The fiery horse with the speed of light, the cloud of dust, and a hearty high old silver, the long ranger. With his faithful Indian companion, Tonto, the masked rider of the planes led the fight for law and order in the early western United States. The stories of his strength and courage, his daring and resourcefulness have come down to us through the generations. And nowhere in the pages of history can one find a greater champion of justice. Return with us now to those thrilling days of yesteryear. Now to the past come the thundering hoofbeats of the great horse, Silver. The Lone Ranger rides again. Come on, Silver! We're heading for Black Canyon. I am Silver! The 14-year-old nephew of the Lone Ranger was an inquisitive boy. And naturally he was curious about the many adventures of the Lone Ranger and Tonto before he met them. He found it difficult to question his uncle because the masked man rarely talked about himself. But Tonto occasionally related one of their true experiences. The grave-faced Indian was more prone to do this when he and the boy were alone. Like the wise men of his race, Tonto knew that Dan would remember true stories long after ordinary lessons had been forgotten. Tonto, tell me something, will you? Uh-huh. Well, the Lone Ranger always helps lawmen, doesn't he? The Lone Ranger help law, not men. Well, isn't that the same thing? Sometime, not all time. Gee, that's what I can't figure out. The law is right, then why isn't it right all the time? Law, right, but not always men who use it honest. Sometime, crook use law. Well, that's what I mean. If the crook was using it, then the Lone Ranger helped the law. Well, he'd be helping a crook, wouldn't he? Oh, Lone Ranger, plenty wise. Him no difference. If crook have law, Lone Ranger take it away. How? Well, one time it happened, just like that. What happened? Tell me about it. Many moons ago, on big plains down south, Lone Ranger and Tonto ride through Black Canyon to town of Kingville. All at once, we hear rifle shots. We stop, plenty quick. Hold still before we go. Hold still before we go. Where did that come from, Tonto? Could you tell? Uh, thick bush over the river. Yes, I think you're right. I... listen. Uh, somebody ride plenty fast. We chase them? Not yet, Tonto. I believe our first job is to investigate that shot. Look, a horse with an empty saddle breaking through that brush. Come on, Silver. Get him on the scout! All right, Silver. Oh, boy! That's it! Oh, oh, Silver! Let's go, let's go! Oh, oh, oh! The horse is a roll, Tonto. It looks familiar to me. Uh-huh. I can't exactly place it, but I... hear that? There's somebody on the other side of this brush. Come on. Oh, they're mad. Him hurt bad. Yes, it's... now I know who that horse belongs to. Dave Jenkins. And this is Dave. Looks like he's been hurt bad. How do you... Who shot you, Dave? I don't know, for sure. They dry-killed Chimino. Easy, Dave. You've got a nasty wound on your side now. I know. Give me your handkerchief, Tonto. I'll make a bandage for that. What is it, Dave? It's a funny thing you're finding me. I was on my way to find you. I'm glad I was near here. Tom Blaine sent me after you. I'm his deputy. Is Sheriff Blaine in trouble? Plenty. Whole towns in trouble. That's why he needs you. Where's the sheriff now? Flat on his back. Mother Carter's boarding house. He was shot. I was only... Does he know who it was? Sheriff Blaine's up against a gang of killers. I can't understand why he sent you alone. Tonto, get there some way. He'll explain. You see, Tom Blaine's not Sheriff of Kingsville anymore. Not really a deputy, but... Easy, Dave. I found you before they took my star away. It's pinned right here in my vest. Take it. Yes. I'll take care of your star, Dave. Thanks. At least I'm leaving the real law in good hands. He's dead, Tonto. You'll find out who's responsible for this murder. It's the last thing we ever do. You bring Dave's body into town. I'm going on ahead. Here, Silver. Why him say Sheriff Blaine? Not Sheriff now. That's what I intend to find out. Dave said the sheriff was at Mother Carter's boarding house. I'll head straight for there. You follow as soon as you can. Right here, Silver. I'll be back in a minute. Mrs. Carter. You heard me. Mother Carter, I've come to see Sheriff Blaine. I want to... If you think you can get... Glory be. It's a long ranger. Where's Sheriff Blaine? Upstairs. Excuse me. I thought you were Bart Higgin or one of his crowd. May I go upstairs? Sure. Thank you. I know Tom's in for you, but... You found your... Yes, he found me. And I promised to bring this to you. Dave's badge. Where'd you get it? Your deputy, Dave Jenkins, is dead, Sheriff. No. He was shot ambushed about 30 minutes ago on the Black Canyon Road. How'd you know? Todd and I heard the shot that we didn't see the killers. And Dave was pretty far gone when we reached him. So they got Dave. He must have figured he was going for help, but they wouldn't have tried. Who's they? Bart Higgin, Kurt Ransom, or any of that parcel of gunslingers they've got working for him. I'm glad to tell me something about it, but he didn't last long enough. Who's Higgin and Ransom? Bart Higgin's a lawyer here in town, and a loan shark. Most every rancher in the valley owes him money. Kurt Ransom's a gambler at the Golden Eagle Cafe. I see. You think they're responsible for Dave's murder? I'm sure of it. And I'm sure they're the same side-winders who threw this load of buckshot I'm carrying in my back.  That's why I sent for you. See, the folks of Kingsville think I'm no longer sheriff. Who is? Bart Higgin. Higgin? I don't understand. Neither did I till this morning. Now it's too late. Unless you can do something. Tell me about it. Well, yesterday afternoon, Bart Higgin came to my office and demanded that I furnish him a deputy sheriff. He wanted a lawman to serve eviction notice on a rancher who owes him some money. Did you do it? Oh, I only had one deputy, Dave Jenkins. I wasn't going to let him help Higgin with any of his skin-flint schemes. What happened? Well, then he offered to serve his own eviction notice. If I'd swear him and his deputy sheriff for eight hours, just long enough to ride out to the valley and back. That's where you made your mistake. Well, how'd you know? Because, according to law, an elected sheriff is unable to perform his duties for any reason like sickness or death. Any legally sworn deputy may assume the office of sheriff. That's just what happened. I made Higgin a temporary deputy and before the eight hours were up, I'd been shot in the back. I'd put you out of the way. How about Dave? Bart declared himself sheriff by a reason of me being unable to serve. Then he called a special meeting of the town council and passed a law making it illegal to carry a gun in Kingsville. Illegal? Higgin made every man in town turn in the shooting irons, including Dave. Then when Jenkins was killed, he didn't even have a gun to defend himself. Not so much as a cap pistol when he left here to try to find you. Part of some nervy schemes, but never one that could equal this. The worst part of it is, I guess it's legal. It may be legal according to Higgin, but that doesn't make it right. If only I wasn't flat on my back. Just a minute. There's something you can do, sheriff. What's that? Swear me in to act in your place. Swear you... What about Higgin? The law doesn't limit the number of your deputies, especially in an emergency. But suppose Bob Higgin was... Let me worry about that. May I wear the star that belonged to Dave Jenkins? Why, sure. Sure you can. Lean over here and I'll pin it on you, all right? There. I'll raise your right hand. All right. You solemnly swear to uphold and enforce the laws of Kings County in the town of Kingsville. Yes, I do. And I depotize you to serve as an officer of the law as sheriff of this county. Thank you. Now what? Well, I'm not going to reveal my new authority until a little later. First, I'd like to discover the reason for it.  Oh, what's wrong? You can't tell from here. I don't want to leave the place. There's a lot of folks milling around an engine they just rolled up. It's tonneau. I'll be back later, sheriff. Who's that turn to the saddle of the second pony? Looks like... Why, it's Dave Jenkins. Jenkins? First, Blaine is shot. Now, Jenkins. Who's the engine? Never saw him before. If Higgins a sheriff around here... All right, that's Kim. Who've you got turn to that pony? Me, fine man, shot. Bring him here. Yeah. It's Dave Jenkins, Bart. He's dead. You've got a lot of nerve, ain't you, engine? Kill a man, then bring the body right into town. Oh, me? Not kill him. Yeah, well, I say you did. And I'm the sheriff here in Kingsville. You're under arrest. Oh, me? Put up your hands. Get his guns, Kurt. Kill, Bart. Just a minute. Nice man. A mouthful. Maybe that's the omber that killed Dave. Look at that stallion he's riding. You better let the Indian keep his guns, Mr. Higgins. Who are you? No, that law can write in here. He didn't kill Jenkins, and you know he didn't. Get the engines, guns, Kurt. I'll keep this man's gum break covered. Hand him over, Redskins. No, me, keep guns. Remember, I warned you. You're under arrest, too. I'll go for that gun. My hand. You asked for it. Did you see that? Shot the gun right out of his hand. Why are you? Oh, oh. Kurt got the same thing. Say, who is that omber with the man? Never saw such shooting. You better leave, Tutto. We'll come back later. Come on, Tim. Get him on the count. Nail him. Why does somebody nail those critters?  Shoot them, omberies. How can anybody else do any shooting when you and Kurt have got the only two guns in town? And they are on the ground. The curtain falls on the first act of our Lone Ranger story. Before the next exciting scenes, please permit us to pause for just a few moments. As Tutto told how he and the Lone Ranger had ridden away from Kingsville, Dan interrupted with a question. Why did you leave? How could you discover who killed Dave Jenkins unless you stayed right there?  But Lone Ranger, no best. We ride back along Canyon Road, then we stop. Who's over here? Who's got hope on the phone? We'll wait here a while, Tutto. I want to change my appearance before I go back to Kingsville. I'll get the other clothes out of the saddlebag. You fix the disguise. Berries like Indian make him. That change Lone Ranger's face. Make him look like rancher. Him change clothes. And plenty quick, nobody know him Lone Ranger. Why did he want to be disguised? Don't tell you. You listen. When Lone Ranger ready, him speak. He tell Tutto plan. I'll go back now, Tutto. I think we can accomplish a lot more this way. Ah, and where you go? First, the Golden Eagle Cafe. The gambler, Kirk, ran some works there. And what me do? Ride a back trail, Tutto. Come into town from the other side. Wait near Mother Carter's boarding house. If I need you, I'll signal. Here, Silver, you think we catch Crook? I hope so. Oh, this is one time the Crooks have the law on their side. Is that a big fella? That makes it more difficult.  Wait for signal. Good. Come on, Silver. To Lone Ranger go. Me wait short time. Then Tutto ride back trail. Ride slow. Other side river. Me almost reach town. Go by cave on river bank. Then me hear voices. Then talk. Me sneak close through tall blasts. Hear men talk plenty. Then tablet into town. Well, we don't move in on the boat until after the driver's delivered it to the bank. That's right. We do the bank signs for it. Ain't you getting kind of fancy, Bart? What difference does it make who we hijack as we get the gold? Because when it's on the stage, it's in the hands of the express company. And they're too big and tough to muck you with. Sometimes banks are tough, too. Not this one. You won't have any trouble. Wish I was sure of it. I'm sure. And I'm the sheriff of Kingsville. Yeah, but Red and me are doing the job. What are you scared of? There isn't a loose gun in the whole town. Kurt and I crowned every one of them. Kurt? What's Kurt and Ransom got to do with it? He's my deputy. That's a laugh. Oh, it's so funny. You two high binders being in the law in Kingsville. I'd never put that one over. Never mind. We did it. Now you want breeze to your part. That gold's as good as in our pockets right now. Eh, one more thing.  Mask? What's wrong with the bandana I always use? There was a mask outlaw in town today. Folks got a good look at him. Is that him? All right. Don't know where I'll rustle a black mask, but I'll try. Kurt and I'll meet you here as soon as it's over. But remember this. Don't get any ideas about a double cross. Or you'll wish you hadn't. You and Ransom remember the same thing. I guess we understand each other. Sure, Sheriff. We understand. That's buddy. I'll see you tonight. Me here, crook talk. Then go meet Lone Ranger. Him not there. I'll talk to wait. Him in Golden Eagle Cafe. All right, stranger. What you drinking? Nothing. I'm looking for Kurt Ransom. Yeah? Where is he? Is Kurt looking for you? Maybe. I guess it didn't catch your name. I didn't mention it. Where's Ransom? Now listen here, stranger. You better not start. If you don't tell me where he is, I'll start looking with this. Where'd you get that gun? Don't you know it's against the law to pack a gun in this town?  If you don't tell me where Ransom is, I'll go around. No, wait a minute. No, wait. All right. Follow me. Kurt, I...  There's a number here asking for you. I don't want to see anybody. Get out of here. I think you'll see me. Hey, what the... Hey, Bartender. I'll handle this alone. Honest, Kurt. All I did was... I said alone. Hey, you can't do anything. Sit down. You can't force your way in here and threaten me with a gun. I'm a deputy sheriff. I'll arrest you. You're wrong, Ransom. I'm the one who'll make the arrest. You're not the law in Kingsville. Wrong again. See, this badge says Deputy Sheriff King County. How can you be? I was sworn in this afternoon by Sheriff Blaine. Blaine's not sheriff. Bart Higgins got the job. You mean he took the job? I don't know who you are, stranger. But you're heading for a lot of trouble. Maybe.  My badge means just as much as yours. More goes the law's on my side. Not out that's true. So we'll settle things without the law. Settle what? I never saw you before. I want to know why you and Higgins shot Sheriff Blaine and killed Dave Jenkins. We didn't. I want to know why you took over the law here in Kingsville. That's none of your business, stranger. Better not go for that gun. You're covered. All right. You'll get a cold wrap. What do you want? Where's Bart Higgins? How do I know?  Cut. I've got everything fixed with Libya Red. They'll meet the stage. Reach Higgins. What the... Who is this? I'm a crazy cop. He walked in here, pulled a gun on me. Thanks for reminding me. Lay your guns on the table. Both of you. Wait a minute, you can't do this. I said lay them on the table. But you won't get away with this very long. There's my gun. Here's mine. The other one too. What do you mean? A pistol in that shoulder holster into your arm. Lay it down. There it is. Good.  Money? No, Higgins. All I want is a company of you and Mr. Ransom. Company. And listen here, I'm the sheriff of this town. It won't be long. That's why I want your company. We're going for a walk. Walk? Just over to Mother Carter's boarding house. You see, Sheriff Blaine wants to legally take back the authority he gave to Higgins yesterday. Take back me. Blaine can't do that. The law says... But I say we're going for a walk. Turn around. Both of you. Go ahead. Now open the door, Higgins. Walk straight ahead and out the front door. Don't try any tricks. You can't do this. Remember, there's a gun in the back of both of you. Let's go. Walk on the door, Higgins. This Carter woman's immediate. She might knock. Why, you low down? All right, Mrs. Carter. Neither of these men is armed. They won't cause any trouble. Who are you? Higgins and Ransom want to turn back to their law badges to Sheriff Blaine. Now you have a gun. Will you keep them covered and take them upstairs? Oh, I'll take them up.  Come on now. Now the Sheriff, I'll be back as soon as I find out who Red and Lefty are. Well, all this was going on. Me wait outside a house. Then lone rangers see Tonto, him stop. Tonto. Huh? Me see you take Higgins and other Crook. Mother Carter will watch them for a few minutes. Now Higgins said something about everything being fixed with two men named Lefty and Red. We've got to find out. Me know. Tonto hear Crook talk in cave by river. There's Red and Lefty. Who are they? Me not see them. They hold a bank after stage, leave gold. They think that easy because nobody have gun. So that's the reason Higgins took over the law in Kingsville to frame a bank robbery. Ah. When, Tonto, do you know? Plenty soon. Stage come in at sundown. Then there's no time to lose. Come on. Stage driver, left this strong box in here on the wall. Will a couple of you boys help me carry it into the bank? Sure, Joe. We'll help you. Come on, Jack, grab a hold. Sure. That's it. Oh, oh, oh. We'll save you the trouble. We'll take that box boys and everything that's in it. Reach, Olivia. Hold up. Not a gun in the whole crowd of them. Where's Hagan the sheriff? Look, it's all one's mask. Same ovary that shot it out with Hagan today. Guess none of you folks are anxious to interfere. Unless you want to taste some lead. Keep everybody stretching, Red. I'll shoot the lock off this box. No, you won't. Who's going to stop me? They're in the shooting iron and the whole parcel of you. That's what you think. Who's he? I never saw him before. Ain't that engine with him? Why you? Oh, my arm. He got him in the arm. No, he didn't. Just shot the gun out of his hand. Yeah, but... I say that's the same kind of shooting that mask man did this afternoon. You, over there. Unless you want some of the same thing, you better drop that gun. Put up your hand there. Up, don't shoot. Hey, stranger, it's a mighty lucky thing you happen to be here. Wasn't luck. Oh, he's wearing a badge. You a lawman? A sheriff, maybe? Just temporarily. Until the law of Kingsville is back where it belongs. All right, Red. Nifty, keep your hands up. Where are you taking the mavericks? To jail? I'm going to turn them over to the sheriff. You mean Bart Hegan? There's only one real sheriff in Kingsville. That's Tom Blaine. All right. Start walking. He's heading for Carter's boarding house. Come on, let's follow him. Take Crook the house. Plenty people follow us. Him, March Crook, up to where Sheriff's sick and dead. There they are, Sheriff. The other part of Hegan and Ransom's plan. Glory be! Who are these varmints? They were going to haul up the bank after Hegan admitted illegal for anyone to carry a gun in Kingsville. So that was it. You're going to appoint a deputy to take them to jail? Sure. Sure I can. Give me the job, Sheriff. I'll handle those sightwinders. Where's Hegan and Ransom? I got him hog-died down in the cellar. Yes, I didn't see it, but after mother brought those two pole cats up here, she took them downstairs again. Must have used little persuasion. I used plenty. Why? They both signed a confession. I've got it right here in black and white. Hegan admits he killed Dave Jenkins. Good. Good. My job's done. Oh, I, uh, here's the badge, Sheriff. It's been a privilege to word. I hate to see you take it off.  Kingsville owes me nothing but a promise to keep the law in honest hands. And with you as Sheriff, I know it'll be that way. Adios. Come on, brother. Who is he, Sheriff? Yeah, that's what I want to know. Who is that man? Oh, I'm sorry. I thought everybody knows. That was a lone ranger. Is a copyrighted feature of the Lone Ranger Incorporated.


We could try simply not provoking a wider war via the continued destruction of Gaza. The US has carried out another air raid in Yemen with targets reportedly including the International Airport in the capital city of Sana'a. This comes a day after US and UK airstrikes on Yemen in retaliation for Houthi attacks on Red Sea commercial vessels. For years, Yemen's Houthi forces have been greatly inconveniencing commercial shipping with their blockade, with reports last month saying Israel's alat port had seen an 85% drop in activity since the attacks began. This entirely bloodless inconvenience was all it took for Washington to attack Yemen, the war-ravaged nation in which the US and its allies have spent recent years helping Saudi Arabia murder hundreds of thousands of people with its own maritime blockades. Yemen has issued defiant statements in response to these attacks, saying they will not go unanswered or unpunished. The Biden administration's dramatic escalation toward yet another horrific war in the Middle East has been hotly criticized by lawmakers on both sides of the aisle, who argue that the attacks were illicit because they took place without congressional approval. This impotent congressional whining will never go anywhere since, as Glenn Greenwald has observed, the US Congress never actually does anything to hold presidents to account for carrying out acts of war without their approval. But there are some worthwhile ideas going around. After the second round of strikes, a Democratic representative from Georgia named Hank Johnson tweeted the following, I have what some may consider a dumb idea, but here it is. Stop the bombing of Gaza, then the attacks on commercial shipping will end. Why not try that approach? By golly, that's just crazy enough to work. In fact, anti-interventionists have been screaming it at the top of their lungs since the standoff with Yemen began. All the way back in mid-October, responsible state crafts treat a Parsi was already writing urgently about the need for a ceasefire in Gaza to prevent it from exploding into a wider war in the region, a position Parsi has continued pushing ever since. As we discussed previously, Israel's US-backed assault on Gaza is threatening to bleed over into conflicts with the Houthis in Yemen, with Hezbollah in Lebanon, with Iran-aligned militias in Iraq and Syria, and even potentially with Iran itself, any of which could easily see the US and its allies committing themselves to a full-scale war. Peace in Gaza takes these completely unnecessary gambles off the table. And it is absolutely within Washington's power to force a ceasefire in Gaza. Biden could end all this with one phone call, as US presidents have done in the past. As Parsi wrote in The Nation earlier this month, quote, In 1982, President Ronald Reagan was disgusted by Israeli bombardment of Lebanon. He stopped the transfer of cluster munitions to Israel and told Israeli Prime Minister Menchem Beghen in a phone call that this is a holocaust. Reagan demanded that Israel withdraw its troops from Lebanon. Beghen caved. Twenty minutes after their phone call, Beghen ordered a halt to the attacks. Indeed, it is absurd to claim that Biden has no leverage, particularly given the massive amounts of arms he has shipped to Israel. In fact, Israeli officials openly admit it. All are missiles, the ammunition, the precision-guided bombs, all the airplanes and bombs. It's all from the US, retired military Major General Yitzhak Brick conceded in November of last year. The minute they turn off the tap, you can't keep fighting. You have no capability. Everyone understands that we can't fight this war without the United States, period, end quote. In the end, you get peace by pursuing peace. That's how it happens. You don't get it by pursuing impossible imaginary ideals like the total elimination of Hamas while butchering tens of thousands of innocent Palestinians. You don't get it by trying to bludgeon the Middle East into passively accepting and active genocide. You get it by negotiation, de-escalation, diplomacy, and détente. The path to peace is right there. The door's not locked. It's not even closed. The fact that they don't take it tells you what these imperialist bastards are really interested in.


talk will be on public key identification schemes based on multivariate quadratic polynomials by Koichi Sakamoto, Taizo Shirai, and Hanonaga Hibatari. Koichi will be the speaker. Thank you, Chair. Today, I'd like to talk to you about our new identification schemes. Our motivation of this work is finding a new alternative to current standard schemes, for example, RSA, for public key identification and digital signature. Especially, we would like to provide an alternative based on a program other than factoring or a discrete log. There are a number of prior works in this area. For example, identification schemes based on permitted kernel problem, syndrome decoding problem, and lattice problem. On the other hand, we focus on another problem called M-key problem. Let's begin with what is an M-key problem? The M-key problem is solving a multivariate quadratic equation system over our finite field. Concretely, given coefficients of the equation system and the problem is finding a solution of a system, and this function consisting of various quadratic polynomials is called M-key function. And this problem is very promising problem for cryptography because of the following three advantages. First, the M-key function can be efficiently implemented. Second, the M-key function can be used as a one way function with very short output, for example, only 80 bits. And the interactability of a random instance have been well-exampled for a long time. Finally, the associated decision version of the M-key problem is NP-complete. In fact, there is no known polynomial time quantum algorithm to solve it in contrast to factoring or discrete log, which variates public key cryptography or NP-KC uses this form of function. But many existing schemes of NP-KC have already shown to be insecure. What is the reason why this happens? Existing design of much varied P-KC is based on a trapster function from composition of easily invertible maps. For example, MI scheme, HFV scheme, and UOV scheme are based on this design. In this design, a secret key consists of these three easily invertible maps. And the public key is the composite function of the three maps. The composite function is a highly limited form of M-key function. But the key recovery problem is not an M-key problem, but another problem whose interactability is still controversial. The problem is called isomorphism of polynomial problem. In fact, this causes that some schemes of NP-KC have been already shown to be insecure. So we employ a totally different design. Our design is based on their knowledge, argument of knowledge for the M-key problem. Of course, there is a trivial construction of such a protocol for any NP-language. So especially, our design is based on a non-trivial and efficient construction by using our original technique. We note that it uses not a composite function, but a random instance of M-key function. In this design, coefficients of the M-key function can be commonly used by all users. So a secret key is an input of the M-key function, and the public key is the output of the function. By using this design, you can get following two advantages. First, the key recovery problem is an M-key problem. In fact, the security of our scheme can be reduced into the interactivity of the M-key problem. Second, the size of our public key is very small. For example, 80 bits for 80-bit security. This is the summary of introduction. First, we focus on the M-key problem, which is very interactable and promising. Then, we introduce a different design than existing M-PKC. The existing M-PKC design of M-PKC is based on a telephoto function. From composition of easily invertible maps. So the key recovery problem is not an M-key problem, but another problem whose interactivity is still controversial. On the other hand, our design is based on what zero-knowledge argument of knowledge for the M-key problem. So the security can be reduced into the interactivity of a random instance of M-key problem. That was the introduction of this talk. Then, I'd like to talk about new technique and construction. Here, we consider two entities, approved by Alice and verify above. And we suppose that both of them have a common instance why of M-key problem. In this situation, approved by Alice asserts that she has a solution of the M-key problem. On the other hand, a verify above checks whether the assertion is true or not through interaction with Alice. In this protocol, if y and x used as a public key and a secret key, then this protocol can be used for a public key identification. The security can be derived from two properties, their knowledge and argument of knowledge. Roughly speaking, their knowledge implies that a verify above cannot any information on the secret key. And argument of knowledge implies that nobody can cheat above in order to construct such a protocol. A cut-on-choose approach is useful. So we review the cut-on-choose approach. In this approach, first, approved by Alice divides her secret into shares. For example, share 0 and share 1. Then, a verify above to this feature she checks, either 0 or 1. Finally, she proves the correctness of the chosen share without revealing her secret itself. For example, a proof of Alice reveals the secret at the share i. Then, a verify above checks the correctness of the share i. For this approach, we should solve how to divide a secret key into shares and how to check the correctness of each share. A property of homomorphism is useful for this approach. For example, in the case of modular exponentiation, if a secret key x is divided into two shares, R0 and R1, then a public key to the power x is correspondingly divided into two parts, each of which is verifiable from either R0 or R1. But the M-cube function is not homomorphism. So we introduce our new original technique for the M-cube function. For our M-cube function F, we consider a situation where a secret key is x and the public key y is f of x. In this situation, you can find the U-through property of the M-cube function. That is, the associated polar form G of the M-cube function F is by linear function. The polar form G is this form of function. By using the U-through property, we can divide a secret key into three shares. First, we divide the secret key x into R0 and R1. Consequently, the public key y is also divided as this equation. Second, we further divide the information on R0. That is, R0 is divided into T0 and T1. And F of R0 is also divided into E0 and E1. Consequently, a public key y is divided as this equation. So the secret key x is divided into the three shares. Here, we note that this part is verifiable from share 0 and share 1 as share 2. And this part is also verifiable from share 1 and share 2. Moreover, no information on the secret key x can be extracted from only two out of the three shares. Using this technique, we construct a protocol. This is our basic protocol. First, approval Alice divides her secret x into three shares. Then, she commits these values. Finally, a verifiable bar chooses a challenge 0. Then, approval Alice reveals share 0 and share 1. If a verifiable bar chooses a challenge 1, then approval Alice reveals share 1 and share 2. Finally, a verifiable bar chooses challenge 2. Then, Alice reveals share 0 and share 2 for the security, which can show the following theorems. First, this protocol is statistically zero-knowledge when the commitment scheme is statistically hiding. Second, this protocol is argumental knowledge for the MQ program with knowledge error two-thirds when the commitment scheme is computationally binding. So the commitment scheme is statistically hiding and computationally binding. Then, this protocol is statistically zero-knowledge argumental knowledge for the MQ program with knowledge error two-thirds. And such a commitment scheme can be constructed from a collision-resistant hash function. For public identification, this protocol should be repeated a number of times because the knowledge error of the protocol is two-thirds. So here, we consider two types of composition, sequential composition and parallel composition. The sequential version of our scheme achieves the security against active attack. The security model supposes an attacker who can interact with an honest prover. On the other hand, the parallel version of our scheme achieves the security against active attack. But the security model supposes only eavesdropper. So the sequential version of our scheme satisfies higher security requirements. But if underlying MQ function is substantially compressing, for example, a map from 160 bits to 80 bits, the parallel version of our scheme also achieves the security against active attack. We compare with public identification schemes based on another program whose associated decision version is NP-complete. Especially, we consider the schemes from three-part still-knowledge argumental knowledge. This table shows the public key size for 80-bit security, the communication data size, the number of arithmetic operations, and required random permutation. These figures are in the case that the protocol is repeated until the impersonation probability is less than 2 to the power of minus 30, which is less than 1 billionth. As you see, the performance of our scheme is highly comparable, especially the public key size is very small, only 80 bits. In addition, our schemes doesn't require any random permutation. Finally, we summarize this work. We propose public key identification schemes based on our MQ program through new design, which is totally different from existing MQC. Technically, the design is based on their knowledge, argument of knowledge for the MQ program. And the advantages of our scheme are the security and the public key size. The security can be reduced into the interactivity of a random instance of MQ program. And the size of our public key is very small. For example, only 80 bits for 80-bit security. Another application, a digital signature scheme based on the MQ program can be also realized. Thank you for your attention. So we have time for one question. OK, so let's thank our speaker again.


Hello everybody, welcome back to another Adobe Photoshop CC 2021 tutorial this one I'm going to show you how to enhance an image in like 90 seconds and really make a nice image pop now This is a great photo, but it's a little bit. I don't know it looks a little washed out to me the colors There's not a lot of contrast the red here looks kind of dull and overall I think we can make this image pop and I know you graphic designers don't like to hear that But that's what it is. We're gonna make it pop. We're gonna make it more vibrant How do we do that first step drag and drop the image in we're going to left click on the lock here The next step is I'm going to create a duplicate copy I'm going to drag and drop it on this little plus icon at the bottom right and that makes sure that we're working non-destructively so now let's get right into this the next step you want to go to window and Click on your properties panel. My property panel is up here, but yours could be anywhere Move down to quick actions and under quick actions You're going to see something called view more left click on view more and then left click now on Enhance image. It is a quick action as I said twice for some reason and now left click on apply It is now going to do some calculations It's using camera raw to do it and presto You're gonna see that it has made some adjustments and it's done a very good job And I'll show you what I mean. Here is the final product. Here is the original image The original image is a little darker. The reds aren't popping the greens a little bit I don't know just a little drab to me and now with the enhanced version. I think it's got better contrast It's a little bit brighter. It's just an overall better image and the thing is you can do it in like 60 seconds So if you've got lots of images that you need to process This is a very quick and efficient way to make your images look great Thanks for watching this tutorial time for stuff coming up. Stay tuned


So, in the last class we looked at a nice property of irreducible Markov chain that is positive recurrent. We said that it is going to be positive recurrent if and only if there exist a probability function pi such that it is the solution of the equation pi equals to pi p right. So, in the last class we just showed existence of such a pi and we argued that that is going to unique right, but yeah one of you pointed who is that you write so about the uniqueness. So, he has a concern that whatever the method we started with we had some way of construction of B n's. So, we had this B n's and this was a vector and we showed that this as n goes to infinity this was converging to some gamma right. So, if I am going to take some we said this was positive, but this is we know this it is unique, but you are starting with some method where you are going to start with some particular sequence and saying that that is going to convert some little. So, this whatever and we somehow translated this we take this pi equals to gamma, but after normalizing this gamma by dividing it by summation of gamma is. So, by this method I mean what are the gamma we got yes this is an unique solution for the method we started with, but if you have started with another method maybe you would have constructed another sequence and looked into its limit maybe what is the guarantee that that would have also given you the same gamma. For uniqueness I want that irrespective of what method you are going to construct when you look at the limit that limit should be the same that is what I wanted to show. So, in a way the last construction we said for that method the gamma you obtained is unique, but we are going to say that that was a kind of hint for this now we are going to say that that is going to be the unique whatever method you are going to use. So, now we are going to argue that if then any other pi exist let us say which also which satisfies the relation pi equals to pi p that must be necessarily the case that that pi is exactly equals to this gamma. So, let us try to argue that. So, in the last class it was clear that this pi is a positive probability vector because all this gamma is a positive. Now, how to show uniqueness? Now, let us say that pi is any probability vector with strictly positive elements in this and then let us say that is a solution of pi equals to pi p. So, some pi which is going to satisfy this now we are going to argue that this pi is nothing but this gamma this pi must be necessarily this gamma. Now, how we are going to show that here this pi is such that summation of pi i equals to 1 and pi i is equals to 0. So, let us assume that this pi is such that this is a probability vector with stick positive elements in it and it satisfies this relation. If it satisfies this relation I already know that by repeating this recursion I should be able to get this for all greater than k equals to greater than or equals to 1. Now, what I do is I will add this for k time. So, this is true for any k equals k right. Now, I will take this for k equals to 1 2 up to n and then add all of them. So, if I add all of them on the right hand side I am going to get n pi and on the right hand side I am going to get and let us divide them by n. Now, this guy on the right hand side if I just slightly reorganize this I know already how to handle this right. We have already dealt with how to handle this we have already said that if this is going to be. So, if only if you are going to look into this quantity this quantity is going to be finite if my state is transient right and in that case if I am going to divide it by n anyway it will go to 0. We have shown that this quantity in the limit as n goes to infinity already goes to 0 if my state is and I am now looking at 1 by n and null recurrent. But if it is not either of these two then it goes to some constant which we said as gamma j right. So, now just do this you let n goes to infinity. So, the left hand side is anyway constant what is changing if the right hand side as n goes changes and where this limit is going we have already shown that this is gamma right. So, then this pi is actually equals to gamma 1 and that gamma is what that gamma is a specific vector which is the limit of my sequences. Now what we have just argued is this pi whatever this pi that satisfies this relation that pi is equals to this gamma right. There is a pi multiplication here, but when you look at component wise that what we showed right. So, now this is in a vector format you take a specific component j in this and now look at for that when we look into that this quantity is going to be turn out to be simple gamma j and now this is like a probability vector right. So, it will just add to 1 and you will just get the constant gamma j for that and now just look at the vector just the same argument we did last time except that I just writing compactly it for the vector here. So, now we have done the what we started proving was only the only if part right we are trying to we will try to show that the necessary condition that if my Markov chain is irreducible and positive recurrent, then the pi that exists here that is there exists a pi which is a solution of this and which is also unique. Now we want to say that suppose let us say we want to show the if part that is the sufficiency what we want to show now if indeed such a relation holds pi equals to pi p for some positive probability vector then my irreducible Markov chain is positive recurrent how we are going to show that. So, we are again going to show this by contradiction suppose assume that you have a irreducible Markov chain and let us say it is it should be of one type either it should be transient positive recurrent or null recurrent suppose let us assume it is transient or null recurrent. So, I am assuming so assume pi equals pi equals to pi p and then Markov chain is transient or null recurrent then for this we know that this quantity here goes to 0 as n goes to 0 and again going back to this previous step here what I have that is pi equals to pi 1 by n and k equals to 1 to n p to the power k and if I apply my limit as n goes to infinity what does this say this says this guy goes to 0 but what is this we have assumed this pi vector to be positive strictly positive. So, this is right away are going to give us a contradiction. So, it must be the last case if this relation holds then none of this should be possible a contradiction infinity. So, this relation should be true for any n. So, this is this relation whatever we wrote this should be true for any k. So, I can just add all of them and get it for any n and then I let n go to infinity ok. So, now our part is complete. So, if you hand up with a irreducible Markov chain then how you are going to ensure that that is going to be positive recurrent what you first do is take this transition probability matrix check whether there it has a solution pi equals to pi p right. If it has a pi that solution and all the components in that are going to be strictly positive then you conclude that that this is a positive recurrent matrix ok. Now, what we did is we took a irreducible class right and we just focused on that. What if my Markov chain has many communicating classes which are all closed for example, how does this result extend? So, let us say I have. So, this is my state space S I have one communicating class 1, one communicating class 2 and one communicating class 1. So, let us say my Markov chain reduced into these three communicating classes and let us say each one of them is a closed communicating class. So, then what would we say ok that is the case just take focus on this and on the states here then you can think of your Markov chain on this state space is irreducible because your entire now state space is one communicating class and then we did just study this. But suppose your Markov chain had multiple things like this. So, how you are going to extend results like this? Now, is it the case that if I look at my solution on the entire space for pi equals to pi p will that pi is going to be unique? Let us do that. Yeah, it could be unique for C 1, C 2 and C 3 that we have already shown ok. Let us say let me take A 1 here which is the solution of this to this. So, be watchful here when I write P 1 here this is the transition probability matrix reduced to the states in this class. So, for this entire thing there is one transition probability matrix which is of size mod s into mod s. But now this P 1 is that portion of this matrix which corresponds to this state space. That is again a transition probability matrix right because you have just restricted yourself to some particular rows here and similarly E 2 equals to now all of this A 1, A 2, A 3 are according to us unique because we have just focused on that particular communicating class. Now, what can we say? So, now extend this to this kind is equals to pi 1 equals to pi 1 over entire p. Now, what is this pi 1? Pi 1 is same as this A 1, but on the places where the states are not included in this state I am just going to append zeros there. They are getting a sense of what I mean by this. So, pi 1 is still on the entire s states, but it is going to be some A 1 here and zeros other way. So, that means this some portion it is A 1 that is corresponding to the states in this class and I am just appending zeros in the other places. Similarly, pi 2 I mean the position where this A 1 and A 2 lies need not be at the same this is just for my representation. So, if you are going to extend like this you can still check that pi 1 here is going to be a solution of this equation still. This is going to be the case because transition from this class to this class or this class to this class those probabilities are going to be zero or these are not there. So, because of that even if you extend it like this this p will be such that for the portions corresponding to zeros here the corresponding elements in p is also going to be zero because the cross probabilities are going to be zero. Just check that this is in this going to be true for all of them. So, let us say this is I have all these are solutions according to our now all of you know what I mean what is a convex combination of two vectors. So, let us take this is a probability vector this is a probability vector this is a probability vector let us take their convex combination will it be probability vector this is going to be probability vector. So, now if I take lambda 1 pi 1 lambda 2 pi 2 plus lambda 3 pi 3 this is going to be where this lambda 1 plus lambda 2 plus lambda 3 is 1. I will take lambda 1 lambda 2 lambda 3 which they are positive and they sum up to 1 and they have taken their convex combination like this. So, let us call this another pi. So, this is the same pi here what I have in the entire Markov chain I have now based on this pi 1 pi 2 pi 3 by taking their convex combination I have obtained another probability vector this satisfies this equation pi equals to pi p. So, so this pi is now I have such a pi where pi is lambda 1 pi 1 but lambda 2 pi 2 plus lambda 3 pi 3. But now is this pi unique here that satisfies this relation pi equals to pi p what exactly right. So, this lambda 3 this convex combination if you change this. So, what are this they are this lambda 1 lambda 2 lambda 3 you take any such thing such that you take any lambda 1 lambda 2 lambda 3 such that it satisfies this then you will get another pi here different different pi which satisfies this. So, on the whole thing this solution pi equals to pi p need not be unique there could be many pies that could solve this and specifically these pies can be obtained as a convex combination of the pies we obtain on each of these communicating classes. Then so, let us look at another example an example so far. Well, let us say I have some 5 states like this starting from 0 1 2 3 and 4. So, let us draw this transitions. So, let us say I have a Markov chain like this. So, think of this initial state as you start throwing some coin here you toss a fair coin when the probability half you enter state 1 with probability half you enter state 3. So, when you enter state 2 1 after that you either go to remain in state 1 or go to state 2 you go to state 2 with probability half sorry 0.8 and similarly when you are in state 2 you go to state 1 with 0.2 or remain there itself. Now, this 5 state Markov chain in how many classes I can derive this into and what are those classes? So, will 1 and 0 communicate no right. So, 0 and 1 cannot be in the same group in the same class and similarly 0 and 3 can be in the same class no they cannot be right, but can 0 1 and 2 can be in the same class and 2 and 3 and 4 can be in the same class. So, then what are the possible classes? 3 classes right to a class 1 class 2 and this has to be in the separate one. Now, what about class 1?  So, fine you can have such a transition like and based on that you can see what are the different classes here. Now, suppose that you want to solve for this equation. It is still not one irreducible class, but suppose let us say you solve this and obtain some solution for this case it looks like the solution is going to be 0 1 by 10 and 4 by 10 1 by 4 and 1 by 10 just check this I am just dumping this on the board. If you are going to start your Markov chain with this distribution as your initial distribution ok. You right now this I have just told you this is a transition I have not told you anything about the initial distribution right. Suppose you solve this and you start this with is your initial distribution. We already said one property what was that property? So, x 1, x 2, x 3 at every point it is going to remain in the same thing right. Now, then you can also then in a way that can also imply that if we start with initial distribution I then my dT mc is stationary. So, you already discussed what is the stationarity of a process right ok. So, if your Markov chain is such that if you are going to start with your initial distribution simply to be this invariant distribution then it is going to be stationary right. So, I am this is not a irreducible Markov chain here right. So, that was the case when it is irreducible. So, now I am not saying that this is going to be reducible this is an arbitrary Markov chain with different different different possible set of classes. Now, you just still take this as the solution of this provided this this pi solution happens to be a probability vector you take it and make it as your initial distribution and then run it then your Markov chain is going to be stationary. Yeah. So, we have only ensured that my pi is going to be unique when I have a irreducible positive recurrent one, but here it is neither positive sorry it is irreducible and I have not yet verified whether this states are recurrent or what. So, I have just said take a solution like this. So, in that case I am not guaranteed to have an unique distribution. So, that is the thing right like if there are multiple solutions which satisfies this and if you are going to start with those different different possibilities then the your stationarity your probability that your Markov chain is going to take a particular state in a particular time that is going to be different it depends on what is the initial distribution you are started with and that you can have multiple possibilities in that case in case if you have many possible pies in that case. Yeah let us say let us try to see a case where such a thing will not happen ok when when is the possibility can you think of any case where this pi equals to pi p cannot be a solution. So, at least there will be always a pi equals to pi p solution will be there right because why we know that we already argued that pi is a stochastic matrix right. So, pi has a eigenvalue of 1 and this pi is what we are basically saying that this pi is nothing but the eigenvector corresponding to that one we will always have, but the question is will this add up to 1 if whether it is going to be going to be if not if it is not going to add up to 1 is there a way you can make it and add up to 1 how normalize this then you will have always one such pi which is going to satisfy this relation right then you can start with that yeah again think of a case when it is possible fine I have one as eigenvalue if that eigenvalue will be such that 0 is the only possible eigenvector yes yeah. So, we have to think about an example for that then in general I do not know like under what condition it holds we have to basically construct an example. So, in a way what this says if suppose pi is equals to 0 is the vector right if rank is not full it can have multiple solution the question is can 0 be a solution no we are not saying p equals to 1. So, this is pi equals to pi p right. So, let us write it as pi into 1 into pi p this is my eigenvalue suppose this is all 0 this is anyway severely satisfied that is not an issue here right. So, think about this is there any transition probability matrix where my pi equals to 0 is the only solution for. So, come with the proper. So, even forget transition probability matrix just construct matrix with this stochastic we will have eigenvalue 1 is there a matrix whose eigenvalue 1 will can have only 0 as the eigenvector that is the case yes then we will have we have an eigenvector when we say by default let us say it makes sense only when the component at least some components are going to be positive right. If that is not the case then this is going to be this relation is going to be satisfied for any constant for any eigenvalue that is what all we are asking now is suppose if I have a such a relation is it always the case that pi i is equals to 0 for some i for some i as long as one component is going to be positive that is fine right I will have I will come up with a vector which will not have all 0. So, what is that matrix or is it the case that whatever p you are going to start with you will end up with this ok just think about this I think we should be able to argue that whatever p you are going to take I will end up with the pi in which at least one of the component is here non-zero. So, because of that I can always discard the all 0 solutions ok. So, it looks like pi here is an eigenvector, but we have to just make sure that our definition of eigenvector is consistent in the sense that that exclude the case that all the components being 0 right we just need to ensure that ok just check about this ok fine. So, now what we have we have just dealt with the case when I have a reducible class how to say whether it is going to be a positive recurrent right. So, now if it is positive not positive recurrent there are that means if it is not possible recurrent that means I will not be able to find a solution pi equals to pi p where pi is going to be my positive is a probability vector with positive components right. And by the way notice that we have also argued that if one of the component is positive in my vector pi it must be the case that all the components are also positive. It is not that only in the solution only one component is positive and other components are going to be 0 ok. So, we showed it right when we discussed the properties of this statement when we said that if one component is going to be strictly it means that all other components are also going to be going to be strictly positive. How to ensure that now what are the other properties do have any other properties to say that if this is not going to be positive I will not end up with such a pi which is a solution of pi equals to pi p and will have all strictly positive element. I will end up with some pi equals to pi p solutions here with some of the components to be 0, but that does not say that my reducible transfer my DTMC is a positive recurrent class. For my DTMC to be positive recurrent I and I want that the solution of this pi equals to pi p will be such that all the components in that are going to be positive ok. So, I first when I have such a big matrix if I want to see that first thing I will do is this irreducible communicating class if that try to find pi equals to pi p solution see if all its components are going to be strictly positive then you are done you know it is a positive recurrent. If you happen to find this pi equals to pi p solutions with some of the component is 0 then you know this is not a positive recurrent. It has to be either transient or non-recurrent how to verify this. So, next we are going to look for a condition when we can say that this is going to be transient ok.


And we're here in Tessaloniki at the Nanotextology Conference, and who are you? Hi Nicolas, nice to meet you. I'm Antigone Alexandro. I will be presenting a talk, an invited talk tomorrow, on ultra sensitive in vitro diagnostics. So what does that mean? So in vitro diagnostics is to put it in a simple way is what you do when you get blood sample and then you use that blood sample to detect different molecules inside the blood sample and to determine if you have certain different types of disease. So what we are doing is doing the same thing, only we can do it in a much more sensitive way. Something like 1000 more sensitive than standard techniques that are used in a medical analysis lab. So now people use a lot of those glucose meters or something like that, but there's some other things people do also that are much more advanced, like when you do blood tests, right? Yes, exactly. And when they do the blood test, you have a technology potentially to do much more detailed. For example, there are cytokines which are involved in inflammatory diseases and they are so-called biomarkers because they sort of tell you if the disease is there or not. And in many cases, these biomarkers, the current technology is not able to detect them with high enough sensitivity. So the concentrations are there, but they are too low to be detected. And what we propose with our nanoparticle-based technology is to be able to detect these biomarkers, these cytokines, which currently it's not possible to detect them unless you have extremely sophisticated technology. So what do you show here? Okay, so this explains our technology. It's similar to the so-called ELISA technology. What you do is you have antibodies on a surface.  You incubate it with the blood sample, which contains different kinds of molecules, which are shown here with different shapes and different colors. Then you rinse and let's say you want to detect cytokine indicative of inflammation. So let's say this is a cytokine. There is a few in your blood sample. So it is recognized by these antibodies, which capture the biomarker. And then you can come and reveal the presence of this biomarker, this green molecule, by another antibody, which will recognize a different part of the same molecule, which is called the capture antibody. Now what you need is a sort of probe to be able to detect the fact that the antibody has bound and the fact that this biomarker is there or was there in your blood sample. And this is where our technology enters. Usually people use techniques that are not very sensitive. But what we propose is to use these lanthanide ion based oxide nanoparticles, which are extremely bright, extremely photo stable. And they give you the possibility to have a very high signal to noise ratio so that you can detect very low concentrations. So if I show you the next slide, this is what you can typically do. There is a notion in this field is the limit of detection. Limit of detection is the lowest concentration you can detect of this given molecule, this given biomarker. And if you use a standard technique that gives you a low signal, if the signal is below this limit of detection, you cannot detect the concentration you are interested in. But in our case what we do, we have a huge increase of the signal due to the very bright probes, luminescence emitting, light emitting probes that we have, the emission that we excite with the laser. And the signal becomes now in our case with our technology much higher than this limit of detection level. And so even a very low concentration becomes detectable. So right now there are clinical studies which use samples and they do the measurements and for certain cytokines, 90% of the samples, they measure nothing. That is, they measure a value that is below the threshold. So they don't know if there is none of the specific cytokines or if the concentration is just too low to detect. And with our technology we will be able now to detect these concentrations and really be able to say if there is some concentration there or not. And in particular we will be able to detect a combination of biomarkers and in that case current molecules that are known to be representative of a disease cannot be used to biomarkers because their concentrations are too low. But now we will be able with this technique which from our laboratory we have commercialized them with a start-up that we co-founded with Max Richley called Lumydix, we will be able to bring new biomarkers to the market because currently biomarkers are there but we cannot detect them. If we are able to detect them there will be new ways of detecting disease more early. So earlier detection means better prognostics for the disease because if you get detected earlier you can treat it earlier and so there is a huge impact that we expect to have with this technology. So these bright dots right here, is it like a new kind of invention that you are able to do this so it's based on established science that's an old way of doing things? Well actually we've been working on this technology with a colleague Thierry Gacouin also working at the Colpol Technique, my institution in Palaisone near Paris. So in my lab and in his lab we have been working on these nanoparticles for about 15 to 20 years now but we've been working on more basic applications like how to label single molecules in the cell membrane and how to track them and see how they diffuse inside the cell membrane and how they are confined in certain cases and how that influences the functioning of the cells. And then at some point we realized that the properties of these particles the fact that they are synthesized easily, they are synthesized in water the fact that they are very photo stable and very stable in dispersion so they don't aggregate. All these properties make them very good candidates for this present application to detect biomarkers in blood or in other human urine samples for example. So how far is this from the market? Actually this startup Lubevix that we co-founded started last year was created last year in July 2018 and we're currently working on the industrialization of the technology and hopefully by next year there will be products in the market. And what are the other things you're working on? You're working on many things or? Well actually we're working with my team together with my colleague Cedric Bouzid. We have a team that is also Nicolas Olivier. We have a team at the laboratory of optics and biosciences in Ecole Polytechnique which is working on let's say as I mentioned earlier understanding cell function and also being able to detect and maybe I can show you another slide being able to detect for example hydrogen peroxide. Hydrogen peroxide, well people used to use it to remove nail polish. Now you don't do it anymore because it's a bit toxic, not highly toxic but you have less toxic solvents that you can use now. Yeah it's just no problem. Maybe you can tell them to speak a bit less loudly. It's just coming right here. And what was known for a long time is that hydrogen peroxide is used by immune cells to kill bacteria so it's a bactericidal activity and you also use it to clean solutions at home sometimes. So what has been found out more recently is that this molecule, hydrogen peroxide is actually also present not only as a toxic molecule but it's also present in very low concentrations, in a very localized manner in time and space and it has a role of a signaling molecule. Now what is signaling for a cell? Signaling is, well cells need to communicate with each other and they need to communicate with the external environment. So there is a signal, there is a molecule coming from the outside. It's detected by so-called receptors on the cell membrane and then there is a transmission of signal so the receptors' role is to detect the signaling molecule and then tell the cell to do something in response to that. For example if you have vascular cells, what their role is to contract for example in response to certain signals. So if these vascular smooth muscle cells, if they detect a molecule called endothelium, this is the signal for the cell to contract. And so the cell has a machinery to transform this outside signal into a cellular response which can be in this case contraction or migration or differentiation or apoptosis. And for that there is an intermediate molecule which is like what is called a cell has a signaling machinery and there is an intermediate molecule. In our case it's also hydrogen peroxide and it's involved in the signaling cascade of detecting the signal from detecting the signal all the way to creating the cell response and in the middle there is this hydrogen peroxide. And what we do is try to understand how this signaling pathway works by detecting this hydrogen peroxide in a very efficient way. Quantitatively we can measure concentrations and resolve in time and in space. And actually it turns out that we use the very same nanoparticles that I mentioned before for ultra sensitive detection. We use these very same particles in a somewhat different situation and the conditions to detect this, to detect with, and so we have a nano sensor. Before we had a label, now we have a nano sensor. And maybe I can show you a slide on this. No, it's perfect. So I can show you how, for example... Is this something that has to do with blood samples also or where do you do this? Actually we do that in cells and that's for basic research so we want to understand what the concentrations of the signaling molecule, what the involved concentrations are and what the time dynamics is. Because we have been able to show with our sensors that the dynamics is different. For example, here is one example. Depending if... Okay, here is our molecule that activates the receptor and then this receptor leads to production of hydrogen peroxide, this signaling molecule that I was mentioning before. And depending on the type of the stimulation of the cell, this is a molecule inducing contraction and here this is a molecule plate-blade-derived growth factor that induces migration. And you can see here that the time scale, the dynamics, the time dynamics, is very different between these two stimulations. So the secondary messenger that we use, that the cell uses is the same, hydrogen peroxide, but the time dynamics is different and the concentration is similar but the time dynamics is different and we are, thanks to this nano sensor based on European ions, we were the first to be able to detect this. And now the next step... So what do you detect? What can we do? Well, we detect as before the luminescence of these nanoparticles. The only difference is that before we would use them. So we remove electrons and then this molecule has oxidative properties. Sorry, we add electrons and this molecule has oxidative properties so it removes electrons from the nanoparticles. So when we add electrons, I don't show it here, but what happens is that the luminescence signal decreases. Then we stimulate the cell and then hydrogen peroxide is produced. Here are the nanoparticles we... Oops, I'm sorry, which we have managed to put inside the cell by relatively standard technique and here is the white light image of the cell and here are the nanoparticles emitting signal luminescence inside the cell. So we follow the luminescence of one single nanoparticle. So the luminescence goes down when we add electrons and then when we stimulate the cell we produce this signaling molecule and then it oxidizes the nanoparticles back to their initial state and then we have a rise in luminescence. And from this rise we can extract very precisely, quantitatively, and time and space results. Time result because you can see we can measure the concentration of hydrogen peroxide as a function of time but we can also measure locally because each nanoparticle, here, each single nanoparticle that you can visualize with a high-performance luminescence microscope gives you the information of the hydrogen peroxide in the very specific location of the cell where the nanoparticle is located. So we have time resolution, space resolution and quantitative information. So what could this possibly help with in the future? Okay, so what I've been saying is more trying to understand how the cells work but it's one very important issue in basic science is you never know what application it will lead to so it's very important to have basic science if you want to have applied science later down on the way and so here is the application. And this is relevant to many different materials, many different particles? Actually, for now, this is the only nanoparticle that we have identified that has all these multiple properties localized and time result detection and quantitative detection. Other nanosensors don't have all these combination, this combination of properties and the next thing, so I've told you this is what we call physiological signaling what happens when the hell is healthy but then this process can get deregulated and then you get deregulated hydrogen peroxide production you get what we call oxidative stress you've probably heard about that it's often mentioned in everyday life about oxidative stress and you have to use antioxidant to remove the oxidative stress it has entered everyday commercials and then you can have if you have deregulation of this hydrogen peroxide production you can have neurodegenerative diseases you can have inflammation, chronic inflammation which is related to cancer and many diseases which are related to inflammation like diabetes or as I said neurodegenerative diseases and so the next step is to try to do this detection in vivo, in vivo meaning in living organisms so we start with mice and so this has not been published yet but what we did was of course there's an ethical committee that validated this research program beforehand we did that with Gustave Roussi which is a hospital which is one of the most well-known cancer hospitals and research centers in France and in Europe and what we do is we have a mouse model of inflammation so we inject the particles in the mouse ear and then we apply a molecule called methyl salicylate which induces inflammation of the ear actually it's what also some people use in sports to heat up their muscles okay in this case you generate an inflammation and here, okay we can see here in this image you can see the particles the emission of the particles that have been injected in the mouse ear and then when you do this okay first we check that we indeed have an inflammation with other standard techniques I will skip this part and then as I showed you here this is the signal of the nanoparticles so I will start the movie now and when I start the movie you will see in the beginning nothing happens the signal, the luminescence signal oops, I'm sorry let's just go back so in the beginning nothing happens then okay I will go back because it goes a bit fast oops, I'm sorry yes, so in the beginning nothing happens then we apply the induction of inflammation so that's when the mouse ear shifts a bit and then if we wait a little we see the increase of the signal that I told you which reflects the production of hydrogen peroxide and other oxidant molecules so here again it's one of the first measurements of this oxidative response which is a pathological condition especially if it becomes chronic and so we have been able to measure as a function of time and for different mouse ears the increase of signal and how it is and from that we can extract the production of hydrogen peroxide so if you remember what I showed you before here the signal is I didn't show it actually but the signal here is much higher than before because the concentrations and the signal related signals for physiological mechanism is much lower here we have a pathological mechanism it's an inflammation it's a reaction of the body to this application of an external molecule and we have a higher signal higher production of hydrogen peroxide so ideally we would like in the long term to be able to do this in humans but this will still require some years of work to come and so these signals and detecting those signals can be useful for many things for yes, as I mentioned there are several pathologies and there are many pathologies where inflammation is involved cardiovascular disease is also involved as an inflammatory response neurodegenerative diseases cancer, many cancer types are related to chronic inflammation like for example the gastric cancer comes from ulcers and ulcer comes from helicobacter pylori bacterium it creates ulcers it creates a chronic inflammation and if nothing is done the chronic inflammation can turn into cancer so all these diseases it's important for all these diseases it's important to be able to measure as many parameters as possible of the disease to be able to do what is called precision or personalized medicine because, okay currently you measure one or two biomarkers but if you can also measure what is the concentration of hydrogen peroxide for a certain patient and how that correlates with the severity of the cancer or with the metastatic activity of the cancer or you can correlate it that we hope to do in the future how it correlates with the efficacy of a certain pharmaceutical treatment so this tool measuring the reactive oxygen species that are produced of which one of the main ones is the hydrogen peroxide this can give a valuable new parameter for a huge number of pathological situations so you can get an injection of something and then all the cancer cells will light up or how does it work well we are currently starting to work on that we want to avoid injecting, we want to avoid injecting the nanoparticles in the human body because there are always toxicity issues so we would like to be able to just introduce the particles do the measurement and then remove them so maybe in the next conference I can tell you more about this and you in Paris, right? Yes, I work in south of Paris at the Ecole Polytechnique which has now merged with four other institutions to form the Institut Polytechnique de Paris and many students in your group or how does it work? Yeah, we are three permanent researchers one technical staff and graduate students postdocs and undergraduate students so typically a group of about 10 people but we are a part of a bigger lab with 50, 60 people the laboratory for optics and biosciences and our team is called nano imaging and nanotechnology is a big deal yes, nano imaging and quantitative biology yes all these nano tools are now almost everywhere in all aspects of life and of course we have to make sure that we use them only where they are really necessary so that the public does not develop Nano addiction? Not nano addiction but an apprehension of these technologies in some cases they are used in sunscreens for example in some cases they are not really necessary so scientists have to be really careful to in terms with interaction with society have to be justified why we use the nanoparticles and that we use them only when they are really necessary because of course there are also human toxicity issue but also toxicity issues to the environment are used at a massive scale and if they are released in the environment you can have issues like we have today with plastic maybe if we don't pay attention we'll have similar issues with nanoparticles so hopefully not but scientists in this field have to be very cautious about issues like that so you'll have a presentation about this tomorrow do you have lots of networking with people from nanotechs? this is really a great occasion to meet very exciting people in different fields in my field of nano biomedicine and nano bio biology but also to see some very exciting plenary talks on other fields of nano sciences so I'm really delighted to be here and I thank the organizers for inviting me


On this episode, I have the honor to chat with Professor Marina Fischer-Kowalski from Boku University who founded the Institute of Social Ecology already some years ago and is the founding figure of the International Society of Industrial Ecology, the European Society of Ecological Economics, and her work is very interdisciplinary. It has evolved over the years, of course, but it includes elements of social sciences, environmental accounting, and some elements of policy and even politics. She has written countless reports, articles and books on the topics of material flow analysis, industrial ecology, ecological economics, and many more. With all that being said, Marina, thank you a lot for taking the time.  Well, I started in the 80s. I've been trained as a sociologist, and my first major work was a social report on inequality and how it had evolved after the Second World War in Austria. So this report was controversial, but in many ways well received, and this trained me to use, on the one hand, all kinds of public statistics. On the other hand, to utilize the discourse between policymakers and scientists to make things change. And my next step was at OECD in Paris. I was involved in the program. It was not social indicators, it was quality of life for example indicators or something like that. I did a lot of work there, I loved it. But all of a sudden the United States of America found that this program is superfluous because if people have money, they live well and it's not necessary to inquire into all other aspects of their living. So this was frustrating for me as you can imagine. And coming back to Austria and having children, there was a big conflict about the atomic energy plant that was planned in Austria and also a big water that they wanted to use for another energy creation. And so, yeah, this was just a private person. Two publishers wrote me later, why don't I do a report on inequality in the environment? And why don't I do, yeah, I said, well, I'm not an expert there. So then you will write in a way that people understand you. So, okay, I agreed. And I did, I did do a book on state of the environment, measures in environmental politics and reporting on the environment between, and that was a trick, the nine Austrian states. So I got them into competition, which... They love that, right? Benchmarking is already back in the day. Right. But that way I met all sorts of experts. I didn't have any natural science education yet. So I really needed other people and I needed all these civil organizations that were fighting for better environment. So I got in the middle of that scene and looked for a new job. And all of a sudden I got offered the chance from an inter- weird, inter-university, interdisciplinary, yeah, belonging to eight universities. They offered me to build up a research area on society and environment. Why not? That's what I did. That's what I did. And that was a wonderful offer. Yeah. So that was the beginning of the Institute of Social Ecology? Yes. That was the beginning in the mid-80s. I had, of course, I was used to international cooperation. So immediately I cooperated with the Wuppertal Institute, University of Leiden. So I found international partners to develop a new agenda. And I had learned while doing this report on the Austrian environment, I had learned that everybody was crazily focused on wastes and emissions. Okay. And I said, my Christ, this is an irrational way to go about this policy agenda. You have to look at the inputs and then you start understanding the outflows. And it makes no sense to stare at thousands of potentially poisonous or whatever outputs without looking at the amounts of inputs and regulate them and not the other. So that makes sense today. I don't know. Yeah. Today it makes sense, but at that time it was weird. And it was, there were a few intelligent offices in the Austrian Ministry of Environment who found that smart and helped me or finance certain research. Yeah. So how it started. That's how it started. And so was that like a first material flow analysis you would say back in the day? Yeah. Yes, we did. I think that's why the first material flow. Okay. In 88, I think. And the other good experience was that you need to collaborate with very different scientists. You have to have interdisciplinary live. Collaboration. And that's much nicer than at most universities because at. At universities, people all the time compete among one another who is more famous and who has the better message and whatnot. Yeah. But if you depend on a physicist to explain to you why or how you can resolve a certain problem and to a chemist and to a biologist and botanist and an agrarian scientist and whatnot. You don't compete with them. That's foolish. Yeah. Yeah. Yeah. Except they're the specific knowledge and by asking them questions that are unusual for them. They learn too. And that's how team building evolved and my original team from the early nineties or late eighties. I mean, many of them are still in my team. So we really. Yeah. We liked it together and we were very productive. Yeah.