A tale of two countries and how they controlled the Coronavirus

Read an article in IEEE Spectrum last week about Taiwan’s response to COVID-19 (see: Big data helps Taiwan fight Coronavirus) which was reporting on an article in JAMA (see Response to COVID-19 in Taiwan) about Taiwan’s success in controlling the COVID-19 outbreak in their country.

I originally intended this post to be solely about Taiwan’s response to the virus but then thought that it more instructive to compare and contrast Taiwan and South Korea responses to the virus, who both seem to have it under control now (18 Mar 2020).

But first a little about the two countries (source wikipedia: South Korea and Taiwan articles):

Taiwan (TWN) and South Korea (ROK) both enjoy close proximity, trade and travel between their two countries and China

  • South Korea (ROK) has a population of ~50.8M, an area of 38.6K SqMi (100.0K SqKm) and extends about 680 Mi (1100 Km) away from the Asian mainland (China).
  • Taiwan (TWN ) has a population of ~23.4M, an area of 13.8K SqMi (35.8K Sq Km) and is about 110 Mi (180 Km) away from the Asian mainland (China).

COVID-19 disease progression & response in TWN and ROK

There’s lots of information about TWN’s response (see articles mentioned above) to the virus but less so on ROK’s response.

Nonetheless, here’s some highlights of the progression of the pandemic and how they each reacted (source for disease/case progression from : wikipedia Coronavirus timeline Nov’19 to Jan’20, and Coronavirus timeline Feb’20; source for TWN response to virus JAMA article supplement and ROK response to virus Timeline: What the world can learn from South Korea’s COVID-19 response ).

  • Dec. 31, 2019: China Wuhan municipal health announced “urgent notice on the treatment of pneumonia of unknown cause”. Taiwan immediately tightened inbound screening processes. ==> TWN: officials board and inspect passengers for fever or pneumonia symptoms on direct flights from Wuhan
  • Jan. 8, 2020: ROK identifies 1st possible case of the disease in a women who recently returned from China Wuhan province
  • Jan 20: ROK reports 1st laboratory confirmed case ==> TWN: Central Epidemic Command Center activated, activates Level 2 travel alert for Wuhan; ROK CDC starts daily press briefings on disease progress in the nation
  • Jan. 21: TWN identifies 1st laboratory confirmed case ==> TWN: activates Level 3 travel alert for Wuhan
  • Jan 22: ==> TWN: cancels entry permits for 459 tourists from Wuhan set to arrive later in Jan
  • Jan 23: ==> TWN: bans residents from Wuhan, travelers from China required to make online health declaration before entering
  • Jan. 24 ROK reports 2nd laboratory confirmed case ==> TWN bans export of facemasks; ROK, sometime around now the gov’t started tracking confirmed cases using credit card and CCTV data to understand where patients contacted the disease
  • Jan. 25: ==> TWN: tours to china are suspended until Jan 31, activates level 3 travel alert for Hubei Province and Level 2 for rest of China, enacts export ban on surgical masks until Feb 23
  • Jan 26: ==> TWN: all tour groups from Wuhan have to leave,
  • Jan. 27: TWN reports 1st domestic transmission of the disease ==>TWN NHIA and NIA (National health and immigration authorities) integrate (adds all hospital) patients past 14-day travel history to NHIA database, all tour groups from Hubei Province have to leave
  • Jan 28: ==> TWN: activates Level 3 travel alert for all of China except Hong Kong and Macau; ROK requests inspection of all people who have traveled from Wuhan in the past 14 days
  • Jan 29: ==> TWN: institutes electronic monitoring of all quarantined patients via gov’t issued cell phones; ROK about now requests production of massive numbers of WHO approved test kits for the Coronavirus
  • Jan. 30: ROK reports 2 more (4 total) confirmed cases of the disease ==> TWN: tours to or transiting China suspended until Feb 29;
  • Jan 31: ==> TWN: all remaining tour groups from China asked to leave
  • Feb 2 ==> TWN extended school break from Feb 15 to Feb 25,gov’t facilities available for quarantine, soldiers mobilized to man facemask production lines, 60 additional machines installed daily facemask output to reach 10M facemasks a day.
  • Feb 3: ==> TWN: enacts name based rationing system for facemasks, develops mobile phone app to allow public to see pharmacy mask stocks, Wenzhou city Level 2 travel alert; ROK CDC releases enhanced quarantine guidelines to manage the disease outbreak, as of today ROK CDC starts making 2-3 press releases a day on the progress of the disease
  • Feb 5: ==> TWN: Zheijanp province Level 2 travel alert, all cruise ships with suspected cases in past 28 days banned, any cruise ship with previous dockings in China, Hong Kong, or Macau in past 14 days are banned
  • Feb 6:==> TWN: Tours to Hong Kong & Macau suspended until Feb 29, all Chinese nationals banned, all international cruise ship are banned, all contacts from Diamond Princess cruise ship passengers who disembarked on Jan 31 are traced
  • Feb 7: ==> TWN: All foriegn nationals with travel to China, Hong Kong or Macau in the past 14 days are banned, all Foreigners must see an immigration officer,
  • Feb 14:==> TWN: Entry quarantine system launched fill out electronic health declaration for faster entry
  • Feb 16: ==> TWN: NHIA database expanded to cover 30 day travel history for travelers form or transited through China, Hong Kong, Macau, Singapore and Thailand.
  • Feb 18 ==> TWN: all hospitals, clinics and pharmacies have access to patients travel history; ROK most institutions postpone the re-start of school after spring break
  • Feb 19 ==> TWN establishes gov’t policies to disinfect schools and school areas, school buses, high speed rail, railways, tour busses and taxis
  • Feb 20 ==> ROK Daegu requests all individuals to stay home
  • Feb 21 ==> TWN establishes school suspension guidelines based on cases diagnosed in school; ROK Seoul closes all public gatherings and protests
  • Feb 24 ==> TWN, travelers with history of travel to china, from countries with level 1 or 2 travel alerts, and all foreign nationals subject to 14 day quarantine (By this time many countries are in level 1-2-3 travel alert status in TWN)
  • Feb 26 ==> ROK opens drive-thru testing clinics, patients are informed via text messages (3 days later) the results of their tests
  • Mar 3? ==> ROK starts selling facemasks at post offices
  • Mar 5 ==> ROK bans the export of face masks

As of Mar 16, (as reported in Wikipedia), TWN had 67 cases and 1 death; and ROK had 8,326 cases and 75 deaths. As of Mar 13 (as reported is Our world in data article), TWN had tested 16,089 and ROK had tested 248,647 people.

Summary of TWN and ROK responses to the virus

For starters, both TWN and ROK learned valuable lessons from the last infections from China SARS-H1N1 and used those lessons to deal better with COVID-19. Also neither country had any problem accessing credit information, mobile phone location data, CCTV camera or any other electronic information to trace infected people in their respective countries.

If I had to characterize the responses to the virus from the two countries:

  1. TWN was seemingly focused early on reducing infections from outside, controlling & providing face masks to all, and identifying gov’t policies (ceasing public gathering, quarantine and disinfectant procedure) to reduce transmission of the disease. They augmented and promoted the use of public NHIA databases to track recent travel activity and used any information available to monitor the infected and track down anyone they may have contacted. Although TWN has increased testing over time, they did not seem to have much of an emphasis on broad testing. At this point, TWN seems to have the virus under control.
  2. ROK was all about public communications, policies (quarantine and openness), aggressively testing their population and quarantining those that were infected. ROK also tracked the goings on and contacts of anyone that was infected. ROK started early on broadly testing anyone that wanted to be tested. Using test results, infected individuals were asked to quarantine. A reporter I saw talking about ROK mentions 3 T’s: Target, Test, & Trace At this point, ROK seems to have the virus under control.

In addition, Asian countries in general are more prone to use face masks when traveling, which may be somewhat restrict Coronavirus transmission. Although it seems to primarily reduce transmission, most of the public in these countries (now) routinely wear face masks when out and about. And previously they routinely wore face masks when traveling to reduce disease transmission.

Also both countries took the news out of Wuhan China about the extent of the infections, deaths and ease of disease transmission as truthful and acted on this before any significant infections were detected in their respective countries

What the rest of the world can learn from these two countries

What we need to take from TWN a& ROK is that

  1. Face masks and surgical masks are a critical resource during any pandemic. National production needs to be boosted immediately with pricing and distribution controls so that they are not hoarded, nor subject to price gouging. In the USA we have had nothing on this front other than requests to the public to stop hoarding them and the lack of availability to support healthcare workers).
  2. Test kits are also a critical resource during any pandemic. Selection of the test kit, validation and boosting production of test kits needs to be an early and high priority. The USA seems to have fallen down on this job.
  3. Travel restrictions, control and quarantines need to be instituted early on from infected countries. USA did take action to restrict travel and have instituted quarantines on cruise ship passengers and any repatriated nationals from China.
  4. Limited testing can help control the virus as long as it’s properly targeted. Mass, or rather less, targeted testing can also help control the virus as well. In the USA given the lack of test kits, we are limited to targeted testing.
  5. Open, rapid and constant communications can be an important adjunct to help control virus spread. The USA seems to be still working on this. Many states seem to have set up special communications channels to discuss the latest information. But there doesn’t seem to be any ongoing, every day communications effort on behalf of the USA CDC to communicate pandemic status.
  6. When one country reports infections, death and ease of transmission of a disease start to take serious precautions immediately. Disease transmission in our travel intensive world is much too easy and rapid to stop once it takes hold in a nation. Any nation today that starts to encounter and infectious agent with high death rates and seemingly easy transmission must be taken seriously as the start of something much bigger.

Stay safe, be well.

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Comments?

Photo Credit(s):

New science used to combat COVID-19 disease

Read an article last week in Science Magazine (A completely new culture on doing research… ) on how the way science is done to combat disease has changed the last few years.

In the olden days (~3-5 years ago), disease outbreaks would generate a slew of research papers to be written, submitted for publication and there they would sit, until peer-reviewed, after which they might get published for the world to see for the first time. Estimates I’ve seen say that the scientific research publishing process takes anywhere from one month (very fast) to 4-8 months, assuming no major revisions are required.

With the emergence of the Zika virus and recent Ebola outbreaks, more and more biological research papers have become available through pre-print servers. These are web-sites which accept any research before publication (pre-print), posting the research for all to see, comment and understand.

Open science via pre-print

Most of these pre-print servers focus on specific areas of science. For example bioRxiv is a pre-print server focused on Biology and medRxiv is for health sciences. On the other hand, arXiv is a pre-print server for “physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.” These are just a sampling of what’s available today.

In the past, scientific journals would not accept research that had been published before. But this slowly change as well. Now most scientific journals have policies gol pre-print publication and will also publish them if they deem it worthwhile, (see wikipedia article List of academic journals by pre-print policies).

As of today (9 March 2020) ,on biorXiv there are 423 papers with keyword=”coronavirus” and 52 papers with the keyword COVID-19, some of these may be the same. The newest (Substrate specificity profiling of SARS-CoV-2 Mpro protease provides basis for anti-COVID-19 drug design) was published on 3/7/2020. The last sentence in their abstract says “The results of our work provide a structural framework for the design of inhibitors as antiviral agents or diagnostic tests.” The oldest on bioRxiv is dated 23 January 2020. Similarly, there are 326 papers on medRxiv with the keyword “coronavirus”, the newest published 5 March 2020.

Pre-print research is getting out in the open much sooner than ever before. But the downside, is that pre-print papers may have serious mistakes or omissions in them as they are not peer-reviewed. So the cost of rapid openness is the possibility that some research may be outright wrong, badly done, or lead researchers down blind alleys.

However, the upside is any bad research can be vetted sooner, if it’s open to the world. We see similar problems with open source software, some of it can be buggy or outright failure prone. But having it be open, and if it’s popular, many people will see the problems (or bugs) and fixes will be rapidly created to solve them. With pre-print research, the comment list associated with a pre-print can be long and often will identify problems in the research.

Open science through open journals

In addition to pre-print servers , we are also starting to see the increasing use of open scientific journals such as PLOS to publish formal research.

PLOS has a number of open journals focused on specific arenas of research, such as PLOS Biology, PLOS Pathogyns, PLOS Medicine, etc.

Researchers or their institutions have to pay a nominal fee to publish in PLOS. But all PLOS publications are fully expert, peer-reviewed. But unlike research from say Nature, IEEE or other scientific journals, PLOS papers are free to anyone, and are widely available. (However, I just saw that SpringerNature is making all their coronavirus research free).

Open science via open data(sets)

Another aspect of scientific research that has undergone change of late is the sharing and publication of data used in the research.

Nature has a list of recommended data repositories. All these data repositories seem to be hosted by FAIRsharing at the University of Oxford and run by their Data Readiness Group. They list 1349 databases of which the vast majority (1250) are for the natural sciences with over 1380 standards used for data to be registered with FAIRsharing.

We’ve discussed similar data repositories in the past (please see Data banks, data deposits and data withdrawals, UK BioBank, Big open data leads to citizen science, etc). Having a place to store data used in research papers makes it easier to understand and replicate science.

Collaboration software

The other change to research activities is the use of collaborative software such as Slack. Researchers at UW Madison were already using Slack to collaborate on research but when Coronavirus went public, they Slack could help here too. So they created a group (or channel) under their Slack site called “Wu-han Clan” and invited 69 researchers from around the world. The day after they created it they held their first teleconference.

Other collaboration software exists today but Slack seems most popular. We use Slack for communications in our robotics club, blogging group, a couple of companies we work with, etc. Each has a number of invite-only channels, where channel members can post text, (data) files, links and just about anything else of interest to the channel.

Although I have not been invited to participate in Wu-han Clan (yet), I assume they usee Slack to discuss and vet (pre-print) research, discuss research needs, and other ways to avert the pandemic.

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So there you have it. Coronavirus scientific research is happening at warp speed compared to diseases of yore. Technologies to support this sped up research have all emerged over the last five to 10 years but are now being put to use more than ever before. Such technological advancement should lead to faster diagnosis, lower worldwide infection/mortality rates and a quicker medical solution.

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Enhanced LIDAR maps out 45km rather than 215m

I’ve used small LIDAR sensors on toy (Arduino based) robots and they operate well within 1m or so. Ultrasonics sensors are another alternative but we found them very susceptible to noise and surface abrasion. With decent LIDAR sensors used in drones and vehicles, they work up to 215m or so.

But research in the lab (ScienceDaily article: Want to catch a photon, start by silencing the sun) has created LIDAR sensors that uses a novel form of analog/optical noise suppression that is capable of using these same LIDAR sensors and using them to map up to 45km of space.

The researchers were able to add a quantum marker to LIDAR beam photon(s) and then filter beam reflections to only honor those reflected photons with the quantum marker. The ScienceDaily article was based on a Nature Communications article, Noise-tolerant single photon sensitive three-dimensional imager.

What’s been changed?

They call the methodology implemented by their device, Quantum Parametric Mode Sorting or QPMS. It’s not intended to compete with software or computational approaches for noise filtering but rather complement those capabilities with a more accurate LIDAR, that can eliminate the vast majority of noise using non-linear optics (see Wikipedia article on Non-linear optics to learn more)..

It turns out the researchers are able to image space with their new augmented LIDAR using a single photon per pixel. They use an FPGA to control the system and programable ODL(optical delay line, delay’s optical signals), with up conversion single photon detector (USPD, that takes one or more photons at one frequency and converts them to another, higher frequency photon) and a silicon avalanche photo diode (SI-APD, which detects a single photon and creates an avalanche [of multiple electrons?] electrical signal from it.

How well does it work?

To measure the resolution capabilities of the circuit they constructed a 50x70mm (~2×2 3/4″) CNC machined aluminums depth resolution calibration device (sort of like an eye chart only for depth perception) see (2c and 2d below) and were able to accurately map the device column topologies.

They were also able to show enhanced perception and noise reduction when obscuring a landscape (Einstein’s head) with an aluminum screen which would be very hard for normal solutions to filter out. The device was able to clearly reconstruct the image even through the aluminum screen.

The result of all this is an all optical fibre noise reduction circuit. I’m sure the FPGA ,SI-APD, USPD, MLL, Transciever, ODL and MEM are electronics or electro-mechanical devices,, but the guts of the enhanced circuit seems all optical.

What does it mean?

What could QPMS mean for optical fibre communications. It’s possible that optical fibres could use significantly less electro-optical amplifiers, if a single photon could travel 45km without noise.

Also LiFi (light fidelity) or open air optical transmission of data could be significantly improved (both in transmission length and noise reduction) using QPMS. And rone could conceivably use LiFi outside of office communications, such as high bandwidth/low-noise, all optical cellular data services for devices. .

And of course boosting LIDAR length, noise reduction and resolution could be a godsend for all LIDAR mapping today. I readi another article (ScienceDaily: Modern technology reveals … secrets of great, white Maya road) about archeologist mapping the (old) Maya road through the jungles of central America using LIDAR equipped planes. I imagine a QPMS equiped LIDAR could map Mayan foot paths.

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Comments?

Using jell-o (hydrogel) for new form of photonics computing

Read an article the other day which blew me away, Researchers Create ” Intelligent interaction between light and meterial – New form of computing, which discussed the use of a hydrogel (like raspberry jell-o) that could be used both as a photonics switch for optical communications and as modifiable material to create photonics circuits. The research paper on the topic is also available on PNAS, Opto-chemical-mechanical transduction in photeresponsive gel elicits switchable self trapped beams with remote interactions.

Apparently researchers have created this gel (see B in the graphic above)which when exposed to laser light interacts to a) trap the beam within a narrow cylinder and or b) when exposed to parallel beams interact such that it boosts the intensity of one of the beams. They still have some work to show more interactions on laser beam(s) but the trapping of the laser beams is well documented in the PNAS paper.

Jell-o optical fibres

Most laser beams broaden as they travel through space, but when a laser beam ise sent through the new gel it becomes trapped in a narrow volume almost as if sent through a pipe.

The beam trading experiment using a hydrogel cube of ~4mm per side. They sent a focused laser beam with a ~20um diameter through an 4mm empty volume and measured the beam’s disbursement to be ~130um diameter. Then the did the same experiment only this time shining the laser beam through the hydrogel cube and over time (>50 seconds) the beam diameter narrows to becomes ~22um. In effect, the gel over time constructs (drills) a self-made optical fibre or cylindrical microscopic waveguide for the laser beam.

A similar process works with multiple laser beam going through the gel. More below on what happens with 2 parallel laser beams.

The PNAS article has a couple of movies showing the effect from the side of the hydrogel. with a single and multiple laser beams.

Apparently as the beam propagates through the hydrogel, it alters the optical-mechanical properties of the material such that the refractive index within the beam diameter is better than outside the beam diameter. Over time, as this material change takes place, the beam diameter narrows back down to almost the size of the incoming beam. They call any material like this that changes its refractive index as chromophores.

It appears that the self-trapping effectiveness is a function of the beam intensity. That is higher intensity incoming laser beams (6.0W in C above) cause the exit beam to narrow while lower (0.37W) intensity incoming laser beams don’t narrow as much.

This self-created optical wave-guide (fibre) through the gel can be reset or reversed (> 45 times) by turning off the laser and leaving the gel in darkness for a time (200 seconds or so). This allows the material to be re-used multiple times to create other optical channels or to create the same one over and over again.

Jell-o optical circuits

It turns out that by illuminating two laser beams in parallel their distances apart can change their interaction even though they don’t cross.

When the two beams are around 200um apart, the two beams self channel to about the size of ~40um (incoming beams at ~20um). But the intensity of the two beams are not the same at the exit as they were at the entrance to the gel. One beam intensity is boosted by a factor of 12 or so and the other is boosted by a factor of 9 providing an asymmetric intensity boost. Unclear how the higher intensity beam is selected but if I read the charts right the more intensely boosted beam is turned on after the the less intensely boosted beam (so 2nd one in gets the higher boost.

When one of the beams is disabled (turned off/blocked), the intensity of the remaining beam is boosted on the order of 20X. This boosting effect can be reversed by illuminating (turning back on/unblocking) the blocked laser. But, oddly the asymmetric boosting, is no longer present after this point. The process seemingly can revert back to the 20X intensity boost, just by disabling the other laser beam again. .

When the two beam are within 25 um of each other, the two beams emerge with the same (or close to similar) intensity (symmetric boosting), and as you block one beam the other increases in intensity but not as much as the farther apart beams (only 9X).

How to use this effect to create an optical circuit is beyond me but they haven’t documented any experiments where the beams collide or are close together but at 90-180 degrees from one another. And what happens when a 3rd beam is introduced? So there’s much room for more discovery.

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Just in case you want to try this at home. Here is the description of how to make the gel from the PNAS article: “The polymerizable hydrogel matrix was prepared by dissolving acrylamide:acrylic acid or acrylamide:2-hydroxyethyl methacrylate (HEMA) in a mixture of dimethyl sulfoxide (DMSO):deionized water before addition of the cross-linker. Acrylated SP (for tethered samples) or hydroxyl-substituted SP was then added to the unpolymerized hydrogel matrix followed by an addition of a catalyst. Hydrogel samples were cured in a circular plastic mold (d = 10 mm, h = 4 mm thick).

How long it will take to get the gel from the lab to your computer is anyones guess. It seems to me they have quite a ways to go to be able to simulate “nor” or “nand” universal logic gates widely used in to create electronic circuits today.

On the other hand, using the gel in optical communications may come earlier. Having a self trapping optical channel seems useful for a number of applications. And the intensity boosting effect would seem to provide an all optical amplifier.

I see two problems:

  1. The time it takes to get to a self trapping channel, 50sec is long and it will probably take longer as you increase the size of the material.
  2. The size of the material seems large for optical (or electronic) circuitry. 4mm may not be much but it’s astronomical compared to the nm used in electronic circuitry

The size may not be a real concern as the movies don’t seem to show that the beam once trapped changes across the material, so maybe it could be a 1mm, or 1um cube of material that’s used instead. The time is a more significant problem. But then again there may be another gel recipe that acts quicker. But from 50sec down to something like 50nsec is nine orders of magnitude. So there’s a lot of work here.

Comments?

Photo Credit(s): all charts are from the PNAS article, Opto-chemo-mechanical transduction in photo responsive gel…

Gaming is driving storage innovation at WDC

I was at SFD19 a couple of weeks ago and Western Digital supplied the afternoon sessions on their technology (see videos here). Phil Bullinger gave a great session on HDDs and the data center market. Carl Che did a session on HDD technology and discussed on how 5G was going to ramp up demand for video streaming and IoT data requirements. Of course one of the sessions was on their SSD and NAND technologies.

But the one session that was pretty new and interesting to me was their discussion on how Gaming and how it’s driving system innovation. Eric Spaneut, VP of Client Computing was the main speaker for the session but they also had Leah Schoeb, Sr. Developer Manager at AMD, to discuss the gaming market and its impact on systems technology.

There were over 100M viewers of the League of Legends World Championships, with a peak viewership of 44M viewers. To put that in perspective the 2020 Super Bowl had 102M viewers. So gaming championships today are almost as big as the Super Bowl in viewership.

Gaming demands higher performing systems

Gaming users are driving higher compute processors/core counts, better graphics cards, faster networking and better storage. Gamers are building/buying high end desktop systems that cost $30K or more, dwarfing the cost of most data center server hardware.

Their gaming rigs are typically liquid cooled, have LEDs all over and are encased in glass. I could never understand why my crypto mine graphics cards had LEDs all over them. The reason was they were intended for gaming systems not crypto mines.

Besides all the other components in these rigs, they are also buying special purpose storage. Yes storage capacity requirements are growing for games but performance and thermal/cooling have also become major considerations.

Western Digital has dedicated a storage line to gaming called WD Black. It includes both HDDs and SSDs (internal NVMe and external USB/SATA attached) at the moment. But Leah mentioned that gaming systems are quickly moving away from HDDs onto SSDs.

Thermal characteristics matter

Of the WDC’s internal NVMe SSDs (WD Black SN750s), one comes with a heat sink attached. It turns out SSD IO performance can be throttled back due to heat. The heatsink allows the SSD to operate at higher temperatures and offer more bandwidth than the one without. Presumably, it allows the electronics to stay cooler and thus stay running at peak performance.

I believe their WD Black HDDs have internal fans in them to keep them cool. And of course they all come in black with LEDs surrounding them.

Storage can play an important part in the “gaming experience” for users once you get beyond network bottlenecks for downloading. For downloading and storage perform well . however for game loading and playing/editing videos/other gaming tasks, NVMe SSDs offer a significant performance boost over SATA SDDs and HDDs.

But not all gaming is done on high-end gaming desktop systems. Today a lot of gaming is done on dedicated consoles or in the cloud. Cloud based gaming is mostly just live streaming of video to a client device, whether it be a phone, tablet, console, etc. Live game streaming is almost exactly like video on demand but with more realtime input/output and more compute cores/graphic engines to perform the gaming activity and to generate the screens in “real” time. So having capacity and performance to support multiple streams AND the performance needed to create the live, real time experience takes a lot of server compute & graphics hardware, networking AND storage.

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So wherever gamers go, storage is becoming more critical in their environment. Both WDC and AMD see this market as strategic and growing, whose requirements are unique enough to demand special purpose products. They bothy are responding with dedicated hardware and product lines tailored to gaming needs.

Photo credit(s): All graphics in this post are from WDC’s gaming session video stream

Breaking IoT security

Earth globe within a locked cage

Read an article the other day (Researchers exploit low entropy of IoT devices to break RSA certificates) about researchers cracking IoT device security and breaking their public key encryption keys. The report focused on PKI and RSA certificates and IoT devices. The article mentioned the research paper describing the attack in more detail.

safe 'n green by Robert S. Donovan (cc) (from flickr)
safe ‘n green by Robert S. Donovan (cc) (from flickr)

RSA certificates publish a public key and the digital signature of the certificate and identify the device that owns the certificate.

What the researchers were able to show was that ~250K keys in IoT device RSA certificates were insecure. They were able to compromise the 250K RSA certificates using a single Microsoft Azure VM and about $3K of computer time.

It turns out that if two RSA certificate public keys share the same factor, it’s much easier to determine the greatest common devisor GCD) of the two public keys than it is to factor any one of them. And once you have the GCD of the two keys, it’s relatively trivial to determine the other factor in a public key. And that’s just what they did.

Public key infrastructure (PKI) encryption depends on asymmetric cryptography using a “public” key to encrypt messages (or to encrypt a one time key to be used in later encryption of messages) and the use of a “private” key to decrypt the message (or keys) and sign digital certificates. There are certificate authorities and a number of other elements used in PKI but the asymmetric cryptography at its heart, rests on the foundation of the difficulty in factoring large numbers but those large numbers need to be random and prime.

True randomness is hard

Just some of the recently donated seeds that are being added to the Reading Food Growing Network seed swap boxes, including some Polish gherkin seeds.

The problem starts with generating truly random numbers in a digital computer. Digital algorithms typically depend on a computer to perform the some set of instructions, in the same way and sequence so as to get the same answer every time we run the algorithm.

But if you want random numbers this predictability of always coming up with the same answer each time results in non-random numbers (or rather random numbers that are the same each time you run the algorithm). So to get around this, most random number generators can make use of a (random) seed which is used as an input to the algorithm to generate random numbers.

However, this seed needs to be a random number. But to create a random number it needs to be generated not with instructions but using something outside the digital computer. One approach noted above is to use a human typing keys to generate a random number to be used as a seed.

The researchers exploited the fact that most IoT devices don’t use a random (enough) seed for their PKI key generation. And they were able to use the GCD trick to figure out the factors to the PKI.

But the lack of true randomness (or entropy) is the real problem. Somehow, these devices need to have a cheap and effective way to generate a random seed. Until this can be found, they will be subject to these sorts of attacks.

… but not impossible to obtain

I remember in times past when tasked to create a public key-private key pair I had to type some random characters. The Public key encryption algorithm used the inter-character time interval of my typing to generate a random seed that was then used to generate the key pair used in the public key. I believe the two keys also need to be prime numbers.

Earth globe within a locked cage

Perhaps a better approach would be to assign them keys from a centralized key distributor. That way the randomness could be controlled by the (key) distributor.

There are other approaches that depend on the sensors available to an IoT device. If the device has a camera or mic, taking raw data from the camera or sound sensor and doing a numerical transform on them may suffice. Strain gauges, liquid levels, temperature, humidity, wind speed, etc. all of these devices have something which senses the world around them and many of these are, at their base, analog sensors. Reading and converting some portion of these analog signals from raw analog to a digital random seed could be very effective way to generate true(r) randomness.

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The paper has much more information about the attack and their results if your interested. They said that ~50% of the compromised devices were from a large network supplier. Such suppliers probably also have a vast majority of devices deployed. Still it’s troubling, nonetheless.

Until changes are made to IoT devices, they will continue to be insecure. Not as much of a problem when they are read only sensors but when the information they sense is used by robots or other automation to make decisions about actions, then having insecure IoT becomes a safety issue.

This is not the first time such an attack was attempted and each time, it’s been very successful. That alone should be cause for alarm. But IoT and similar devices are hard to patch in the field and their continuing insecurity may be more of a result of the difficulty of updating a large install base than anything else.

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Designing living machines

Read an article the other day in PNAS (A scaleable pipeline for designing reconfigurable organisms) which described an approach to designing and constructing living organisms to perform real world actions. One could call these living machines or biological robots (biobots). There’s an appendix to the paper which provides supplementary information.

The intention of the pipeline is to expand the modern design space from construction materials, chemical process, electronics and mechanical devices to the domain of living things. Thereby create objects that perform functions for mankind, that are operate well with living things, are more resilient and have a benign impact on the environment.

The Biobot design pipeline stage 1

The design process begins using an evolutionary algorithm which takes as input an organism goal or action (i.e., moving so many body lengths for minutes) and the cell types to be used in constructing the organism and randomly generates potential organism designs.

In the current process there are two cell types (red and cyan) one is passive (scaffolding) and the other is active and provides movement power.

Designing and manufacturing reconfigurable organisms. A behavioral goal (e.g., maximize displacement), along with structural building blocks [here, contractile (red) and passive (cyan) voxels], are supplied to an evolutionary algorithm. The algorithm evolves an initially random population and returns the best design that was found.

Once a set of randomized designs using the two cell types have been determined, each undergoes a computerized simulation (in a physics engine that simulates gravity and liquid environment) to see how well the possible organism perform.

All designs are ranked in how well the achieve they performe and the best of these are used as seeds for another round of evolutionary design exploration. This uses these good designs and randomly changes some aspect of them to create another set of organism designs to test out.


Designing reconfigurable organisms. For a given goal, 100 independent evolutionary trials were conducted in silico (A–C). Each colored line represents the velocity of the fastest-moving design within its clade. Each genome (D) dictates anatomy and behavior by determining where and how voxels are combined, and whether they are passive (cyan) or contractile (red; E).

At some point, the evolutionary design exploration-simulation process stops when it has determined a set of workable organism designs which can achieve the goals set out for them.

The workable organism designs are then subjected to two rounds of filtering. The first filter is tests the designs for resilience to noise. This is done by putting the designs through another set of computer simulations that include noise. Some of the workable organism designs will still perform well in noisy environment and others will not.

The organism designs that perform well in noisy environments, are deemed resilient and are then fed into the next filtering stage of the pipeline.

The resilient workable organism designs are then filtered by whether they can be constructed with the current processes. Even though all the organisms are made up of the two cell types, not all of them can be realized given the current process.

After this point we have a set of designs that

a) Achieve the requested goal in simulation;

b) Perform well in noisy environment simulations; and

c) Can be constructed with the current processes and cell types.

The Biobot design pipeline stage 2

The next steps in the organism design pipeline all take place in the real world. The set of selected designs are constructed/manufactured and set into a petri dish to see how well they perform in real life .

The two cell types used in the current process are derived from the Xenobus (frog) embryos and consist of stem cells (passive) and heart muscle cells (active). The building of organism designs is done through layering of stem cells and then surgically or using cauterization to remove cells not part of the design. After the stem cells are placed then heart muscle cells can be layered on in a similar fashion.

There’s no control mechanism whatsoever other than the surfaces designed for the organism. Xenobus heart muscle cells automatically contract and when combined with other heart muscle cells, all the muscle cells contract in waves.

The design of the organism is such that the contractions propel the organism to move and explore the environment (the intended goal). The designed organisms are placed in a Petri dish and then observed over a period of time to see how well the perform the desired action.

Successful designs can then be seeded back into the start of the evolutionary exploration to generate even better designs. Simulations can also be adjusted with feedback from the real world behavior of the designed organisms. At some point the best designed organisms can be used in the real world.

Why biobots

Although the example had a goal to explore its environment. other goals could be readily used as well. Some of the ones mentioned in the paper are manipulating and gathering together some compounds/elements/particles in a volume. These could be used to clear a viscous solution of some impurities.

Another organism could be designed to have a pouch within which they can store and transport objects (or drugs).

Designed organisms could operate together in a solution with some organisms performing one function while others perform other functions. Organism designs could eventually be combined into one organism that performs more functions.

One nice aspect of biobots is that they can be squeezed, perturbed in many ways including being cut and they repair themselves and continue to operate.

One could design an organism to reproduce in a suitable environment or even designed to age and die after a specific time period.

The end goal seems to be to create living machines that can be used to operate in the environment or a body. Biobots could be designed to clear away plaque from a blood vessels or to dismantle malignant tumors. They could conceivably be constructed from a person’s own cells to operate for days-weeks-months in a body and then dissolve to be reused/disposed of just like any other biological material in a human.

So now we can design biobots.

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Just in case you wanted to try your hand at designing living organisms yourself. The researchers have all open sourced thei code for the evolutionary design exploration, computerized simulation, noise and build ability filtering which is available on github. The actual manufacturing/construction of the designed organism would need to be done in a lab.

Photo credit(s): All images are from the paper and its supplementary appendix.

Weight Agnostic Neural Networks (WANNs)

Read an article the other day (Neural Networks Can Drive Without [weight] Learning) about a new form of deep learning neural network (NN) that is not dependent on the weights assigned to network nodes. The new NN is called WANN (Weight Agnostic NN). There’s also a scientific paper (on Github, Weight Agnostic Neural Networks) that describes WANNs in more detail.

How WANNs differ from normal NN

If I understand them properly, WANNs are trained, but instead of assigning weights during training, WANN networks architectures (nodes and connections) are modified and optimized to perform well against the training data.

Indeed, most NN start out with assigning random weights to all network nodes and then these weights are adjusted through the training cycle, until the NN performs well on the training data. But NN such as these, have a structure (# nodes/layer, # layers, connectivity type, etc.) defined by the researcher, that is stable and unchanging during a training-validation cycle. If the NN model is not accurate enough, the researcher has two choices, find better data or change the model’s structure. WANNs start and end with changing the model’s structure.

With WANNs they start out with a set of NN architectures (#nodes/layer, #layers, connection types, etc). Each NN architecture is evaluated against the training data with a single shared randomized weight. That shared weight is altered (randomly) for a training pass and the model evaluated for accuracy.

At the end of a WANN training pass you have a set of evaluation metrics for each model structure. The resultant WANNs are then ordered by performance and complexity. The highest performing networks are then used to create a new population (set) of WANN architecture to be tested and the process iterates from there. This would presumably continue until you have reached a plateau of accuracy statistics across a number of shared randomized weights. And this would be the WANN model used for the application

Why WANN?

For a normal NN, each node weight would be adjusted automatically and independently at the end of each training batch. There would, of course, be a large number of batches, causing each weight in the NN nodes to be altered (via floating point arithmetic). So the math would be floating point arithmetic*#nodes*#layers*# of training batches (* # training passes (or epochs).

WANNs avoid this inner loop math altogether. Instead they would need to test a model on a number of shared random weights. This would presumably be done after a complete training pass (each epoch). And even if you had the same number of WANN models as nodes in a normal NN, the computations would be much less. Something on the order of #models * # epochs (each training pass [or epoch] could conceivable test a different shared random weight).

Another advantage of WANNs is that they result in simpler, less complex NN models (# nodes, # layers, # of connections, etc.) than normal DL NNs. Simpler NN models could be very useful for IoT applications, where computational power and storage is limited.

The main disadvantage of WANNs is that they aren’t as accurate as normally (weight adjusted) NNs. However, once you have a WANN, you can always elect to re-train it in the normal fashion by adjusting weights to gain more accuracy. And doing so would likely be much closer to a more complex NN model that was trained from the start by altering weights.

WANNs are more like nature

Human and other mammal (probably avian, aquatic, etc as well) seem to be born with certain innate abilities, visual, perceptive, mobility and with certain habits such as nursing, facial mimicking, hunger-feeding, etc. Presumably these innate abilities and habits are hardwired neuron networks that don’t depend on envirnonmental learning. Something that they are all born with.

Concievably WANNs could be consider similar to these hardwired (unlearned) neuron networks. WANNs could be used in a similar fashion to embed certain innate habits and abilities into robots or other automation that could be further trained with their interactions with their environment

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The Github paper has an online WANN model widget with a slider where you can alter a shared random weight and see its impact on the operation of a the widget. Playing with this, the only weight that seems to have a significant impact on the actions of the widget is zero…

Photo Credit(s): “Neural Connections In the Human Brain” by Image Editor is licensed under CC BY-NC-ND 2.0