The PEOPLE OF THE STATE OF NEW YORK.
Mr. Aaron E. Koota, New York City, for respondent.
Petitioner refused to testify before a New York grand jury which was investigating the alleged association of city policemen with criminals, racketeers, and gamblers in Kings County. He was convicted of criminal contempt and sentenced to one year's imprisonment. We granted certiorari, 347 U.S. 1010, 74 S.Ct. 869, 98 L.Ed. 1134, to determine whether, under the circumstances here presented, petitioner was deprived of his liberty without due process of law in being punished for his refusal to testify. Cf. Adamson v. People of State of California, 332 U.S. 46, 54, 67 S.Ct. 1672, 1676, 91 L.Ed. 1903.
Petitioner refused to answer the question on the ground that his answer might tend to incriminate him. He made a statement in which he claimed that his waiver of immunity was invalid since he had not understood its significance when he signed it, and no one had explained it to him. He expressed doubt as to his status as a witness and his privileges and duties as such.
Petitioner was taken before the County Court of Kings County to clarify his status. It was there held, after a hearing, that the waiver was valid because petitioner had fully understood its significance when he signed it. Petitioner was directed to answer the question which he had been asked. He returned to the grand jury, but persisted in his refusal to testify. He was thereupon indicted for criminal contempt, tried by a jury, and convicted. His conviction was affirmed by the Appellate Division in a short memorandum opinion, 282 App.Div. 775, 122 N.Y.S.2d 478, and by the New York Court of Appeals without opinion, 306 N.Y. 747, 117 N.E.2d 921. The Court of Appeals did amend its remittitur to show that the question of whether petitioner had been deprived of liberty without due process of law had been raised and passed upon. 306 N.Y. 875, 119 N.E.2d 45.
Petitioner contends that this Court must here determine whether the Fourteenth Amendment prevents a State from imprisoning an individual for refusing to give self-incriminatory testimony. In so doing he ignores the crucial significance of the immunity statute in this case. We simply hold that under the circumstances here presented petitioner was not deprived of any constitutional rights in being punished for his refusal to testify.
The immunity statute is crucial in this case because it removed any possible justification which petitioner had for not testifying. If petitioner had not executed a waiver of immunity, it is clear beyond dispute that he would have had to testify;6 the statute would have provided him with immunity from prosecution on the matters on which his testimony was sought, and thus his testimony could not possibly have been self-incriminatory.7 The waiver of immunity, although it does affect the possibility of subsequent prosecution, does not alter petitioner's underlying obligation to testify. Much of the argument before this Court has been directed at the question of whether the waiver of immunity was valid or invalid, voluntary or coerced, effectual or ineffectual. That question is irrelevant to the disposition of this case for on either assumption the requirement to testify, imposed by the grant of immunity, remains unimpaired.
First, assume that the waiver was valid. Any testimony which the petitioner gave could then have formed the basis for a subsequent prosecution, and the State would here be punishing the petitioner for his refusal to provide such self-incriminatory testimony. But, since we are assuming the validity of the waiver, such a situation would be simply the result of a voluntary choice to waive an immunity provided by the State.
The waiver of immunity from prosecution may, on the other hand, be regarded as invalid. Petitioner argues at some length that the waiver was obtained by a 'pattern of duress and lack of understanding.' He points to the circumstances attending the signing of the waiver: the size of the room, the number of policemen who simultaneously executed waivers, the speed with which the waivers were obtained, the lack of counsel, etc.8 He also points to the provisions of the New York Constitution and the City Charter requiring him to sign the waiver or lose his job.9 In addition he claims that the waiver was stale and thus ineffective since over 21 months had elapsed from the date of its execution to his refusal to testify. We fail to see where petitioner's arguments lead. If the waiver is invalid, the immunity from prosecution persists, and in the presence of such immunity petitioner's testimony could not possibly be self-incriminatory. It must be remembered that this conviction is for refusing to testify. The invalidity of the waiver may be made a defense to subsequent prosecution, where it would be a proper matter for disposition; it is no defense to a refusal to testify.
Petitioner suggests that his refusal to testify may have been justified by the uncertainty existing at the time he was directed to testify. That uncertainty was only as to whether or not he could be prosecuted for criminal activity which might be revealed in his testimony. As a matter of state law, a defense to the crime of criminal contempt may be provided when such uncertainty reaches a sufficiently high point.10 But the Constitution does not require the definitive resolution of collateral questions as a condition precedent to a valid contempt conviction. Cf. Cobbledick v. United States, 309 U.S. 323, 327, 60 S.Ct. 540, 542, 84 L.Ed. 783. The petitioner knew that however the question of the validity of the waiver might be resolved, he was obliged to testify. In persisting in his refusal after being directed to testify he could be punished for contempt. The law strives to provide predictability so that knowing men may wisely order their affairs; it cannot, however, remove all doubts as to the consequence of a course of action.
The judgment below is accordingly affirmed.
Mr. Chief Justice WARREN, with whom Mr. Justice CLARK joins, concurring.
I concur in the opinion and judgment of the Court, but would add that substantial federal questions may arise if the petitioner is again called upon to testify concerning bribery on the police force while he was an officer and if he is thereafter denied immunity as to any offenses related to the investigation.
This Court has never held that a State, in the absence of an adequate immunity statute, can punish a witness for contempt for refusing to answer self-incriminatory questions. A case involving such facts has never been presented here.1 Nor is this such a case, since New York, by § 381 of the Penal Law, has granted immunity. Petitioner was obliged to answer the questions as would by any witness in the State of New York. If he had signed no waiver, he concededly would have been compelled to testify, since under § 381 of the New York Penal Law he would be entitled to immunity. The fact that he signed a waiver, even assuming it to be invalid as he claims, certainly cannot relieve him from the duty of every citizen to testify. His failure to so testify, therefore, placed him in contempt of court and subject to the punishment accorded him in this case.
Moreover, a state immunity statute—like any other state statute—must be applied uniformly unless there is some reasonable ground for classification; otherwise, the Equal Protection Clause of the Fourteenth Amendment is violated.3 After a city employee suffers the primary sanction of the constitutional and charter sections—namely, loss of his position—it may well be that the waiver cannot to used to send him to the penitentiary for bribery when the same sanction would not be imposed on other witnesses giving like testimony.
However, as already noted, we do not reach these questions here.
In order to keep his job as a New York City policeman petitioner signed a paper waiving immunity he would otherwise have had from prosecution under state law as to matters he might testify about before a grand jury. Twenty-two months later, long after he had resigned as a policeman, he was brought before a county grand jury. He was asked whether he had ever accepted bribes while he was a policeman. Acceptance of bribes is a New York felony punishable by ten years' imprisonment. Petitioner refused to answer the questions claiming a federal constitutional and state privilege against self-incrimination. For refusal to answer he was sentenced to twelve months in prison. The Court holds that New York can thus imprison petitioner 'for his refusal to provide such self-incriminatory testimony.' I do not agree that New York can do this consistently with the Federal Constitution.
For reasons stated on other occasions I believe the Fourteenth Amendment makes the Fifth Amendment applicable to the States. See, e.g., Adamson v. People of State of California, 332 U.S. 46, 68, 67 S.Ct. 1672, 1683, 91 L.Ed. 1903. And the Fifth Amendment accords an unqualified privilege to persons to be silent when asked questions, answers to which would make those persons witnesses against themselves. See, e.g., Blau v. United States, 340 U.S. 159, 161, 71 S.Ct. 223, 224, 95 L.Ed. 170. Even under the other view of the Fourteenth Amendment, that it does not make the Fifth Amendment applicable to the States and that under some circumstances States may compel persons to testify against themselves, this Court has held many times that a State may not convict a person on testimony it coerced from him. E.g., Leyra v. Denno, 347 U.S. 556, 558, 74 S.Ct. 716, 717, 98 L.Ed. 948; Ashcraft v. State of Tennessee, 322 U.S. 143, 155, 64 S.Ct. 921, 927, 88 L.Ed. 1192; cf. Rochin v. People of California, 342 U.S. 165, 72 S.Ct. 205, 96 L.Ed. 183. Coercing testimony for that purpose is equally obnoxious to the Fourteenth Amendment. However its action is described, the State is seeking to coerce this petitioner to give testimony to help bring about his conviction for crime. For it is certainly coercion to throw a man into jail unless he agrees to testify against himself.
The Court approves the dilemma in which New York places petitioner. He must give evidence which might convict him of a felony or go to jail for refusing to give that evidence. The Court says, however, that petitioner's dilemma is 'simply the result of a voluntary choice to waive an immunity provided by the State.' There of course may be some doubt as to how 'voluntary' this 'choice' was. In any event it is a completely novel idea that a waiver device of this kind can destroy constitutional protections. It is nothing more nor less than a wholesale blanket agreement that a person will not claim a constitutional privilege with reference to anything he has ever done in the past or that he may do in the future in connection with his job. So far as I know it has never been held before that the privilege against self-incrimination or any other Bill of Rights safeguard can be bargained away far in advance of the day when needed as protection against the overreaching power of government.
The Court's holding appears to approve a dangerous technique whereby both State and Federal Governments can compel people to convict themselves out of their own mouths. Are we to infer that the Federal Government is now free to compel its millions of employees permanently to waive their privilege against self-incrimination or lose their jobs? Surely private employers are not now free to compel their employees to waive this and other constitutional privileges. This might be highly satisfactory to those who believe that the privilege against compulsory self-incrimination has no proper place in our Bill of Rights. But that provision was designed as a continuing rigid safeguard against ruthless exercise of governmental power.* That it sometimes permits people to escape conviction for offenses is no sufficient reason for reading it out of the Constitution. Those who wrote the provision are bound to have known that it would have the effect of making it harder for the Government to convict people accused of crime. Exactly that effect results from all of the procedural provisions of the Bill of Rights, including the right to be heard, to have a lawyer, to be confronted by witnesses, to be informed of the nature of the offense charged, and to be tried by jury. This holding weakens these and other ancient safeguards which to me represent great landmarks in the never-ceasing struggle of men to be free from despotic governmental powers. See dissent in Feldman v. United States, 322 U.S. 487, 494—503, 64 S.Ct. 1082, 1085—1089, 88 L.Ed. 1408.
See also New York Code of Criminal Procedure, § 10.
To the same effect were §§ 584 and 996 of the Penal Law which dealt with the crimes of conspiracy and gambling. These statutes have since been amended. New York Laws 1953, c. 891.
'I, Michael J. Regan, of No. 3819 Harper Avenue, Bronx, * * * of The City of New York pursuant to the provisions of Section 2446 of the Penal Law of the State of New York, do hereby waive all immunity which I would otherwise obtain from indictment, prosecution, punishment, penalty or forfeiture for or on account of or relating to any transaction, matter or thing concerning which I may testify or produce evidence, documentary or otherwise, before the Grand Jury of the County of Kings, in its investigation above entitled or in any other investigation or other proceeding, before any judge or justice, court or other tribunal, conducting an inquiry for legal proceeding relating to the acts of said John Doe, Michael J. Regan, or of any other person.
'I do hereby further waive any and all privileges which I would otherwise obtain against the use against me of the testimony so given or the evidence so produced upon any criminal investigation, prosecution or proceeding.
The questionnaires never were completed.
See Brown v. Walker, 161 U.S. 591, 16 S.Ct. 644, 40 L.Ed. 819; cf. Counselman v. Hitchcock, 142 U.S. 547, 12 S.Ct. 195, 35 L.Ed. 1110.
Petitioner does not challenge the sufficiency of the immunity provided.
There was testimony that the waiver was obtained in a room which measured '10 10, or 12 12, approximately,' containing a desk 'about 60 2' (sic) and a bench upon which 'about five people could sit.' About 35 waivers were obtained in a period of 25 minutes. An assistant district attorney made a single speech explaining the nature of the immunity. Immediately after executing the waiver, petitioner testified that he had signed the waiver voluntarily, that it had been explained to him, and that he understood its meaning. Twenty months thereafter petitioner reaffirmed its execution without raising any objection to its validity. It was some twenty-one months after its execution that petitioner challenged the validity of the waiver for the first time. The trial court left the question of the validity of the waiver to the jury. Its verdict of guilty indicates its finding on this matter. The conviction was affirmed by both appellate courts, but we cannot be sure that the affirmance sustained the finding on this matter for the appellate courts may have viewed the question of the validity of the waiver as irrelevant to their decision as we do to ours.
It might be pointed out that, as far as the record shows, this objection was at no point raised below. It appears for the first time in the Petition for Certiorari.
People ex rel. Hofsaes v. Warden, 302 N.Y. 403, 98 N.E.2d 579.
Compare Twining v. State of New Jersey, 211 U.S. 78, 29 S.Ct. 14, 53 L.Ed. 97 (jury instruction authorizing the jury in a criminal case to draw an unfavorable inference from the accused's failure to take the stand); Adamson v. People of State of California, 332 U.S. 46, 67 S.Ct. 1672, 91 L.Ed. 1903 (state law permitting prosecutor and trial judge to comment on the accused's failure to take the stand); Snyder v. Commonwealth of Massachusetts, 291 U.S. 97, 105, 54 S.Ct. 330, 332, 78 L.Ed. 674 (denial of permission to the accused to accompany jury on visit to scene of crime); Palko v. State of Connecticut, 302 U.S. 319, 325 326, 58 S.Ct. 149, 151—152, 82 L.Ed. 288 (state statute allowing appeal by State in criminal cases).
Cf. Terral v. Burke Construction Co., 257 U.S. 529, 42 S.Ct. 188, 66 L.Ed. 352.
See, e.g., Dowd v. United States ex rel. Cook, 340 U.S. 206, 71 S.Ct. 262, 95 L.Ed. 215, and Cochran v. State of Kansas, 316 U.S. 255, 62 S.Ct. 1068, 86 L.Ed. 1453.
'If a man has done wrong, he should be punished. But the evidence against him should be produced, and evaluated by a proper court in a fair trial. Neither torture nor an oath nor the threat of punishment such as imprisonment for contempt should be used to compel him to provide the evidence to accuse or to convict himself.' Griswold, The Fifth Amendment Today, 7—8.


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Q: Should I update sources.list.d jessie->stretch? I am upgrading from jessie to stretch, I understood that I have to replace jessie by stretch in the sources.list but what about those in sources.list.d/
I have these sources under sources.list.d/:
mysql.list:
deb http://repo.mysql.com/apt/debian/ jessie mysql-apt-config
deb http://repo.mysql.com/apt/debian/ jessie mysql-5.6
deb http://repo.mysql.com/apt/debian/ jessie mysql-tools
deb-src http://repo.mysql.com/apt/debian/ jessie mysql-5.6

nodesource.list:
deb https://deb.nodesource.com/node_10.x jessie main
deb-src https://deb.nodesource.com/node_10.x jessie main

php.list (for some reason this was already on stretch):
deb https://packages.sury.org/php/ stretch main

A: Yes, After upgrading to debian stretch, you need to edit the 3rd party repository by replacing the jessie codename with stretch under /etc/apt/sources.list.d/ .
sudo sed -i 's/jessie/stretch/g' /etc/apt/sources.list.d/{nodesource,mysql}.list

Then run:
sudo apt update
sudo apt upgrade

redpajama_set_name: RedPajamaStackExchange


No matter your budget, we can help you build something beautiful in your home. Customized cabinetry gives your home a unique look, and we work with you to design and build your perfect built-ins, kitchen cabinets, and more.
Bigstone Custom Cabinets was built to offer customized cabinetry options to customers at competitive prices. With each project, we strive to build something amazing and beautiful for you.
When you partner with Bigstone Custom Cabinets, you get high quality cabinets, exemplary service, and reasonable and competitive prices. We help you design, build, and install custom cabinets everyone will love.
At Bigstone Custom Cabinets, we're proud of what we do for our customers every single day. Designing, building, and installing cabinetry is our core competency; it's what we live, do, and breath.
They did an amazing job on our bathroom vanity. They listened, explained, and took time to make sure we got what we wanted. Thank you!
Love the cabinets that Big stone made for our home! Highly recommend them and have people already asking who made our cupboards for our kitchen!
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Your home (or business) is a reflection of you and your life. We strive to enhance each customer's space with cabinets that match your life. Whether that means incorporating special storage spaces, or making sure that the finish and colors match the rest of your decor, our specially trained designers will bring your vision to life.
Want the custom kitchen of your dreams, but need to stay within your budget? You can have both when you partner with Bigstone Custom Cabinets. Get an amazing kitchen at a competitive price.
Need help designing the perfect bathroom around your favorite soaking tub? Talk to a Bigstone Custom Cabinets design expert to create a brilliant bathroom that adds value to your home and happiness to your life.
Built-in cabinets add immediate elegance and sophistication to any room. Let Bigstone Custom Cabinets design, build, and install customized built-ins that bring any room in your home alive with vibrancy, beauty, and functionality.
Custom cabinets help bring functionality to commercial spaces because cabinets help create comfort out of chaos. Get the right kind of cabinets that your employees and visitors both admire and use efficiently.
Bigstone Custom Cabinets was started by Gerald Finnman in May of 1990 on their family farm. He started out by building Travco camp trailers, Gerald spent long nights to make deadlines. Since then, Gerald has grown his business into one of the biggest players in the local custom cabinet industry. Bigstone currently employs around twenty staff members including a sales manager, production manager, purchaser and a knowledgeable team of interior designers and production staff.


text:
About KeyLogic
Anticipatory Service
Federal Civilian
Systems Architecture and Software Development
Business Opportunity Tracking System
Software Internationalization
Call Center Data Warehousing
Veracity: Full Lifecycle Budget Management
KeyInsight: Integrated Project Portfolio Management
KeyCertainty: Biometrics Solution Optimization
Cloud Implementation
Business Intelligence and Data Analysis
BI/Information Visualization
EVM/PM
R&D Technology and Project Management
R&D Technology Management
Program Planning & Strategy
Resiliency Services
Software and System Engineering
Intelligence Community Capabilities
System Development Lifecycle
IV&V
Army Benet Labs
DHS EAGLE II
FBI IT Triple S
GSA IT Schedule 70
GSA MOBIS
GSA OASIS
GSA PES
IRS TIPSS-4
Partner Contracts
Department of Energy MESA
NAVAIR Biometric Operations
Why KeyLogic?
Whether it's the latest big contract win, recognition for our employees or a corporate event, you can read about the most recent KeyLogic happenings.
KeyLogic Names Former NASA Executive John Marinaro as Vice President of Federal Civilian Division
Morgantown, WV – Sept. 7, 2017 – KeyLogic Systems, Inc., a leading professional services and engineering firm, today announced John D. Marinaro as Vice President of their Federal Civilian Division. Marinaro joins KeyLogic from the National Aeronautics and Space Administration (NASA), where he was most recently the Director, Safety Engineering Technical Excellence, and Aerospace Consultant. In his new role, Marinaro will oversee KeyLogic's federal civilian portfolio.
Marinaro's experience and expertise in leading mission assurance for NASA, with a focus on quality engineering, will be used to provide insight and continuous improvement to KeyLogic's processes, software development, and service delivery. His perspective as a government professional enhances the KeyLogic Anticipatory Service delivery model that is intended to provide effective results and solutions with a focus on performance and strategic management.
"My entire career has been focused on developing high performance teams through the culture, process and systems," said Marinaro. "I look forward to bringing this perspective and focus to KeyLogic and their clients."
In 2009, Marinaro entered the Federal Senior Executive Service and was responsible for developing and managing the NASA University for Safety, including promoting technical excellence throughout the NASA safety community. He served as the Executive Director of the NASA Aerospace Safety Advisory Panel in the wake of the Space Shuttle Columbia tragedy. Marinaro has also had the responsibility for more than 40 major NASA mission independent verification and validation (IV&V) projects, including Space Shuttle and the International Space Station.
"We are thrilled that John will be joining KeyLogic as we look to build upon our recent string of successes over the past several years," said, Jon Hammock, KeyLogic President and Chief Executive Officer. "John's depth of knowledge, expertise, and breadth of experience in the federal government will be an asset to all of our customers. "
In Marinaro's 33 years of military, government and industry service, he has worked for Naval Air Systems Command (NAVAIR), Federal Aviation Administration (FAA), and NASA. Marinaro has 23 years of military service in the U.S. Marine Corps, Navy Reserve and Army National Guard. He is also a retired Commissioned Officer and Army Aviator from the West Virginia Army National Guard.
Marinaro has a Master of Science in aeronautics from Embry-Riddle Aeronautical University and a Bachelor of Science in electronics from Southern Illinois University, focused on electrical engineering and digital computing.
KeyLogic is a highly successful provider of professional and engineering services. We specialize in solutions that enable our customers to make better decisions for their organization. KeyLogic's performance has earned the company a solid reputation for high standards, proactive solutions, and an outstanding commitment to the customer, best exemplified by the fact we have never had a one-time federal customer — all of our customers have provided repeat business. To learn more, visit www.keylogic.com.
Quick List of KeyLogic About
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About | Capabilities | Experience | Working With KeyLogic | Careers | Contact Us
Copyright © 2013 KeyLogic Systems, Inc.

redpajama_set_name: RedPajamaCommonCrawl


text:
Netflix to invest up to $100m in a NYC production hub
According to NY Governor Cuomo, Netflix will be investing up to $100 million in a NYC production hub. The company will include six sound stages in Brooklyn and an expanded office in Manhattan's Flatiron District.
Disney's new streaming app will have every Disney movie every made
Disney's highly anticipated streaming service just got some extra bonus points. The streaming service, Disney Plus, will be launching later this year, and will include..
Netflix is now boasting 125 million subscribers
In Netflix's first quarterly report for 2018 financial year, we've learned several points, especially that the company isn't slowing down in subscriber growth. Over the..
Comcast is now bundling Netflix into cable packages
Netflix and Comcast working together? Sounds a big strange, right? Well, you're not alone. Comcast's Xfinity cable bundle now includes Netflix. Despite the two companies..
Netflix has opened its public bug bounty program
Today, Netflix has announced that it's public bug bounty program. Prior to opening this program, the company carefully had a vulnerability disclosure program in place..
Netflix decides to not move forward with 'patches' for kids
Previous reports suggested that Netflix was testing a new rewards program wherein children could earn "patches" for watching shows like Fuller House and Series of..


When you are deep within the earths crust be cautious and keep an eye on the rigs storage capacity and the fuel level. If your storage is full then you can no longer mine anymore materials, if your fuel runs out then you will be stranded underground which is definitely not good. Have fun!
Mega Miner is a first-class mining game. Play it on this page in your browser, for free. The game is powered by Flash to work smoothly in most browsers. This game has received 238,448 plays and has been rated 9.2 / 10 by 1,293 people. Did you like playing Mega Miner? Then take a look at our other mining games or to Mine Blocks and Mister Mine.
Use WASD or arrow keys to control the miner and spacebar to interact.
Click 'Allow' to play Mega Miner! Click 'Always allow' to play Mega Miner!


The Coalition for Community Energy (C4CE) is excited to run another series of webinars after our 2017 webinars showcased many of the questions in the small scale solar guide with answers and real life examples from our member groups.
The webinars run from 6pm to 7 pm on Tuesdays during September, October and November 2018 and are generally related to the great work of our community energy groups.
Recording webinars: We recommend you attend these event when they occur. However, for those who can't make it to a particular webinar we will be recording them and publishing them at a later date. To be informed when a recording is available for a particular webinar, please register for that webinar.
25 September 2018 Enova's fundraise and their ambition to bring community owned energy retailing to all of us.
Video and Q&A and our slides from the night can be downloaded here.
9 October 2018 CORENA presents… Andrew Stock and the politics of climate and energy.
Video and our slides from the night can be downloaded here.
6 November 2018 Revolving Funds Rock!
13 November 2018 Upper Yarra community hydro.
The Upper Yarra community energy group is about launching its run-of-the-river hydro power station near Warburton. You can see the timelapse videos here. Come and talk to the project architects and partners.
20 November 2018 Solar Gardens – how can I own solar power if I can't put it on my roof?
Community Power Agency and the Institute for Sustainable Futures are nearing the end of their social access solar gardens project. Come and hear from them and their two Pilot projects in Shoalhaven and Blacktown.


DETROIT, March 13 (Reuters) - The leader of the United Auto Workers union on Wednesday warned that job security and preventing the shift of U.S. jobs to Mexico would be top priorities in contract talks with Detroit's automakers slated for later this year.
This year's contract talks between the UAW and General Motors Co, Ford Motor Co and Fiat Chrysler Automobiles NV are likely to be contentious, with both sides focused on healthcare costs and the use of temporary workers.
The auto industry is expected to hit a downturn following an unprecedented run of strong sales dating to the end of the Great Recession.
The talks will come after GM's announcement in November it will close five North American plants producing less-popular sedans.
Just last week, the last Chevrolet Cruze rolled off the line at GM's plant in Lordstown in northeastern Ohio.
The closing has drawn a lawsuit from the United Auto Workers union and significant political blowback, including from Republican U.S. President Donald Trump.
Boosting American manufacturing jobs was a cornerstone of Trump 's successful 2016 campaign and Ohio is a key state for his 2020 reelection chances.
The UAW has vowed to fight for GM to reopen Lordstown with a new vehicle.
Analysts, however, say the chances of a new product being assigned to Lordstown are slim as Lordstown was one of several money-losing GM plants running well below capacity.
GM Chief Executive Mary Barra has said the cuts will help the company's long-term financial stability. The automaker expects they will improve annual free cash flow by $6 billion by the end of 2020 - and fund new electric and self-driving vehicles.


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Jill Waage, Editor-in-chief of Traditional Home magazine, has enlisted the best of the best of California design to have an intimate discussion that deep dives into modernist icons like Herman Miller, Eileen Gray, Hans Knoll and Charlotte Periand and the influences they have on luxury design in the present day. Christopher Kennedy, Joe Lucas, Adam Hunter and Janice Barta will be sharing insight on how these icons of the past are still applicable to the luxury interiors they are designing today.
The organizer of this event is Traditional Home Magazine.


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WEYMOUTH midfielder Josh Wakefield tapped home Josh McQuoid's pinpoint pass in the 88th minute to earn his side a dramatic and deserved 1-1 draw with Beaconsfield Town at the Bob Lucas Stadium.
Brendan Matthew had earlier given the Rams a lead they nearly held on for, but Weymouth's resolve sees them preserve their unbeaten start to the Evo-Stik Southern Premier South campaign.
Weymouth made just one change to their starting XI, with Stephane Zubar returning to replace Matt Casey in the Terras' defence.
Casey dropped to the bench, taking Tiago Sa's spot in the dugout in the only other change to Weymouth's matchday squad since the dramatic 2-1 win over Taunton Town on Tuesday.
The hosts, who stretched their unbeaten home run in the league to 17 games, started brightly, monopolising possession in the opening exchanges.
Rams' forward Matthew looked a handful with his pace and runs in behind but Weymouth were able to contain the striker before Josh McQuoid curled an effort at Ravan Constable after a smart dummy by Brandon Goodship.
Weymouth continued to dominate the ball but, after Charlie Davis was dispossessed, Matthew was supplied in behind only to fire his shot well wide.
Mark Molesley's men were diligent off the ball, winning it back almost instantly with Josh Carmichael leading the pressing, and a similar passage of play set Abdulai Baggie free on the right but his cross was too tall for Goodship who headed skywards on the stretch.
The Terras were perhaps shy of pulling the trigger, Baggie and Goodship the culprits, after a sweeping break from the former, but Carmichael followed up with a rasping 30-yard drive that just strayed wide.
Baggie and Goodship were linking well and the Sierra Leone winger curled a left-footed shot after intricate interplay between the two.
At the centre of Weymouth's forward play, Baggie released Ben Thomson with a clever flick only to see the forward shoot tamely at Constable.
Weymouth again retrieved the ball, this time through McQuoid, and he fed Goodship to test Constable with a central effort from 20 yards before two balls across the six-yard box went out of reach of the Terras as the half ended goalless.
But Beaconsfield delivered a blow in the 48th minute through the dangerous Matthew, who made Weymouth pay after breaking the defensive line for a third time by slotting home past Seabright.
The Rams surged forward again in a brief mini-spell, right-back Omar Vassell trying his luck from 20 yards before a Wes Daly free-kick was flicked on by Matthew to centre-back Reece Yorke, who blazed over.
Sub Wakefield helped regain the initiative for the hosts and his combination with Goodship conjured a chance that he flashed against the near-post side-netting.
Rams' boss Gary Meakin was sent to the stands on the hour, before Weymouth spurned two huge chances, the first coming from Goodship, who was played in by Baggie 12 yards out following Carmichael's raking ball out wide, only to blast at Constable.
Goodship then turned creator when he unleashed Thomson before offloading a return pass to McQuoid who once more failed to beat the solid Rams' gloveman.
Matthew then took an age to walk off after sustaining an injury as Beaconsfield desperately tried to run the clock down.
And their blatant efforts proved in vain when Weymouth snatched a deserved equaliser two minutes from time.
Captain Jake McCarthy was clattered into on the edge of the box but McQuoid swept up the loose ball, drove to the byline and squared for Wakefield to nudge home from two yards to spark rapturous celebrations.
Seven minutes of added time followed but Beaconsfield came closest to winning it, McCarthy forced into an heroic sliding tackle on sub Tre Mitford to conserve Weymouth's point and unbeaten record this season.
Terras: Seabright, Wells, Buckley, Carmichael, McCarthy, Zubar (Baker 79), Baggie, Davis (Wakefield 56), Thomson, Goodship, McQuoid (Diaz 90+3). Subs not used: Thomas, Casey.


For the first time in 63 years, the congregation of Breakers Church of God is preparing to move into a new building.
The new church, dedicated to the late Pastor Esau Brooks for his many years of labor for the Lord, will cost around $500,000 to erect. The congregation is hoping to move into the building by late summer of next year.
Located just down the road from the old worship center, the new church will be equipped with Sunday school classrooms, a pastor's office, rest rooms and a kitchenette.
Church member Meredith Rankine is looking forward to the new church's opening.
She said the new building is needed because the existing structure on the beach is in poor condition.
It was incorporated as the Church of God Full Gospel Hall in George Town and no longer owned by the congregation of Breakers.
Church of God Full Gospel Hall Board Trustee Araunah Powery said as far as he knew, the Breakers congregation was allowed to use the church until the passing of Mr. Brooks. Then the property was passed on to Full Gospel Hall church. He said at the time of writing that there were no plans yet as to what will become of the old building, which was to be discussed in the next church meeting. He said the last thing brought up about the little church was the people of Breakers had offered to buy it, but that did not happen.
The story of the establishment of the Church of God Breakers goes back to the late 1940's when the late Ezra Sheets, a pastor from Upland, California, decided to hold "cottage" meetings in the yards of various residents in Breakers.
They were so well attended that he thought a church was needed.
Pastor Sheets flew back to California, sold his home and returned to Cayman to use the proceeds to build the little church in Breakers.
To show their appreciation, Kampton Webster and his wife Ella donated the land on which the church now stands. Through the dedicated labors of the residents of Breakers, the original church was built.
During the process of building the church, Pastor Sheets also included provisions for a cistern so residents would no longer have to collect water from the rock well.
The residents of Breakers saw the results of their back breaking labor come to fruition when the church was dedicated and officially opened in 1952.
Pastor Sheets led the congregation in Breakers until his death, at which time Pastor Luke Forbes took the reins. Upon the death of Pastor Forbes, the pastorship was continued by Pastor Brooks, who passed away Aug. 18 at 82 years of age.
The church still continues services with an enrollment of about 60 attendees.


This is the forum to list any and all resources related to intercultural competence development and assessment from a wide variety of disciplines and fields. Resources can include articles, books, book chapters, websites, You Tube clips, etc. These can be new resources and they can also be resources that you personally have found to be very helpful in your own work. Let's help each other know more about the ICC-related resources that exist around the globe!


Compared with the prior year the indicator rose barely by 2.95 percent, revised figures from European Commission show. Cleaning compounds export grew slightly from 36 732 768 Euro to 37 817 800 Euro, as it follows the tendency of the previous year when it went up by 1.12 percent. It's new all-time high since 2003. Croatia saw a rise of 129.89% in cleaning compounds export over the past 15 years. As well as it has had the CAGR of 0.04 percent over the last 5 years with a negligible overall growth since the start of observations. It averaged 26 185 655.47 Euro from 2003 to 2017 and it attained its minimum of 16 450 420 Euro in 2003.


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I ordered this dress blue for a formal event I attended and it arrived so quickly! Much earlier than I had anticipated.


A living roof can turn a plain old bird feeder into something truly remarkable. A layer of sedum plants atop the bird table is ever so pretty, creates a real talking point and will also attract butterflies and bees when it's in flower.
This is a brilliant way to use up any offcuts left over from making a green roof on your shed.
Use the timber to create an edging all the way around the roof – this will stop the wind lifting and shifting the sedum matting. You will end up with a tray on top of the roof which needs to be around 5cm deep.
Drill holes in the edging approximately 1 cm from the bottom – to allow excess water to drain away.
Treat the timber with a protective wood stain.
Measure the roof and cut the polyethene, the drainage mat (if needed), the water retention mat and the Enviromat to the right size.
Always cut components before putting them on the roof – you don't want to be slicing through the waterproofing!
Drainage mat – you only need this on a flat roof.
This project could also be adapted to suit a rabbit hutch, dog kennel or even a bee hive.


text:
\section{Introduction}
\label{sec:intro}
Deep learning, with its remarkable progress in recent years~\cite{AlexNet,Goodfellow-et-al-2016}, is ripe for applications in physics~\cite{carleo-RevModPhys.91.045002}. A particular instance having general applicability to physical problems is the classification of arbitrary convex geometrical shapes embedded in an $N$-dimensional space~\cite{PhysRevResearch.2.023169}. Having a mathematical framework to understand this class of problems and a solution that scales efficiently with the dimension $N$ is essential. With increasing effective dimensionality of the system,
including parameters and data, determining the geometry with measurements across the full parameter space may become prohibitively expensive. However, as we show, qualitative information about the boundaries defining the structures of interest may suffice for classification.

We propose a new framework for classifying simple high-dimensional geometrical structures: {\it ray-based classification}. Rather than working with the full $N$-dimensional data tensor, we train a fully connected DNN using one-dimensional representations in $\mathbb{R}^N$, called ``rays'', to recognize the relative position of features defining a given structure. We position the boundaries of this structure relative to a point of interest, effectively ``fingerprinting'' its neighborhood in the $\mathbb{R}^N$ space. The ray-based classifier is motivated primarily by experiments, particularly those in which sparse data collection is impractical. Our approach not only reduces the amount of data that needs to be collected, but also can be implemented {\it in situ} and in an online learning setting, where data is acquired sequentially.

We test the proposed framework using a modified version of the ``Quantum dot data for machine learning'' dataset~\cite{ml-data} developed to study the application of convolutional neural networks (CNNs) to enhance calibration of semiconductor quantum dot devices for use as qubits~\cite{Loss98-QCD}. Tuning these devices requires a series of measurements of a single response variable as a function of voltages on electrostatic gates. As the number of gates increases~\cite{Zajac16-SGA,Mukhopadhyay18-2DD}, heuristic classification and tuning becomes increasingly difficult, as does the time it takes to fully explore the voltage space of all relevant gates. The specific geometry of the response in gate-voltage space corresponds to the number and position of populated quantum dots, which is valuable information in the process of tuning of these systems.

Previous work has shown both theoretically~\cite{Kalantre17-MLD} and experimentally~\cite{Zwolak20-AQD} that an image-based CNN classifier for 2D volumes, \textit{i.e.}, solid images, combined with conventional optimization routines, can assist experimental efforts in tuning quantum dot devices between zero-, single- and double-dot states. Here, we consider a double- and triple-dot system. We show that using ray-based classification, the quantity of data required (and thus the time required) for identifying the state of the quantum dot system can be drastically reduced compared to an imaged-based classifier.

\section{A Framework for Ray-Based Classification}
\label{sec:framework}
\begin{figure}
  \begin{minipage}[c]{0.45\textwidth}
    \includegraphics[width=\textwidth]{gfx/ray-vis.pdf}
  \end{minipage}\hfill
  \begin{minipage}[c]{0.5\textwidth}
    \caption{(a) Visualization of a ray $\mathfrak{R}(x_o,x_f)$ from $x_o$ to $x_f$ in $\mathbb{R}^3$. Different colors of polytopes represent different classes. (b) A side-view of the polytopes with two features marked along the ray. The red mark denotes a critical feature. (c) Visualization of the $M$-projection from point $x_o$ with $6$ rays (denoted by black arrows) for two different polytopes in $\mathbb{R}^3$. Note that both $M$-projections include a ray that does not have a critical feature.} \label{fig:rays-vis}
  \end{minipage}
  \vspace{-2pt}
\end{figure}

Consider Euclidean space $\mathbb{R}^N$ with its conventional 2-norm distance function $d$, and a polytope function $p : \mathbb{R}^N \rightarrow \{0,1\}$. The set of points where $p(x) = 1$ constitutes the boundary of a collection of polytopes. For example, a polytope function producing a square in $\mathbb{R}^2$ centered at the origin is $p(x_1,x_2)=\{1 \text{ if } |x_1|+|x_2| = 1; 0 \text{ elsewhere}\}$, where $(x_1, x_2)\in\mathbb{R}^2$. In our quantum dot applications a value of $p=1$ indicates the location where an electron is transferred in or out of a dot.

\begin{defn}[Rays]\label{def:rays}
	Given $x_o, x_f\in\mathbb{R}^N$, the {\bf\emph{ray}} $\mathfrak{R}_{x_o,x_f}$ emanating from $x_o$ and terminating at $x_f$ is the set $\{x\,|\,x = (1-t) x_o + t x_f, t \in [0,1]\}$ \emph{(see Fig.~\ref{fig:rays-vis}(a) for a depiction of a ray in $\mathbb{R}^3$)}.
\end{defn}
\vspace{-2pt}
In practical applications, rays have a natural granularity that depends on the system as well as the data collection density. For quantum dots, the device parameters define an intrinsic separation between critical features that gives the scale of the problem. We refer to granularity of rays in terms of pixels.

To assess the geometry of a polytope enclosing any given point $x_o$, we consider a collection of rays of a fixed length $r$ centered at $x_o$. The rays are uniquely determined by a set of $M$ points on the sphere $\mathcal{S}^{N-1}_r$ of radius $r$ centered at $x_o$, $\mathcal{P}:=\{x_m\in\mathcal{S}^{N-1}_{x_o}(r)\,|\,1\le m \le M\}$. We call a set of $M$ rays, $\mathcal{R}_M:=\{\mathfrak{R}_{x_o,x_m}\,|\, x_m \in \mathcal{P}\}$, an {\bf\emph{$M$-projection}} (see Fig.~\ref{fig:rays-vis}(c) for visualization in $\mathbb{R}^3$).

\begin{defn}[Feature]
	Given a ray $\mathfrak{R}_{x_o,x_f}$ and a polytope function $p$, a point $x\in\mathfrak{R}_{x_o,x_f}$ is a {\bf\emph{feature}} if $p(x)=1$.
\end{defn}
\vspace{-2pt}
Figure~\ref{fig:rays-vis}(b) shows two features along a sample ray in $\mathbb{R}^3$. Features along a given ray define its {\bf\emph{feature set}}, $F_{x_o,x_f} := \{x\in\mathfrak{R}_{x_o,x_f}\,|\,p(x)=1\}$, with a natural order given by the 2-norm distance function $d:{x_o}\times F_{x_o,x_f} \rightarrow \mathbb{R}^+$. In general, $F_{x_o,x_f}$ could be empty. Using a decreasing {\bf\emph{weight function}} $\gamma:\mathbb{R}^+\rightarrow[0,1]$ we can assign a weight to each feature, effectively defining the {\bf\emph{weight set}} $\Gamma_{x_o,x_f}$ corresponding to its feature set $F_{x_o,x_f}$ as $\Gamma_{x_o,x_f} = \{\gamma(d(x,x_o))\, |\, x \in F_{x_o,x_f}\}$. The actual choice of function $\gamma$ needs be altered to fit the problem itself and can be considered another hyperparameter that can help optimize the machine learning process. For the quantum dot case, we chose $\gamma(n)=\nicefrac{1}{n}$.

The assumption that the weight function $\gamma$ is monotonic in distance lets us define a ray's {\bf\emph{critical feature}} as the point $x \in F_{x_o,x_f}$ with highest (i.e., {\bf\emph{critical}}) weight $W_{x_o,x_f}=\gamma(d(x,x_o))$. If $F_{x_o,x_f}=\varnothing$, we put $W_{x_o,x_f}=0$. This allows us to ``fingerprint'' the space surrounding point $x_o$.

\begin{defn}[Point fingerprint]
	Let $x_o\in\mathbb{R}^N$ be a point from which a collection of rays $\mathcal{R}_M=\{\mathfrak{R}_{x_o,x_f^1},\dots,\mathfrak{R}_{x_o,x_f^M}\}$ emanate. The {\bf\emph{point fingerprint}} of $x_o$ is the $M$-dimensional vector consisting of the rays' critical weights: $\mathcal{F}_{x_o}=\big(W_{x_o,x_f^1},\,\dots,\,W_{x_o,x_f^M}\big)$.
\end{defn}

\begin{wrapfigure}[23]{R}{0.6\textwidth}
\vspace{-23pt}
\begin{minipage}{0.6\textwidth}
\begin{algorithm}[H]
\caption{Ray-based fingerprinting algorithm}
\label{alg:data_classification}
{\it {\bf Step 1.} Find $M$-projection centered at $x_o$ given $r$.}
\begin{algorithmic}[1]
\State {\bf Input:} $x_o$, $r$, a set $\mathcal{P}$ of $M$ points on the $(N-1)$-sphere
\State $m \gets 1$; $\mathcal{R}_M \gets$ empty list
\For{$m=1$ to $M$}
    \State Find $m$-th ray $\mathfrak{R}_{x_o,x_f^m}$ and append it to the list $\mathcal{R}_M$.
\EndFor
\State {\bf Return:} List of $M$ rays $\mathcal{R}_M$.
\end{algorithmic}
{\it {\bf Step 2.} Fingerprint $x_o\in\mathbb{R}^N$ using rays in $\mathcal{R}_M$ from Step 1.}
\begin{algorithmic}[1]
\State {\bf Input:} $\mathcal{R}_M$, $\gamma : \mathbb{R}^+ \rightarrow [ 0,1 ]$
\State $m \gets 1$; $\mathcal{F}_{x_o} \gets$ empty list
\For{$m=1$ to $M$}
\State Find the feature set $F_{x_o,x_f^m}$.
\If {$F_{x_o,x_f^m} \neq\varnothing$}
    \State \begin{varwidth}[t]{0.87\linewidth} Identify the critical feature $x_i^m$, find $W_{x_o,x_f^m}$ and append  it to the list $\mathcal{F}_{x_o}$.\end{varwidth}
\Else
    \State Append $0$ to the list $\mathcal{F}_{x_o}$.
\EndIf
\EndFor
\State {\bf Return:} The point fingerprint vector $\mathcal{F}_{x_o}$.
\end{algorithmic}
\end{algorithm}
\end{minipage}
\end{wrapfigure}

This point fingerprint $\mathcal{F}_{x_o}$ of $x_o$ is the primary object of the ray-based classification framework. If sufficiently many rays in appropriate directions are chosen from $x_o$, the fingerprint is sufficient, at least in principle, to qualitatively determine the geometry of the convex polytope enclosing $x_o$. Due to the cost of experimental data acquisition, determining {\it how few} rays are sufficient for a machine learning algorithm to make this determination is of crucial importance. Looking to establish a correspondence between the fingerprint $\mathcal{F}_{x_o}$ of point $x_o$ and the class of the polytope enclosing this point, we define the following problem:

\begin{problem}
	Given a set of bounded and unbounded convex polytopes filling an $N$-dimensional space and belonging to $C$ distinct classes, $C \in \mathbb{N}$, and a point $x_o\in\mathbb{R}^N$, determine to which of the classes the polytope enclosing $x_o$ belongs.
\end{problem}

A solution to this problem in the supervised learning setting can be obtained by training a DNN with the input being the point fingerprint and the output identifying an appropriate class. The procedural steps for the proposed classification algorithm for $N$-dimensional data in the form of pseudocode are presented in Algorithm~\ref{alg:data_classification}.

\section{Experiments: Classifying Shapes in 2D and 3D}\label{sec:experiment}
\begin{figure}[b]
  \centering
  \includegraphics[width=0.94\linewidth]{gfx/problem-setup.pdf}
  \vspace{-5pt}
  \caption{(a) A sample $2D$ map generated with the quantum dot simulator~\cite{Zwolak18-QLD} showing the different bounded and unbounded polytopes in $\mathbb{R}^2$ with $12$ evenly distributed rays overlaid on 2D scans like the ones used in Ref.~\cite{Zwolak18-QLD}. (b) The average trends of the fingerprints with $M=12$ rays. Fingerprints for SD$_L$ and SD$_R$ are out of phase, as expected from the the curvature of lines defining these states and SD$_C$ is shifted by $\nicefrac{1}{4}$ of the period). The colors (labels) are consistent between both panels.}
  \label{fig:sample_2D_map}
\end{figure}

The ray-based data is generated using a physics-based simulator of quantum dot devices~\cite{Zwolak18-QLD}. An example of a simulated measurement, like the ones typically seen in the laboratory, is shown in Fig.~\ref{fig:sample_2D_map}(a). The $x$ and $y$ axes represent a subset of parameters that can be changed in the experiments (here, gate voltages) and the curves where the signal strength is equal to 1 represent the device response to a change in electron occupation. The slopes of those lines correspond to the location of the quantum dots with respect to the gates. The device states manifest themselves by different bounded and unbounded shapes defined by these curves, as shown in Fig.~\ref{fig:sample_2D_map}(a). Previous work has confirmed the reliability of a dataset generated with this simulator for the case of a CNN used with $2D$ images, finding an accuracy of $95.9\,\%$ (standard deviation $\sigma=0.6\,\%$) over 200 training and validation runs performed on distinct datasets~\cite{Zwolak18-QLD}. Here, we use a modified version of this dataset, splitting the single-dot (SD) class into 3 distinct classes based on the dot location (Left, Center, Right) as suggested by experimentalists. No-dot (ND) and double-dot (DD) classes are unchanged.

To test the ray-based classification framework in 2D, we use $20$ realizations of $2D$ maps qualitatively comparable to the one shown in Fig.~\ref{fig:sample_2D_map}(a). Using a synthetic dataset allows us to systematically vary the length of the rays and their number. A regular grid of 1,369 points is used for sampling, resulting in a dataset of 27,380 fingerprints. We consider five datasets of $M$-projections, with $M=3,4,5,6,\mathrm{and }12$ evenly spaced rays. The ray length is varied between $10$ and $80$ pixels (where 30 pixels is the average separation between transition lines in the simulated devices). We ran 50 training and validation tests per combination of rays' number and length (with data divided 80:20). For testing, we generated a separate dataset based on three distinct devices. This allows us to both better determine the classification error for the most efficient number and length combinations of rays and to study the failure cases over the device layout (see Fig.~\ref{fig:perf_fc_vis}).

Figure~\ref{fig:perf_var_rays}(a) shows the performance of the ray-based classifier. The accuracy of the classifier increases with the total number of points measured for a fixed number or rays, as expected. However, for a fixed number of points, increasing the number of rays does not necessarily lead to increased accuracy. This is because with a fixed number of points and point density, increasing the number of rays naturally results in shorter rays. Rays shorter than the radius of the interior diameter of the shapes leads to empty feature sets, resulting in uninformative fingerprints. Increasing the number or size of hidden layers in the DNN does not further improve the accuracy (see Table A.1).

\begin{figure}[t]
  \centering
  \includegraphics[width=0.95\linewidth]{gfx/2D-3D-perf.pdf}
  \caption{Classifier performance for varying numbers of rays as a function of the total number of (a) pixels measured and averaged over $N=50$ training runs for the double-dot system and (b) voxel number averaged over $N=10$ training runs for the triple-dot system. The black dashed line in (a) represents the benchmark from Ref.~\cite{Zwolak18-QLD}. The black vertical lines in (b) represent the minimum data requirements for CNN classifier with 3 orthogonal 2D slices (as depicted in insert (B), dotted line), large 2D scan (dashed line), and a full 3D CNN (solid line). Insert (A) shows the $M$-projection with 6 rays. In both panels, the connecting lines are a guide to the eye only and the $3\,\sigma$ confidence bands.}
  \label{fig:perf_var_rays}
\end{figure}

To test the proposed framework with triple-dot systems~\cite{Schroer2007}, we generated a dataset by sampling 17,576 fingerprints from a single simulated device with three dot gates. We varied the number of rays between 6 and 18, while keeping the length of the rays fixed at 60 voxels. For each configuration, we performed $N=10$ training and validation runs (with data divided 80:20). As shown in Fig.~\ref{fig:perf_var_rays}, the classifier accuracy improved from 66.2 \% ($\sigma$ = 0.3 \%) for 6 rays to 79.9 \% ($\sigma$ = 0.3 \%) for 18 rays.

\section{Summary}\label{sec:concl}
While our analysis is performed using simulated data, its true advantage becomes clear when put in the context of experiments. Depending on the resolution, measuring $12$ rays of length $80$ (total of $960$ data points) is equivalent to measuring the full $2D$ image ($900$ data points) as in Ref.~\cite{Zwolak20-AQD}. With 6 rays of length 60 pixels, only $360$ data points are needed, resulting in $60\,\%$ reduction of the data needed to obtain accuracy of $96.4\,\%$ ($\sigma=0.4\,\%$). The reduction for 3D data is even more significant.

In conclusion, we have defined a framework to generate a low-dimensional representation of geometrical shapes in a high-dimensional space. We have empirically shown that the ray-based framework is an effective solution for cutting down the measurement cost while preserving high-accuracy of classification on the quantum dot dataset. If the ray-based classifier were implemented in a scheme to tune the double dot, as in Ref. \cite{Zwolak20-AQD}, this reduction in data collection significantly improves its viability as a replacement to a hand-tuning scheme by a human operator. We expect this approach will find many related applications outside of the quantum dot domain.

\section*{Broader Impact}
The authors believe that research presented in this paper does not have ethical aspects. This work furthers the efforts to automate and scale up quantum dot-based quantum computing to larger and more impactful devices. Our approach can also be extended to setups involving the estimation of quantum states in solid-state and atomic experiments, as well as tuning and scalability of other quantum computing architectures. A ray-based approach may find use classifying point clouds if sufficiently ordered. Extending our technique to include intersection points beyond the first critical feature may allow for identification of non-convex shapes in higher dimensions.

\begin{ack}
This research was sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. S.K. gratefully acknowledges support from the Joint Quantum Institute (JQI) -- Joint Center for Quantum Information and Computer Science (QuICS) Lanczos graduate fellowship. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.
\end{ack}

\section{Introduction}
\label{sec:intro}
Deep learning, with its remarkable progress in recent years~\cite{AlexNet,Goodfellow-et-al-2016}, is ripe for applications in physics~\cite{carleo-RevModPhys.91.045002}. A particular instance having general applicability to physical problems is the classification of arbitrary convex geometrical shapes embedded in an $N$-dimensional space~\cite{PhysRevResearch.2.023169}. Having a mathematical framework to understand this class of problems and a solution that scales efficiently with the dimension $N$ is essential. With increasing effective dimensionality of the system,
including parameters and data, determining the geometry with measurements across the full parameter space may become prohibitively expensive. However, as we show, qualitative information about the boundaries defining the structures of interest may suffice for classification.

We propose a new framework for classifying simple high-dimensional geometrical structures: {\it ray-based classification}. Rather than working with the full $N$-dimensional data tensor, we train a fully connected DNN using one-dimensional representations in $\mathbb{R}^N$, called ``rays'', to recognize the relative position of features defining a given structure. We position the boundaries of this structure relative to a point of interest, effectively ``fingerprinting'' its neighborhood in the $\mathbb{R}^N$ space. The ray-based classifier is motivated primarily by experiments, particularly those in which sparse data collection is impractical. Our approach not only reduces the amount of data that needs to be collected, but also can be implemented {\it in situ} and in an online learning setting, where data is acquired sequentially.

We test the proposed framework using a modified version of the ``Quantum dot data for machine learning'' dataset~\cite{ml-data} developed to study the application of convolutional neural networks (CNNs) to enhance calibration of semiconductor quantum dot devices for use as qubits~\cite{Loss98-QCD}. Tuning these devices requires a series of measurements of a single response variable as a function of voltages on electrostatic gates. As the number of gates increases~\cite{Zajac16-SGA,Mukhopadhyay18-2DD}, heuristic classification and tuning becomes increasingly difficult, as does the time it takes to fully explore the voltage space of all relevant gates. The specific geometry of the response in gate-voltage space corresponds to the number and position of populated quantum dots, which is valuable information in the process of tuning of these systems.

Previous work has shown both theoretically~\cite{Kalantre17-MLD} and experimentally~\cite{Zwolak20-AQD} that an image-based CNN classifier for 2D volumes, \textit{i.e.}, solid images, combined with conventional optimization routines, can assist experimental efforts in tuning quantum dot devices between zero-, single- and double-dot states. Here, we consider a double- and triple-dot system. We show that using ray-based classification, the quantity of data required (and thus the time required) for identifying the state of the quantum dot system can be drastically reduced compared to an imaged-based classifier.

\section{A Framework for Ray-Based Classification}
\label{sec:framework}
\begin{figure}
  \begin{minipage}[c]{0.45\textwidth}
    \includegraphics[width=\textwidth]{gfx/ray-vis.pdf}
  \end{minipage}\hfill
  \begin{minipage}[c]{0.5\textwidth}
    \caption{(a) Visualization of a ray $\mathfrak{R}(x_o,x_f)$ from $x_o$ to $x_f$ in $\mathbb{R}^3$. Different colors of polytopes represent different classes. (b) A side-view of the polytopes with two features marked along the ray. The red mark denotes a critical feature. (c) Visualization of the $M$-projection from point $x_o$ with $6$ rays (denoted by black arrows) for two different polytopes in $\mathbb{R}^3$. Note that both $M$-projections include a ray that does not have a critical feature.} \label{fig:rays-vis}
  \end{minipage}
  \vspace{-2pt}
\end{figure}

Consider Euclidean space $\mathbb{R}^N$ with its conventional 2-norm distance function $d$, and a polytope function $p : \mathbb{R}^N \rightarrow \{0,1\}$. The set of points where $p(x) = 1$ constitutes the boundary of a collection of polytopes. For example, a polytope function producing a square in $\mathbb{R}^2$ centered at the origin is $p(x_1,x_2)=\{1 \text{ if } |x_1|+|x_2| = 1; 0 \text{ elsewhere}\}$, where $(x_1, x_2)\in\mathbb{R}^2$. In our quantum dot applications a value of $p=1$ indicates the location where an electron is transferred in or out of a dot.

\begin{defn}[Rays]\label{def:rays}
	Given $x_o, x_f\in\mathbb{R}^N$, the {\bf\emph{ray}} $\mathfrak{R}_{x_o,x_f}$ emanating from $x_o$ and terminating at $x_f$ is the set $\{x\,|\,x = (1-t) x_o + t x_f, t \in [0,1]\}$ \emph{(see Fig.~\ref{fig:rays-vis}(a) for a depiction of a ray in $\mathbb{R}^3$)}.
\end{defn}
\vspace{-2pt}
In practical applications, rays have a natural granularity that depends on the system as well as the data collection density. For quantum dots, the device parameters define an intrinsic separation between critical features that gives the scale of the problem. We refer to granularity of rays in terms of pixels.

To assess the geometry of a polytope enclosing any given point $x_o$, we consider a collection of rays of a fixed length $r$ centered at $x_o$. The rays are uniquely determined by a set of $M$ points on the sphere $\mathcal{S}^{N-1}_r$ of radius $r$ centered at $x_o$, $\mathcal{P}:=\{x_m\in\mathcal{S}^{N-1}_{x_o}(r)\,|\,1\le m \le M\}$. We call a set of $M$ rays, $\mathcal{R}_M:=\{\mathfrak{R}_{x_o,x_m}\,|\, x_m \in \mathcal{P}\}$, an {\bf\emph{$M$-projection}} (see Fig.~\ref{fig:rays-vis}(c) for visualization in $\mathbb{R}^3$).

\begin{defn}[Feature]
	Given a ray $\mathfrak{R}_{x_o,x_f}$ and a polytope function $p$, a point $x\in\mathfrak{R}_{x_o,x_f}$ is a {\bf\emph{feature}} if $p(x)=1$.
\end{defn}
\vspace{-2pt}
Figure~\ref{fig:rays-vis}(b) shows two features along a sample ray in $\mathbb{R}^3$. Features along a given ray define its {\bf\emph{feature set}}, $F_{x_o,x_f} := \{x\in\mathfrak{R}_{x_o,x_f}\,|\,p(x)=1\}$, with a natural order given by the 2-norm distance function $d:{x_o}\times F_{x_o,x_f} \rightarrow \mathbb{R}^+$. In general, $F_{x_o,x_f}$ could be empty. Using a decreasing {\bf\emph{weight function}} $\gamma:\mathbb{R}^+\rightarrow[0,1]$ we can assign a weight to each feature, effectively defining the {\bf\emph{weight set}} $\Gamma_{x_o,x_f}$ corresponding to its feature set $F_{x_o,x_f}$ as $\Gamma_{x_o,x_f} = \{\gamma(d(x,x_o))\, |\, x \in F_{x_o,x_f}\}$. The actual choice of function $\gamma$ needs be altered to fit the problem itself and can be considered another hyperparameter that can help optimize the machine learning process. For the quantum dot case, we chose $\gamma(n)=\nicefrac{1}{n}$.

The assumption that the weight function $\gamma$ is monotonic in distance lets us define a ray's {\bf\emph{critical feature}} as the point $x \in F_{x_o,x_f}$ with highest (i.e., {\bf\emph{critical}}) weight $W_{x_o,x_f}=\gamma(d(x,x_o))$. If $F_{x_o,x_f}=\varnothing$, we put $W_{x_o,x_f}=0$. This allows us to ``fingerprint'' the space surrounding point $x_o$.

\begin{defn}[Point fingerprint]
	Let $x_o\in\mathbb{R}^N$ be a point from which a collection of rays $\mathcal{R}_M=\{\mathfrak{R}_{x_o,x_f^1},\dots,\mathfrak{R}_{x_o,x_f^M}\}$ emanate. The {\bf\emph{point fingerprint}} of $x_o$ is the $M$-dimensional vector consisting of the rays' critical weights: $\mathcal{F}_{x_o}=\big(W_{x_o,x_f^1},\,\dots,\,W_{x_o,x_f^M}\big)$.
\end{defn}

\begin{wrapfigure}[23]{R}{0.6\textwidth}
\vspace{-23pt}
\begin{minipage}{0.6\textwidth}
\begin{algorithm}[H]
\caption{Ray-based fingerprinting algorithm}
\label{alg:data_classification}
{\it {\bf Step 1.} Find $M$-projection centered at $x_o$ given $r$.}
\begin{algorithmic}[1]
\State {\bf Input:} $x_o$, $r$, a set $\mathcal{P}$ of $M$ points on the $(N-1)$-sphere
\State $m \gets 1$; $\mathcal{R}_M \gets$ empty list
\For{$m=1$ to $M$}
    \State Find $m$-th ray $\mathfrak{R}_{x_o,x_f^m}$ and append it to the list $\mathcal{R}_M$.
\EndFor
\State {\bf Return:} List of $M$ rays $\mathcal{R}_M$.
\end{algorithmic}
{\it {\bf Step 2.} Fingerprint $x_o\in\mathbb{R}^N$ using rays in $\mathcal{R}_M$ from Step 1.}
\begin{algorithmic}[1]
\State {\bf Input:} $\mathcal{R}_M$, $\gamma : \mathbb{R}^+ \rightarrow [ 0,1 ]$
\State $m \gets 1$; $\mathcal{F}_{x_o} \gets$ empty list
\For{$m=1$ to $M$}
\State Find the feature set $F_{x_o,x_f^m}$.
\If {$F_{x_o,x_f^m} \neq\varnothing$}
    \State \begin{varwidth}[t]{0.87\linewidth} Identify the critical feature $x_i^m$, find $W_{x_o,x_f^m}$ and append  it to the list $\mathcal{F}_{x_o}$.\end{varwidth}
\Else
    \State Append $0$ to the list $\mathcal{F}_{x_o}$.
\EndIf
\EndFor
\State {\bf Return:} The point fingerprint vector $\mathcal{F}_{x_o}$.
\end{algorithmic}
\end{algorithm}
\end{minipage}
\end{wrapfigure}

This point fingerprint $\mathcal{F}_{x_o}$ of $x_o$ is the primary object of the ray-based classification framework. If sufficiently many rays in appropriate directions are chosen from $x_o$, the fingerprint is sufficient, at least in principle, to qualitatively determine the geometry of the convex polytope enclosing $x_o$. Due to the cost of experimental data acquisition, determining {\it how few} rays are sufficient for a machine learning algorithm to make this determination is of crucial importance. Looking to establish a correspondence between the fingerprint $\mathcal{F}_{x_o}$ of point $x_o$ and the class of the polytope enclosing this point, we define the following problem:

\begin{problem}
	Given a set of bounded and unbounded convex polytopes filling an $N$-dimensional space and belonging to $C$ distinct classes, $C \in \mathbb{N}$, and a point $x_o\in\mathbb{R}^N$, determine to which of the classes the polytope enclosing $x_o$ belongs.
\end{problem}

A solution to this problem in the supervised learning setting can be obtained by training a DNN with the input being the point fingerprint and the output identifying an appropriate class. The procedural steps for the proposed classification algorithm for $N$-dimensional data in the form of pseudocode are presented in Algorithm~\ref{alg:data_classification}.

\section{Experiments: Classifying Shapes in 2D and 3D}\label{sec:experiment}
\begin{figure}[b]
  \centering
  \includegraphics[width=0.94\linewidth]{gfx/problem-setup.pdf}
  \vspace{-5pt}
  \caption{(a) A sample $2D$ map generated with the quantum dot simulator~\cite{Zwolak18-QLD} showing the different bounded and unbounded polytopes in $\mathbb{R}^2$ with $12$ evenly distributed rays overlaid on 2D scans like the ones used in Ref.~\cite{Zwolak18-QLD}. (b) The average trends of the fingerprints with $M=12$ rays. Fingerprints for SD$_L$ and SD$_R$ are out of phase, as expected from the the curvature of lines defining these states and SD$_C$ is shifted by $\nicefrac{1}{4}$ of the period). The colors (labels) are consistent between both panels.}
  \label{fig:sample_2D_map}
\end{figure}

The ray-based data is generated using a physics-based simulator of quantum dot devices~\cite{Zwolak18-QLD}. An example of a simulated measurement, like the ones typically seen in the laboratory, is shown in Fig.~\ref{fig:sample_2D_map}(a). The $x$ and $y$ axes represent a subset of parameters that can be changed in the experiments (here, gate voltages) and the curves where the signal strength is equal to 1 represent the device response to a change in electron occupation. The slopes of those lines correspond to the location of the quantum dots with respect to the gates. The device states manifest themselves by different bounded and unbounded shapes defined by these curves, as shown in Fig.~\ref{fig:sample_2D_map}(a). Previous work has confirmed the reliability of a dataset generated with this simulator for the case of a CNN used with $2D$ images, finding an accuracy of $95.9\,\%$ (standard deviation $\sigma=0.6\,\%$) over 200 training and validation runs performed on distinct datasets~\cite{Zwolak18-QLD}. Here, we use a modified version of this dataset, splitting the single-dot (SD) class into 3 distinct classes based on the dot location (Left, Center, Right) as suggested by experimentalists. No-dot (ND) and double-dot (DD) classes are unchanged.

To test the ray-based classification framework in 2D, we use $20$ realizations of $2D$ maps qualitatively comparable to the one shown in Fig.~\ref{fig:sample_2D_map}(a). Using a synthetic dataset allows us to systematically vary the length of the rays and their number. A regular grid of 1,369 points is used for sampling, resulting in a dataset of 27,380 fingerprints. We consider five datasets of $M$-projections, with $M=3,4,5,6,\mathrm{and }12$ evenly spaced rays. The ray length is varied between $10$ and $80$ pixels (where 30 pixels is the average separation between transition lines in the simulated devices). We ran 50 training and validation tests per combination of rays' number and length (with data divided 80:20). For testing, we generated a separate dataset based on three distinct devices. This allows us to both better determine the classification error for the most efficient number and length combinations of rays and to study the failure cases over the device layout (see Fig.~\ref{fig:perf_fc_vis}).

Figure~\ref{fig:perf_var_rays}(a) shows the performance of the ray-based classifier. The accuracy of the classifier increases with the total number of points measured for a fixed number or rays, as expected. However, for a fixed number of points, increasing the number of rays does not necessarily lead to increased accuracy. This is because with a fixed number of points and point density, increasing the number of rays naturally results in shorter rays. Rays shorter than the radius of the interior diameter of the shapes leads to empty feature sets, resulting in uninformative fingerprints. Increasing the number or size of hidden layers in the DNN does not further improve the accuracy (see Table A.1).

\begin{figure}[t]
  \centering
  \includegraphics[width=0.95\linewidth]{gfx/2D-3D-perf.pdf}
  \caption{Classifier performance for varying numbers of rays as a function of the total number of (a) pixels measured and averaged over $N=50$ training runs for the double-dot system and (b) voxel number averaged over $N=10$ training runs for the triple-dot system. The black dashed line in (a) represents the benchmark from Ref.~\cite{Zwolak18-QLD}. The black vertical lines in (b) represent the minimum data requirements for CNN classifier with 3 orthogonal 2D slices (as depicted in insert (B), dotted line), large 2D scan (dashed line), and a full 3D CNN (solid line). Insert (A) shows the $M$-projection with 6 rays. In both panels, the connecting lines are a guide to the eye only and the $3\,\sigma$ confidence bands.}
  \label{fig:perf_var_rays}
\end{figure}

To test the proposed framework with triple-dot systems~\cite{Schroer2007}, we generated a dataset by sampling 17,576 fingerprints from a single simulated device with three dot gates. We varied the number of rays between 6 and 18, while keeping the length of the rays fixed at 60 voxels. For each configuration, we performed $N=10$ training and validation runs (with data divided 80:20). As shown in Fig.~\ref{fig:perf_var_rays}, the classifier accuracy improved from 66.2 \% ($\sigma$ = 0.3 \%) for 6 rays to 79.9 \% ($\sigma$ = 0.3 \%) for 18 rays.

\section{Summary}\label{sec:concl}
While our analysis is performed using simulated data, its true advantage becomes clear when put in the context of experiments. Depending on the resolution, measuring $12$ rays of length $80$ (total of $960$ data points) is equivalent to measuring the full $2D$ image ($900$ data points) as in Ref.~\cite{Zwolak20-AQD}. With 6 rays of length 60 pixels, only $360$ data points are needed, resulting in $60\,\%$ reduction of the data needed to obtain accuracy of $96.4\,\%$ ($\sigma=0.4\,\%$). The reduction for 3D data is even more significant.

In conclusion, we have defined a framework to generate a low-dimensional representation of geometrical shapes in a high-dimensional space. We have empirically shown that the ray-based framework is an effective solution for cutting down the measurement cost while preserving high-accuracy of classification on the quantum dot dataset. If the ray-based classifier were implemented in a scheme to tune the double dot, as in Ref. \cite{Zwolak20-AQD}, this reduction in data collection significantly improves its viability as a replacement to a hand-tuning scheme by a human operator. We expect this approach will find many related applications outside of the quantum dot domain.

\section*{Broader Impact}
The authors believe that research presented in this paper does not have ethical aspects. This work furthers the efforts to automate and scale up quantum dot-based quantum computing to larger and more impactful devices. Our approach can also be extended to setups involving the estimation of quantum states in solid-state and atomic experiments, as well as tuning and scalability of other quantum computing architectures. A ray-based approach may find use classifying point clouds if sufficiently ordered. Extending our technique to include intersection points beyond the first critical feature may allow for identification of non-convex shapes in higher dimensions.

\begin{ack}
This research was sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. S.K. gratefully acknowledges support from the Joint Quantum Institute (JQI) -- Joint Center for Quantum Information and Computer Science (QuICS) Lanczos graduate fellowship. The views and conclusions contained in this paper are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by the National Institute of Standards and Technology.
\end{ack}

redpajama_set_name: RedPajamaArXiv


One of the smallest books in the Bible is the story of Ruth the Moabitess. The book of Ruth is very short, and can be hard to find. It is the eighth book of the Bible. After the books of Moses, come Joshua, Judges, then Ruth. This short and sometimes neglected story has no obvious miracles, no mighty and divine interventions. And yet, it offers us a great deal, as we shall see.
We gather in the church on Sabbath but not to worship not some random idea, or fuzzy, amorphous, impersonal alien being. We gather to worship our Creator/Re-creator. The gospel of John says that in the beginning was the Word and the Word was with God, and the Word was God. He was in the beginning with God. This "He" was in the beginning with God the Father. Who is "He?" He is Jesus. The Bible says that All things were made through Him, and without Him was not anything made that was made (John 1:1-3).
14 And the Word became flesh and dwelt among us, and we have seen His glory, glory as of the only Son from the Father, full of grace and truth (John 1:5, 10, 14).
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Protestants long have understood the "man of sin" in this passage as indicating the power of the papacy or as being the pope of Rome. But now there is a new Pope, Francis I. Stories are appearing in the news outlets of his great humility, kindness, and solidarity with the poor. No doubt there is something in these stories. However, we shall be looking at what the Scriptures say in this book about the Second Coming of Jesus and what then shall happen to those who prefer to generate their own "good news," those who place themselves in the temple of God and proclaim themselves to be god (2 Thessalonians 2:4).
Jesus: Lord of All or Nothing?
Jesus gave a warning very meaningful for us today. He warned against worshiping Him in vain, teaching for doctrines the mere commandments of men (Matthew 15:8, 9). Jesus was quoting from Isaiah.