unreliable.
    \item Human users may fall prey to biases when interacting with XAI tools.
    \item There are no objective measures of explainability.
    \item XAI may work on an aggregate level, but not on an individual level.
    \item Evidence-based assessment is the proper way to evaluate medical (AI) interventions, not XAI.
    \item In the medical domain, we cannot trade performance for explainability.
    \item XAI is not suitable for all users.

\end{enumerate}

\begin{figure}[h]
    \centering
    \includegraphics[width=0.9\textwidth]{figures/overview.png}
    \caption{A visual summary of the criticisms and the related opportunities.}
    \label{fig:overview}
\end{figure}

\subsection*{XAI techniques may be incorrect or unreliable.}

\textbf{Criticism.} XAI techniques are sometimes faulty: they can be fooled with adversarial attacks or with noise.\cite{slack2019, heo2019}  Moreover, for the XAI techniques that approximate an original black-box model, there can be an additional discrepancy between the explanation and the real model behavior, resulting in unreliable explanations.\cite{rudin_stop2019}

\noindent
\textbf{Discussion.} While these points are true, the mitigating factor here is that every technology has failure modes. As long as we have awareness and know how to counter undesired effects, such techniques can still be useful. Furthermore, some such negative results can also be a pointer on how to improve XAI techniques; \textit{e.g.} adversarial attacks on SHAP (one of the techniques offering individual-level explanations) suggest that out-of-distribution samples should not be employed in approximate SHAP calculations.

\subsection*{Human users may fall prey to biases when interacting with XAI tools.}

\textbf{Criticism.} Users of XAI techniques struggle to understand what is a good explanation and often fall prey to biases –- e.g. preferring explanations that confirm their own beliefs, the so-called confirmation bias –- or misuse XAI tools.\cite{kaur2020, rosenfeld2021}

\noindent
\textbf{Discussion.} Such remarks are on point and should be addressed when designing interfaces of AI tools and training programs, but they do not constitute a sufficient reason to give up on XAI. It is very well documented that many researchers misunderstand and misuse $p$-values \cite{Wasserstein.2016}, but this is not a good argument to abandon significance testing. A similar point could be made about conventional medical procedures, where different kinds of bias have been documented. \cite{Schwartz.2014} The best solution for such problems is to have the users properly trained; notably, when it comes to medical devices the training of users is quite well regulated; \textit{e.g.} in Europe via the Medical Device Regulation and the ISO standards.\cite{mdr}

\subsection*{There are no objective measures of explainability.}

\textbf{Criticism.} There is no obvious ground truth for explainability, and therefore there is no straightforward way to assess how good an XAI technique really is.\cite{zhou2021, yang2019}

\noindent
\textbf{Discussion.} This surely is a barrier, and more work is required to establish how the reliability of XAI tools can be tested; what is a good explanation, and what is useful to the user/developer/regulator are questions that have an intrinsic psychological nature and would benefit from more principled and widespread experimentation. Several of these concerns have been raised recently, \cite{Rudin.2022}  and substantial work has  already been done in investigating what constitutes an appropriate and useful explanation.\cite{slack2019, huysmans2011, miller2018, jacovi2022, krawiec2021} These efforts include eliciting users' preferences with user studies, and deriving insights on the notion of explainability from the perspective of social sciences and related fields. Observing this need for an interdisciplinary structure, many scientists have recently participated in projects, workshops, discussion groups that involve colleagues working in diverse areas.

\subsection*{XAI may work on an aggregate level, but not on an individual level.}

\textbf{Criticism.} Ghassemi and colleagues distinguish XAI techniques used in the specific (individual level) case and in the general case, finding that they can be of use in the latter but not in the former.\cite{Ghassemi.2021} The reason is that users too often cannot decide if the explanation is sensible for an individual sample and have to base their judgment on an unwarranted intuition.

\noindent
\textbf{Discussion.} While this risk is concrete, it is important to realize that this criticism mostly applies to certain kinds of data. In images, single input features (i.e. pixels) have no intrinsic meaning; only certain patterns of feature activation have meaning. When a certain area of an image is highlighted we simply do not know if what we recognize (\textit{e.g.} the shape of a kidney) is the same as what the AI recognizes, because we have no access to the high-level representations of the machine. Hence, we cannot be sure whether the machine is recognizing the kidney as the salient part of the image at hand, or if there is some other spurious reason. There are however data types, like Electronic Health Records, where single input features have a very clear semantics. If, for instance, the patient's values of BMI and glucose are singled out as salient by an explainability technique, this may be enough for a clinical user to consider whether the AI's output is sensible.

Thus in some applications, having XAI developed on an individual level in a way that allows users to 'check' the reason(s) behind the prediction, can be a way to find and escalate possible problems of the AI at an aggregate level. Especially when the data and the AI environment are of high complexity, we need oversight mechanisms to continuously monitor and detect unforeseen failure modes, and individual users can play a role in such monitoring processes.
Furthermore, the explanations of AI predictions will typically be one of the pieces of information that clinicians take into account. In current practice, for example, physicians obtain results of different tests, which at times may even be potentially conflicting, \textit{e.g.} radiopaque lesion resembling infectious mass in a CT scan but lack of elevated infection markers. Such different factors are weighed in, each with its degree of uncertainty, to arrive at a decision. In this workflow, the input(s) of XAI techniques can be contrasted with other sources of information and be instrumental in i) achieving a deeper insight into the clinical case at hand and ii) decide whether to (not) trust the AI.

\subsection*{Evidence-based assessment is the proper way to evaluate medical interventions, not XAI.}

\textbf{Criticism.} According to this criticism, randomized controlled trials (RCTs) and evidence-based assessment are better ways to evaluate medical interventions than XAI. The primary goal is to have interventions that work, even if we do not understand why. Black-box solutions are used in medicine already, so we do not need XAI as long as we have established that a tool or intervention is effective. The desire for explainability is secondary.\cite{mccoy2021}

\noindent
\textbf{Discussion.} It is indisputable that XAI should not be conceived as a replacement of evidence-based instruments to validate model effectiveness. However, the criticism raises a false dichotomy: evidence-based evaluation tools and XAI can coexist, and we do not need to abandon one in favor of the other. We do not need to choose between a thorough evaluation and an explainable model: thorough evaluation should be a default (and it often is, given current regulations), and the need for explainability can be discussed separately.

Furthermore, framing the discourse about explainability as a binary classification between primary and secondary requirements is needlessly reductionist. Surely there can be trade-offs with accuracy that we must be cognizant of (more on this later), but the benefit of explainability may pay off in other areas, \textit{e.g.} the discovery of biased outcomes for certain subgroups, or the disentangling of causal effects from spurious ones. As put forward by various governing bodies, explainability is one aspect within a set of requirements that are partially interdependent and should be balanced against each other. The pursuance of explainability will have different priority depending on the application. An AI tool to speed up reconstruction of images in the back-end might not need explainability, while a decision support tool to predict risk of complications might need it if the risks are to be discussed and assessed with a patient. A blanket statement declaring explainability 'secondary' does not help in determining which case is which.

A final point should be made about validation of AI tools and evidence-based medicine (EBM). EBM can rely on a stable set of protocols that are established and widely used for the certification of new interventions. It should be noted however that, despite its indisputable importance, EBM is not a laser-precise discipline: e.g. there is a vast body of literature highlighting the fact that clinical trials are often not reflecting clinical practice, or have non-representative populations, or are incomparable with each other, or become quickly outdated.\cite{solomon2011}  Furthermore, as argued by several researchers,\cite{cartwright2012, Greenhalgh.2014, solomon2011, worrall2007} RCTs do not always control for the biases they are intended to control, they do not produce reliably generalizable knowledge, or they can create unnecessary constraints on clinical testing. In addition, obtaining statistically significant evidence is often extremely time-consuming and expensive. Finally, the success rate of clinical trials for medications is famously low.\cite{wong2019} Against this backdrop, we should be very careful to promote the initiation of many studies or trials testing the effectiveness of tools whose inner workings are entirely unknown, i.e. black boxes. Explainability is a precious tool not just for the end user, but also for the developer: the vast majority of models are discarded before reaching the test phase because they are found to be clinically implausible thanks to explainability techniques. In this sense, while we do not dispute the high position of RCTs in the hierarchy of evidence,\cite{djulbegovic2017} explainability is a complementary tool that can even help expedite or improve the effectiveness of RCTs and EBM as a whole.

\subsection*{In the medical domain, we cannot trade performance for explainability.}

\textbf{Criticism. }The trade-off between explainability and performance may induce people to use simpler and less accurate models, rather than more effective ones. This is a problem, as we may be exchanging a further improvement in medical outcomes with a debatable increase in transparency. There are several medical interventions (\textit{e.g.} some drugs) for which a mechanistic explanation is not known, but they are nevertheless proven to be effective and widely used. If we are accepting of black-box interventions in healthcare, we can allow effective AI models to be opaque as well.\cite{mccoy2021}

\noindent
\textbf{Discussion.} It is indeed the case that some explainability techniques (or simpler, more interpretable models) can result in lower performance. Whether this is desirable is a decision that should be taken case-by-case. Moreover, the analogy with medications, and the fact that we are 'fine' with them being black-boxes, seems misguided. There are already different tools in medical practice for which different levels of understanding are required. For example, as opposed to drugs, radiography machines can only be operated by specialists. Hence for complex tools we do require users to have some knowledge of the inner workings of the device. The reason underlying this choice is that there are failure modes we want to be able to control.
Moreover, the fact that now we do not require explanations for some interventions (as long as they are proven to work) does not mean that this is the desirable way forward.  On the contrary, in some cases this could be construed as a problem. Arguably, we should keep pushing research to understand why certain treatments are effective. Along this line of reasoning, if we can build a tool with an intrinsic ability to explain itself -- at a bearable cost in terms of trade-offs – we should definitely pursue it.

Finally, a 'simple' algorithm is not necessarily less accurate. When coupled with feature engineering or feature reduction, even those 'simple' algorithms may achieve good performances. This criticism thus overlooks an important line of work on inherently interpretable methods that are aiming at matching the performances of more complex models.\cite{rudin_stop2019, letham2015} Figure \ref{fig:compas_example} showcases a simple model which is easy to understand and achieves high accuracy.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.4\textwidth]{figures/COMPAS_example.png}
    \caption{Example of an accurate white-box algorithm for the COMPAS recidivism problem (Figure 4 in \cite{Hu.2019}).}
    \label{fig:compas_example}
\end{figure}

\subsection*{XAI is not suitable for all users.}

\textbf{Criticism.} XAI techniques can be used to support the work of different professionals interacting with an AI tool. Beside regular users, other roles include the developers, the medical experts, the regulators, and so on, each with different requirements and needs for explainability. Arguably, XAI techniques could be useful for trained professionals such as developers or auditors, but could be dangerous when shown to untrained users.

\noindent
\textbf{Discussion.} This point has some overlap with points 2 and 4, but it is nonetheless important to stress that XAI techniques will almost never offer a one-size-fits-all solution. Different kinds of interaction with the machine will be required depending on the purpose of the participating human; it is highly likely that different techniques will answer different needs.\cite{Arrieta.2020} Current research extends to interactive machine learning to provide user-targeted explanations.\cite{sokol2020} As to the potential misuse or misunderstanding of XAI by simple users, the solution lies in a combination of robust methodology, proper training, and expectation management.

\section{Should we downsize our efforts in XAI for healthcare?}

We have reviewed some of the core criticisms offered in the literature against the widespread use of XAI techniques in medical contexts. Some of the qualms, as well as some of the counterpoints, are in fact not limited to healthcare applications and can be recast at a more general level. We should however be mindful of how the debate is influenced by the higher stakes present in safety-critical AI applications in the health realm.
It is worth remarking that demoting the usefulness of explainable medical AI means stifling the efforts to get human oversight over a new technology that is still in its infancy but can be deployed at scale very rapidly, potentially affecting a large swath of the population. XAI is not a silver bullet that will solve all of AI's problems, and in particular it is not a substitute for rigorous evaluation of model performance. Nonetheless, XAI tools can be of value in several circumstances.
Human oversight over machines is still an important tenet, and the introduction of more black-boxes is not going to stem the loss of human control in a quickly digitizing world. It has been argued that augmentation of human capacities is preferable to automation,\cite{acemoglu2017} since the latter leads to deskilling as well as detachment and unemployment spikes. Augmentation presupposes the possibility of a human-machine synergy; in other words, a way for humans to relate and engage with AI.

Different public bodies, including the European Union, are discussing regulations for AI products. While the field of XAI may not yet be at a level of maturity where XAI techniques are embedded into guidelines in the form of hard requirements, this does not entail that XAI techniques should be sidelined or disregarded. Said techniques can be employed to exercise an additional layer of control over AI products; as with all tools, practitioners should be aware of the potential failure modes. As for guidelines, it could be meaningful to allow the use of XAI techniques -- without enforcing a specific choice -- and require an argument of why a certain technique was chosen.

We noted in the introduction the inherent tension in the request for explainability: while we want machines to perform at super-human level, we also want them to be understandable by humans. This is however just another case of competing objectives, not a good reason to toss one of the objectives out the window (for another example of competing objectives: saving healthcare costs and keeping people alive). The challenge is to find a trade-off that is satisfactory, a challenge that is extremely case-dependent.
The question should be: while explainability may not be a requirement for healthcare interventions tout court, is AI a technology on which we think "extra" explainability is needed? This technology has peculiar aspects that set it apart, including the potential to be prejudiced against subgroups based on gender, ethnic background, and socioeconomic status,\cite{fitzgerald2017} the fact that it may lose reliability when the input data changes,\cite{ulmer_ood2020}  or that it may rely on spurious correlations found in the data.\cite{calude2017} For these reasons, coupled with the potential of rapid and impactful adoption, we argue that a higher level of oversight is preferable compared to regular interventions.

\section{Conclusion}\label{sec:conclusion}

Despite its infancy, the research on XAI is growing very rapidly. Thanks to this growth, new techniques are tested extensively by many researchers in the field, and the duration to become a time-tested approach is much shorter than it has been in the past. Given the arguments we discussed, and the aforementioned possibility to rapidly hone useful techniques, we conclude that we should step up our efforts in XAI research for healthcare, as this research program is promising and addresses a core issue. Ultimately, we still want clinicians to be the mediators that can bridge the coldness of algorithms and scientific protocols with the human experience of the patient. If clinical users of medical AI cannot interact with machines, if their role becomes that of mere executors of interventions that are proven to be effective, the sensitive task of providing care could well become de-humanizing.

\section{Introduction}

Along with the blooming of Artificial Intelligence (AI) and the accompanying increase in model complexity, there has been a surge of interest in explainable AI (XAI henceforth), namely AI that allows humans to understand its inner workings.\cite{doshi-velez2017, linardatos2020, Gilpin.2018, biran2017, doran2017} This interest is particularly keen in safety-critical domains such as healthcare, where it is perceived that XAI can engender trust, help monitoring bias, and facilitate AI development.\cite{doshi-velez2017, lipton2018} XAI has already shown to improve clinicians' ability to diagnose and assess prognoses of diseases as well as assist with planning and resource allocation. For example, Letham and colleagues developed a stroke prediction model that matches the performance of the most accurate machine learning algorithms, while remaining as interpretable as conventional scoring methods used in clinical practice.\cite{letham2015}

Despite the enthusiasm, and a growing community of researchers devoting energy to XAI, there is currently no consensus on the reliability of XAI techniques and several researchers have cast serious doubts on whether XAI solutions should be incorporated into guidelines and standards, or even deployed at all.\cite{mccoy2021, Ghassemi.2021}

Undoubtedly, there is an inherent tension between the desire for machines performing better than humans, and the desire for machines providing human-understandable explanations. Together with their super-human capacities -- such as the ability to juggle dozens or hundreds of factors -- the sub-symbolic character of statistical learning techniques contributes to rendering machine learning models opaque to humans.

There is a wide variety of techniques for XAI, and many categorizations have been proposed in the literature. Techniques can roughly be grouped into local vs. global, and model-specific vs. model-agnostic approaches. Local methods aim to explain model outputs for individual samples, while global methods focus on making models more explainable at an aggregate level. Model-specific methods are tailored to explain a specific type of model, while model-agnostic methods can be applied to a range of different models. Many of the well-known techniques yield post-hoc explanations, meaning that they generate explanations for already trained, so-called 'black-box', models. Alternatively, there exist approaches that are inherently explainable, also known as white-box models, such as decision trees and linear regression models. A detailed taxonomy is beyond the scope of this paper; for an extensive overview we refer the reader to existing reviews.\cite{carvalho2019, molnar_book2022, Ras.2022}

What is important to stress is that, in order to overcome the opaqueness of black-box models, XAI techniques often bring in additional layers of complexity, such as:
\begin{itemize}
    \item explanations themselves can be inaccurate or deceiving,\cite{rudin_stop2019, Ghassemi.2021}
    \item since an objective ground truth is lacking, it is hard to evaluate XAI techniques,\cite{zhou2021, yang2019}
    \item trade-offs exist between explainability and other desiderata such as high performance.\cite{adadi2018, weld2019}
\end{itemize}

What role is left then for XAI in the medical realm, where adoption and trust of new technologies are notoriously complex? Given the wealth and heterogeneity of XAI techniques, as well as the doubts and limitations mentioned in the literature, the answer to this question is not straightforward.

In this Viewpoint we collect, review and appraise the criticisms that are leveled against XAI. We conclude that, despite several critical points of attention, there is no decisive argument to abandon the research for better XAI techniques, especially in the medical field, where a higher level of oversight is preferable.

\section{Criticisms to Explainable AI}

We begin by recollecting the main doubts raised about the usefulness and efficacy of XAI, in no particular order; each point is then expanded and discussed. The criticisms, along with the main counterpoints discussed below, are visually represented in Figure 1.
\begin{enumerate}
    \item XAI techniques may be incorrect or unreliable.
    \item Human users may fall prey to biases when interacting with XAI tools.
    \item There are no objective measures of explainability.
    \item XAI may work on an aggregate level, but not on an individual level.
    \item Evidence-based assessment is the proper way to evaluate medical (AI) interventions, not XAI.
    \item In the medical domain, we cannot trade performance for explainability.
    \item XAI is not suitable for all users.

\end{enumerate}

\begin{figure}[h]
    \centering
    \includegraphics[width=0.9\textwidth]{figures/overview.png}
    \caption{A visual summary of the criticisms and the related opportunities.}
    \label{fig:overview}
\end{figure}

\subsection*{XAI techniques may be incorrect or unreliable.}

\textbf{Criticism.} XAI techniques are sometimes faulty: they can be fooled with adversarial attacks or with noise.\cite{slack2019, heo2019}  Moreover, for the XAI techniques that approximate an original black-box model, there can be an additional discrepancy between the explanation and the real model behavior, resulting in unreliable explanations.\cite{rudin_stop2019}

\noindent
\textbf{Discussion.} While these points are true, the mitigating factor here is that every technology has failure modes. As long as we have awareness and know how to counter undesired effects, such techniques can still be useful. Furthermore, some such negative results can also be a pointer on how to improve XAI techniques; \textit{e.g.} adversarial attacks on SHAP (one of the techniques offering individual-level explanations) suggest that out-of-distribution samples should not be employed in approximate SHAP calculations.

\subsection*{Human users may fall prey to biases when interacting with XAI tools.}

\textbf{Criticism.} Users of XAI techniques struggle to understand what is a good explanation and often fall prey to biases –- e.g. preferring explanations that confirm their own beliefs, the so-called confirmation bias –- or misuse XAI tools.\cite{kaur2020, rosenfeld2021}

\noindent
\textbf{Discussion.} Such remarks are on point and should be addressed when designing interfaces of AI tools and training programs, but they do not constitute a sufficient reason to give up on XAI. It is very well documented that many researchers misunderstand and misuse $p$-values \cite{Wasserstein.2016}, but this is not a good argument to abandon significance testing. A similar point could be made about conventional medical procedures, where different kinds of bias have been documented. \cite{Schwartz.2014} The best solution for such problems is to have the users properly trained; notably, when it comes to medical devices the training of users is quite well regulated; \textit{e.g.} in Europe via the Medical Device Regulation and the ISO standards.\cite{mdr}

\subsection*{There are no objective measures of explainability.}

\textbf{Criticism.} There is no obvious ground truth for explainability, and therefore there is no straightforward way to assess how good an XAI technique really is.\cite{zhou2021, yang2019}

\noindent
\textbf{Discussion.} This surely is a barrier, and more work is required to establish how the reliability of XAI tools can be tested; what is a good explanation, and what is useful to the user/developer/regulator are questions that have an intrinsic psychological nature and would benefit from more principled and widespread experimentation. Several of these concerns have been raised recently, \cite{Rudin.2022}  and substantial work has  already been done in investigating what constitutes an appropriate and useful explanation.\cite{slack2019, huysmans2011, miller2018, jacovi2022, krawiec2021} These efforts include eliciting users' preferences with user studies, and deriving insights on the notion of explainability from the perspective of social sciences and related fields. Observing this need for an interdisciplinary structure, many scientists have recently participated in projects, workshops, discussion groups that involve colleagues working in diverse areas.

\subsection*{XAI may work on an aggregate level, but not on an individual level.}

\textbf{Criticism.} Ghassemi and colleagues distinguish XAI techniques used in the specific (individual level) case and in the general case, finding that they can be of use in the latter but not in the former.\cite{Ghassemi.2021} The reason is that users too often cannot decide if the explanation is sensible for an individual sample and have to base their judgment on an unwarranted intuition.

\noindent
\textbf{Discussion.} While this risk is concrete, it is important to realize that this criticism mostly applies to certain kinds of data. In images, single input features (i.e. pixels) have no intrinsic meaning; only certain patterns of feature activation have meaning. When a certain area of an image is highlighted we simply do not know if what we recognize (\textit{e.g.} the shape of a kidney) is the same as what the AI recognizes, because we have no access to the high-level representations of the machine. Hence, we cannot be sure whether the machine is recognizing the kidney as the salient part of the image at hand, or if there is some other spurious reason. There are however data types, like Electronic Health Records, where single input features have a very clear semantics. If, for instance, the patient's values of BMI and glucose are singled out as salient by an explainability technique, this may be enough for a clinical user to consider whether the AI's output is sensible.

Thus in some applications, having XAI developed on an individual level in a way that allows users to 'check' the reason(s) behind the prediction, can be a way to find and escalate possible problems of the AI at an aggregate level. Especially when the data and the AI environment are of high complexity, we need oversight mechanisms to continuously monitor and detect unforeseen failure modes, and individual users can play a role in such monitoring processes.
Furthermore, the explanations of AI predictions will typically be one of the pieces of information that clinicians take into account. In current practice, for example, physicians obtain results of different tests, which at times may even be potentially conflicting, \textit{e.g.} radiopaque lesion resembling infectious mass in a CT scan but lack of elevated infection markers. Such different factors are weighed in, each with its degree of uncertainty, to arrive at a decision. In this workflow, the input(s) of XAI techniques can be contrasted with other sources of information and be instrumental in i) achieving a deeper insight into the clinical case at hand and ii) decide whether to (not) trust the AI.

\subsection*{Evidence-based assessment is the proper way to evaluate medical interventions, not XAI.}

\textbf{Criticism.} According to this criticism, randomized controlled trials (RCTs) and evidence-based assessment are better ways to evaluate medical interventions than XAI. The primary goal is to have interventions that work, even if we do not understand why. Black-box solutions are used in medicine already, so we do not need XAI as long as we have established that a tool or intervention is effective. The desire for explainability is secondary.\cite{mccoy2021}

\noindent
\textbf{Discussion.} It is indisputable that XAI should not be conceived as a replacement of evidence-based instruments to validate model effectiveness. However, the criticism raises a false dichotomy: evidence-based evaluation tools and XAI can coexist, and we do not need to abandon one in favor of the other. We do not need to choose between a thorough evaluation and an explainable model: thorough evaluation should be a default (and it often is, given current regulations), and the need for explainability can be discussed separately.

Furthermore, framing the discourse about explainability as a binary classification between primary and secondary requirements is needlessly reductionist. Surely there can be trade-offs with accuracy that we must be cognizant of (more on this later), but the benefit of explainability may pay off in other areas, \textit{e.g.} the discovery of biased outcomes for certain subgroups, or the disentangling of causal effects from spurious ones. As put forward by various governing bodies, explainability is one aspect within a set of requirements that are partially interdependent and should be balanced against each other. The pursuance of explainability will have different priority depending on the application. An AI tool to speed up reconstruction of images in the back-end might not need explainability, while a decision support tool to predict risk of complications might need it if the risks are to be discussed and assessed with a patient. A blanket statement declaring explainability 'secondary' does not help in determining which case is which.

A final point should be made about validation of AI tools and evidence-based medicine (EBM). EBM can rely on a stable set of protocols that are established and widely used for the certification of new interventions. It should be noted however that, despite its indisputable importance, EBM is not a laser-precise discipline: e.g. there is a vast body of literature highlighting the fact that clinical trials are often not reflecting clinical practice, or have non-representative populations, or are incomparable with each other, or become quickly outdated.\cite{solomon2011}  Furthermore, as argued by several researchers,\cite{cartwright2012, Greenhalgh.2014, solomon2011, worrall2007} RCTs do not always control for the biases they are intended to control, they do not produce reliably generalizable knowledge, or they can create unnecessary constraints on clinical testing. In addition, obtaining statistically significant evidence is often extremely time-consuming and expensive. Finally, the success rate of clinical trials for medications is famously low.\cite{wong2019} Against this backdrop, we should be very careful to promote the initiation of many studies or trials testing the effectiveness of tools whose inner workings are entirely unknown, i.e. black boxes. Explainability is a precious tool not just for the end user, but also for the developer: the vast majority of models are discarded before reaching the test phase because they are found to be clinically implausible thanks to explainability techniques. In this sense, while we do not dispute the high position of RCTs in the hierarchy of evidence,\cite{djulbegovic2017} explainability is a complementary tool that can even help expedite or improve the effectiveness of RCTs and EBM as a whole.

\subsection*{In the medical domain, we cannot trade performance for explainability.}

\textbf{Criticism. }The trade-off between explainability and performance may induce people to use simpler and less accurate models, rather than more effective ones. This is a problem, as we may be exchanging a further improvement in medical outcomes with a debatable increase in transparency. There are several medical interventions (\textit{e.g.} some drugs) for which a mechanistic explanation is not known, but they are nevertheless proven to be effective and widely used. If we are accepting of black-box interventions in healthcare, we can allow effective AI models to be opaque as well.\cite{mccoy2021}

\noindent
\textbf{Discussion.} It is indeed the case that some explainability techniques (or simpler, more interpretable models) can result in lower performance. Whether this is desirable is a decision that should be taken case-by-case. Moreover, the analogy with medications, and the fact that we are 'fine' with them being black-boxes, seems misguided. There are already different tools in medical practice for which different levels of understanding are required. For example, as opposed to drugs, radiography machines can only be operated by specialists. Hence for complex tools we do require users to have some knowledge of the inner workings of the device. The reason underlying this choice is that there are failure modes we want to be able to control.
Moreover, the fact that now we do not require explanations for some interventions (as long as they are proven to work) does not mean that this is the desirable way forward.  On the contrary, in some cases this could be construed as a problem. Arguably, we should keep pushing research to understand why certain treatments are effective. Along this line of reasoning, if we can build a tool with an intrinsic ability to explain itself -- at a bearable cost in terms of trade-offs – we should definitely pursue it.

Finally, a 'simple' algorithm is not necessarily less accurate. When coupled with feature engineering or feature reduction, even those 'simple' algorithms may achieve good performances. This criticism thus overlooks an important line of work on inherently interpretable methods that are aiming at matching the performances of more complex models.\cite{rudin_stop2019, letham2015} Figure \ref{fig:compas_example} showcases a simple model which is easy to understand and achieves high accuracy.

\begin{figure}[h]
    \centering
    \includegraphics[width=0.4\textwidth]{figures/COMPAS_example.png}
    \caption{Example of an accurate white-box algorithm for the COMPAS recidivism problem (Figure 4 in \cite{Hu.2019}).}
    \label{fig:compas_example}
\end{figure}

\subsection*{XAI is not suitable for all users.}

\textbf{Criticism.} XAI techniques can be used to support the work of different professionals interacting with an AI tool. Beside regular users, other roles include the developers, the medical experts, the regulators, and so on, each with different requirements and needs for explainability. Arguably, XAI techniques could be useful for trained professionals such as developers or auditors, but could be dangerous when shown to untrained users.

\noindent
\textbf{Discussion.} This point has some overlap with points 2 and 4, but it is nonetheless important to stress that XAI techniques will almost never offer a one-size-fits-all solution. Different kinds of interaction with the machine will be required depending on the purpose of the participating human; it is highly likely that different techniques will answer different needs.\cite{Arrieta.2020} Current research extends to interactive machine learning to provide user-targeted explanations.\cite{sokol2020} As to the potential misuse or misunderstanding of XAI by simple users, the solution lies in a combination of robust methodology, proper training, and expectation management.

\section{Should we downsize our efforts in XAI for healthcare?}

We have reviewed some of the core criticisms offered in the literature against the widespread use of XAI techniques in medical contexts. Some of the qualms, as well as some of the counterpoints, are in fact not limited to healthcare applications and can be recast at a more general level. We should however be mindful of how the debate is influenced by the higher stakes present in safety-critical AI applications in the health realm.
It is worth remarking that demoting the usefulness of explainable medical AI means stifling the efforts to get human oversight over a new technology that is still in its infancy but can be deployed at scale very rapidly, potentially affecting a large swath of the population. XAI is not a silver bullet that will solve all of AI's problems, and in particular it is not a substitute for rigorous evaluation of model performance. Nonetheless, XAI tools can be of value in several circumstances.
Human oversight over machines is still an important tenet, and the introduction of more black-boxes is not going to stem the loss of human control in a quickly digitizing world. It has been argued that augmentation of human capacities is preferable to automation,\cite{acemoglu2017} since the latter leads to deskilling as well as detachment and unemployment spikes. Augmentation presupposes the possibility of a human-machine synergy; in other words, a way for humans to relate and engage with AI.

Different public bodies, including the European Union, are discussing regulations for AI products. While the field of XAI may not yet be at a level of maturity where XAI techniques are embedded into guidelines in the form of hard requirements, this does not entail that XAI techniques should be sidelined or disregarded. Said techniques can be employed to exercise an additional layer of control over AI products; as with all tools, practitioners should be aware of the potential failure modes. As for guidelines, it could be meaningful to allow the use of XAI techniques -- without enforcing a specific choice -- and require an argument of why a certain technique was chosen.

We noted in the introduction the inherent tension in the request for explainability: while we want machines to perform at super-human level, we also want them to be understandable by humans. This is however just another case of competing objectives, not a good reason to toss one of the objectives out the window (for another example of competing objectives: saving healthcare costs and keeping people alive). The challenge is to find a trade-off that is satisfactory, a challenge that is extremely case-dependent.
The question should be: while explainability may not be a requirement for healthcare interventions tout court, is AI a technology on which we think "extra" explainability is needed? This technology has peculiar aspects that set it apart, including the potential to be prejudiced against subgroups based on gender, ethnic background, and socioeconomic status,\cite{fitzgerald2017} the fact that it may lose reliability when the input data changes,\cite{ulmer_ood2020}  or that it may rely on spurious correlations found in the data.\cite{calude2017} For these reasons, coupled with the potential of rapid and impactful adoption, we argue that a higher level of oversight is preferable compared to regular interventions.

\section{Conclusion}\label{sec:conclusion}

Despite its infancy, the research on XAI is growing very rapidly. Thanks to this growth, new techniques are tested extensively by many researchers in the field, and the duration to become a time-tested approach is much shorter than it has been in the past. Given the arguments we discussed, and the aforementioned possibility to rapidly hone useful techniques, we conclude that we should step up our efforts in XAI research for healthcare, as this research program is promising and addresses a core issue. Ultimately, we still want clinicians to be the mediators that can bridge the coldness of algorithms and scientific protocols with the human experience of the patient. If clinical users of medical AI cannot interact with machines, if their role becomes that of mere executors of interventions that are proven to be effective, the sensitive task of providing care could well become de-humanizing.

redpajama_set_name: RedPajamaArXiv


We know it sounds strange, but we are CERTAIN that the below video will completely fill your soul with happiness.
Magical YouTuber Mimine uses mini ingredients and a mini kitchen to create mini masterpieces.
She's created a tiny ramen before as well as a tiny fried rice.
This time, Mimine has made our eyes water with the cutest, smallest doughnuts ever.
It's basically a childhood barbie house turned into a full-blown bakery and we are OBSESSED.
They are just SO cute! Who would've thought eating 15 doughnuts would ever be acceptable!


Minimum 10 people - A two scrambled or fried egg sandwich with ham, bacon, sausage or hash and cheese on english muffin, rye, wheat, white or a hard roll, served with home fries.
Minimum 10 people - Scrambled eggs, home fries, bacon and sausage. Served with 1 piece of French toast. Includes butter, syrup and all supplies.
Minimum 10 people - Two slices of cinnamon french toast per person with bacon and sausage. Served with butter and syrup.
Minimum 10people - Same as Scrambled Egg Breakfast above but with an egg casserole instead of scrambled eggs.
Original - Eggs scrambled with onions, peppers and cheddar cheese.
Western - Eggs scrambled with ham, green pepper, onions and cheese.
Spanish - Eggs scrambled with peppers, cheddar cheese and topped with salsa.
Minimum 10 people - Two blueberry pancakes per person with bacon and sausage. Served with butter, syrup.
Minimum 10 people - Two blueberry or old-fashioned pancakes & two scrambled eggs per person with bacon, sausage. Served with butter and syrup.
Freshly ground and brewed right before delivery, with creamer, sweeteners and all supplies. Regular, decaf available.
Filled with 10 cups of fresh brewed coffee and creamer, sweeteners and all supplies.
Filled with 15 cups of fresh brewed coffee and creamer, sweeteners and all supplies.
Orange, apple-cranberry, white or ruby red grapefruit.
Disposable wire rack with water pan and sterno $10.00 ea.


you can all join in.
I have just found this blog from Lily Bette's cards and crafts. Unfortunately google wont let me join more blogs so I have clicked to follow by email. I have to make some Teddy bear cards so will join in this challenge soon.


MC Assembly Partners with KT Technical Sales, Inc.
Home/MC News/MC Assembly Partners with KT Technical Sales, Inc.
MC Assembly, a leading electronic manufacturing service provider, has partnered with KT Technical Sales Inc. as a vertical market representative to expand the company's sales presence in the Southwest region of the United States.
Under a recently finalized contract, KT Technical Sales will now offer MC Assembly services to Original Equipment Manufacturing (OEM) customers. This will in effect open the door as in introduction to MC Assembly capabilities which can support high volume electronic manufacturing service operations.
Prunier calls this partnership a "good fit" and said working with them should shorten the sales cycle times significantly. At the same time working with MC Assembly expands KT Technical Sales Inc.'s service offerings to include high volume services like system integration services and box build; opening the door for both companies to grow new business.
KT Technical Sales Inc. first worked with MC Assembly in 2009 in a supplier role. At the time, MC Assembly used some of their cables in box builds for medical screening machines. Since then the relationship between the two companies has grown.
"We're looking forward to expanding our relationship with MC Assembly and helping them build a larger presence in the Southwest region," Thompson said.
KT Technical Sales Inc.'s focus is to be "An Engineering Design & Manufacturing Solutions Provider". The company has a very diversified customer base that is very strong with defense and military, but also includes medical, communication and industrial customers.
About KT Technical Sales KT Technical Sales, Inc. (www.kttechsales.com) adds value to your efforts in a seamless manner. We employ a "WIT" (whatever it takes) approach, ensuring we meet, and ultimately exceed your expectations. We maintain a worldwide network of business partners that allow us to develop and foster relationships with both customers and suppliers. We handle the pressures of coordinating the design and movement of your products from NPI to Production worldwide. Our mission is to be an extension of your company's procurement and engineering teams, providing you with quality low cost solutions. We offer a variety of products and services from design to production, allowing our customers to focus on what they do best. At KT Technical Sales, Inc. we're ready and equipped to support your unique challenges.


For the past two decades, Children of Persia has grown into a large network of individuals sharing the conviction that the act of kindness and giving in creating opportunities for children, no matter how small.
creates a small ripple that can create a sea of change.
Our goal is to make the hope of a brighter future, a reality for many children whose youth is short-lived under the hardship of family, social, and economic responsibilities, and their struggle for their daily living.
COP is, a 501 (c) 3 nonprofit organization, with OFAC Licenses for funding projects in Iran.
With A Heartfelt Thank You!
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A planning application (ref 17/01091/FUL )has been made to build a gymnastics centre and carpark for 100 cars on an area that is currently wildlife rich woodland, which is part of Great Hollands Recreation Ground.
Protect and establish public open space for recreation, play, sport, health, biodiversity.
Make sure public open space supports well-being; encourages participation; and facilitates social inclusion.
The proposed development will require the destruction of 85 trees over a large wooded area, resulting in the loss of mature wildlife habitat.
The area is well established and rich in wildlife, all of which are part of the Bracknell Forest's rich biodiversity and far too valuable to current and future generations of our town to destroy.
During the Preliminary Ecological Assessment evidence of potential Badger activity was found in the form of a large mammal burrow dug into the side of a bank and numerous runs visible through the grassland and scrub. The site is also close to other habitats that could be used by badgers for foraging.
The habitats within the site are highly suitable for foraging bats. A number of the mature trees also have potential features that may be utilised by bats for roosting. A detailed assessment of trees with features observed from the ground should be conducted to determine if dusk/dawn surveys are required. Bat activity surveys are also recommended to establish if any flight path or foraging areas will be impacted.
The West European Hedgehog and Common Toad are UK BAP priority species. As these species are in the wider countryside and there is the possibility that it could occur on site.
felling, removal or lopping of: woodland, field hedgerows and/or lines of trees with connectivity to woodland, mature trees with obvious holes, cracks or cavities or ivy covered.
Floodlighting of: any building within 50m of woodland.
The parking for the proposed centre is at the back of the building, turning woodland into a car park for 100 cars. This means that the car park is very close to a number of properties within Staplehurst as it would back onto their gardens. Noise from cars / vehicles arriving and leaving the site, as well as light from the centre itself as the car park and building will need to be illuminated as the setting is semi-rural. All of which could have an adverse effect on the existing wildlife.
Most visitors will use motor vehicles to travel to and from. Only residents within the adjacent areas of Wooden Hill, some parts of Great Hollands and Home Farm can easily reach the site on foot, therefore a significant increase in traffic is likely.
South Road, Wooden Hill, Staplehurst and surrounding roads become congested when events such as Football / Boot camps / Park run etc. are taking place on the recreation ground– the gymnastics club will increase traffic significantly, and for a more prolonged period.
In close proximity the community already has a Gymnastics facility at Pinewood which is approx. ½ a mile away from the site.
When considered in conjunction with the loss of woodland for the 900+ housing development at the nearby Road Research Lab site in Crowthorne, this is unacceptable.
You can read the full application on the Bracknell Forest Planning Site.


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Attention Anyone Who Loves Lake Tahoe: Devastating Fires Could Damage Its Iconic Blue Waters
By Rachel Becker | CalMatters
Published Sep 2, 2021 10:30 AM
In this July 2014 image, the famously clear water of Emerald Bay lies under blue skies at Lake Tahoe. The massive Caldor Fire has raised questions about damage to the lake.
(Sean Gallup
Smoke and ash from wildfires near Lake Tahoe — one of the deepest lakes in the world — is already clouding the lake's famously clear water, researchers say.
While the long-term effects are unclear, ash and soot are now coating the surface of the High Sierra lake and veiling the sun, which can disrupt the lake's ecosystem and its clarity. More debris and sediment are likely to wash into the lake from runoff and rain this fall and winter.
"It's not going to turn the lake green or anything like that, in my opinion. But certainly the clarity of the lake, how deep you can see in the lake, could be affected for several years," said Randy Dahlgren, emeritus professor of soils and biogeochemistry at the University of California, Davis. "It all depends on Mother Nature."
Researchers are now trying to figure out what the residue and flames from the Caldor Fire, which crossed a granite ridge and spread into the Lake Tahoe basin on Monday, could mean for the iconic cobalt-blue lake.
"We've never had a fire of this extent before…This one is off the charts," said Geoffrey Schladow, director of the University of California, Davis' Tahoe Environmental Research Center.
Lake Tahoe is blanketed by smoke from the rapidly spreading Caldor Fire on Aug. 27, 2021
(Noah Berger
AP)
The research center's tests of the lake already show that its clarity declined in recent days, although Schladow said it may be temporary. The changes could be caused by a combination of factors: smoke preventing sunlight from penetrating the lake's depths, ash muddying its water or more algae growing near its surface.
"Normally around this time of year, we would expect to see down maybe 65 feet. Right now we're seeing down maybe 50 feet," Schladow said.
Over the past half-century, the alpine lake has lost 40% of its clarity, largely due to runoff containing particles and plant-feeding nitrogen and phosphorus. In recent years, its clarity — a sign of its improving health — has begun to stabilize as state and local officials in California and Nevada took steps to protect the lake.
The degree of damage to Lake Tahoe will depend on the degree of devastation to the forest and buildings around it. It also will depend on the months ahead: Severe rain following a fire forces more sediment and nutrients into the runoff and ultimately the lake.
For fine particles, nutrients, and toxic chemicals, "most deposition occurs on the land and continues to be washed into the lake many months after the fires have been extinguished when winter returns," a 2020 report by UC Davis' Tahoe Environmental Research Center.
Nestled into the elbow between California and Nevada, Lake Tahoe is a hub of outdoor recreation in both summer and winter, drawing in more than $3 billion in tourism dollars yearly, according to the Tahoe Prosperity Center.
During the fires, Schladow's team is taking measurements every few days, venturing out on the water to collect water samples and measure UV radiation, water clarity, nutrients and algae.
"The eeriest thing is that here we were at the end of August, on Lake Tahoe. And you could not hear another boat on the lake. That's sort of unheard of in the middle of the day at Lake Tahoe in August," Schladow said.
Lake Tahoe and its surrounding lands and waters are home to abundant wildlife, including Lahontan cutthroat trout, which are a threatened species, as well as mountain whitefish, black bears, beavers, marmots, deer, raptors, rare flowering plants and a wingless stonefly that lives on the bottom of the deep lake and provides food for fish.
"I am not as worried about the clarity but the sensitive, endemic (only found in one location) species that are hanging on in the lake…The clarity may be able to rebound but will we lose these species that are already having a tough time?" said Sudeep Chandra, professor of biology at the University of Nevada, Reno.
As the Caldor Fire fills the air with smoke, researcher Brandon Berry replaces the particle sampler on one of UC Davis' research buoys on Lake Tahoe on Aug. 25, 2021.
( Brant Allen
UC Davis courtesy of S Geoffrey Schladow)
Alexander Forrest, an associate professor at UC Davis, has launched an autonomous underwater vehicle that seesaws up and down through the water column, crossing the lake once every 15 hours to measure particle sizes and concentrations. His team expects to collect the robot and retrieve its data in a few weeks.
"There's this sort of layer on the surface and then below, you stir it up with your hand, and you can see these particles drifting around in the water," Forrest said. "What we're trying to sort of elucidate or try to understand is, what are the implications?"
Soot and ash, which can wash into the lake if it falls within its expansive watershed, can have different effects on Lake Tahoe's algae at different depths. Particles can darken the water and cool the lake — making it less hospitable to some algae. But it also can introduce nutrients, such as nitrogen, that algae and other organisms feed on, potentially fueling algal blooms at the surface.
"The whole food web is being turned on its head, I expect," Schladow said. "The measurements we're taking (are) to see how true that is and how things are changing."
That's what happened at Castle Lake in Siskiyou County after smoke choked the region for 55 days in the summer of 2018, a team of researchers from the University of Nevada, Reno recently reported. Light dimmed in the waters, and temperatures dropped. Algae flourished in shallow waters but dwindled to near zero in the depths. Trout disappeared from the edges of the lake.
"As a result, you completely restructure the internal architecture of a lake," said Chandra, one of the study's authors. "The question for Lake Tahoe becomes, does the internal architecture restructure for a short period of time and rebound? Or does it restructure more permanently?"
The question for Lake Tahoe becomes, does the internal architecture restructure for a short period of time and rebound? Or does it restructure more permanently?
— Sudeep Chandra, University of Nevada, Reno
In recent weeks, the region has been choked with smoke from fires burning throughout Northern California, turning the air hazardous. On Monday, mandatory evacuations from the Caldor Fire spread to South Lake Tahoe, home to resorts and camps that usually are full in late summer.
The Caldor Fire has burned about 300 square miles of El Dorado County over the past 15 days. The fire spilled into the Tahoe Basin on Monday and is lapping at the edges of the Upper Truckee River Watershed, the biggest contributor to the waters of Lake Tahoe.
"Conceivably, much of the upper Truckee River Watershed could burn," said Dahlgren. "That's the worst-case scenario."
Fires can also contaminate water used for drinking supplies.
Scientists fear that the extreme wildfire season could be signs of the damage that climate change will inflict on waterways like Lake Tahoe in years to come.
"That may be a warning or a harbinger of what the future may have in store for Tahoe and for other lakes," Schladow said.
"If we're going to have these big fires every summer, and we start getting these temporary effects every year, at what point does that temporary become the norm?"
Climate Emergency Questions
Fires. Mudslides. Heat waves. What questions do you need answered as you prepare for the effects of the climate emergency?
Caldor Fire Forces All Residents Of South Lake Tahoe To Evacuate
Here Are The National Forests In California That Are Closed (And When They Will Reopen)
LA Explained: Wildfires Are Getting Worse. What You Need to Know


Holistic Therapy Services Ltd - IPHM will endeavour to reply to your enquiry as soon as possible but at busy times your patience is required. All our correspondence is completed by email and we will contact you within 24hrs.
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IMPORTANT NOTICE FOR CURRENT MEMBERS - Ensure you include your IPHM number when contacting us in order for us to deal with your request more quicky and effeciently. Your iphm number can be found on your certificate.


Ever tried capturing the eeriness of a foggy landscape? You'll know it's trickier than it looks to recreate what your eyes can see in all its atmospheric glory. Take a look at these fog photography tips to help you capture shots that are more exciting than flat.
You've got to seek out the fog, you can't just wait for it to come to you. Planning is one of the biggest challenges and takes a great deal of perseverance. Don't be put out if it takes longer to find the mist than you'd like – after all, you can't do anything about the weather.
Make sure to check weather forecasts regularly: especially for early mornings and night fog photography. Fog forms when cold air passes over warmer water or moist land and is most likely to be found around sunrise or sunset. Just be prepared to set early morning alarms to make the most of it!
Once you've found the mist, make sure your camera is set up properly for the types of photos you want to take.
Getting the right exposure levels is one of the most difficult aspects of fog photography. If you've taken photos of sandy beaches or snow, you'll know just how tricky this can be to get right. If your exposure is too low, this can make the mist look too dark, causing the photograph to be too grey. So try overexposing the shot to help make the fog lighter. As the sun starts to rise, keep an eye on the exposure, and adjust it regularly to accommodate the increasing light.
If you want to capture the fog, it's best to work with manual focus. Auto-focus works by detecting the differences in contrast, but there is little to no contrast in foggy conditions. Combining this with low light levels will make it even more difficult for your camera to focus on anything. Instead, choose your focal point and adjust your lens by hand. For example, you could focus on a tree that's surrounded by the fog, or perhaps use a foreground focal point such as a fence, person or other subject close to the camera. Why not experiment with different levels of focus, and preserve all the differences in a premium photo book?
It's vital to keep your camera steady when you're taking pictures in fog. It's texture and dispersal can vary massively, which is a great opportunity to take photographs with a wide variety of effects and moods. The best way to capture these differences is with shutter speed. A quicker shutter speed prevents the texture from becoming too smooth, perfect for taking pictures when the haze is almost cloud like. Use a longer shutter speed if you want the haze to look like one long, thick continuous patch.
Using a wide angled lens is one of the best ways to capture photos of the fog. Wide, misty landscapes look stunning, and if you're lucky, you'll be able to capture the start and end of a hazy patch.
Top tip: Out and about in the fog? You'll find much higher levels of condensation. Watch out for small droplets of water getting into your lens. Make sure to bring a lens cloth with you, so the droplets don't end up ruining your photos.
-Silhouettes: Fog does a great job of emphasising shapes in photos. Textures and contrasts are reduced, leaving your subject as a single dark colour.
-Black and white fog photography: We recommend shooting in colour, and turning the photos into black and white later. It's a perfect way to add atmosphere.
-Light rays: If there's a light source, fog can make it much more concentrated. Try taking photos of light rays through a group of trees.
For a lot of photographers, brushing up photos using editing software is par for the course. Maximising the contrast is common, but avoid it for photos of misty conditions! Adjusting the contrast means your photos will lose their soft lighting.
Getting the perfect shot takes a great deal of patience and practice. With the tips above, you'll know how to photograph in fog like a pro. Looking for a way to preserve your moody, atmospheric photos? Wall art is a perfect way to display your best images.


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Daniel Dae Kim revisits 2001 anthrax attacks in 'The Hot Zone: Anthrax'
November 30, 2021 by News Desk
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ABC News published this video item, entitled "Daniel Dae Kim revisits 2001 anthrax attacks in 'The Hot Zone: Anthrax'" – below is their description.
Daniel Dae Kim discusses his role in National Geographic limited series "The Hot Zone: Anthrax," about the anthrax attacks after 9/11, and talks about increasing minority representation in Hollywood.
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River Valley Athletic Club is dedicated to providing the best swim training in the St. Croix Valley, through superior instruction by a professional staff of knowledgeable instructors in a fun atmosphere where learning is enjoyable and meets the needs of every student.
River Valley Athletic Club's robust swim program is open to both RVAC members and guests. Our program provides American Red Cross swim lessons to swimmers of all ages and abilities. Whether in a group or individual setting, we offer experienced Red Cross certified instructors to help swimmers reach their goals while learning important skills to keep safe in and around the water.
By registering for RVAC swim lessons, participants, or their parents/guardians agree to RVAC Swim Lesson Policies.
This program builds swimmer readiness by emphasizing having fun in the water with a parent.
Children must be able to submerge their head under water and be in the class without a parent in the water. They will learn kicking, front & back floating, beginning swimming skills, water safety and water fun.
Register Now: 19-PS4, Tuesday AND Thursday, May 7 - May 23, 2019 (6 classes), 6:00 pm - Almost Full!
Must be able to swim independently at least 25 feet. Will work on swimming skills on their front and back, underwater swimming, water safety and basic safety rules. Most advanced level for preschool swimmers.
Learn to enter and exit the water safely, open eyes underwater and retrieve objects, swim on front and back using alternate arm action, float on front and back, follow basic water safety rules, breath control, and use a life jacket.
Review skills from Level 1 and also learn to enter water by stepping or jumping from the side, swim on side, tread water, submerge head, perform front and back glide, and swim on front and back using combined strokes.
Students review skills from Level 2 and also learn to bob with head submerged, survival float, butterfly kick and body motion, breaststroke kick, scissors kick, tread water, rotary breathing, perform front and back crawl, reaching assist, and HELP and Huddle positions.
Students develop confidence in the skills learned in Level 3 and also learn open turns on front and back, swim on side using scissors kick, safe diving rules, perform a throwing assist, breaststroke, butterfly, elementary backstroke, swim underwater, tread water and care for choking victim.
Students learn further coordination and refinement of strokes from previous level. They also learn to tread water using two different kicks, survival swimming, perform rescue breathing, front flip turn, backstroke flip turn, sidestroke, pike surface dive and tuck surface dive.
Level 6 is designed to focus on preparing students to participate in more advanced courses including Water Safety Instructor and Lifeguard Training Courses. This level refines the strokes so students swim with ease, efficiency, power and smoothness over great distances.
Non-competitive class for kids interested in improving technique and building strength. Coach Tara Noer will focus on the four competitive strokes, butterfly, backstroke, breaststroke, and freestyle. Kids must have shown proficiency in Red Cross Level 4 or above.