We pre­sent Imagen, a text-to-im­age dif­fu­sion model with an un­prece­dented de­gree of pho­to­re­al­ism and a deep level of lan­guage un­der­stand­ing. Imagen builds on the power of large trans­former lan­guage mod­els in un­der­stand­ing text and hinges on the strength of dif­fu­sion mod­els in high-fi­delity im­age gen­er­a­tion. Our key dis­cov­ery is that generic large lan­guage mod­els (e.g. T5), pre­trained on text-only cor­pora, are sur­pris­ingly ef­fec­tive at en­cod­ing text for im­age syn­the­sis: in­creas­ing the size of the lan­guage model in Imagen boosts both sam­ple fi­delity and im­age-text align­ment much more than in­creas­ing the size of the im­age dif­fu­sion model. Imagen achieves a new state-of-the-art FID score of 7.27 on the COCO dataset, with­out ever train­ing on COCO, and hu­man raters find Imagen sam­ples to be on par with the COCO data it­self in im­age-text align­ment. To as­sess text-to-im­age mod­els in greater depth, we in­tro­duce DrawBench, a com­pre­hen­sive and chal­leng­ing bench­mark for text-to-im­age mod­els. With DrawBench, we com­pare Imagen with re­cent meth­ods in­clud­ing VQ-GAN+CLIP, Latent Diffusion Models, and DALL-E 2, and find that hu­man raters pre­fer Imagen over other mod­els in side-by-side com­par­isons, both in terms of sam­ple qual­ity and im­age-text align­ment.

A small cac­tus wear­ing a straw hat and neon sun­glasses in the Sahara desert.

A small cac­tus wear­ing a straw hat and neon sun­glasses in the Sahara desert.

A photo of a Corgi dog rid­ing a bike in Times Square. It is wear­ing sun­glasses and a beach hat.

A photo of a Corgi dog rid­ing a bike in Times Square. It is wear­ing sun­glasses and a beach hat.

Sprouts in the shape of text ‘Imagen’ com­ing out of a fairy­tale book.

Sprouts in the shape of text ‘Imagen’ com­ing out of a fairy­tale book.

A trans­par­ent sculp­ture of a duck made out of glass. The sculp­ture is in front of a paint­ing of a land­scape.

A trans­par­ent sculp­ture of a duck made out of glass. The sculp­ture is in front of a paint­ing of a land­scape.

A sin­gle beam of light en­ter the room from the ceil­ing. The beam of light is il­lu­mi­nat­ing an easel. On the easel there is a Rembrandt paint­ing of a rac­coon.

A sin­gle beam of light en­ter the room from the ceil­ing. The beam of light is il­lu­mi­nat­ing an easel. On the easel there is a Rembrandt paint­ing of a rac­coon.

Visualization of Imagen. Imagen uses a large frozen T5-XXL en­coder to en­code the in­put text into em­bed­dings. A con­di­tional dif­fu­sion model maps the text em­bed­ding into a 64×64 im­age. Imagen fur­ther uti­lizes text-con­di­tional su­per-res­o­lu­tion dif­fu­sion mod­els to up­sam­ple the im­age 64×64→256×256 and 256×256→1024×1024.

* We show that large pre­trained frozen text en­coders are very ef­fec­tive for the text-to-im­age task.

* We show that scal­ing the pre­trained text en­coder size is more im­por­tant than scal­ing the dif­fu­sion model size.

* We in­tro­duce a new thresh­old­ing dif­fu­sion sam­pler, which en­ables the use of very large clas­si­fier-free guid­ance weights.

* We in­tro­duce a new Efficient U-Net ar­chi­tec­ture, which is more com­pute ef­fi­cient, more mem­ory ef­fi­cient, and con­verges faster.

* On COCO, we achieve a new state-of-the-art COCO FID of 7.27; and hu­man raters find Imagen sam­ples to be on-par with ref­er­ence im­ages in terms of im­age-text align­ment.

* Human raters strongly pre­fer Imagen over other meth­ods, in both im­age-text align­ment and im­age fi­delity.

A photo of a An oil paint­ing of a

in a gar­den. on a beach. on top of a moun­tain.

There are sev­eral eth­i­cal chal­lenges fac­ing text-to-im­age re­search broadly. We of­fer a more de­tailed ex­plo­ration of these chal­lenges in our pa­per and of­fer a sum­ma­rized ver­sion here. First, down­stream ap­pli­ca­tions of text-to-im­age mod­els are var­ied and may im­pact so­ci­ety in com­plex ways. The po­ten­tial risks of mis­use raise con­cerns re­gard­ing re­spon­si­ble open-sourc­ing of code and demos. At this time we have de­cided not to re­lease code or a pub­lic demo. In fu­ture work we will ex­plore a frame­work for re­spon­si­ble ex­ter­nal­iza­tion that bal­ances the value of ex­ter­nal au­dit­ing with the risks of un­re­stricted open-ac­cess. Second, the data re­quire­ments of text-to-im­age mod­els have led re­searchers to rely heav­ily on large, mostly un­cu­rated, web-scraped datasets. While this ap­proach has en­abled rapid al­go­rith­mic ad­vances in re­cent years, datasets of this na­ture of­ten re­flect so­cial stereo­types, op­pres­sive view­points, and deroga­tory, or oth­er­wise harm­ful, as­so­ci­a­tions to mar­gin­al­ized iden­tity groups. While a sub­set of our train­ing data was fil­tered to re­moved noise and un­de­sir­able con­tent, such as porno­graphic im­agery and toxic lan­guage, we also uti­lized LAION-400M dataset which is known to con­tain a wide range of in­ap­pro­pri­ate con­tent in­clud­ing porno­graphic im­agery, racist slurs, and harm­ful so­cial stereo­types. Imagen re­lies on text en­coders trained on un­cu­rated web-scale data, and thus in­her­its the so­cial bi­ases and lim­i­ta­tions of large lan­guage mod­els. As such, there is a risk that Imagen has en­coded harm­ful stereo­types and rep­re­sen­ta­tions, which guides our de­ci­sion to not re­lease Imagen for pub­lic use with­out fur­ther safe­guards in place.

Finally, while there has been ex­ten­sive work au­dit­ing im­age-to-text and im­age la­bel­ing mod­els for forms of so­cial bias, there has been com­par­a­tively less work on so­cial bias eval­u­a­tion meth­ods for text-to-im­age mod­els. A con­cep­tual vo­cab­u­lary around po­ten­tial harms of text-to-im­age mod­els and es­tab­lished met­rics of eval­u­a­tion are an es­sen­tial com­po­nent of es­tab­lish­ing re­spon­si­ble model re­lease prac­tices. While we leave an in-depth em­pir­i­cal analy­sis of so­cial and cul­tural bi­ases to fu­ture work, our small scale in­ter­nal as­sess­ments re­veal sev­eral lim­i­ta­tions that guide our de­ci­sion not to re­lease our model at this time.  Imagen, may run into dan­ger of drop­ping modes of the data dis­tri­b­u­tion, which may fur­ther com­pound the so­cial con­se­quence of dataset bias. Imagen ex­hibits se­ri­ous lim­i­ta­tions when gen­er­at­ing im­ages de­pict­ing peo­ple. Our hu­man eval­u­a­tions found Imagen ob­tains sig­nif­i­cantly higher pref­er­ence rates when eval­u­ated on im­ages that do not por­tray peo­ple, in­di­cat­ing  a degra­da­tion in im­age fi­delity. Preliminary as­sess­ment also sug­gests Imagen en­codes sev­eral so­cial bi­ases and stereo­types, in­clud­ing an over­all bias to­wards gen­er­at­ing im­ages of peo­ple with lighter skin tones and a ten­dency for im­ages por­tray­ing dif­fer­ent pro­fes­sions to align with Western gen­der stereo­types. Finally, even when we fo­cus gen­er­a­tions away from peo­ple, our pre­lim­i­nary analy­sis in­di­cates Imagen en­codes a range of so­cial and cul­tural bi­ases when gen­er­at­ing im­ages of ac­tiv­i­ties, events, and ob­jects. We aim to make progress on sev­eral of these open chal­lenges and lim­i­ta­tions in fu­ture work.

An art gallery dis­play­ing Monet paint­ings. The art gallery is flooded. Robots are go­ing around the art gallery us­ing pad­dle boards.

An art gallery dis­play­ing Monet paint­ings. The art gallery is flooded. Robots are go­ing around the art gallery us­ing pad­dle boards.

A ma­jes­tic oil paint­ing of a rac­coon Queen wear­ing red French royal gown. The paint­ing is hang­ing on an or­nate wall dec­o­rated with wall­pa­per.

A ma­jes­tic oil paint­ing of a rac­coon Queen wear­ing red French royal gown. The paint­ing is hang­ing on an or­nate wall dec­o­rated with wall­pa­per.

A gi­ant co­bra snake on a farm. The snake is made out of corn.

A gi­ant co­bra snake on a farm. The snake is made out of corn.

We give thanks to Ben Poole for re­view­ing our man­u­script, early dis­cus­sions, and pro­vid­ing many help­ful com­ments and sug­ges­tions through­out the pro­ject. Special thanks to Kathy Meier-Hellstern, Austin Tarango, and Sarah Laszlo for help­ing us in­cor­po­rate im­por­tant re­spon­si­ble AI prac­tices around this pro­ject. We ap­pre­ci­ate valu­able feed­back and sup­port from Elizabeth Adkison, Zoubin Ghahramani, Jeff Dean, Yonghui Wu, and Eli Collins. We are grate­ful to Tom Small for de­sign­ing the Imagen wa­ter­mark. We thank Jason Baldridge, Han Zhang, and Kevin Murphy for ini­tial dis­cus­sions and feed­back. We ac­knowl­edge hard work and sup­port from Fred Alcober, Hibaq Ali, Marian Croak, Aaron Donsbach, Tulsee Doshi, Toju Duke, Douglas Eck, Jason Freidenfelds, Brian Gabriel, Molly FitzMorris, David Ha, Philip Parham, Laura Pearce, Evan Rapoport, Lauren Skelly, Johnny Soraker, Negar Rostamzadeh, Vijay Vasudevan, Tris Warkentin, Jeremy Weinstein, and Hugh Williams for giv­ing us ad­vice along the pro­ject and as­sist­ing us with the pub­li­ca­tion process. We thank Victor Gomes and Erica Moreira for their con­sis­tent and crit­i­cal help with TPU re­source al­lo­ca­tion. We also give thanks to Shekoofeh Azizi, Harris Chan, Chris A. Lee, and Nick Ma for vol­un­teer­ing a con­sid­er­able amount of their time for test­ing out DrawBench. We thank Aditya Ramesh, Prafulla Dhariwal, and Alex Nichol for al­low­ing us to use DALL-E 2 sam­ples and pro­vid­ing us with GLIDE sam­ples. We are thank­ful to Matthew Johnson and Roy Frostig for start­ing the JAX pro­ject and to the whole JAX team for build­ing such a fan­tas­tic sys­tem for high-per­for­mance ma­chine learn­ing re­search. Special thanks to Durk Kingma, Jascha Sohl-Dickstein, Lucas Theis and the Toronto Brain team for help­ful dis­cus­sions and spend­ing time Imagening!

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The EU Commission’s draft reg­u­la­tion on pre­vent­ing and com­bat­ing child abuse is a frontal at­tack on civil rights. And the EU Commission is push­ing for this draft to be­come law with Trump-like ex­ag­ger­a­tions.

As cit­i­zens we can ex­pect more from the EU Commission. The least we can ask for when the Commission wants to in­tro­duce sur­veil­lance mech­a­nisms that will im­mensely weaken Europe’s cy­ber­se­cu­rity would be hon­est com­mu­ni­ca­tion.

No one de­nies that child sex­ual abuse is a big is­sue that needs to be ad­dressed. But when propos­ing such dras­tic mea­sures like CSAM scan­ning of every pri­vate chat mes­sage, the ar­gu­ments must be sound. Otherwise, the EU Commission is not help­ing any­one — not the chil­dren, and not our free, de­mo­c­ra­tic so­ci­eties.

The EU Commission has man­aged to push three ar­gu­ments into the pub­lic de­bate to swing the pub­lic opin­ion in fa­vor of scan­ning for CSA ma­te­r­ial on every de­vice. But the ar­gu­ments are bla­tantly wrong:

One in Five: The EU Commission claims that One in Five chil­dren in the EU would be sex­u­ally abused.

AI-based sur­veil­lance would not harm our right to pri­vacy, but save the chil­dren.

90 % of CSAM would be hosted on European servers

The EU Commission uses the ‘One in Five’ claim to jus­tify the pro­posed gen­eral mass sur­veil­lance of all European cit­i­zens.

Yes, child abuse is an im­mense prob­lem. Every ex­pert in the field of child pro­tec­tion will agree that pol­i­tics need to do more to pro­tect the most vul­ner­a­ble in our so­ci­ety: chil­dren.

Nevertheless, the ques­tion of pro­por­tion must be looked at very closely when it comes to CSAM scan­ning on our per­sonal de­vices:

Is it okay that the EU in­tro­duces mass

sur­veil­lance mech­a­nisms for all EU cit­i­zens in an at­tempt to tackle child sex­ual abuse?

To find an an­swer to this ques­tion, I would like to ask the EU Commission sev­eral ques­tions:

There is no sta­tis­tic to be found that sup­ports the ‘One in Five’ claim. This fig­ure is promi­nently put on a web­site by the Council of Europe, but with­out giv­ing any source.

According to the World Health Organization (WHO) 9.6% of chil­dren world­wide are sex­u­ally abused. Contrary to the EU fig­ures, this data is based on a study, an analy­ses of com­mu­nity sur­veys.

Nevertheless, let’s ig­nore the European Commission’s ex­ag­ger­a­tion of af­fected chil­dren as the num­ber pub­lished by the WHO is still very high and must be ad­dressed.

The WHO num­ber sug­gests that more than 6 mil­lion chil­dren in the EU suf­fer from sex­ual abuse.

Consequently, we can agree that the EU must do some­thing to stop child sex­ual abuse.

Another ques­tion that is very im­por­tant when in­tro­duc­ing sur­veil­lance mea­sures to tackle child sex­ual abuse is the one of ef­fec­tive­ness.

If mon­i­tor­ing of our pri­vate com­mu­ni­ca­tion (CSAM scan­ning) would help save mil­lions of chil­dren in Europe from sex­ual abuse, many

peo­ple would agree to the mea­sure. But would that ac­tu­ally be the case?

On the same web­site that the EU Commission claims that ‘1 in 5’ chil­dren are af­fected, they also say that “Between 70%

and 85% of chil­dren know their abuser. The vast ma­jor­ity of chil­dren are vic­tims of peo­ple they trust.”

This begs the ques­tion: How, just how, is scan­ning for CSAM on every chat mes­sage go­ing to help pre­vent child sex­ual

abuse within the fam­ily, the sports club or the church?

To find out whether mon­i­tor­ing of pri­vate mes­sages for CSA ma­te­r­ial may help tackle child sex­ual abuse, we must take a look at ac­tual mon­i­tor­ing data that is al­ready avail­able.

As an email provider based in Germany we have such data. Our trans­parency re­port shows that we are reg­u­larly re­ceiv­ing valid telecom­mu­ni­ca­tions sur­veil­lance or­ders from German au­thor­i­ties to pros­e­cute po­ten­tial crim­i­nals.

One could think that Tutanota as a pri­vacy-fo­cused, end-to-end en­crypted email ser­vice would be the go-to place for crim­i­nal of­fend­ers, for in­stance for shar­ing CSAM. In con­se­quence, one would ex­pect the num­ber of court or­ders is­sued in re­gard to “child pornog­ra­phy” to be high.

In 2021 we re­ceived ONE telecom­mu­ni­ca­tions sur­veil­lance or­der based on sus­pi­cion that the ac­count was used in re­gard

to “child pornog­ra­phy”.

This is 1,3% of all or­ders that we re­ceived in 2021. More than two thirds of or­ders were is­sued in re­gard to “ransomware”; a few in­di­vid­ual cases in re­gard to copy­right in­fringe­ment, prepa­ra­tion of se­ri­ous crimes, black­mail and ter­ror.

Numbers pub­lished by the German Federal Office of Justice paint a sim­i­lar pic­ture: In Germany, more than 47.3 per cent of the mea­sures for the sur­veil­lance of telecom­mu­ni­ca­tions ac­cord­ing to § 100a StPO were or­dered to find sus­pects of drug re­lated of­fenses in 2019. Only 0.1 per cent of the or­ders - or 21(!) in to­tal - where is­sued in re­la­tion to “child pornog­ra­phy”.

In 2019, there were 13.670 cases of child abuse ac­cord­ing to the

sta­tis­tic of the German Federal Ministry of the Interior

in Germany.

If we take these num­bers to­gether, there were 13.670 chil­dren abused in Germany in 2019. In only 21 of these cases a

telecom­mu­ni­ca­tions sur­veil­lance or­der was is­sued.

It be­comes ob­vi­ous that the mon­i­tor­ing of telecom­mu­ni­ca­tions (which is al­ready pos­si­ble) does not play a sig­nif­i­cant role to track down per­pe­tra­tors.

The con­clu­sion here is ob­vi­ous: ‘More sur­veil­lance’ will not bring ‘more se­cu­ri­ty’ to the chil­dren in Europe.

Similarly to the ‘One in Five’ claim, the EU Commission claims that 90% of child sex­ual abuse ma­te­r­ial is hosted on

European servers. Again the EU Commission uses this claim to jus­tify its planned CSAM scan­ning.

However, even ex­perts in this field, the German eco Association that works to­gether with the au­thor­i­ties to take down CSAM (Child Sexual Abuse Material), state that “in their es­ti­ma­tion, the num­bers are a long way from the claimed 90 per­cent”. Alexandra Koch-Skiba of the eco Association also

says: “In our view, the draft has the po­ten­tial to cre­ate a free pass for gov­ern­ment sur­veil­lance. This is in­ef­fec­tive and il­le­gal.

Sustainable pro­tec­tion of chil­dren and young peo­ple would in­stead re­quire more staff for in­ves­ti­ga­tions and com­pre­hen­sive pros­e­cu­tion.”

Even German law en­force­ment of­fi­cials are

crit­i­ciz­ing the EU plans be­hind closed doors. They ar­gue that there would be other ways to track down more of­fend­ers. “If it’s just about hav­ing more cases and catch­ing more per­pe­tra­tors, then you don’t need such an en­croach­ment on fun­da­men­tal rights,” says an­other long­time child abuse in­ves­ti­ga­tor.

It is un­be­liev­able that the EU Commission uses these ex­ag­ger­a­tions to swing the pub­lic opin­ion in fa­vor of CSAM scan­ning. It

looks like the ar­gu­ment ‘to pro­tect the chil­dren’ is used to in­tro­duce Chinese-like sur­veil­lance mech­a­nisms. Here in Europe.

The Cat S22 Flip takes the cell phone back to what it should be… a phone.  Made for those who want a de­vice as sim­ple to use as it is tough, the Cat S22 Flip fea­tures phys­i­cal but­tons and a large touch screen, let­ting you choose how you in­ter­act with it. The Cat S22 Flip’s ‘Snap it to End it’ call­ing gives you con­fi­dence that when it is closed the call is over.

Android™ 11 (Go Edition)

Programmable PTT Button

IP68 & MIL-SPEC 810H

Drop tested up to 6ft on to steel

Waterproof to a depth of 5ft for up to 35 mins

The Cat S22 Flip brings the worlds biggest op­er­at­ing sys­tem, Android™ 11 (Go Edition) and its Play Store to the tra­di­tional cell­phone de­sign so you no longer have to choose be­tween a con­ven­tional cell­phone or a smart­phone. Powerful speak­ers help you hear in the loud­est of en­vi­ron­ments, and a larger bat­tery keeps the Cat S22 Flip go­ing, so no mat­ter if you are a first re­spon­der on the front line or a farmer out in the field, the Cat S22 Flip is a phone you can de­pend on.

Engineered to the high­est rugged stan­dards, the Cat S22 Flip is every­thing you ex­pect from a Cat phone, with the hinge alone is tested 150 thou­sand times. The Cat S22 flip fea­tures the same IP68 and MIL-SPEC 810H rat­ing as our larger phones, mean­ing it can be dropped, dunked and washed reg­u­larly us­ing the harsh­est of chem­i­cals, bleaches and san­i­tiz­ers. So you can wash it thor­oughly and reg­u­larly, help­ing to keep you and those around you safe from germs.

The Cat S22 Flip is de­signed to work in the tough­est of en­vi­ron­ments so you don’t have to worry about your de­vices. This is backed up with our 2 year war­ranty so you can stay con­fi­dent that no mat­ter what hap­pens.

The Cat S22 Flip built for American en­ter­prise. With its rugged build and Android Go op­er­at­ing sys­tem, the Cat S22 Flip the per­fect phone for a huge range of work­ers from those on the front line to those in the field and many more.

Android™ 11 (Go Edition) is the lighter ver­sion of Google’s Android™ sys­tem, giv­ing you ac­cess to key apps and se­cu­rity ben­e­fits of Android with­out the need for a larger, ex­pen­sive de­vice, mak­ing it the per­fect op­tion for whether you are look­ing for a de­vice for your­self or your team.

Up to 6ft on to steel­Han­dles low to high tem­per­a­ture dif­fer­ences be­tween -13°F to 122°F for up to 30 min­s­Pres­sur­ized al­co­hol abra­sion tests at 500gF/cm2 over hun­dreds of cy­cles

By con­tin­u­ing to use this site you con­sent to the use of cook­ies on your de­vice as de­scribed in our pri­vacy pol­icy un­less you have dis­abled them. You can change your cookie set­tings at any time but parts of our site will not func­tion cor­rectly with­out them.

Other App From Our Hands

Unprecedented ev­i­dence from in­ter­nal po­lice net­works in China’s Xinjiang re­gion proves prison-like na­ture of re-ed­u­ca­tion camps, shows top Chinese lead­ers’ di­rect in­volve­ment in the mass in­tern­ment cam­paign.

The Xinjiang Police Files are a ma­jor cache of speeches, im­ages, doc­u­ments and spread­sheets ob­tained by a third party from con­fi­den­tial in­ter­nal po­lice net­works. They pro­vide a ground­break­ing in­side view of the na­ture and scale of Beijing’s se­cre­tive cam­paign of in­tern­ing be­tween 1-2 mil­lion Uyghurs and other eth­nic cit­i­zens in China’s north­west­ern Xinjiang re­gion.

The files have been au­then­ti­cated through peer-re­viewed schol­arly re­search. Investigative re­search teams from over a dozen global me­dia out­lets have also ver­i­fied por­tions of the data.

Read our in-depth re­ports of the Xinjiang Police Files

View PowerPoints and im­ages show­ing po­lice se­cu­rity drills in Xinjiang’s camps and vil­lages

Watch as Dr. Adrian Zenz, an in­ter­na­tional ex­pert on in­ter­nal Chinese gov­ern­ment doc­u­ments and the Xinjiang in­tern­ment cam­paign, breaks down the con­tents of the Xinjiang Police Files, why they are im­por­tant, and how civil so­ci­ety and gov­ern­ments should re­spond.

A pro­ject of the Victims of Communism Memorial Foundation. ​

Here’s the sum­mary of the hard­ware and the soft­ware that pow­ers Healthchecks.io.

Since 2017, Healthchecks.io runs on ded­i­cated servers at Hetzner. The cur­rent lineup is:

All servers are lo­cated in the Falkenstein data cen­ter park, scat­tered across the FSN-DCx data cen­ters so they are not all be­hind the same core switch. The monthly Hetzner bill is €484.

* Systemd man­ages ser­vices that need to run con­tin­u­ously (haproxy, ng­inx, post­gresql, etc.)

* Wireguard for pri­vate net­work­ing be­tween the servers. Tiered topol­ogy: HAProxy servers can­not talk to PostgreSQL servers.

* Netdata agent for mon­i­tor­ing the ma­chines and the ser­vices run­ning on them. Connected to Netdata Cloud for easy overview of all servers.

* HAProxy 2.2 for ter­mi­nat­ing TLS con­nec­tions, and load bal­anc­ing be­tween app servers. Enables easy rolling up­dates of ap­pli­ca­tion servers.

* PostgreSQL 13, stream­ing repli­ca­tion from pri­mary to standby. No au­to­matic failover: I can trig­ger failover with a sin­gle com­mand, but the de­ci­sion is man­ual.

* hchk, a small ap­pli­ca­tion writ­ten in Go, han­dles ping API (hc-ping.com) and in­bound email.

* NGINX han­dles rate lim­it­ing, sta­tic file serv­ing, and re­verse prox­y­ing to uWSGI and hchk.

Healthchecks.io, the cron job mon­i­tor­ing ser­vice, uses cron jobs it­self for the fol­low­ing pe­ri­odic tasks:

* Once a day, make a full data­base backup, en­crypt it with gpg, and up­load it to AWS S3.

* Once a day, send “Your ac­count is in­ac­tive and is about to be deleted” no­ti­fi­ca­tions to in­ac­tive users.

* Once a day, send “Your sub­scrip­tion will re­new on …” for an­nual sub­scrip­tions that are due in 1 month.

* My main dev ma­chine is a desk­top PC with a sin­gle 27″ 1440p dis­play.

* Sublime Text for edit­ing source code. A com­bi­na­tion of meld, Sublime Merge and com­mand-line git for work­ing with git.

* Yubikeys for sign­ing git com­mits and log­ging into servers.

* Fabric scripts for de­ploy­ing code and run­ning main­te­nance tasks on servers.

* A ded­i­cated lap­top in­side a ded­i­cated back­pack, for deal­ing with emer­gen­cies while away from the main PC.

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CF33-hNIS-antiPDL1 for the Treatment of Metastatic Triple Negative Breast Cancer

This phase I trial tests the safety, side ef­fects, and best dose of CF33-hNIS-antiPDL1 in treat­ing pa­tients with triple neg­a­tive breast can­cer that has spread to other places in the body (metastatic). CF33-hNIS-antiPDL1 is an on­colytic virus. This is a virus that is de­signed to in­fect tu­mor cells and break them down.

Documented in­formed con­sent of the par­tic­i­pant and/​or legally au­tho­rized rep­re­sen­ta­tive

* Assent, when ap­pro­pri­ate, will be ob­tained per in­sti­tu­tional guide­lines

Agreement to re­search biop­sies on study, once dur­ing study and end of study, ex­cep­tions may be granted with study prin­ci­pal in­ves­ti­ga­tor (PI) ap­proval

Histologically con­firmed metasta­tic triple neg­a­tive breast can­cer. Triple neg­a­tive sta­tus will be de­fined as es­tro­gen re­cep­tor (ER) and prog­es­terone re­cep­tor (PR) =< 10% by im­muno­his­to­chem­istry (IHC) and HER2 neg­a­tive, per American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guide­lines

Patients must have pro­gressed on or been in­tol­er­ant of at least 2 prior lines of ther­apy for ad­vanced/​metasta­tic dis­ease. Patients that qual­ify for im­munother­apy and/​or PARP in­hibitors must have pro­gressed on or been in­tol­er­ant of these agents

Fully re­cov­ered from the acute toxic ef­fects (except alope­cia) to =< grade 2 to prior anti-can­cer ther­apy

Must have a su­per­fi­cial tu­mor (cutaneous, sub­cu­ta­neous), breast le­sion or nodal metas­tases amenable to safe re­peated in­tra­tu­moral in­jec­tions per treat­ing physi­cian and in­ter­ven­tional ra­di­ol­o­gist re­view

Absolute neu­trophil count (ANC) >= 1,500/mm^3

* NOTE: Growth fac­tor is not per­mit­ted within 14 days of ANC as­sess­ment un­less cy­tope­nia is sec­ondary to dis­ease in­volve­ment

Platelets >= 100,000/mm^3

* NOTE: Platelet trans­fu­sions are not per­mit­ted within 14 days of platelet as­sess­ment un­less cy­tope­nia is sec­ondary to dis­ease in­volve­ment

Serum cre­a­ti­nine =< 1.5 mg/​dL or cre­a­ti­nine clear­ance of >= 50 mL/​min per 24 hour urine test or the Cockcroft-Gault for­mula

Agreement by fe­males and males of child­bear­ing po­ten­tial* and their part­ners to use an ef­fec­tive method of birth con­trol (defined as a hor­monal or bar­rier method) or ab­stain from het­ero­sex­ual ac­tiv­ity for the course of the study through at least 6 months af­ter the last dose of pro­to­col ther­apy

* Childbearing po­ten­tial de­fined as not be­ing sur­gi­cally ster­il­ized (men and women) or have not been free from menses for > 1 year (women only)

Chemotherapy, bi­o­log­i­cal ther­apy, im­munother­apy or in­ves­ti­ga­tional ther­apy within 14 days prior to day 1 of pro­to­col ther­apy

Major surgery or ra­di­a­tion ther­apy within 28 days of study ther­apy

Has re­ceived a vac­ci­na­tion within 30 days of first study in­jec­tion

History of al­ler­gic re­ac­tions at­trib­uted to com­pounds of sim­i­lar chem­i­cal or bi­o­logic com­po­si­tion to study agent

Patients with a known his­tory of he­pati­tis B or he­pati­tis C in­fec­tion who have ac­tive dis­ease as ev­i­denced by he­pati­tis (Hep) B sur­face anti­gen sta­tus or Hep C poly­merase chain re­ac­tion (PCR) sta­tus ob­tained within 14 days of cy­cle 1, day 1

Another ma­lig­nancy within 3 years, ex­cept non-melanoma­tous skin can­cer

Patients may not have clin­i­cally un­sta­ble brain metas­tases. Patients may be en­rolled with a his­tory of treated brain metas­tases that are clin­i­cally sta­ble for >= 4 weeks prior to start of study treat­ment

Any other con­di­tion that would, in the Investigator’s judg­ment, con­traindi­cate the pa­tien­t’s par­tic­i­pa­tion in the clin­i­cal study due to safety con­cerns with clin­i­cal study pro­ce­dures

Prospective par­tic­i­pants who, in the opin­ion of the in­ves­ti­ga­tor, may not be able to com­ply with all study pro­ce­dures (including com­pli­ance is­sues re­lated to fea­si­bil­ity/​lo­gis­tics)

I. To de­ter­mine the safety and tol­er­a­bil­ity of a novel chimeric on­colytic or­thopoxvirus, on­colytic virus CF33-expressing hNIS/​Anti-PD-L1 an­ti­body (CF33-hNIS-antiPDL1), by the eval­u­a­tion of tox­i­c­i­ties in­clud­ing: type, fre­quency, sever­ity, at­tri­bu­tion, time course, re­versibil­ity and du­ra­tion ac­cord­ing to Common Terminology Criteria for Adverse Events (CTCAE) 5.0 cri­te­ria. I. To de­ter­mine the op­ti­mal bi­o­logic dose (OBD) (defined as a safe dose that in­duces an im­mune re­sponse in tu­mors [increase check­point tar­get PD-L1 by at least 5% and/​or in­crease T cell in­fil­tra­tion by at least 10%]) and the rec­om­mended phase II dose (RP2D) for fu­ture ex­pan­sion trial.II. To de­ter­mine tu­mor re­sponse rates by Response Evaluation Criteria in Solid Tumors (RECIST) ver­sion (v)1.1 (primary) and im­mune-mod­i­fied (i)RECIST (secondary).III. To doc­u­ment pos­si­ble ther­a­peu­tic ef­fi­cacy and eval­u­ate pro­gres­sion-free sur­vival, over­all sur­vival and re­sponse.I. To de­ter­mine the im­mune and ge­nomic pro­files of tu­mors be­fore and af­ter CF33-hNIS-antiPDL1 ther­apy.Pa­tients re­ceive CF33-hNIS-antiPDL1 in­tra­tu­morally (IT) on days 1 and 15. Treatment re­peats every 28 days for up to 3 cy­cles in the ab­sence of dis­ease pro­gres­sion or un­ac­cept­able tox­i­c­ity.Af­ter com­ple­tion of study treat­ment, pa­tients are fol­lowed up at 30 days, then every 3 months for 1 year.

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Which tri­als are right for you?

Use the check­list in our guide to gather the in­for­ma­tion you’ll need.

… in which we look at one or two ways to make life eas­ier when work­ing with Python reg­u­lar ex­pres­sions.

tl;dr: You can com­pose ver­bose reg­u­lar ex­pres­sions us­ing f‍-‍strings.

Here’s a real-world ex­am­ple — in­stead of this:

Read on for de­tails and some caveats.

Formatted string lit­er­als (f‍-‍strings) were added in Python 3.6, and pro­vide a way to em­bed ex­pres­sions in­side string lit­er­als, us­ing a syn­tax sim­i­lar to that of str.for­mat():

Verbose reg­u­lar ex­pres­sions (re. VERBOSE) have been around since for­ever, and al­low writ­ing reg­u­lar ex­pres­sions with non-sig­nif­i­cant white­space and com­ments:

Once you see it, it’s ob­vi­ous — you can use f‍-‍strings to com­pose reg­u­lar ex­pres­sions:

It’s so ob­vi­ous, it only took me three years to do it af­ter I started us­ing Python 3.6+, de­spite us­ing both fea­tures dur­ing all that time.

Of course, there’s any num­ber of li­braries for build­ing reg­u­lar ex­pres­sions; the ben­e­fit of this is that it has zero de­pen­den­cies, and zero ex­tra things you need to learn.

Because a hash is used to mark the start of a com­ment, and spaces are mostly ig­nored, you have to rep­re­sent them in some other way.

The doc­u­men­ta­tion of re. VERBOSE is quite help­ful:

When a line con­tains a # that is not in a char­ac­ter class and is not pre­ceded by an un­escaped back­slash, all char­ac­ters from the left­most such # through the end of the line are ig­nored.

That is, this won’t work as the non-ver­bose ver­sion:

… but these will:

The same is true for spaces:

When com­pos­ing regexes, end­ing a pat­tern on the same line as a com­ment might ac­ci­den­tally com­ment the fol­low­ing line in the en­clos­ing pat­tern:

This can be avoided by al­ways end­ing the pat­tern on a new line:

While a bit cum­ber­some, in real life most pat­terns would span mul­ti­ple lines any­way, so it’s not re­ally an is­sue.

Because f‍-‍strings al­ready use braces for re­place­ments, to rep­re­sent brace quan­ti­fiers you must dou­ble the braces:

Maybe you’d like to use ver­bose regexes, but don’t con­trol the flags passed to the re func­tions (for ex­am­ple, be­cause you’re pass­ing the regex to an API).

So, you can do this:

That’s it for now.

Learned some­thing new to­day? Share this with oth­ers, it re­ally helps!

Lots of other lan­guages sup­port the in­line ver­bose flag, too! You can build a pat­tern in whichever lan­guage is more con­ve­nient, and use it in any other one. Languages like…

C (with PCRE — and by ex­ten­sion, C++, PHP, and many oth­ers):

grep (only the GNU one):

Java (and by ex­ten­sion, lots of JVM lan­guages, like Scala):

Notable lan­guages that don’t sup­port in­line ver­bose flags out of the box: