Johnny dollar. Hi, Johnny. This is Harvey Wakeman. Well, it's state unity life down here in New York. Sure. I know how are you? They got me a problem. Well, you're not alone in that or rather one of my clients has every of a man named Thomas Rayburn Morgan No, I don't recall. I don't have no reason to he's a broker Johnny a stock for in New York and Jay Well, what seems to be well, I'll tell you what he called me up on the phone Oh, do Johnny dollar right away you and nobody else. Did he tell you what his problem? No, he didn't say what's bothering him Not the slightest hint, but he did make it plain. There's no time to wait Well, unless I have some idea I promised I'd call you right away. So I've called you so his office address is 138 27 North Carmer Street over there in New York. So grab your hat and get on down to see him. Okay?  Well, sure. Go ahead, Johnny. Don't get sore. I said just that he's a very important client. So anything we can do I said that unless I have some idea just what this man Morgan wants me for Johnny Johnny boy Didn't you hear me? I haven't got the least idea. What's troubling mr. Morgan? Maybe I better phone him myself No, no, no, no, there is no point in calling him. He'll only say to come and see him and he'll tell you what it's all about himself Well, and I'm sorry, but these blind assignments for the birds you tell him I'm not interested unless I can find out exactly And I'll tell you this too Johnny considering the size of all the policies we've written for No, I'm wondering that when I call him back and tell him you're on the way No, now listen a guy like you comes high and that you have to be sure of a nice big fee and Johnny when you see how generous He can be when he wants you said fee What's your guess? Thomas Rayburn Morgan, right? I'm on my way. Yes radio network brings you Bob Reddick in the exciting adventures of the man with the Action-Pact expense account America's fabulous freelance insurance investigator yours truly Johnny dollar Expense accounts submitted by a special investigator Johnny dollar to the state unity life insurance company, New York City office Following is an account of expenses incurred during my investigation of the lone wolf matter Expense account item one Well as long as mr. Morgan is supposed to be such a generous soul call it $30 even for the plane to Newark in a cab To the brokerage office on North Commerce Mr. Morgan turned out to be a friendly Distinguished prosperous-looking man of about 50 They also looked more than just a little worried Sit down dollar sit down, please right Morgan. Thanks. No. No. No, I'm I'm the one to say thanks I mean to you for coming here Strictly speaking. This is not an insurance matter, but to tell you the truth You're the only one I know of who might be able to help me in this situation Now why is that sir? Well, I mean help me with a proper regard for its confidential aspects You see I I haven't dared to contact the police about this matter not even the authority down there in Philadelphia No, why Philadelphia? Well, that's where the letter came from. What letter is that mr. Morgan? You see if the facts were to become public knowledge Well, it could mean only one thing Complete and utter ruin not only for me. I don't care about that But for my family my wife and two splendid young sons Suppose you tell me what it's all about, sir, but I must be sure Absolutely sure that I can depend on you to to keep this completely confidential believe me I'll pay you well for it very well. Just go ahead sir. All right Yeah, read it for yourself Okay Dear Thomas or should I say Danny Fairland? Danny Fairland that was a name that I I used when I was younger and I But but read on please all right You may be surprised to hear from me after all this time That doesn't mean I haven't kept myself. We're informed about you Ever since that day some years ago when our little investment scheme took a sizable sum of money from Handful of gullible suckers You've done well Thomas. I Am sure your share of the money is what started you so nicely Is that true mr. Morgan just just read on please all right However, I was somewhat less fortunate and especially recently things have gone very badly for me But I am certain you will be only too glad to help me out. Let us say for the sake of our old friendship And because so far I have never revealed the details of our illicit enterprise to the authorities or anyone else After all I fully realize that such a revelation Just what such a revelation I do to you my friend your business and of course your nice family Now in view of your being in a position to help me what I suggest you do is this Make a couple of withdrawals from your bank account in cash Until you have say your $10,000 on hand For a man of your means this shouldn't be too difficult. It'll take very long I'll contact you as to where and when you may deliver this money Not not blackmail read on sir One other thing being a man of conscience nothing been always afraid at some time I might die without opportunity to get our crime off my conscience. I Have carefully written it out in all detail Including of course your part in it I have given this to a friend in a sealed envelope Needless to say should anything happen to me any Trouble of any kind This friend would have immediately turned it over to the newspapers. Do you see? Thomas I shall call you shortly. There at your office rather than needlessly bother your fine family Your old friend HBW. That's very neat. Mr. Morgan neat Mr. Dollar and just who is HBW His name is Henry B. Wolfe and this crime he talks about a most reprehensible stock swindle But I was young and inexperienced when he proposed it to me It it simply looked to me like an easy way to accumulate money enough to start this business And I thought without hurting anyone But it was wrong and we took thousands from innocent people Then aren't your skirts just as dirty as his mr. Morgan? Well, they're not quite mr. Dollar Well, I mean except for this blackmail bit. No, no Because at the time I kept a list of all the people we'd defrauded and then later anonymously I paid them all back with considerable interest why anonymously who could heaven's man Don't you see if what I'd done would have become known don't you see what it would do to this stockbroke Which business of mine it would ruin me but far more important though it would Needlessly hurt my wife my family the kind of looks as though he has you over a barrel, isn't it? This man wolf has nothing to lose and you have plenty and he knows it. What can I do mr. Dollar? What can you do? You know something mr. Morgan. Yes That's a good question Johnny dollars adventures take him to many out-of-the-way places around the world CBS news takes you to every corner of the globe where trained reporters are constantly on the alert to bring you accurate accounts Of what happens the very minute it happens You can always count on CBS news to be first and most accurate with the news Here expanded CBS news on CBS radio every hour on the hour every weekday. It's your clue to what's new everywhere We'd also like to remind you that today begins National Hospital Week During the year one out of every eight Americans receives some type of hospital care in addition your community hospital stands ever ready to Serve you in case of a disaster or an epidemic It is the training center for doctors nurses and medical research scientists Your community hospital needs and deserves your support You can give that support not only financially But also by serving as a hospital volunteer and by encouraging the young people in your community to enter health careers Remember your hospital is a community partnership Morgan convinced me that he was telling the truth That his ex-powel Henry Wolf had engineered the stock swindles some 25 years before That he himself had made complete restitution and then some for his part in the affair But how to locate this Henry B. Wolf The only helpful information on that letter was a Philadelphia postmark and Philadelphia is a pretty big town But he said that he would telephone me mr. Dollar. Yeah, I know and tell you where and when to hand over the $10,000 have you done that done what he told you to drawing the amount from the bank Oh, what else could I do if I set the police on his trail if he's arrested in jailed His friend will release the sealed envelope to the press and I'll be ruined And if I myself try to to stop them in any way, I I mean when I deliver the money to them the Same thing will happen stop him. You mean by putting a bullet through him something like that Believe me if it weren't for my family mr. Doc. Oh No, no, I don't mean that I just couldn't do a thing like that But what can I do? Just pay him off is what it looks like doesn't it ten thousand dollars. That's right and When he runs out another ten thousand and another and another You have the same hold on you in a year in ten years that he has right now Then you must do something to stop him now and you must do it now Okay How's about letting your secretary go out and pick me up a handful of magazines Magazines mr. Dollar what for below said that he'd call you here at the office didn't mean yes He did so right here is where I'm staying But mr. Dollar any better ideas Until we find out where and when and how he wants that money I See and when we do that depends now How about her getting me a stack of reading matter In the three days that followed I plowed through four mystery magazines three paperback novels a lot of back copies of the Wall Street Journal and all The daily newspapers available including the funny papers When Morgan went out to lunch, I went to lunch When he went home at night to his beautiful place and upper Montclair I went along with him He introduced me to his wife as an old college chum spending a few days in town But I think mrs. Morgan kind of wondered why more of our small talk wasn't about the old school tie She was a lovely woman. I could well understand why Morgan would never want to hurt her She showed me pictures of the boys. They were good-looking kids too both going to Princeton both doing very well But then the afternoon of the third day there in the office came the phone call that we'd been waiting for Had a sign from mr. Morgan. I carefully picked up the extension phone. I'm still on Henry I'm sure you're prepared for anything Henry. I know I know I guess you've made that very clear But now listen if I do give you this money But what guarantee can I have that you won't be back for more? Where do you want it when?  Dollar your pal wolf is a real clever fellow It looks from here as though any interference in this plan of his from anybody can only end up in disaster for you Well, doesn't it? Yes. Yes, of course it does But you well I thought that somehow you could help just take my advice and be there in Monroe Park tonight and hand over that Money to him so long mr. Morgan. No, no, please please mr. Dollar. See you sometime Item 2 a dime for a telephone call and then item 3 1170 to some early cocktails and dinner at the tavern with an old friend of mine Judge Amos Ordway retired The judge had been around for a long time and has a good memory Yes, Johnny. I recall that investment swindle very well And some of the people seeking their money back from those two swindlers were clients of mine Are you mean from Henry wolf and mr. Thomas Thomas? Oh No, no, no, no, it was wolf and a fellow by the name of Danny Fairland At least that's what he called himself Would there still be a case against this Danny Fairland even if he were calling himself something else now No, when the people got their money back and then some and withdrew their charges. Well, nonetheless judge Just remember this Johnny Laws governing the handling of securities weren't what they are today Possibly I say possibly at that time that operation could have been construed as being within the law barely But even more important Suppose the last guilty of the two perhaps the one who called himself Danny Fairland Suppose that ever since then he has lived a holy exemplary life More than merely made up for his youthful indiscretion. I see Yes, in other words, you know full well the name of the man who used to call himself Danny Fairland Now what are you talking about Johnny? How could I possibly know anything about him now? I see Well, thanks Incidentally, though yes, sir if you should happen to need the services of a good completely dependable stockbroker here in town May I recommend a man by the name of Thomas R. Morgan? It was after dark by now, but I still had plenty of time item for a dollar even for a cab to the southern edge of Monroe Park Trying to bribe a certain key from one of the guards about to go off duty wouldn't work So I did the only thing I could think of the moment and help myself to it after roughing him up a little But then tucking a sawbuck into one of his pockets As a result The appointed meeting place a little tool shed there in a dark tree covered corner of the park held not only me but the guard He lay quietly among the rakes and shovels securely bound and gagged at the time passed slowly nine o'clock Ten o'clock Began to get chilly uncomfortable, and I began to feel a bit uneasy Finally as a distant clock boomed out 11. I heard footsteps outside the shack Footsteps of only one however pacing slowly back and forth. I Hope it was Thomas Morgan But what if wolf played it smart and didn't show Gently Gently as possible I Opened the door a bit just a crack And then I froze because I heard the steps of another man Good evening Thomas Henry. I didn't think you were coming. You said 11 o'clock What's it?  You mean if you should do this to me again exactly And I probably will There's nothing you can do about it ever Now there's money, please Very well, Henry Here you are Brenda, thank you And now Thomas will you aren't going to count it? Or should I you know, I trust you implicitly Even as you can trust me you know leave by the south gate and go home. I'll Just wait here to be sure that you do Good night Thomas Not bad for an evening's work Not bad is right buddy. That is for me. I'll take that dough from you. Come on. Come on Thanks For you blackmail racket, huh? What are you talking about what I heard from inside the shack put down that gun now and listen to me Now you listen to me, buddy You got that poor guy on a string so maybe I can take him to huh you sure you somebody's holding some dope on I'm in a sealed envelope. No, that's what you told him So if I get the envelope whatever's in it I can put the bee on him, too I don't know please you don't understand no and whatever it is you know about him You're doing okay, and now friend when you leave me to that envelope. I'll have a racket of my own, right? So buddy if you want to stay alive you take me to that envelope, but there is none.  That was just a bluff to keep them on the hook. I swear that it was only a bluff You think for a minute I'd ask anyone else in with they are the thing like this. You mean you're just alone wolf Mr. Wolf believe me. I'm the only one It happens to be my job my name is Johnny dollar Investigator that's right. Your friend Morgan is a client of mine. I see now. Should we take a little walk? Why are you kidding? You're all through wolf. You bet you are That's where you're wrong daughter You're forgetting that the only names connected with that stock swindle years ago were yours and that of a man who called himself Danny Fairland listen Danny listen if a crook like you tried to claim that today Danny Fairland It's somebody else you think anybody believe you Yes Because daughter if I tell what I know you'll only hang yourself Not for the swindle that nobody cares about anymore, but for this blackmail bit As for any tales about the past you might think of telling you can take my word for it. They'll never hit the papers You see I know a judge well Mr. Wolf turn him in Take the chance that he might be able to hurt Thomas Morgan or Let him go With the full knowledge that I could prove a charge of blackmail against it What would you have done? Oh Well, that's just exactly what I did Expense account total including incidentals 8840 Yours truly Johnny dollar Oh here is our star to tell you about next week story next week a raging blizzard in the middle of May and The pure white snow carries the mark of death Join us. Won't you yours truly Johnny dollar? Starring Bob Reddick is written by Jack Johnstone produced and directed by Bruno Zerato, Jr. Musical supervision by Ethel Huber Here in our cast were Santa's hortega as Henry B. Wolf Sam Gray is Thomas Morgan William Redfield is Harvey Wakeman Robert Briden as judge Amos Horkway Be sure to join us next week same time same station for another exciting story of yours truly Johnny dollar This is Art Hanna speaking the laughs are on Arthur Godfrey every weekday on the CBS radio network


Everybody Matt here for late in sports cars are up in one box of 2021 22 Donner's optic basketball. This is for Eric W Good luck Eric. Let's do it. John. There's JT Thor Dwight Howard Carmelo Anthony all-stars silver. I say a liver 75 years Franz Wagner rookie insert Those 75 years inserts are so cool. Hi Jones This is red to 99 Lonzo ball Kuzma silver I say a time and 99 retro series auto Ralph Sampson. You go Eric by Howard Carmelo Scotty Barnes rated rookie grows Clay Brooks and Emmys Kuta Kita Rated rookie silver here Wiggins Scotty Lewis rated rookie and bam Quetta, there's Josh giddy rated rookie John Wall and orange Jimmy Butler to one ninety nine LeBron stargazing Keon Johnson and rated rookie orange to one ninety nine Brandon Boston, Jr. Jaden Springer and rated rookie silver Cade Cunningham. There you go Eric. Nice Cade Cameron Thomas and Duncan. That's man Devin Durant Herbert Jones Zion Joshua Primo. There's Clay Isaiah Jackson and this is lime green to one forty nine shake. You'll just Alexander All right last pack for you. Good luck. Okay Murray. That is Miles Bridges Silver All right, Eric that'll do it for your box. Thank you for the purchase appreciate that we'll get these right out to you you


You must forgive it Talking about it is a is a big first step. It's like group therapy Oh, yeah, you know being honest about it and saying well honestly, this was my past and You were honest with me about your past and I feel insecure about what you did because Blah blah blah. I'm sorry about that. I mean this is a big step. You know being truthful Means a lot. I mean look look at a online dating. It don't that only works According to how honest a person is on their profile and whether or not other people read it Nobody reads it doesn't mean anything, but I'm saying honesty is a big step dating and relationships So it's a it's a foundation. It's pretty solid Donald Trump Promised he would deport 11 million undocumented Mexican immigrants Wait if they are undocumented they are unaccountable How can he identify them? Why don't you go up to the companies that are hiring all the undocumented?  Yeah, forget it. It's not the Taj Mahal ain't gonna happen Most Mexicans are not lazy or criminal Many work at more than one job They provide a valuable service despite Trump's vicious slander Ben Carson said that if Jews had guns There would never have been a Holocaust Ridiculous Jews had guns and Used them during the war saw uprising the Jews were smashed Carson said the Egyptian pyramids were for storing grain not to bury the pharaohs a Pyramid shape with narrow corridors is stupidly wrong for storage of grain Impractical yeah Carson also described how easy it would be to drive Isis out of the Middle East That is like when big Janie proclaimed that war in Iraq would give us an easy victory You know it will see it will Reinforces my view of the United States of America today is just one big gigantic insane asylum Some other Republicans still scream about the attack on Benghazi Republicans will not face facts or admit Then Republicans refuse to pay for increased security at the US compound in Benghazi What is the matter with Americans who welcome this nonsense The acceptance of these Republican candidates as front-runners seems to prove that it pays to be ignorant you know Watching the Donald Trump documentary I hate to admit it, but you know some of Donald Trump's hecklers are Also ignorant to like remember the what do you mean some of you remember the guy who says John what do you mean John McKay does a war hero hero? Yeah, and Trump says War hero he wasn't a warrior hero. He got he got captured. I like people that weren't captured now That was silly too.  Oh You're gonna vote Democrat, but you're not looking at the overall picture. I says what is the overall picture? I know what he was getting to Yeah, he was getting to the overall picture of Salvation and judgment and all that stuff. I says listen you poluka There is no evidence I Know what the evangelical agenda is I says there's no evidence At a fertilized egg or an embryo that breathes like a fish as a human baby.  They're called audition Cultist tradition cultist tradition, which is a perception of I an unproven Well incorrect ideology because it's not of the Bible though like you told me you taught me the first Life begins when God gave the first breath to Adam Period And how is the grave that's great The origins of the word hell I Was highly offended Disrespected and suddenly Saddened by Amy Dickinson's Response to a query And I am confident I am representing the overall sentiment felt by my fellow Hindu community members as well Depends what it was about a Simple Google search will show any internet user that the Nazi and Hindu swastikas Are two symbols with entirely different meaning as that is very true They're not swastikas the shape of the swastika the rotary the identical rotary shape of The swastika is found in many cultures throughout the world and it does not represent Nazi or fascism or or a Nazi form of government the Hindu swastika is a symbol of suspiciousness and is extremely important and Highly revered in our culture the swastika was used by Hindus and other ancient cultures for Thousands of years before it was adopted by the Nazis. That's very true in the 1920s ancient symbol The fact that the two symbols bear a slight resemblance to each other Does not in any way condone Nazi beliefs Diwali and the Hindu New York New Year's excuse me took place two weeks ago and The family reference in the piece Had drawn those symbols on their front doorstep For religious purposes Just as all my friends and family did And we have been living in the United States for 20 plus years Well the flag The symbol of Sicily The island of Sicily where many of my relatives from my ancestry is literally the head of Medusa with legs her legs forming a swastika shape Exactly like a swastika and it is an ancient symbol. I think it even I think it goes really really far back I don't know if it goes back as far as Babylon, but it goes way back Dickinson is a highly well-branded individual And I am surprised she answered this question in such an uninformed manner I would expect someone with such strong influence on readers To clarify the distinct difference between the two and to raise awareness about the acceptance and understanding of different cultures Well, if you did the research You viewers out there you Google it you will see That it is a very ancient symbol It comes in different forms, but its basic shape is the same. It's like a like a rotor It's like like a never-ending circular Motion Actually spherical There are many spherical symbols Circle never go never never ends goes on and on right that's true Cyclical cycles But anyway, that's about that's it really unless you have something on Trumpy or Um Yeah, we can wait on you. Yeah, we we did pretty good. It was a very invigorating Educational show for this week progressive discussions, but we'll see you next time have a safe and fun filled weekend and Have a good next week And we'll see you next week following week. God willing On this countdown to happy Festivus for the rest of us Salute to Krampus Who's German with a very long tongue? Very long time and I imagine very talented time Yours is okay, but not as nearly as nearly as long as Krampus Need as long as gene Kiss either gene Simmons Simmons.  That's pretty long Now um I am going to make Drive drive after martinis On Christmas Eve. I'm going to make it for myself and William H. Morrow Our commercial voice over for specialists and shaken off sturd, of course, and I Learned something new all the time. I learned something about vermouth vermouth is a wine that was originally used of Medicinally because it has extracts of many herbs in it, you know, there's the first vermouth Was Presented I think in turn Italy. I'm telling you you never stop learning no matter how old you are That's what I'm trying to get at every week that goes by. I learned new things so There is somebody's walking around thinking that they're a know-it-all guru And they're on top of Mount Olympus and that's it forget it It's all it's all delusional because you really never stop. It's a closed mind It's a closed mind mind Every time you think you know it all you don't you know, and it could be it could be any field the history of Like I told them well, I haven't told them yet, but You know Helder Gondra of oh, I'm sorry Helder Gondra in the clubs Portugal and Zay Ricardo Zay Ricardo Who's the chess player like yourself a chess enthusiast? I told I'm going to tell Hader Helder that if you go to the tourist area on the coast of Portugal, which I imagine is Lisbon and Being that you make you're very artistic with wood with wooden toys wooden exercise clubs and such and calisthenics equipment in all made out of wood Lot of beachwood there. Anyway, if you make chess sets if you make hand-carved medieval Chess sets out of wood that is big tourist souvenir That's a gold mine Literally because no matter where I've ever been Internationally at a resort. I saw lots of chess sets From that region it just you know, what can I say? It's a big Tourist gift souvenir item, so anyway, we'll see you next time I'm progressive discussions Believe it or not, I have one last piece of my sister Lisa's sugar-free pumpkin pie Pumpkin pies and her sugar-free home baked apple pie was outstanding outstanding No need for sugar in these pies It was outstanding. I Tasted apple the apples were crispy. They weren't mushy. I Tasted the cinnamon and I Tasted the you know the butter and in the in the pie crust and everything and it was enough sweetness That were in the apples. There was no need for the any added sugar So that's it. We'll see you next time You know, there's really no need for this but Americans are they have this obsession with with with sugar and and and Excessive amounts of salt. There's no need for it. So they can taste. Oh Yeah, it drowns out the natural flavors of the food you're eating Yeah Well, when you go to if you go to a Brazilian Rodizio barbecue restaurant, I watched them the only thing they sprinkle on their meat is either sea salt or kosher salt They don't they don't they don't pile on sauce and Dry rub, but I personally like Memphis style Barbecue with the dry rub, but they don't they just put the salt on and they and they allow the wood From the that they use to barbecue to flavor the meat Anyway, that's another talk show Bye-bye


Hey everybody, it's Brian. Today we're going to be discussing variables and data types. I'm going to make the assumption that this is not your first programming language, and if it is, I'm going to beg you, go learn a different language first. C++ has an incredibly steep learning curve. Go learn Python, Java, Visual Basic, C-Sharp, and then come back. Okay, moving on. A variable. A variable, as you know, is simply something that changes. It's a declaration you make that you have this thing, which we're not going to define yet, you have this thing, and that this thing will change. A good example of a variable would be age. Your age will change. Now a variable can be typed. What I mean by that is these are the different types of variables you can have. There's many, many more, but these are the basic ones. The fundamental data types, if you will. Character. It's basically one character, like the letter C here. If I could highlight it, the letter C here is a character. In C++, a string, this is just an array of characters. We'll get into that into a different tutorial. You can see it's one byte and it can be signed or unsigned. What does signed and unsigned mean? Well, signed, which all variables are signed by default, means that it can be negative. You see negative 128 to 127. Unsigned means 0 through 255. It still has the same range, but notice that one can be negative and one can't. Whenever you see unsigned, just know that it means it can never be negative. If you try to give it a negative, you're going to get some weird number or a compiler error. Your program will just crash. Short int. This is a small integer. If you come from Visual Basic 6 land, that's the standard integer size. Int. That is a bigger number. That would be like an equivalent to a VB6 long. Long int, which is a massive number. Bulls, those are like light switches, just true or false. Float for floating point processes. For example, video games use floating point to determine the trajectory of the grenade you just threw. Floating points are not accurate. They're only accurate to a certain number of digits. You see this approximately seven digits. It's accurate up to about seven digits. Double precision floating point number. Long double precision floating point number. W care underscore T, wide character. What is that? I believe W care underscore T is a unit code. Correct me if I'm wrong, but I believe that's a unit code. Those are the basic data types. You notice size is very important. Why is that? Well, C++ is cross-platform in theory, but in practice it can get a little tricky. For example, an integer is four bytes, so it says, but on some systems it's actually six. Some systems it's eight. Some systems it's four. You can't really depend on the size being constant. If you're going to do cross-platform programming, which our tutorials intend to using the Qt framework, you should use a framework that is intended to be cross-platform. Otherwise, you will run into some size issues with your variables. Now, without further ado here, we've got our basic template. I've already added some code. As you can see, we've got an unsigned integer age. Remember, unsigned means it can never be negative. Int, eggs, this is a sign. Remember, all variables are signed by default six. So let's just play around here. Cout, age, and we're going to just add an inline here. And then we're going to say age equal negative 23. And then we're going to say Cout, age, and L. So we're just going to print two lines here. And after the first line, we're going to actually set age to negative 23. And I know you're sitting there saying, hold the phone. We just said age can't be negative. Why are you doing that? Well, I want to show you what happens. There's a common programming mistake with variables. Let's run this. There's some errors. Undeclared identifier, Cout. What the heck? Okay, I see. Sorry, guys. Long day. Let's run this again. Now, we've got some interesting results here. Zero and, well, it looks like four trillion. Where'd that number come from? Well, remember, age can't be negative. So what it does is it goes to the largest number and counts backwards. So let's try a short int, unsigned short int. Let's recompile this and see what happens. Notice how we've still got this number, but it's very big. It's not a negative. Well, it's because it went to its maximum range. Let's flip back to our types here. We got short int. 65,535. 65,513. So you might have guessed it went to the maximum and reduced it by 23. That's how it works. So if you're working with your code and you get some really weird numbers, check to make sure you're not passing a negative. Other than that, it's just very standard. You declare signed or unsigned. You give it the type. You give your variable a name and then you set the value.  Run time check. The variable age is being used without being initialized. We didn't initialize a variable. Therefore, it technically doesn't exist, so we can't use it. Well, I know this is kind of a short tutorial. I just wanted to let you guys know how variables work in C++. Like I said, I'm making the assumption this is not your first language, so this should just be a very quick review for you guys. This is Brian. Thank you for watching. I hope you found this video educational and entertaining.


Throughout human history there have been numerous examples of the application of chemical and biological agents used as weapons during the course of warfare. In the 20th century, their devastating potential has become apparent to terrorists. And although there have been many global efforts to restrict chemical, biological, radiological and nuclear materials, the emergence of CBR and E-terrorism has become a significant threat, requiring immediate attention and a call for collective action. Terrorism is evolving to the changes in our socio-political environment. Our political history, our technology, the way we communicate. And these changes have led that terrorist organizations and individuals have changed the way they operate, the way they acquire funding and deploy capabilities beyond our imagination. These factors place a priority on the need for law enforcement to be aware of all of the issues, be better equipped to deal should a catastrophe happen and to do so by playing a leading role in the cooperation between multi-agencies. In dealing with CBR and E- incidents, we all have to work together. And it's quite often the case that it's law enforcement who has that coordination role between all multi-agency partners. As the global law enforcement entity, Interpol is moving towards the forefront of the global response on all forms of terrorism by assisting member countries in the prevention and disruption of terrorist activities through identifying members of known transnational terrorist networks and their affiliates. This is done by tackling the main enabling factors of their activities, travel and mobility, online presence, weapons and materials and finances. The CBR and E-subdirectorate forms part of the weapons and materials segment of a broader counter-terrorism strategy. Its methodology for countering the threat of CBR and E consists of three main pillars. Information sharing and intelligence analysis, capacity building and training and operational and investigative support.


Hello, I'm Andre and I'm going to present our paper, low-weight discrete logarithms and subset sum in 2 to the power of 0.65n with polynomial memory, which is a joint work with Alexan Amai. So for the structure of the talk, we'll first talk about the subset sum problem and some non-basic algorithms to solve it with a polynomial amount of memory and then we'll dive into the low-weight discrete logarithm problem and in the end we come back to the subset sum problem and our results here. So the subset sum problem is defined by given n numbers a1 to an defined modulo 2 to the n and a target which is also a number defined modulo 2 to the n and a weight parameter omega and we are asked to find a subset of these ai's that sum to t modulo 2 to the n and the size of this subset shall be of size omega n and we represent this subset via this binary solution vector e and we only consider random instances so random instances mean instances where the ai's are chosen uniformly at random and why shall we consider the subset sum problem at all from a cryptographic perspective it's mostly due to its cryptanalytic applications meaning there are several problems of cryptographic interests which can be expressed as some pictorial approximative versions of the subset sum problem as for example the decoding of random binary linear codes or the low-weight discrete logarithm problem and hence more efficient algorithms for the subset sum problem often directly directly relate to more efficient algorithms for these respective problems so let us start with a very basic algorithm solving the subset sum problem with a polynomial amount of memory only so this algorithm has a meet in the middle strategy to actually find the solution vector e here e is of weight n over 2 so we fix the weight parameter omega to one half in the subset sum setting so because the weight parameter of one half is in terms of weight the worst case for for this problem and it's easy to generalize the algorithms to arbitrary weight but for a more easy exposition we stick with one half for the moment in the low-weight discrete logarithm case we will have to keep track of this weight parameter so but now we stick with one half so we split the vector e in two addons x and y where x has its weight only in the first half and y only in the second half now we meet in the middle algorithm would enumerate all x and y to finally find the solution vector e but we cannot afford to use this as we want to use only a polynomial amount of memory we define functions instead we define a function f and function g which map a subset to the corresponding subset sum or t minus the corresponding subset sum modulo two to the n over two here's this modulus is more like a technical need to actually apply memoryless collision search algorithms to those functions so we will now find collisions between those functions which we can do with a polynomial amount of memory using standard techniques and then a collision between those function relate to some x plus y which at least fulfill the identity or whose corresponding subset sum is at least equal to the target on the lower half of the bits and obviously a solution also fulfills this identity so how does the algorithm work finally so we search for collisions between f and g this relates then or collision then relates to some x plus y who whose corresponding subset some matches t on the lower bits and then we check if the identity is fulfilled on all bits if not we have to search for a new collision if it is we can output the solution and if you calculate the running time of this algorithm which is simply the time it takes to find the collision between those functions times the number of collisions that does exist as we need to make sure to find our golden collision you will conclude that the time complexity is 2 to the 0.75n so next we'll see an improvement made by Becker-Corot and Jue to this algorithm and how did they improve on this algorithm they did so by using the representation technique which is originally from how Graf Graham and Jue and this technique is also a key ingredient for our new algorithms and hence we'll describe the idea of this technique in the context of memoryless collision finding or in the next slides so our actual setting is with our two functions f and g which map from some domains to the same range and there does exist a collision between x and y that somehow relate to our e so in our case x plus y is equal to e and we chose x and y of this weight disjoint form and as we do so there is only a single decomposition of e in x and y of that form so that x and y add up to e and the goal of the representation technique is now to actually increase the domain of the functions which might sound bad in first place as collision search procedure then needs longer to actually find the collision between those functions but hopefully after we increase the domain there are several useful collisions so meaning collisions that actually relate to our e so collisions where x plus y is equal to e and how do becker-coronju achieves this they do this by spreading the weight of the addons x and y over the full n coordinates instead of using this weight disjoint form so by doing this they get actually many representations of the same vector e in the form of different x and y that add up to e so in summary the becker-coronju algorithm works similar as before but uses enlarged domain sizes which actually lead to more collisions but also to many good collisions so collisions relating to our e and the time complexity heavily depends on the proportion of good collisions among all collisions as the inverse proportion is the expected number of collisions we have to search for until we find a good collision and of course the time it takes to find a single collision tc here and even if the time complexity to find such a single collision tc grows due to the increased domain sizes the technique by becker-coronju does improve this proportion so much that they end up with a better algorithm of time complexity 2 to the 0.72 n and as i said in the beginning it's quite easy to generalize these algorithms to arbitrary weight so on the next slide we'll see a plot where we see the time complexity exponent of both algorithm as a function of omega so here on the y-axis we see the time complexity exponent and on the x-axis we see the weight omega and we see for weight of 0.5 we see the known complexity exponent 0.72 for bcj and 0.75 for the folklore algorithm but not only in the case of weight of 0.5 but regardless with which rate you choose the bcj algorithm outperforms the folklore approach so we keep this in mind and now want to talk about the low weight discrete logarithm problem so the low weight discrete logarithm problem is defined over some group g of order roughly 2 to the n which is generated by some known generator g and we get some group element beta and a weight parameter omega and we're looking for the discrete logarithm of beta to base g which we call alpha and this alpha satisfies g to the alpha is equal to beta and the weight of alpha shall be omega n so weight here corresponds to the weight in binary representation so firstly let us cover some facts regarding this problem so there is a time lower bound for all algorithms solving this problem in the generic group model which is actually the square root of the search space so the search space here are all n bit numbers of weight omega n in binary representation the amount of these numbers is n choose omega n and the square root of this is actually a time lower bound for generic algorithms and the only algorithm that's known so far that achieves this time lower bound for all omega is the meet in the middle algorithm so the downside of this algorithm is that the memory complexity of this algorithm is equal to the time complexity so also equal to the lower bound so the time lower bound so if you consider the discrete logarithm problem without the low weight terminus then this is actually subsumed by the above definition for the case of omega equal to one half since if you consider normally if you consider discrete logarithms you choose a random discrete logarithm and with reasonable probability the weight and binary representation of this discrete logarithm is around n over 2 and there's actually another algorithm for solving discrete logarithms that's by Pollard the Polaro algorithm that has a time complexity of 2 to the 0.5 n and polynomial memory requirement unfortunately this algorithm does not depend on the weight so it's the time complexity exponent stays at 0.5 regardless of the weight but in the case of omega equal to one half this is actually the time lower bound so just in the case in the usual case the algorithm by Pollard achieves the time lower bound and processes a polynomial memory complexity so in our paper we give a nice relation between the low weight discrete logarithm problem and the subset sum problem showing that both algorithms we've seen before so the folklore algorithm and the BCJ algorithm are also applicable to the low weight discrete logarithm problem so let us have a look on the landscape of algorithms we know so far for solving the discrete logarithm problem so that only have a polynomial memory requirement so here we have the folklore algorithm and the BCJ algorithm also applicable to the low weight discrete logarithm problem the algorithm by Pollard which has always a time complexity exponent of 0.5 for all weights and we know there is this lower bound here the dotted line but there is no algorithm that has only polynomial memory requirement achieving this lower bound for all weights so now we want to present an improved algorithm for the low weight discrete logarithm problem that leverages the representation technique a little more so for this case we define again two functions f1 and f2 now not mapping to some subset sums but to g to the power of x respectively beta times g to the power of minus y so then we're again searching for collisions between those functions where a collision now relates to some x and y where g to the x plus y is equal to beta which is of course also equal to g to the alpha so in other words x plus y is equal to alpha which we'll use to represent alpha as the sum of many different x and y so recall that in the subset sum case we also represented our solution e as the sum of some x and y where we chose the weight of these addons to be half the weight of the solution vector and in the subset sum case this actually makes a lot of sense since the addition is performed component wise over that so if we for example would add some weight to x and y then for sure we could not end up with e as their sum as either we would have too many nonzero coordinates or we would have coordinates equal to two in our resulting vector what is both not what we are looking for but now in the discrete logarithm case this x plus y is actually computed over that modulo the group order but let's for a moment forget about this modulo reduction so actually x plus y is the summation of two natural numbers and if we look at the binary representation of such a summation we can actually have carry bits so if we have in binary representation one plus one that actually produces a zero in the resulting vector and a carry bit so two ones in x and y produces a single one in the output vector alpha on the output in the binary representation so if we now add some weight to x and y we can still end up with our alpha of weight omega n so the idea is now to increase the weight of the addons a little more which increases our domain size but hopefully also it increases the number of representations so the question is which weight or do we have to choose for x and y so that we can expect them to add up to alpha and we call this weight of the addons phi of omega and actually calculated it and here we see a plot where the green line is actually the weight you should choose for your addons and in comparison to omega over two and we see that in most cases we have to choose choose a way higher weight of the addons than we did before so in summary we use again these functions f1 and f2 but now we use as a domain all numbers of weight or weight in binary representation phi of omega times n which increases the domain size but also gives us more representations and again we can calculate the time complexity as the proportion of good collisions among all collisions to the minus one times the time it takes to find a single collision and if you calculate this you'll end up with this cryptic formula here dependent on some binary entropy functions which is admittedly hard to pass so on the next slide we'll see the updated landscape of algorithms we know so far for solving the low weight discrete logarithm problem so here we have again the folklore and the bcj algorithm and the algorithm by pollard and the lower bound but now we have additionally our new algorithm as a red solid line we see that it interpolates to pollard's algorithm for weight 0.5 and it outperforms the bcj algorithm for all weights but still it does not attain as a lower bound by schub so to reach this lower bound actually we'll once forget about our polynomial memory restriction so we want to present a time memory trade-off now by employing a technique called parallel collision search that's actually able to produce a time memory trade-off for collision finding by giving the algorithm some memory I don't want to dive into detail on this technique but I'll just tell you so much if we employ our algorithm with as much memory as it needs to find collisions which is actually this inverse proportion then the time complexity is improved by this square root factor and this lets the time complexity exactly hit the time lower bound for generic algorithms of course we can also apply this parallel collision search technique to the bcj algorithm and as we can we'll see a comparison of our algorithm improved by parallel collision search and the bcj algorithm improved by parallel collision search on the next slide so here we have in green the bcj algorithm time and memory complexity memory as a dashed dotted line and in red our time and memory complexity we see that in terms of memory as well as in terms of time we outperform the bcj technique and recalls that this red solid line is actually the time lower bound for generic algorithms so if we compare our algorithm to the meet in the middle algorithm we achieve the same time complexity but we improve on memory complexity so that's actually what I wanted to tell you about the discrete logarithm problem let's now go back to the subset sum problem so recall our folklore algorithm for subset sum which where we had this functions f and g and we were searching for collisions between them where collision actually gave us some x plus y whose corresponding subset sum is equal to t on the lower half of the bits and then we check this identity or we check if the identity holds on all bits and if it does not we search for a new collision and we have to do this until we find a collision that actually sums to our e so we are in other words we are performing an exhaustive search over the remaining half of the bits and actually we want to get rid of this exhaustive search by introducing a layered collision search so instead of doing this this exhaustive search we introduce a new function h1 that actually outputs these upper not matching bits of the corresponding subset sum and as it is a function it also needs some input the input is here some s where s is a starting point for a collision search procedure between f and g so usually a collision search starts at some point of the domain and then you compute an iterative chain of applications of the function until the chain collides with itself and this starting point is the input to our outer function h1 here and then we need a second function h2 which is actually quite similar it also matches the starting point of a collision search between two other inner functions to the upper bits but not the upper bits now but t minus the upper bits or more precisely the upper bits of t minus the upper bits of this corresponding subset sum and it has a difference that a collision in these inner functions does the corresponding subset sum of this collision does match the zero on the lower bits and not the target so it does not matter which output of h1 and h2 we take if we sum it together then the corresponding subset sum matches t on the lower bits so now we can search for a collision between these outer functions and if we find such a collision this actually relates to some element x1 y1 x2 y2 which if you sum it together the corresponding subset sum has or matches t on all coordinates of course still this x1 plus y1 plus x2 plus y2 does not have to be a binary vector so we would have to check this in the end so by actually introducing this layered collision search we end up with nested row structure which is actually inspired by the work of Dino, Dunkelmann, Keller and Shamir from crypto 2016 where the small rows here correspond to an application of the outer function and actually a collision search in these inner functions and if you then perform a collision search between the outer functions you actually end up with a giant row structure where the colliding of the collision on between the outer function is represented by this outer row collision so again the time complexity is equal to the proportion of good collisions among all collisions to the minus one times the time it takes to find a single collision and here this proportion is actually equivalent to the probability that the resulting collision is a binary vector and if you calculate this you end up with an algorithm attaining time complexity of 2 to the 0.65 n so let me summarize our results so we in our paper we give a more general problem which we call a group subset sum not in this talk but in our paper which actually subsumes both problems so the low weight discrete logarithm problem and the subset sum problem so enabling us to apply algorithms that were known for either one of the problems as for example the folklore algorithm as well as algorithms only known for the subset sum problem to both problems additionally we give also improved algorithms for both problems so for the low weight discrete logarithm problem we could give improved polynomial memory algorithms and we could reduce the memory complexity of the meeting the middle algorithm in the subset sum problem or in the case of the subset sum problem we introduced a nested collision search which which actually enabled us to construct an algorithm of time complexity 2 to the 0.65 n which only uses a polynomial amount of memory thank you very much