Look no further than this attractively priced 3 bedroom row unit condominium in the Katimavik neighbourhood of Kanata in Ottawa. Katimavik is a neighbourhood in Kanata South and is located West of Ottawa. It is bordered by Eagleson Road to the East and Hazeldean Road to the South. The Carp River borders Katimavik in the West while the HWY 417 borders it to the North.
This 3 bedroom condo is located on McDermot Court, a quiet condo complex with no through traffic. McDermot Court is located in Kanata's South end and is in close proximity to many groceries, shopping malls, specialty shops and many other businesses. If you like the outdoors McDermot Court is in close proximity to the Old Quarry Trail off of Eagleson Road and across from the Hazeldean Mall at the corner of Hazeldean Road and Eagleson Road. Other businesses in the area include LCBO, Petvalue, Anytime Fitness, Shoppers Drug Mart, M&M Food Market and Laura's Your Independent Grocer.
This row house condominium was built in the late 1980s by Wedgewood. The condo complex has a combination of 2 and 3 bedrooms condos. 49 McDermot Court is listed for sale at $214,900 and boats 3 bedrooms and 1 and a half bathroom. The home is bright and offers an open living and dining room floor plan. The main level has laminate floors throughout the dining and living area while the kitchen has ceramic tiles. The living room is bright with a large patio door giving out to the private fenced in yard. A nice wood fireplace sits in the corner of the living room perfect for winter entertaining of family and friends.
The 2nd level of this Katimavik condo has 3 bathrooms including a master bedroom with a full wall of closet space. The full bathroom has an upgraded vanity and mirror as well as a bathtub with shower head. All bedrooms upstairs have laminate floors and good size closets.
The basement has a family room and a small powder room as well as a utility room that will provide you with additional storage as well as in-unit laundry.
Two parking spots will be included with the sale price. The exterior has little maintenance and the condo management company handles clearing the snow and cutting the grass in the McDermot Court condo complex. Some of the recent upgrades include new windows which were installed in 2015.
Why rent when you can own? Based on a $214,900 purchase price, a 3.99% 5-Year Fixed Rate Mortgage and a 5% down payment you would have a monthly mortgage payment of $1,073. Taxes for 2018 we're $187 a month of $2,252 a year. Condo fees for the condo unit and 2 parking spots are $320 a month and include water, sewer, snow removal, building an insurance and the management fee. Your total monthly payments on this home would be $1,580 + Hydro and Gas.
If you are interested in learning more about this beautiful 3 bedroom, 1.5 bathroom condo at49 McDermot Court in Kanata, call Roch St-Georges at 613-889-7732.


When configuring saving settings for the JPEG format in the Saving Images section of the Configure digiKam window, you can choose between medium and high chroma subsampling, or disable it altogether. But what is chroma subsampling and what does it do? As you may know, JPEG is a lossy format, which means that it trades quality for a smaller size. One of the methods for achieving a smaller size is to store color information (chroma) at a lower resolution than brightness (a.k.a intensity or luma) data. This is possible due to the fact that the human eye is less sensitive to color than brightness, so discarding color information doesn't produce a perceptible loss of quality.
Most graphical applications do that by using the 2×2 chroma subsampling method that breaks the image into 2×2 pixel blocks and only stores the average color information for each 2×2 pixel group. digiKam deploys two subsampling schemes. The 4:2:2 ratio is used for the Medium chroma subsampling that reduces color resolution by 1/3 with no perceptible difference. The High chroma subsampling uses the 4:1:1 ratio and reduces color resolution by half. This results in higher compression but tends to alter colors. The Chroma subsampling Wikipedia article provides more information about chroma subsampling and various schemes.
Unscientific tests show that the subsampling methods deployed by digiKam can achieve up to 20% size reduction (i.e., a ~19MB JPEG file can be compressed to ~15MB using the high chroma subsampling). For collections containing thousands of photos, this translates into significant disk space and bandwidth savings.
This article is a part of the digiKam Recipes book. Buy your copy to get the most out of digiKam.
The book Digikam Recipes looks very interesant. It is traduced to other lenguages? I want buy it, but only in spanish because my english is bad.
No, digiKam Recipes is available in English only.


Is your Whirlpool direct drive washer making loud/strange noises in spin cycle or during agitation? This post will show you how to isolate your Whirlpool transmission from your washer in order to troubleshoot what the cause of the noise is. This post will also show you how to replace the transmission in your direct drive washing machine. For your convenience, you can read the step by step instructions or watch the video at the bottom of this post. If you need to replace your Whirlpool transmission, you can either replace your transmission with a used transmission or purchase a new one. The most common direct drive transmission is part number 3360629, but you can insert your model number in the search box at the bottom of the page to be sure you have the correct transmission for your washing machine.
First, let's identify if you have a Whirlpool direct drive washer. If you look under your machine, and it looks almost exactly like the picture below, you have a direct drive washer. Most appliance repair guys would tell you to keep these machines as long as you can. They are good reliable machines, easy to fix, and parts for the most part are cheaper then most of the newer appliance coming out. If you determine that you do have a direct drive washing machine, continue to the next step.
I'd recommend moving your washing machine to the garage or outside for the sake of room and to not make a mess. If you choose to do the repair in your laundry room, you will need to unplug your washer from the wall outlet and turn off the water to your machine. You will also need to unhook your hoses from the back of the machine.
The first thing that you need to do is open up the lid and remove your detergent dispenser. You can pull it right up with your hands, no tools required. Next remove the dust cap. Now remove the 7/16 hex head bolt. Once the bolt is removed, you can remove the agitiator by pulling up on it from the bottom (watch your face).
After the agitator is removed you will see two small parts that located on the spline of the transmission remove those and set them aside (picture coming soon).
Now you can carefully place the washer on its back. Be very careful with the inlet valve. (The plastic part where your hoses screw in the back of your washer.) Once you washer is on it's back, remove the pump by popping off the two clips that hold the pump in place. Move the pump out of the way. Remove the wiring harness from the motor. Remove the plastic clip on the transmission that holds the wiring harness. Now remove the (3) 1/2″ bolts that hold the transmission into place.
Move pump out of the way.
Remove wiring harness from motor.
Remove wiring harness from transmission.
Remove the (3) 1/2″ bolts from transmission.
Once the 3 bolts are removed from the transmission, the transmission along with the motor can be pulled out. If you are going to replace your transmission, at this point, you can remove the motor from the transmission and then hook your motor up to the new transmission, but to test the transmission, we will put the washing machine back on it's feet. Open up the control panel and then pop the two golden clips that secure the cabinet to rear panel of the machine. Now we can run the wring harness back to the motor, carefully plug the washer into the wall outlet and then put the washer into spin cycle. (You can also run this test in the agitation cycle, but you would need to fill the washer up with water to do so.) . If your transmission is making funky noises, and you know the motor coupling is good, replace your transmission. If your transmission isn't making any funky noises, you know that your problem is down stream, for example your the shoes on your basket drive may be dragging.
Wiring harness plugged back into the motor.


Therefore, we are a Swiss company that is fully sourced in Switzerland and also produces entirely in Switzerland. We are located in Freienbach, canton Schwyz.
We only source organic food within a radius of just a few miles around. We find everything we need to feed our insects, such as dry foods, e.g. ground seeds, and fresh food from producers, e.g. organic vegetables that are not nice enough for conventional sale.
Our experience is based on more than 25 years of know-how with insect breeding. In addition, Entomos is the first company in Switzerland to obtain permission to grow and sell edible insects in Switzerland. This know-how coupled with our pioneering spirit means that our efforts to contribute to a strong, innovative, competitive, state-of-the-art, and more sustainable Switzerland are every day a huge motivation and responsibility. We are motivated to open the door to a new nutritional era of the edible insects in Switzerland, which is a smart alternative to meet the huge food challenges ahead of us.
The production costs are a huge challenge that we have to manage.
Our production costs are in Swiss francs, which obliges us to guarantee the best quality and consistency with our edible insect farms for an indisputably competitive price. Currently we are not making any profits because we have to keep our products accessible in a highly competitive food market. However, we bet that we become more and more efficient in livestock farming every day, that we can achieve economies of scale (if we are able to convince more consumers, the growth of our volumes will allow us to better allocate our fixed costs, so final prices for consumers will go down). And as soon as we reach the breack-even, we will re-invest in the production tool to increase automation, thus further reducing production costs. Yes, we have work in front of us!
Are all the Entomos edible insects yet really 100% organic and Swiss made?
We still need a few weeks to deliver ONLY organically produced Swiss insects. The products that are not yet Swiss made by Entomos are coming from the EU and are labeled accordingly. The foreign companies has been audited and certified according the Swiss legislation. If you want to be notified as soon as all our Acheta and Locusta are organically made in Switzerland by Entomos and Insekterei, please subscribe to our newsletter (at the button of the page).


On October 24, 2017, a reader of our website is scheduled to fly KLM Royal Dutch Airlines from Durham Teesside Valley airport in England to Amsterdam, where he will change planes to Seattle, while his final destination is Kalispell in Montana, USA.
But first he calls KLM to ask whether he can use an EMD voucher issued by Delta Air Lines to pay for his excess baggage. The agent tells him all he needs to do is give the reference number at check-in and it would be fine.
At check-in, however, the attendant asks for a debit card to charge him an excess baggage fee. Instead of the debit card he hands her the voucher, which states that it would cover payment for excess baggage. The clerk says: "I have never seen one of these" and turns to her colleague, who has never seen it either.
Insofar we know, both the agent on the phone and the check-in attendant are wrong. Although EMD vouchers issued by Delta cannot be directly used at KLM, they can be used on Delta's website, for example for a payment to KLM. Obviously, KLM staff's training is insufficient in that it does not include how to use EMD vouchers.
The attendant says she cannot accept the voucher and urges him to pay by debit card. When our reader refuses to give his card based on the information he received by phone, she says that his baggage will be removed from the plane to a secure room. And she adds: "If after 12 hours it is not collected we will dispose of it." Remember that the customer will not even arrive home within 12 hours!
When he arrives in the USA he calls KLM. The agent apologizes for the clerk's error and tells him his luggage would be recovered and returned to him. This never happened. Although he was offered some electronic vouchers they failed to compensate him for the disappearance of his baggage.
Interestingly, a Delta agent with whom he speaks, tries to shift the blame by suggesting to instigate a claim with Alaska Airlines as they were responsible for the final leg of his journey to Montana.
We asked KLM to comment on this experience and to tell us their view of this affair. Although KLM does not deny the story, they refuse any comment. This does not seem totally incomprehensible, for even the best spin doctor would have a hard time to justify KLM's "disposal" of a customer's luggage.
Thus, we do not know what KLM means by disposing of a passenger's baggage. It may mean anything: auctioning off a customer's belongings, giving them to KLM employees, destroying them, or whatever. KLM refuses to specify it. We do not know either how often this occurs. But we do know that if our reader had known in advance about KLM's disposal policy, he would have booked another carrier.


Severe cash constraints faced by the Greek Government due to a pretty demanding schedule of interest and amortization payments in the remainder of 2015 have lately engineered a new explosion of sovereign bond spreads and rekindled fears of a GRexit down the road. Such fears have been exacerbated further in late April 2015 as the progress in implementing the February 20th 2015 Eurogroup agreement has proven to be rather slow and the cash-strapped Greek Government is struggling to meet sizeable debt service obligations. As a result, media reports had been speculating on a number of disastrous scenarios, ranging from the imposition of capital controls or the payment of civil servants and various state suppliers with promissory notes to a sovereign default, either within or outside the Economic and Monetary Union. This paper refrains from analyzing the legal and technical complications involved in the materialization of any of the aforementioned scenarios. Instead, it leans on purely economic and political economy considerations to argue that calls for exit are ill advised, potentially involving immense risks not only for Greece, but also for the EMU project as a whole. We take a close look at Greece's past history of drachma devaluations and their outcome, the current high sovereign indebtedness, and the country's persisting competitiveness gap vis-a-vis its main trading partners as well as the effects of financial contagion during the ongoing European Sovereign Debt Crisis. We explain why a GRexit would be a hugely suboptimal (and, in fact, a highly dangerous) strategy to address these problems.
via GRexit and Why It Will Not Happen: Catastrophic for Greece and Destabilizing for the Euro by Platon Monokroussos, Theodoros G. Stamatiou, Stylianos Gogos :: SSRN.
This entry was posted in Blogpost and tagged breakup, euro, greece, grexit on June 27, 2015 by brianmlucey.
The future of the euro — what happens if a Member State leaves?
The continued viability of the Eurozone as a single currency area is challenged from time to time. Some doubt the ability of Italy and other Member States to remain in the zone, given their difficulties in complying with the applicable budgetary rules. Even the French Prime Minister has commented that life was sometimes easier with the franc. This article considers some of the consequences which may ensue in the event that a Member State felt compelled to seek an exit from the Eurozone, and to reintroduce its own national currency. The Treaty does not legislate for this type of situation and any such attempt would clearly cause severe strains at a political level. But it would also have a major impact on the financial markets. In particular, there may be doubts about the currency in which continuing monetary obligations should be settled. This article seeks to analyze the legal issues which would arise in this context.
via The future of the euro — what happens if a Member State leaves?.
Can we unscramble the euro mess? Can we afford not to?
The greek mess shows how debased the whole Euro project has become. Its time to think the previously unthinkable and consider how to unscramble the eggs. Its time to think about how to back out from the Euro.
This entry was posted in Blogpost, Journalism and tagged Academia, breakup, economy, euro, greece, grexit on June 27, 2015 by brianmlucey.
What this means in detail is dissected in the newspapers and blogs. See in particular Constantin Gurdgiv, the Irish times, and the guardian for good and differing takes.
In practice the ECB will purchase lots of govt debt in the secondary markets, subject to the countries whose bonds are being purchased in effect being under ECB and Commission scrutiny. These bond purchases will be sterilised: there will be no net addition to euro area liquidity.
That's nice…but it's not what's needed.
First, who died (apparently modern capitalism…) and made ECB god? They're a central bank, and it's not at all clear that they should be overseeing fiscal actions. They should, like a good central bank, concentrate on monetary issues. Of course, absent reasonable or any actions from the governments, someone has to oversee the fisc, so perhaps we shouldn't be so critical.
Fourth, this is a beefed up version of the previous ECB acronym soup….each was unveiled as the solution, six months later they all had failed. Central banks and the currencies they supervise are in the end creatures of confidence. Serial overselling of a solution and serial failure does not inspire confidence. Combined with gross politicisation of monetary policy (if the Bundesbank wants to run Europe, they can't…) confidence and indeed faith in the ECB and the euro is not high.
This entry was posted in Blogpost and tagged bonds, debt, ECB, euro on September 7, 2012 by brianmlucey.
Ireland is not in Europe..
This is an expanded and updated version of an opinion piece published in the Irish Examiner on Saturday 4 August 2011.
National Culture is a funny fuzzy concept. Anyone who has ever dealt across different societies knows that cultures and how things are reflected in them differ widely. It can be thought of in this context not as the "high culture" so derided by Goering and the other Nazis who declared, "When I hear the word culture I reach for my gun", but as the complex interwebbing of how we operate as a society. If we have learned nothing else form this crisis it is that economics is, and must be treated as a social analysis. Culture in this context then includes religion, the way we formulate laws, the holistic way in which society works. A widely cited definition of culture is "is that complex whole which includes knowledge, belief, art, morals, law, custom, and any other capabilities and habits acquired by man as a member of society". We are familiar with this in Ireland. There is a culturally accepted norm that we do not, generally, evict people. There is a cultural norm that we do not generally either whistleblower or reward those that do so (we are indeed only now getting round to protecting same). We might as economic analysts decry the effects of these, but that they are cultural norms is something we cannot deny. And culture is very slow to change.
Its a funny feeling to be on the (kinda) same side as Mitt Romney, who caused uproar when in Israel he suggested that the culture of a country powerfully influences its economic prosperity, pointing favourably to Israel in contrast to the palestinians. This did not go down well in many quarters. On parsing this in the National Review he seems to equate culture with freedom and no doubt there is a linkage but the relative lack of freedom of the average palestinian is as much a reflection of their being a geopolitical chewtoy as some form of inate cultural preference. And the massive aid channeled to the Israelis by the USA over the decades has had some influence one might imagine on their economic development. But Romney is right in one sense, that culture, defined broadly, does matter for economic outcomes.
Culture, or proxies for same, has only recently been accepted as a valid part of modern economic analysis. While cognate disciplines, in particular international management and latterly international finance scholars have adopted many measures that proxy for aspects of culture mainstream economics has been to date slowly to take these on board. This is despite the fact that the most cited social scientist by some measures is a Dutch scholar, Hofstede, who's work on culture from the 1980s has been of profound importance to how international business scholarship has progressed. I had the experience dealing with Irish economists on presenting work ( see here and here )involving this scholar's concepts to be told by such things as "what we cant measure is not amenable to being modeled and as such is not useful" and "sure that's not economics so why would we read or cite it". If economics is to move forward it needs to escape from the blind alleys of needless formalism and the conflation of assumptions made for modeling purposes as being actual representations of reality. At the leading edge this is already the case but we know from studies of the philosophy of science that the body of knowledge rarely moves until a crisis. Perhaps this is the crisis.
Hofstede proxies culture as being on four (latterly five) dimensions. He suggested from his work with managers and later extended that societies could be ranked along how masculine they were, how great a proclivity they had for individualism, the extent to which people were and were accepting of being distant from power, whether the society is long or short term orientated, and the extent to which they would avoid uncertainty. Over the years other scholars, in particular those associated with the world values survey have amended and extended this basic insight but the work and measures of hofstede still dominate.A vast literature in international business has found that these cultural dimensions are associated with many economic phenomena. Thus we find that even when we account for other issues (different accounting standards, different legal systems, different ways of dealing with creditors) relative cultural distance is associated with lower foreign direct investment, lower degrees of mergers and acquisitions, and lower portfolio investment. That's perhaps not very surprising – all things being equal people feel more comfortable doing business with people "like themselves".What is perhaps more interesting is how the individual elements of culture impact.
In hofstede metrics Ireland is low on power distance (information flow and management tends to be informal and not hierarchical), highly individualistic and masculine (encouraging of risk taking and focused on winning for personal gain), and low on uncertainty avoidance (risk is rewarded and technicalities tend to be ignored) and on long-term orientation (we tend to not pass up short-term gain even if that may have long-term deleterious consequences). These traits are associated in recent research with for example earnings management in companies, and to more inefficient banks. Lower uncertainty and shorter term orientation is associated with low levels of cash buffers and thus an inability to ride out shocks. This analysis can be carried further : countries (such as Greece) with low individuality, low masculinity and high uncertainty avoidance are more prone to tax evasion ; higher uncertainty avoidance is associated with more severe impacts from crises. Low power distance and high individuality (as is the case in Ireland or Germany but not Greece) are associated with easier adoption of new work practices. The list can be extended greatly. The reality is that there is a growing body of literature, mostly outside economics but increasingly seeping into it that shows that we ignore national culture at our peril.
One of the things that the Eurozone crisis has exposed is the divide in Europe. There are many divides: Germanic neomercantilism versus communitarian economic policies, countries with zombie banks and those that have banks in the process of zombification, the UK versus everyone else. But one that in many senses may underlie this is the cultural divide. An increasing amount of culture studies use the framework of the world values survey. This is a complex instrument but it in essence can be reduced to two scales : how traditional/open is a society and how collective/individual is it. What is remarkable is when one plots these measures we find groupings of countries fall in place in accordance with our preconceptions : latin American countries fall mostly in one area, east Asian in another and so on.What is striking when we examine this is how non-homogenous the EU appears. The other issue is how much of an outlier Ireland appears when viewed through this cultural lens. Culturally we are much more similar to latin American countries than we are to northern European or even Mediterranean countries. The prevailing approach in Europe at present is to make us all good germans it seems. That wont happen. An approach that recognizes that there are deep cultural divisions, that these reflect in different economic outcomes and approaches, and that a union which wishes to thrive must acknowledge, accept and manage with these is one that will be much more likely to stand the test of time. Rather than becoming poor imitations of germans this approach would urge us to become better Irish, greek or Italians.
This entry was posted in Blogpost, Journalism and tagged banks, business, cris, crisis, culture, Distance, ECB, economics, economy, euro, Europe, Hypocrisy, Israel, Mitt Romney, Palestine, Palestinians on August 4, 2012 by brianmlucey.


text:
Home Life Global K-pop fans emerge as political force
Global K-pop fans emerge as political force
Some tech-savvy followers of K-pop music have emerged as increasingly active players in American politics, but in the birthplace of the genre, South Korean fans are wary that their favourite artists will be pulled into foreign partisan fights. Reuters

redpajama_set_name: RedPajamaCommonCrawl


The El Campeón course at the Mission Inn Resort is one of two outstanding championship courses onsite. Dating back to 1917, and being one of the oldest courses in the South, El Campeón boasts 85-feet of elevation changes which create spectacular rolling fairways and undulating greens. Quite unusual for Florida.
Consistently ranked a 'Top 25 US Golf Course' on GolfAdvisor, and the perfect foil to the inland links style Los Colinas (also at the Mission Inn Resort), El Capeón is one of the finest courses in Florida and would be an ideal addition to any Orlando golf trip.


text:
Vivo Z10 launched in India with 6-inch 18:9 display and 24 MP Selfie Camera
The Vivo Z10 is actually a re-branded Vivo V7+ that was launched in India last year in September
By Sagar Bakre	 On Jul 3, 2018 Last updated Jul 3, 2018
Earlier last month, Chinese smartphone brand Vivo launched the Vivo Y83 in India. And now today, Vivo has further expanded its portfolio of products in India with the launch of Vivo Z10. The Vivo Z10 isn't exactly a new smartphone though, instead, it's actually a re-branded Vivo V7+ that was launched in India last year in September.
As the Vivo Z10 is a re-branded V7+, it's needless to say that the design and underlying hardware of the Z10 is the same as that of the V7+. The Z10 sports a 6-inch FullView display that has aspect ratio of 18:9 and a resolution of 1440 x 720 pixels.
Under the hood, the Vivo Z10 comes with Qualcomm's Snapdragon 450 SoC which is paired with 4 GB RAM. The smartphone boots up to Android Nougat which is a shame, and there's no word on when Vivo will roll-out the Oreo update for the Z10.
For photography, the Vivo Z10 features a 16 MP camera at the back along with a 24 MP camera on the front which is accompanied by "Moonlight Selfie" which creates "lighting effects of a professional photography studio, giving you soft radiant complexion".
The Vivo Z10 sports a fingerprint scanner at the back, which apart from unlocking the phone, can also be used as a camera shutter button to take selfies. That said, the Z10 also comes with Face Access that uses face recognition to unlock the smartphone.
Lastly, the Vivo Z10 is offered in three different colors – Black, Gold and Blue – and, it ships with a 3225 mAh battery that fuels the entire package.
Having said that, it's worth mentioning that while all specifications of the Z10 are exactly the same as that of the V7+, there is actually one difference between the both – the Vivo V7+ comes with 64 GB of internal storage whereas the Vivo Z10 comes with 32 GB of internal storage.
Vivo Z10 Specifications
CPU: Snapdragon 450 octa-core processor
Operating System: Funtouch OS 3.2 based on Android 7.1.2 Nougat
Display: 6-inch FullView IPS display with 1440 x 720 pixel resolution, 2.5D curved glass and Gorilla Glass 3 protection
Rear Camera: 16 MP with PDAF, f/2.0 aperture and LED flash
Front Camera: 24 MP with f/2.0 aperture, Portrait Mode, Beauty Mode and Moonlight Selfie Flash
External Storage: Expandable via microSD card (dedicated slot)
SIM: Dual Nano SIM
Connectivity: 4G LTE, GPS, Bluetooth 4.2, Wi-Fi, FM Radio, 3.5 mm headphone jack, micro USB 2.0, USB OTG
Other: Fingerprint Scanner, Face Access
Colors: Black, Gold, Blue
Vivo Z10 Price in India and Availability
Availability: Available only in Karnataka, Telangana, Andhra Pradesh and Tamil Nadu through offline retail stores
India LaunchVivo IndiaVivo Z10
Press render of Samsung Galaxy Tab S4 leaked online with trimmed bezels
ASUS ZenFone 5Z India price leaked ahead of tomorrow's launch


Cary, NC – The lease-up of The Franklin at Crossroads, a $45 million luxury mid-rise apartment community developed by Dewitt Carolinas in late 2015, defies market odds. The 199 upscale apartment homes, 5668 square foot clubhouse and resort-style pool are located on 17 acres in Cary, NC.
Yorktown, VA - Courts of Yorkshire Downs apartment community has been a mainstay in Yorktown since 1987. Known for its many amenities, and its superior location, a recent investment will result in a modern transformation of this well-established community. The renovation will include updates to the one, two and three bedroom apartments as well as upgrades to the clubhouse and landscaping. The community will feature all new LED lighting and new energy efficient appliances.
Richmond, VA – Encompassing 45 designated historic districts, The Richmond Metro area has experienced a resurgence, being dubbed a "foodie town" and attracting new residents. This is due in part to the transformation that has taken place in the area's multifamily housing options, bringing life back to historic districts through the redevelopment of dated and underused buildings into boutique apartment communities that revitalize neighborhoods and create a sense of place for residents.


text:
\section{Introduction}
\label{sec:introduction}
Ultrasound imaging is a valuable tool to support non-invasive diagnosis and for use during interventions, as it is portable, affordable and does not subject the patient or staff to ionizing radiation.
It is not, however, without problems.
One of the major differences between CT and MRI on the one hand and ultrasound on the other hand is the presence of speckle, a grainy, noiselike image degradation that even affects tissue regions that are homogeneous in their microstructure such as liver or thyroid tissue (\textit{e}.\textit{g}.  see \autoref{fig:thyroid-comparison-a}).
This speckle can make interpretation of the images difficult---especially for users with limited experience working with ultrasound.
Consequently, most---if not all---ultrasound systems apply speckle reduction techniques after image formation, where the amount of "graininess" that is retained usually can be controlled by the user through a linear combination or blending of filtered and unfiltered data, depending on the clinical task and user preference.
\autoref{fig:thyroid-comparison} shows a thyroid image before and after filtering with our method compared to a state-of-the-art method and outlines a way to preserve a controlled amount of speckle.

\begin{figure}[t]
     \centering
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 45 0 11, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_original.png}
         \caption{Unfiltered}
         \label{fig:thyroid-comparison-a}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth, trim=0 45 0 11, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_OBNLM.png}
         \caption{Filtered: OBNLM}
         \label{fig:thyroid-comparison-zoom-b}
     \end{subfigure}

     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 21 0 0, clip]{images/exp1198_thyroid15_filtered.png}
         \caption{Filtered: Ours}
         \label{fig:thyroid-comparison-c}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 21 0 0, clip]{images/exp1198_thyroid15_blended.png}
         \caption{Blending}
         \label{fig:thyroid-comparison-d}
     \end{subfigure}
    \caption{Comparison of image appearance of a thyroid.
    Unfiltered (a), despeckling with a state-of-the-art method (OBNLM) (b), despeckling with our method (c) and a linear combination of (a) and (c) in (d).}
    \label{fig:thyroid-comparison}
\end{figure}

The speckle patterns that emerge in ultrasound images are directly influenced by the location of sub-resolution scatterers in the medium.
Being (spaced) sub-resolution, the reflections caused by the individual scatterers cannot be separated by the ultrasound imaging system.
The result is interference between the echoes of different amplitude and phase, leading to an apparent echogenicity that changes depending on the spatial configuration of the scatterers within each resolution cell and the orientation of transmitting and receiving elements.
In addition to speckle, there is also electronic noise that degrades ultrasound images, which is not the focus of this work.
Speckle, while sometimes referred to as noise, is a deterministic phenomenon.
In fact, if it were possible to place an ultrasound transducer at the exact same location when repeating a scan, the resulting speckle pattern would be exactly the same.

\section{Related Works and Contribution}
Conventional despeckling approaches such as speckle-reducing anisotropic diffusion (SRAD) \citep{SRAD_yu2002speckle} and the Optimized Bayesian non-local means filter (OBNLM) \citep{OBNLM_coupe2009nonlocal}---itself building upon non-local means (NLM) \citep{NLM_buades2005non}---have been studied extensively.
We refer the interested reader to the review from \cite{DENOISING_REVIEW_sagheer2020review}.
Especially the latter method OBNLM exhibits excellent despeckling performance, but requires application-specific tuning and significant processing time.

\cite{ESAOTE_cammarasana2021universal} alleviate both these concerns by using a regression CNN that is trained against images despeckled with a tuned, computationally expensive conventional method, thereby allowing the CNN to replicate its results while lowering the execution time.

In other areas different to ultrasound imaging, there exists a body of work on the task of image denoising without requiring perfectly matched pairs of corrupted and clean data.
In Noise2Noise \citep{Lehtinen2018}, a CNN learns to restore images from a number of different corruptions, by using a corrupted image as input and a second image of the same "scene" with a different realization of the corruption as training target.
The authors show that this can reach the performance of methods training against clean images.
Noise2Void \citep{NOISE2VOID_krull2019noise2void} takes this even further by requiring only unpaired corrupted images, it requires however, strong assumptions on the corruption, namely zero-mean and per-pixel independent noise.

Given the difficulties of acquiring clean images in many medical imaging modalities, these general approaches have been further specialized.
In optical coherence imaging, a few well-chosen modifications to the acquisition setup can allow for the acquisition of two independent speckle instances with otherwise unchanged scanning setup \citep{OPTICAL_yin2020speckle}, effectively allowing for direct use of Noise2Noise.
However, this method cannot be directly applied to ultrasound imaging, as these modifications are not possible in ultrasound scanning setups.

In this work, we build on the concept of exploiting two independent \emph{corrupted} observations with the same scanning setup and geometry, and transfer this to a generalizable method for ultrasound imaging.
In this way, we present a technique for learning a CNN-based ultrasound despeckle filter only from images containing speckle.
To do so, we apply the concept of Noise2Noise \citep{Lehtinen2018}, combined with a scheme for ultrasound simulation for data generation, and modifications to the training loss to enhance the image appearance in a method we call Speckle2Speckle.
By simulating different observations of the same general anatomical geometry but with different scatterer locations, we create images with uncorrelated speckle patterns.
In our qualitative comparison and quantitative evaluation with state-of-the-art approaches, we show that the method can remove speckle from real ultrasound data efficiently while not relying on manual data annotation or acquisitions, while the application of the filter is several orders of magnitude faster.

\section{Method}
In this section we first summarize the original formulation of Noise2Noise, followed by a description how we create simulated ultrasound images to be used with a denoising approach like this, and the modifications that were required to account for the peculiarities of ultrasound.
\autoref{fig:method-overview} shows a comparison between the concepts for conventional (clean-target) \citep{ESAOTE_cammarasana2021universal}, Noise2Noise and Speckle2Speckle filter learning.

\begin{figure}[htb]
    \setlength{\belowcaptionskip}{\baselineskip}
    \centering
    \begin{subfigure}[b]{\columnwidth}
        \centering
        \includegraphics[scale=0.55,page=1]{images/Method_graphic-crop.pdf}
        \caption{Clean target}
        \label{fig:method-overviews_a}
    \end{subfigure}
    \hfill
    \begin{subfigure}[b]{\columnwidth}
        \centering
        \includegraphics[scale=0.55,page=2]{images/Method_graphic-crop.pdf}
        \caption{Noise2Noise}
        \label{fig:method-overview_b}
    \end{subfigure}
    \par
    \begin{subfigure}[b]{\columnwidth}
        \centering
        \includegraphics[scale=0.55,page=3]{images/Method_graphic-crop.pdf}
        \caption{Ours}
        \label{fig:method-overview_c}
    \end{subfigure}
    \caption{Overview of training a model with corrupted data input to clean data output (a), Noise2Noise training with both corrupted data inputs and outputs of different realizations (b), and Speckle2Speckle training with corrupted realizations of ultrasound data simulated from the same tissue model (c).}
    \label{fig:method-overview}
\end{figure}

\subsection{Noise2Noise}
\label{subsec:noise2noise}
Noise2Noise \citep{Lehtinen2018} is a deep-learning based technique for general image restoration that does not require clean, uncorrupted data for training.
In a conventional restoration approach, one would use corrupted images as input and clean images of the same scene as the training target.
In their work however, Lehtinen \textit{et al}.  derive, from a statistical perspective, that a network trained with infinite samples can learn to estimate the expectation of the target samples.
This allows the input images as well as the target images to be corrupted with noise, like image noise observed with digital image sensors.
The authors show that the networks trained in this manner perform on-par with networks trained against clean data.

These relaxed requirements on the training targets allow for the application of this technique to situations where the acquisition of clean targets is either costly or impossible all-together.
In their work, Noise2Noise using a modified U-Net (following \cite{UNET_ronneberger2015u}, see \autoref{fig:architecture}) is applied to a number of different image corruption scenarios ranging from photographs subjected to Gaussian-, Poisson- and Bernoulli-noise or text-overlays, Monte-Carlo rendered images, and even MRI acquisitions within the randomly sampled k-space.
\begin{figure}[htb]
    \centering
    \includegraphics[width=0.9\textwidth]{images/Architecture_Layers.pdf}
    \caption{Network architecture of Noise2Noise and Speckle2Speckle. Numbers left of each box show the sizes of the feature maps, exemplified with a 128 by 128 pixel input. The number of channels per feature map is given by the numbers above the boxes. Different arrows represent different operations. The rightmost convolution (marked with $*$) has linear activation.}
    \label{fig:architecture}
\end{figure}

\subsection{Speckle2Speckle: Data}
\label{subsec:speckle2speckle_data}
Ultrasound imaging is a perfect example for a situation where a noise-free target image is difficult to obtain: since diffuse scattering is present for any ultrasound acquisition, speckle patterns remain visible at any time for acquired echo data.
Thus, one can argue that in ultrasound imaging, there is no image without speckle.
The strong correlation between the location and scattering behaviour of the tissue inhomogeneities causing the echos and the locations of extrema in the observed reflections (\textit{i}.\textit{e}.,  the speckle pattern), however, makes the acquisition of multiple images with independent corruption (of the desired echogenicity measurement) difficult for real acquisitions.

However, we can instead turn to realistic ultrasound simulations, where we can exploit the specific control over individual scatterers to generate an arbitrary amount of corrupted datasets for a given setup.
The training data for Speckle2Speckle is generated with the simulation software Field II \citep{jensen1992, jensen1996}.
Images are generated in pairs, using independent in-silico scatterer phantom datasets (collections of scatterers with associated scattering strengths).
These in-silico scatterer phantom datasets are derived from the same geometric phantom, which is a collection of 3D shapes each of which with a number of parameters describing their appearance in ultrasound imaging and scatterer parameters:
\begin{itemize}
    \item scatterer density
    \item scatterer amplitude distribution
    \item presence or absence of an interface
    \item interface scatterer density
    \item interface scatterer amplitude distribution
\end{itemize}
Based on the these properties, this approach allows for the generation of scatterer phantom datasets with independent scatterer locations and thus ideally suited for approaches such as Noise2Noise.
Scatterer locations can be generated randomly, but since the "macro-scale" geometry (e.g. areas of higher scattering) is the same, the ultrasound images created from the different scatterer phantoms look structurally identical and even the speckle distributions are identical.
The main difference, however, is that the speckle pattern "instance" is different, for which an example is shown in \autoref{fig:insilico_phantom_images_a} and \autoref{fig:insilico_phantom_images_b}.
\begin{figure}[htb]
     \centering
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/img_linear_das_2020-10-17_validation_43_0.png}
         \caption{Instance 1}
         \label{fig:insilico_phantom_images_a}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/img_linear_das_2020-10-17_validation_43_1.png}
         \caption{Instance 2}
         \label{fig:insilico_phantom_images_b}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/img_linear_das_2020-10-17_validation_43_2.png}
         \caption{Average}
         \label{fig:insilico_phantom_images_mean}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/img_linear_das_2020-10-17_validation_43_3.png}
         \caption{Interface map}
         \label{fig:insilico_phantom_images_interfaces}
     \end{subfigure}
        \caption{Comparison of the different image types created through simulation:
        Ultrasound images of two different scatterer instances from the same phantom geometry (a) and (b), average of nine ultrasound images from the same geometry (c), interface map derived from the phantom geometry (d).}
        \label{fig:insilico_phantom_images}
\end{figure}

While the training is performed with pairs of scatterer instances, we use a different target for the testing and validation.
In order to facilitate the evaluation of speckle removal in the absence of noise-free images, we compare the output of our method with an image that is composed by averaging multiple ultrasound images of the same phantom geometry but different scatterer instances.
For the training and validation sets, we generated 10 instances per phantom, one of which is used (randomly) as input, the other nine are averaged.
\autoref{fig:insilico_phantom_images_mean} shows the average of nine images corresponding to the same geometry as in \autoref{fig:insilico_phantom_images_a} and \autoref{fig:insilico_phantom_images_b}.

In addition to the ultrasound images, we also derive an interface map directly from the geometric phantom.
Being of the same size and spatial origin as the simulated ultrasound images, it contains whether an interface of a shape was present at each location.
\autoref{fig:insilico_phantom_images_interfaces} shows the interface map associated with the geometry used for the simulations of the ultrasound images in \autoref{fig:insilico_phantom_images}.
This is later utilized to adapt the training loss to the challenges of ultrasound imaging in order to achieve enhancements of prominent interfaces in the output.

Following this general scheme, a dataset was generated with a training set consisting of 1000 image pairs while the validation and test sets each contain 100 phantom geometries with 10 speckle instances per phantom geometry, resulting in 4000 simulated images in total.
Fundamentally, the size of the dataset was limited by the simulation time, where each image required roughly between one and two days on a single CPU core to simulate (depending on the concrete number of scatterers and the CPU performance), although parallelization between images was trivial.

In order to limit the simulation run-time, the scatterer densities values were determined as high as required such that further increase did not cause a change in the speckle patterns.
The probabilities of geometries being anechoic and the scatterer amplitude distributions were determined arbitrarily to ensure visibility of the different regions.
The specific parametrization is listed in \autoref{tab:phantomparameters}.
\begin{table}[t]
\centering
\begin{tabular}{l|l|l}
\hline
Region              & Parameter                      & Value                                                                                \\ \hline
Background          & probability anechoic           & 0.4                                                                                \\
                    & scatterer density              & \SI{0.333e10}{\meter^{-3}}                                                                                \\
                    & scatterer amplitude            & \makecell[tl]{normal distributed $\mathcal{N}(0, \sigma_{\text{bg}}^2)$; \\ $\sigma_{\text{bg}}$ drawn per phantom from $\mathcal{N}(1, 0.5^2)$}    \\ \hline
Inclusion           & random objects                 & spheroids \& cuboids, uniformly distributed                                                                                \\
                    & number per phantom             & 100                                                                                \\
                    & extents                        & drawn per axis uniformly from \SI[number-math-rm = \mathnormal, parse-numbers = false]{[1, 5]}{mm}                                                                            \\
                    & probability anechoic           & 0.4                                                                                \\
                    & scatterer density              & \SI{0.333e10}{\meter^{-3}}                                                                                \\
                    & scatterer amplitude            & \makecell[tl]{normal distributed $\mathcal{N}(0, \sigma_{\text{inc}}^2)$; \\ $\sigma_{\text{inc}}$ drawn per inclusion from $\mathcal{N}(4, 2^2)$} \\ \hline
Inclusion Interface & probability interface          & 0.5                                                                                \\
                    & scatterer density              & \SI{5e6}{\meter^{-2}}                                                                                \\
                    & scatterer amplitude            & \makecell[tl]{constant per inclusion,\\ drawn per inclusion from $\mathcal{N}(14, 2^2)$}                                       \\ \hline
\end{tabular}
\caption{Simulated phantom parameters and values}
\label{tab:phantomparameters}
\end{table}

The simulated imaging protocol used focused imaging and it followed the same restrictions as an actual focused scan using a CPLA12875 probe with a cQuest Cicada (both Cephasonics, CA, USA) would be subject to.
\autoref{tab:acquisitionparameters} contains the detailed acquisition parameters.
\begin{table}[htb]
\centering
\begin{tabular}{l|l}
\hline
Parameter                  & Value           \\ \hline
transducer                 & CPLA12875       \\
number elements            & 128             \\
element pitch              & \SI{0.30}{mm}   \\
elevation focus            & None            \\
transmit frequency         & \SI{7}{MHz}     \\
transmit pulse             & bipolar         \\
imaging depth              & \SI{60}{mm}     \\
focus depth                & \SI{30}{mm}     \\
steering                   & \SI{0}{\degree} \\
number scanlines           & 128             \\
TX aperture size           & 32              \\
RX aperture size           & 64              \\ \hline
\end{tabular}
\caption{Simulated acquisition parameters and values}
\label{tab:acquisitionparameters}
\end{table}

The output of the simulations was ultrasound radio-frequency channel data, which was subsequently processed to ultrasound images using SUPRA \citep{Goebl2018supra} with parameters as in \autoref{tab:reconstructionparameters}.
After beamforming, the resulting images were converted to PNGs and interface indicator maps for inclusions with an interface were created by examining the geometry at every pixel location for the presence of an interface.
\begin{table}[htb]
\centering
\begin{tabular}{l|l}
\hline
Parameter        & Value                    \\ \hline
beamforming      & DAS with hamming window  \\
dynamic range    & 70 dB                    \\
image resolution & 0.075 mm                 \\
image width      & \SI{37.6}{mm} $\widehat{=}$ 502 pixels \\
image height     & \SI{60.0}{mm} $\widehat{=}$ 801 pixels \\ \hline
\end{tabular}
\caption{Image reconstruction parameters and values}
\label{tab:reconstructionparameters}
\end{table}

\subsection{Speckle2Speckle: Learning}
The network architecture employed in Speckle2Speckle follows Noise2Noise (see \autoref{subsec:noise2noise}).

The training loss, however, was adjusted for the peculiarities of ultrasound imaging.
While the original Noise2Noise approach used different loss data terms depending on the noise, we extended the data term $L_2$ with a term to enhance the appearance of interfaces in the images, as interfaces retain key information for interpreting ultrasound imaging data.

The interface term is based on the known locations of interfaces given by the interface indicator map $I_\text{i}$.
We chose $I_{\text{i}G} = g_\text{i}*I_\text{i}$ as spatial weight for the interface loss, where $g_\text{i}$ is a 2D Gaussian with standard deviation $\sigma_i$.
This serves to i) extend the range of the interface effects from the potentially sharp peaks in the interface map, as well as ii) achieve a smooth transition between the effects of the data term and the interface term.
$\tilde{I}_{\text{i}G} = 1 - I_{\text{i}G}$ is the inverse weight.
With that, we formulate the overall loss $L_D$ as
\begin{align}
    &L_D (I_\text{o}, I_\text{t}, I_\text{i}) =
    L_2 (I_\text{o} \odot \tilde{I}_{\text{i}G}, I_\text{t} \odot \tilde{I}_{\text{i}G}) + \lambda S_D (I_\text{o}, I_\text{t}, I_\text{i}),\\
    &S_D (I_\text{o}, I_\text{t}, I_\text{i} )= \frac{1}{|\Omega|} \sum_{x\in \Omega} \left((I_\text{o}*g_\text{psf} - I_\text{t}) \odot I_{\text{i}G}\right)^2,
\end{align}
where $I_\text{o}$ is the network output, $I_\text{t}$ the target image, $\lambda$ the weighting of the interface term, $\odot$ denotes the Hadamard product (element-wise multiplication) and $g_\text{psf}$ a filter kernel of the point spread function (PSF).
The notion is: In the target image, the interfaces are smoothed by the PSF by the process of imaging.
Whereas in the output image the goal is to reduce the blurring, \textit{i}.\textit{e}.,  increase the sharpness.
Consequently, the term $I_\text{o}*g_\text{psf} - I_\text{t}$ penalizes deviations in $I_\text{o}$ from sharp peaks.
The PSF in this case could be the result of a per-image estimation, learned for one specific system, manually determined for one system or treated as hyper-parameter.
We assumed the PSF to be an anisotropic Gaussian, with $\sigma_\text{psf}^x = \text{FWHM}_\text{lat}/2$ and $\sigma_\text{psf}^y = \text{FWHM}_\text{ax}/2$ determined experimentally from a simulated point reflector, where we measured the full width half maximum (FWHM) in lateral and axial directions.
The hyper-parameters and their values are listed in \autoref{tab:hyperparameters}.
\begin{table}[htb]
\centering
\begin{tabular}{l|l}
\hline
Parameter                       & Value      \\ \hline
$\lambda$                       & \num{500}  \\
learning rate                   & \num{3e-5} \\
$\text{epochs}$                 & \num{1000} \\
$\sigma_\text{i}$ (interface)   & \SI{0.375}{mm} $\widehat{=}$ 5 pixels \\
$\sigma_\text{psf}^y$ (axial)   & \SI{0.075}{mm} $\widehat{=}$ 1 pixel \\
$\sigma_\text{psf}^x$ (lateral) & \SI{0.525}{mm} $\widehat{=}$ 7 pixels\\ \hline
\end{tabular}
\caption{Training hyper-parameters and their values}
\label{tab:hyperparameters}
\end{table}

\section{Evaluation}
In order to achieve a fair comparison we evaluate our approach to a group of well-established and state-of-the-art speckle filters, and provide qualitative comparisons and quantitative evaluations for the simulated, phantom, and in-vivo datasets in the following.
To this end, we compare the results of our method against SRAD\footnote{implementation from \cite{SRAD_code}} \citep{SRAD_yu2002speckle},
median filter\footnotemark{}, bilateral filter\footnotemark[\value{footnote}] \citep{BILAT_tomasi1998bilateral},
non-local means\footnotemark[\value{footnote}] (NLM) \citep{NLM_buades2005non}, \footnotetext{implementation from MATLAB R2020b}
and optimized Bayesian non-local means\footnote{implementation from \cite{OBNLM_code}} (OBNLM) \citep{OBNLM_coupe2009nonlocal}.
\autoref{tab:othermethod_parameters} contains the parametrization used for the mentioned methods.
\begin{table}[htb]
\centering
\begin{tabular}{l|l|l}
\hline
Method    & Parameter              & Value \\ \hline
SRAD      & number of iterations   & 200   \\
          & lambda                 & 0.1   \\ \hline
Median    & window size            & 15    \\ \hline
Bilateral & degree of smoothing    & 0.05  \\
          & spatial sigma          & 5     \\ \hline
NLM       & degree of smoothing    & 0.075 \\
          & search windows size    & 101   \\
          & comparison window size & 21    \\ \hline
OBNLM     & search area size       & 101   \\
          & patch size             & 45    \\
          & degree of smoothing    & 1.05  \\ \hline
\end{tabular}
\caption{Comparison method parameters and their values after manual tuning on the image in \autoref{fig:qualitative_comparison_invivo-a}.}
\label{tab:othermethod_parameters}
\end{table}

\subsection{Qualitative comparison}
\begin{figure}[htb]
\setlength{\belowcaptionskip}{\baselineskip}
     \centering
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/img_linear_das_2020-10-17_validation_43_orgHighlight.png}
         \caption{Original}
         \label{fig:qualitative_comparison_simulation-a}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/img_linear_das_2020-10-17_validation_43_mean.png}
         \caption{Average image}
         \label{fig:qualitative_comparison_simulation-b}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/imageCompressed_speckle_instance_43_1_SRAD.png}
         \caption{SRAD}
         \label{fig:qualitative_comparison_simulation-c}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/imageCompressed_speckle_instance_43_1_MED.png}
         \caption{Median}
         \label{fig:qualitative_comparison_simulation-d}
     \end{subfigure}

     \par
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/imageCompressed_speckle_instance_43_1_BILAT.png}
         \caption{Bilateral}
         \label{fig:qualitative_comparison_simulation-e}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/imageCompressed_speckle_instance_43_1_NLM.png}
         \caption{NLM}
         \label{fig:qualitative_comparison_simulation-f}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/imageCompressed_speckle_instance_43_1_OBNLM.png}
         \caption{OBNLM}
         \label{fig:qualitative_comparison_simulation-g}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{0.24\linewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_simulation/linear_das_2020-10-17_validation_43_0_speckle2speckle.png}
         \caption{Ours}
         \label{fig:qualitative_comparison_simulation-h}
     \end{subfigure}
        \caption{
    Qualitative comparison on a simulated image.
    The original speckled image (a), the average of nine different scatterer instantiations (b), output of SRAD (c), median filter (d), bilateral filtering (e), NLM (f), OBNLM (g) and ours (h).}
        \label{fig:qualitative_comparison_simulation}
\end{figure}
\emph{Simulated data.}
In the simulated image from the validation set (see \autoref{subsec:speckle2speckle_data}) shown in \autoref{fig:qualitative_comparison_simulation}, we can visually compare the result of applying the different methods directly with the average image of nine speckle instances.
It is clear that SRAD (c), median filtering (d) and the bilateral filter (e) mostly change the appearance of the speckle, but don not fully remove it.
NLM (f), OBNLM (g) and our method (h) all remove the speckle very well.
In the outlined area in the top of the image, the less dense speckle is not fully removed by OBNLM.
Another noteworthy region is the rectangular object in the top left that has a bright interface in the input as well as the average image.
NLM and OBNLM do not retain this feature with high contrast, whereas our method shows contrast close to the average image.
The elliptic object in the bottom right appears in our method not as clearly as it is visible in the average image, we believe this is due to comparatively low brightness of interface around it.
The other methods, however, also struggle to highlight the object.

\begin{figure}[htb]
\setlength{\belowcaptionskip}{\baselineskip}
     \centering
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/plots/rect_linearProbe_IPCAI_128-2_0_beamformed_original.png}
         \caption{Original}
         \label{fig:qualitative_comparison_experimental-a}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_SRAD.png}
         \caption{SRAD}
         \label{fig:qualitative_comparison_experimental-c}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_MED.png}
         \caption{Median}
         \label{fig:qualitative_comparison_experimental-d}
     \end{subfigure}

     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_BILAT.png}
         \caption{Bilateral}
         \label{fig:qualitative_comparison_experimental-e}
     \end{subfigure}

     \par

     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_NLM.png}
         \caption{NLM}
         \label{fig:qualitative_comparison_experimental-f}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_OBNLM.png}
         \caption{OBNLM}
         \label{fig:qualitative_comparison_experimental-g}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth]{images/qualitative_comparison_experimental/linearProbe_IPCAI_128-2_0_beamformed_denoised.png}
         \caption{Ours}
         \label{fig:qualitative_comparison_experimental-h}
     \end{subfigure}
     \hfill
     \hspace{\sevenimagewidth}
        \caption{
    Qualitative comparison on an experimental phantom image.
    The original speckled image (a), the output of SRAD (b), median filter (c), bilateral filtering (d), NLM (e), OBNLM (f) and ours (g).
    The marked regions in the inclusion and background are used for quantitative evaluation.}
        \label{fig:qualitative_comparison_experimental}
\end{figure}
\emph{Phantom data.}
The experimental image in \autoref{fig:qualitative_comparison_experimental} shows an ultrasound quality assurance phantom (Model 040GSE, CIRS Inc., Norfolk, VA,
USA).
The image was acquired with a CPLA12875 probe and a cQuest Cicada (both Cephasonics, CA, USA), with equivalent imaging parameters as used for the simulations (see \autoref{tab:acquisitionparameters}), but with an imaging depth of \SI{45}{mm}.
Being acquired from a physical phantom, there is no average image as reference, but the distinct regions in the phantom are constructed as regions of homogeneous scattering.
The image shows a background region, several string reflectors, as well as two different circular hyper-echoic inclusions.
The result of SRAD (b), median (c) and bilateral (d) show the same general patterns as with the simulated data---not succeeding in completely removing the speckle.
NLM (e), OBNLM (f) and our method (g) are again close to that goal.
While NLM shows the smoothest image among the three methods and shows a sharp edge of the bright inclusion, it suppresses the string responses more.
The result of OBNLM is almost as smooth as NLM overall, but it shows inhomogeneities around the bright inclusion and its outline is not as well defined as with the other methods.
Our method---while smooth locally---shows a certain inhomogeneity in the background, but does not suppress the point reflectors as strongly and has a clear demarcation of both inclusions.

\begin{figure}[htb]
\setlength{\belowcaptionskip}{\baselineskip}
     \centering
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/plots/rect_invivo_2021-01-17_image_16_1_original.png}
         \caption{Original}
         \label{fig:qualitative_comparison_invivo-a}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_SRAD.png}
         \caption{SRAD}
         \label{fig:qualitative_comparison_invivo-c}
     \end{subfigure}
     \hfill

     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_MED.png}
         \caption{Median}
         \label{fig:qualitative_comparison_invivo-d}
     \end{subfigure}

     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_BILAT.png}
         \caption{Bilateral}
         \label{fig:qualitative_comparison_invivo-e}
     \end{subfigure}

     \par

     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_NLM.png}
         \caption{NLM}
         \label{fig:qualitative_comparison_invivo-f}
     \end{subfigure}
     \hfill
     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_OBNLM.png}
         \caption{OBNLM}
         \label{fig:qualitative_comparison_invivo-g}
     \end{subfigure}
     \hfill

     \begin{subfigure}[b]{\sevenimagewidth}
         \centering
         \includegraphics[width=\textwidth,trim=0 37 0 0, clip]{images/qualitative_comparison_invivo/invivo_2021-01-17_image_16_1_denoised.png}
         \caption{Ours}
         \label{fig:qualitative_comparison_invivo-h}
     \end{subfigure}
     \hfill
     \hspace{\sevenimagewidth}
        \caption{
    Qualitative comparison on an in-vivo image.
    The original speckled image (a), the output of SRAD (b), median filter (c), bilateral filtering (d), NLM (e), OBNLM (f) and ours (g).
    The marked region in the thyroid is used for quantitative evaluation.}
        \label{fig:qualitative_comparison_invivo}
\end{figure}
\emph{In-vivo data.}
In the in-vivo image in \autoref{fig:qualitative_comparison_invivo}---a cross-sectional view of the thyroid of a healthy volunteer acquired with the same configuration as the phantom data with \SI{35}{mm} depth---we can see that SRAD (b), median (c) and bilateral filtering (d) perform better than in the simulated and experimental cases, although NLM (e), OBNLM (f) and our method (g) exhibit a significantly more homogeneous appearance in the thyroid and especially OBNLM and our method retain more sharpness in comparison.
While OBNLM better captures the muscle interfaces over the carotid artery on the right of the image compared to our method, it is also less smooth in the overall area of the carotid.
Our method does not retain said interfaces completely, but is not disturbed by the clutter within the artery.

\subsection{Quantitative evaluation}
\label{subsec:quantitative_comparison}
\emph{Simulated data.}
For the quantitative evaluation in the simulated case we utilize the average images as an approximation of the speckle-free image.
We perform this for all 100 simulated images in the validation set and the corresponding average images.
The input images are scaled to the range $[0, 1]$ before application of any of the methods.
\autoref{tab:quantitative_comparison_simulation} shows the mean and standard deviation of the mean squared errors (MSE) and the mean absolute differences (MAD) between the considered methods and the average images.
\begin{table}[htb]
\centering
\begin{tabular}{l|r@{ }l|r@{ }l}
\hline
Method    & \multicolumn{2}{l|}{MSE ($\times 10^{-3}$)}        & \multicolumn{2}{l}{MAD ($\times 10^{-2}$)}  \\ \hline
Input     & 5.87           & $\pm$ 1.31           & 5.86           & $\pm$ 0.87           \\
SRAD      & 2.33           & $\pm$ 0.86           & 3.61           & $\pm$ 0.76           \\
Median    & 1.82           & $\pm$ 0.25           & 3.25           & $\pm$ 0.33           \\
Bilateral & 1.48           & $\pm$ 0.20           & 2.94           & $\pm$ 0.30           \\
NLM       & 1.27           & $\pm$ \bfseries 0.09 & 2.70           & $\pm$ \bfseries 0.13 \\
OBNLM     & 1.48           & $\pm$ 0.15           & 2.87           & $\pm$ 0.20           \\
Ours      & \bfseries 1.20 & $\pm$ 0.11           & \bfseries 2.60 & $\pm$ 0.20           \\ \hline
\end{tabular}
\caption{Quantitative comparison on 100 simulated images from the validation set. It shows the mean and standard deviation of the MSE and the MAD between images processed with each methods (and the unprocessed input) against the average images of nine speckle instances.
Lowest error mean and standard deviation for both MSE and MAD are highlighted in bold without implying significance.}
\label{tab:quantitative_comparison_simulation}
\end{table}
All methods besides SRAD and median show a similar performance with respect to MSE and MAD, where our method yields the best result.
In combination with the clearly varying image appearance of the different methods, this emphasizes the trade-offs between the methods compared here.

\begin{figure}[htb]
     \centering
     \begin{subfigure}[b]{\columnwidth}
         \centering
         \includegraphics[width=0.7\textwidth]{images/plots/intensity_violin_experimental_bg.pdf}
         \caption{Background}
         \label{fig:quantitative_intensity_violin_experimental-a}
     \end{subfigure}

     \par
     \begin{subfigure}[b]{\columnwidth}
         \centering
         \includegraphics[width=0.7\textwidth]{images/plots/intensity_violin_experimental_inclusion.pdf}
         \caption{Hyperechoic Inclusion}
         \label{fig:quantitative_intensity_violin_experimental-b}
     \end{subfigure}
        \caption{Image intensity distribution within homogeneous background region (a) and hyperechoic inclusion (b) of the experimental images.}
        \label{fig:quantitative_intensity_violin_experimental}
\end{figure}
\begin{figure}[htb]
    \centering
    \includegraphics[width=0.7\columnwidth]{images/plots/intensity_violin_invivo_thyroid.pdf}
    \caption{Image intensity distribution within homogeneous thyroid region of the in-vivo images.}
    \label{fig:quantitative_intensity_violin_invivo}
\end{figure}

\emph{Phantom and in-vivo data.}
In the experimental and in-vivo images it is not possible to observe different speckle instantiations and thus no average image can be computed.
Consequently, we focus on homogeneous image regions, namely the hyperechoic inclusion and the background for the phantom dataset (see \autoref{fig:qualitative_comparison_experimental-a}) as well as the thyroid in the in-vivo dataset (see \autoref{fig:qualitative_comparison_invivo-a}).
Within those regions of assumed homogeneous scatterer distribution the ideal speckle removal filter would result in a flat response.
\autoref{fig:quantitative_intensity_violin_experimental} shows violin plots of the intensity distribution in the marked regions of the phantom images, while \autoref{fig:quantitative_intensity_violin_invivo} shows those of the in-vivo image regions.
The results of NLM, OBNLM and our method on the experimental image show very similar distributions; SRAD, median and bilateral-filter however are less tightly packed.
This generally is matched by the evaluation for the in-vivo image data, but the distribution of our method is less pronounced than NLM and OBNLM.

In \autoref{tab:quantitative_comparison_experimental_invivo}, we show the image intensity standard deviations within the homogeneous image regions.
As expected from the violin plots, SRAD, median, and the bilateral-filter exhibit larger standard deviations in the three regions than NLM, OBNLM and our method.
While our method does not outperform NLM and OBNLM---which share best performance within the three regions and were tuned on the thyroid image---it shows how well our method translates to real ultrasound data, despite not having been trained with real images.
\begin{table}[htb]
\sisetup{detect-weight}
\centering
\begin{tabular}{l|ll|l}
\hline
\multicolumn{1}{l|}{Region} & \multicolumn{2}{l|}{Experimental  ($\times 10^{-2}$)}                       & \multicolumn{1}{l}{In-Vivo  ($\times 10^{-2}$)} \\ \hline
\multicolumn{1}{l|}{Method} & \multicolumn{1}{l}{Background} & \multicolumn{1}{l|}{Inclusion} & \multicolumn{1}{l}{Thyroid} \\ \hline
Input                       & 7.10               & 7.58           & 8.96                    \\
SRAD                        & 3.55               & 3.96           & 5.89                    \\
Median                      & 3.37               & 3.83           & 6.05                    \\
Bilateral                   & 2.39               & 2.69           & 5.42                    \\
NLM                         & 1.49               & \bfseries 0.96 & 4.21                    \\
OBNLM                       & \bfseries 1.32     & 1.60           & \bfseries 4.20          \\
Ours                        & 1.61               & 1.51           & 4.54                    \\ \hline
\end{tabular}
\caption{Quantitative evaluation on the experimental and in-vivo images and shown in \autoref{fig:qualitative_comparison_experimental-a} and \autoref{fig:qualitative_comparison_invivo-a} respectively. The table shows the image intensity standard deviation in homogeneous regions in the background and foreground of the experimental images, and within the thyroid in the in-vivo case.
Lowest standard deviation per region highlighted in bold without implying significance.}
\label{tab:quantitative_comparison_experimental_invivo}
\end{table}

\section{Discussion}
The qualitative and quantitative evaluations show that Speckle2Speckle works well in the simulated images, performing on-par with or even outperforming NLM and OBNLM, as can be seen in \autoref{fig:qualitative_comparison_simulation} and \autoref{tab:quantitative_comparison_simulation}.
It also performs well in real acquisitions, although no real images have been used for training.
There are shortcomings however, especially regarding the overall smoothness as in the background area and the reproduction of the point scatterers in \autoref{fig:qualitative_comparison_experimental}.
Yet, it seems the highlighting of interfaces translates well to real images, with the exception of the linear structures in the muscle layer above the carotid artery in \autoref{fig:qualitative_comparison_invivo}.

The execution time of our method is significantly lower than for NLM and OBNLM.
While the used NLM and OBNLM implementations (CPU only) take in the range of minutes to execute, our method can be performed within tens of milliseconds on a recent GPU, making it suitable for real-time use.

\section{Conclusion}
We have shown with Speckle2Speckle a technique for training despeckling filters purely on simulated data as well as the generation of the simulated data.
The concept is intriguing, since it allows for the generation of multiple speckle realizations from the same underlying geometry.
The learned filter translates well to real acquisitions, performing similar to NLM and OBNLM, but only requiring a fraction of the execution time.

Since no clean training data is required, the number of simulations is drastically reduced compared to approaches that would use average images were used as target images.
It also enables creation of matching maps with high-level info on the structures present in the image that can be used to guide the outcome of the method during training---as we have done with interfaces in this case.

Including single point scatterers and/or strings in the simulated images could improve the reproduction of those structures.
They could be included in the interface map used for sharpening or placed in a separate map all-together, only depending on the desired appearance.
This aspect of using additional geometric maps to tune the result for particular locations could possibly be exploited beyond speckle removal, for example for clutter filtering in blood vessels within the same filter pass.

\acks{We acknowledge the important conversations with our colleagues Maximilian Baust, Maria Tirindelli, and Stefan Wörz.}

\ethics{The work follows appropriate ethical standards in conducting research and writing the manuscript, following all applicable laws and regulations regarding treatment of animals or human subjects.}

\coi{We declare we do not have conflicts of interest.}

redpajama_set_name: RedPajamaArXiv


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text:
Red Cross urges people to donate after winter storm cancellation - DatelineCarolina
Red Cross urges people to donate after winter storm cancellations
Monday, February 17, 2014 5:16 PM EST
Layne Bee, a biology teacher at Spring Valley High School in Columbia, donates blood at the Columbia American Red Cross on Monday afternoon. Photo by Sarah Ellis
A box containing a dozen one-pint units of blood awaits processing Monday at the Columbia American Red Cross donation center. Photo by Sarah Ellis
Stephen Hudson is the regional communications officer for the Palmetto SC Region of the American Red Cross. He said the Red Cross is in need of donations of all blood types. Photo by Sarah Ellis
By Jay Michaels
Layne Bee sat back and clenched her fist steadily, sending blood from her arm through the IV tube and into the blood bag.
Off work for President's Day, the Spring Valley High School biology teacher spent part of her Monday afternoon donating blood at the Red Cross on Columbia's Bull Street. A sign outside read, "Heroes needed to help boost the blood supply."
Bee said she was moved to donate after she received several calls from the Red Cross and heard that last week's storms caused some blood shortages.
The Red Cross is urging eligible blood and platelet donors to give after the storms forced 40 Red Cross blood drives across the state to be canceled, resulting in more than 1,300 uncollected blood and platelet donations.
Five hundred donations are needed daily just to cover the region's 54 hospitals, said Jamie Muldrow, the Red Cross' communications program manager. The Palmetto SC Region provides blood products to hospitals in 35 counties in South Carolina.
Thirty blood drives in the the Carolinas Blood Services Region, which includes some South Carolina counties, were canceled due to the inclement weather, said Kara Dudley, the region's communication programs manager. The cancellations resulted in 900 uncollected units of blood, she said.
She said the Red Cross is calling and emailing all eligible blood and platelet donors and everyone whose scheduled donation was canceled to help offset a weather-related shortfall in donations. Eligible donors are anyone who has not donated in the past 56 days.
"It is not a matter of making up for the lost numbers," Muldrow said. "It is a matter of rescheduling with donors as well as having sponsors reschedule blood drives."
Muldrow said platelet donors as well as blood donors of all blood types are needed now and will be needed in the coming weeks to help ensure blood and platelets are available for patients.
Winter storms caused the cancellation of about 265 Red Cross blood drives in the South and along the East Coast, resulting in nearly 9,300 uncollected blood and platelet donations. Before the last week, back-to-back storms already had canceled more than 1,000 blood drives in 34 states and Washington, D.C.
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- Additional reporting by Sarah Ellis.


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text:
Q: Swift-SceneKit-Can not load '.scn' file from 'art.scnassets' I'm trying to create a new SCNScene from 'diceCollada.scn' file.
But this file won't be loaded.

This file is in "ARDicee/art.assets" folder.
Not only "diceCollada.scn", but also it cannot load the default "ship.scn".
I don't know why it doesn't load files.

Here is my code.

import UIKit
import SceneKit
import ARKit

class ViewController: UIViewController, ARSCNViewDelegate {

    @IBOutlet var sceneView: ARSCNView!

    override func viewDidLoad() {
        super.viewDidLoad()

        // Set the view's delegate
        sceneView.delegate = self

        // Show statistics such as fps and timing information
        sceneView.showsStatistics = true

        // Create a new scene. ---------- The error is here ---------------
        guard let diceScene = SCNScene(named: "art.scnassets/diceCollada.scn") else {
            fatalError()
        }
        // Setting node
        if let diceNode = diceScene.rootNode.childNode(withName: "Dice", recursively: true) {
            diceNode.position = SCNVector3(x: 0, y: 0, z: -0.1)
            sceneView.scene.rootNode.addChildNode(diceNode)
        }
    }

    override func viewWillAppear(_ animated: Bool) {
        super.viewWillAppear(animated)
        if ARWorldTrackingConfiguration.isSupported {

            // Create a session configuration
            let configuration = ARWorldTrackingConfiguration()

            // Run the view's session
            sceneView.session.run(configuration)
        }
    }

    override func viewWillDisappear(_ animated: Bool) {
        super.viewWillDisappear(animated)

        // Pause the view's session
        sceneView.session.pause()
    }
}

Xcode - Version 14.1
macOS Ventura - Version 13.0.1
GitHub - This project

I also tried to create SCNScene another way.
override func viewDidLoad() {
        super.viewDidLoad()

        // Set the view's delegate
        sceneView.delegate = self

        // Show statistics such as fps and timing information
        sceneView.showsStatistics = true

        // --- Another way to create SCNScene ---
        let filePath = URL(fileURLWithPath: "/Applications/xcode/Development/ARDicee/ARDicee/art.scnassets/diceCollada.scn")
        do {
            let diceScene = try SCNScene(url: filePath)
            if let diceNode = diceScene.rootNode.childNode(withName: "Dice", recursively: true) {
                diceNode.position = SCNVector3(x: 0, y: 0, z: -0.1)
                sceneView.scene.rootNode.addChildNode(diceNode)
            }
        } catch {
            print(error)
        }
    }

But it gave this error.

Error Domain=NSCocoaErrorDomain Code=260 "The file "diceCollada.scn" couldn't be opened because there is no such file." UserInfo={NSFilePath=/Applications/xcode/Development/ARDicee/ARDicee/art.scnassets/diceCollada.scn, NSUnderlyingError=0x282924570 {Error Domain=NSPOSIXErrorDomain Code=2 "No such file or directory"}}

I'm trying to create a new SCNScene from 'diceCollada.scn' file.

A: If the app cannot load the spaceship from the apple template, the project might be broken somehow. Try to create a brandnew, default SceneKit/ARKit project, compile it directly and check if the spaceship loads correctly. If yes, copy and paste the code from your current project into the new one. If the spaceship does not even load from a fresh template, your xCode installation could be broken. You can also clean your project build folder or delete derived data, there are articles here on how to do such things. In addition you could share your project here on StackOverflow, so that we can have a look at it.

A: Resetting the path to command-line tools
There are some issues in your command line tools. As a result, the content in the art.scnassets folder isn't readable. In order to fix this, you need to install the latest version of Command_Line_Tools for Xcode 14.1 and then execute the following commands in Terminal:
sudo xcode-select --reset

sudo xcode-select -switch /Library/Developer/CommandLineTools

Reinstalling command-line tools
Or you can remove the old version of command line tools and install the new one using Terminal:
sudo rm -rf /Library/Developer/CommandLineTools

sudo xcode-select --install

Then restart your Mac.
I had the same problem and these steps helped me.
Renaming
If the above steps still do not help, rename the art.scnassets folder to artisan.scnassets.

A: Literally lost a day trying to fix this doing all the above - and anything else I found online - reinstalled XCode, command line tools etc etc nothing worked - BUT the below just worked for me, so posting in case it prevents someone from losing hours like myself:

*

*Copy the contents of the art.scnassets folder to a safe location on hard drive (e.g. a new folder on desktop) - but NOT include the "art.scnassets" folder, just everything below it.

*Created a new "art.scnassets" folder (Xcode Menu File>New File>Scenekit Catalog> "art.scnassets").

*Drag and drop (copy) the contents from the safe hard drive location in there, and it worked.
So for some reason the folder itself had been corrupted - for an unknown reason, it worked for 2 years on that app - and renaming it or manipulating its contents did nothing, and recreating a new one solved it.

redpajama_set_name: RedPajamaStackExchange


text:
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I love him who is of a free spirit and a free heart: thus is his head only the bowels of his heart; his heart, however, causeth his self-overcoming.
Lo, I am a herald of the lightning, and a heavy drop out of the cloud: the lightning, however, is the Superman.
WHAT I SEE: What is great goes beyond what is good, because it must overcome all of what has been. The brave man knows not how to live except if he be somewhere beyond what has been conceived, understood, or tamed with words like safe, good, and virtuous. The brave man overcomes even his own teachings and beliefs. He is born anew, not just a time or two. He is born anew every moment, and so for him, every moment is it's own eternity, cause, and reason. Every moment is sublime.


While employee volunteering has existed in organizations for long, the push by the millennials to engage themselves in such experiences has strongly enhanced the focus on such programs. The Deloitte Millennial Survey provides powerful evidence of how millennials are regarding business as a force for positive social impact.
People Matters and Goodera are excited to present how focusing on employee volunteering programs can impact employee engagement, team alignment, business success and much more. The campaign will feature articles, case studies, and expert interviews that will help you reflect on your learning practices.
Why every leader should volunteer?
Want to engage your employees in a social cause?
When organizations do it right!
Does your company support employee volunteering?