Hello. My name is Jerry Cooperstein. I'm the Director of Training at the Linux Foundation. Today, I'm going to give you a sense of what we do in one of our popular classes, LF312, which is developing applications for Linux. And I'm going to do a section briefly from the middle of the class on how to create and destroy processes. This would normally be reached on the end of the second day or the beginning of the third day of a five-day class, which has 30 some odd sessions. This is session number 15. And before we get to this point, we will have already discussed things like how to compile integrated environments to use in Linux, how to make debugging memory allocation and extensive discussion of file, input, output, writing, reading, and other more advanced IO commands, as well as earlier session on processes which explain what they are and how they work in a Linux and Unix context. So here we're going to talk about how to create them, how to destroy them, etc. So let's get started. And I'm going to begin by showing section 15, which has several sections first about different methods of creating processes. In particular, it talks about system fork and exec. And then also a system called clone, which is unique to Linux, does not exist in other Unixes, which creates a processes with some very special properties. And then we'll talk about different methods of exiting or destroying processes. And we'll talk about waiting, how to wait for a process to be completed. And then we'll talk about daemon processes, which you kind of start in background with a limited set of commands. In subsequent sections during the week, we'll go into great detail on things like inter-process communication, talking about all the different methods of doing that. We'll talk a lot about networking and sockets. We'll talk about multi-threaded processes. We have several sections on how to create multi-threaded programs and write them in an intelligent way. So this is just sort of giving you the setting of how that fits into all that. The first function we discuss for creating a process in Linux is the system command. The system command is very simple. You simply call system and give it as an argument. The program or command that you would like to create a new process such as in this little example down here in the middle of a print statement. And that simply starts a new process and does whatever you put as the argument for the system function. Now, the problem with system, while it's very easy to use, it's expensive because it creates a whole new fork, which we'll talk about next to do something in a pipe in order to get access to the output. It's very sensitive to things in your environment such as various environmental variables. So it's easy for somebody to do an exploit where they make your command do something very different. All they have to do is reset your path in your environment, for instance, so it's very insecure. And it certainly should never be used for anything which is run with super user or administrator privilege. I often tell people it's okay to use system when you're doing a quick hack and trying to figure out how to do something, but I would never put it in a program that I'm delivering to somebody who's paying for me to do it. It's insecure and it's kind of expensive, but this is simplest and quickest way to do something. The fork system call creates what's known as a child process. This is an extremely common thing to do throughout the history of UNIX and certainly Linux for decades. Once the child process is created, it's a full peer to the parent. It has complete access to all the memory, file descriptors, environmental variables and everything associated with the parent. So it's a very, very common way to create new processes. In fact, the way most of them are created in Linux or any kind of UNIX type of operating system. When the child is created while it receives a complete copy of memory, etc., from the parent, it uses what's called a copy-on-write technique, which means the child shares these resources with the parent until one of them makes a change. So for instance, if there's a memory region, they both share the same memory region until the child or the parent writes to it in which case they get their own copy. And this is done on a page-by-page basis rather than the whole memory region. So like most architectures in page of memory is 4 kilobytes. So if you have like a 4 megabyte buffer and you only change a few bytes in it in the child compared to the parent, you only allocate the memory for one page rather than the whole 4 megabytes. So it's a far superior way to do things than to simply give the child a complete copy of everything to begin with. So copy-on-write is important and Linux has very advanced methods for doing copy-on-write. We mentioned in the class a little bit about Vfork, which is an older UNIX command, which is like fork, but the parent is blocked from running until the child is done and has a true share of memory. But these days it's kind of obsolete, but you'll find that in old programs, so Linux knows how to implement it properly. So when you run the fork command, you get something like this where in your main program you call fork. And then after that, there are two processes running through your code. That's why it's called fork. You can think of a left fork and a right fork. In this first branch here is the child process, and the second one is the parent process. So you have two different things running through your code from then on. Later I'll talk about how to wait. You can have the parent wait for the child to finish before going to do something else, for instance, and that's very normal. The next way to create a new process is with the exec function. Actually there's a family of exec functions, exec ve, exec le, etc., as itemized in the text here. Each one of them is the same except they specify the arguments in different ways. The arguments include the arguments to the program you want to run as a process and various environmental variables that should be set if you need to do that for the program that runs. Once the new process starts, the exec function never returns and the old process that called exec is over, and the new process obtains the process ID of the original process. So it's like create, replace, kind of method. It's extremely common to do this in Linux or any kind of Unix operating system. And in the text we give various examples of how to launch it to do various things, and I recommend you look at that, but I won't get into the details here today. The other way to create processes or threads in Linux is with the clone command, clone function. The clone function is unique to Linux. It's not used in other Unixes. It's not a POSIX command. It's the underlying method in which all new processes are made in Linux in our kernel. Internals class we talk about how clone really works inside the Linux kernel. What makes it different than an exec or a simple trial process is that you can control exactly what resources are shared between the parent and the child created process. You can decide to share only memory. You can decide to share memory and signal handlers. You can decide to share memory and environmental variables, etc. There are several different flags that you can control, that control exactly which resources are shared and which are kept independent. If you keep things basically independent subject to copy and write techniques, you get a traditional Unix fork command, and inside the kernel in Linux the clone, the fork command is cloned with certain kinds of arguments that include things are only shared until resource changes such as a memory region. If you want to do a multi-threaded program, for instance using the P-threads library, the posix threads library, which we talk about in a later session, then you call the clone command with the flags specified that everything should be shared. So that, for instance, memory is shared means that if the child process makes a change in memory, the parent sees it immediately. So that's all done through one command with different flags on the user side. And on the next page in the text we go through these different flags and talk about what happens if they're set or not set, etc., and talk about the arguments to the clone function. The one argument to the clone function that's a little tricky is the second argument, which is the child stack pointer. And we discuss in detail how you set that when you're doing a fork, how you set that when you're doing a multi-threaded program, etc. And we have a lab that experiments with this so people learn how to do it more thoroughly. The next section, the fifth section in this chapter is on exiting. And it just talks about what happens when a process terminates either normally or abnormally, how exit codes are handled, etc., and various kinds of exit handlers you can put in so that you can deal in different ways with successful terminations, unsuccessful terminations for different reasons and how your program can be informed about what the reason for termination is so it can take an appropriate response to that. The next section is on constructors and destructors. Using the GNU-Live-C, G-Live-C, it's possible to have certain operations happen every time you create a new process. Those are constructor functions. And other things happen every time the process terminates. Those are destructor functions. And we teach people how to do that and once again we have some labs in which people experiment with that. So this is code that's not directly part of your application but gets called whenever things start up or finish. And then the next section here is on waiting. We talk about what happens when a process waits for another one to terminate. Typically parent processes wait for child. Processes to terminate either normally or abnormally. And so we go into detail into the wait function, how to check the return value to see why a process is terminated, etc. We also talk about what are called zombie functions, zombie processes. A zombie process is a process which has terminated but nobody has asked why or whether it has terminated. So in that sense it's a zombie because it gives up all resources so it's really kind of dead but it's the walking dead. It's still persistent until somebody asks what happened to it. And then we have a lab that plays around with zombie processes and tries to see what happens to them. Linux does not tend to generate a lot of these zombie or so-called defunct processes unlike some other operating systems. Because when the parent dies, child processes are adopted by process number one, the init process. And the init process periodically checks on the status of the children and waits for them. There's generally somebody always waiting to see what happens to you if you happen to die. And then I guess the last section in this section is on what are called daemon or daemon processes. It's a variation on fork and exec where you create a child process which has rather limited set of privileges. This is often done for background services, etc. Because you never want them to have more privilege than they absolutely need for security and stability reasons. And so we show people how to set up daemon processes, etc. And we also have labs that do that. So typical of our training sessions, each session has a bunch of different laboratory exercises. In this case, in this chapter, they're all C programs that you have to write. And they demonstrate many different features. The first one has to do with forking and the second one has to do with zombies and how to kill them, which is always fun. The third is more about waiting, how to successfully wait for a process to be finished and reap its exit code. The next one is on cloning and it teaches you that unique Linux function clone and how to create a variety of different kinds of child processes with variations on how many resources they share. And then the next one is on exiting, playing around with exit handlers. There's another exercise here on memory mapping, which ties into earlier sections. And we talk about how to use anonymous memory mapping to share memory between a parent and a child, instead of having them have independent memory. So it doesn't stand on its own with this chapter, but we've already talked about memory mapping. And so this flushes that out some more. And then the last exercise is on creating a daemon process and showing you how to do that. So that's a fuel for what we do. You know, obviously we would take much longer in the class and we would give people a long time to do the laboratories. There's too many to do in the normal class time, so people will pick a few of them that interest them the most and maybe play with some of the others. I'd like to thank you for viewing this brief introduction to LF312, Developing Applications for Linux. On our website we have detailed information and outlines for the class showing all the different sessions and what we discuss in them. I recommend highly you look at that and consider signing up for the class. People really enjoy it and they learn a lot. So we look forward to you getting in touch with us about it.


Hi everyone, this is Dr. Rajesh Yenivas. I'm a consultant orthopedic surgeon in Manipal Hospital, Porthodam. I'm specialized in joint replacements and sports medicine. We do all joint replacements, mainly the knee, hip, shoulder and elbow. And sports medicine is a specialization in orthopedics which deals with ligament and muscle injuries in all the joints. Total knee replacement. Total knee replacement basically means replacing the damaged cartilage with the implant. And we do this quite often. This is commonly done joint. And the common cause for this is one is age-related wear and tear. Nowadays, because of sedentary lifestyle, we eat, place a crucial root. And if there's any previous injuries which have been under this, that can lead to advanced wear and tear of the joint. And now in this, we replace all the part of the damaged cartilage with the implant and we keep high quality polymer in the joint for the easy moment. And nowadays, we have got advanced materials like ceramic, which has got less wear and tear and more life. Typically, this lasts for around 26 to 28 years. The patient will be made to walk after 6 to 8 hours after the surgery. The patient will be made to use washroom immediately after 6 to 8 hours. The patient will be started knee bending. The physio will begin soon after the surgery. The physiotherapy is as equally important as the surgery. The admission will be there for 48 to 72 hours based on the recovery. And the patient will be having frequent follow-ups for 2 to 3 weeks. And the patient will be walking without any support after 4 weeks. And the coming to the complications of the total knee joint replacement first and the main thing is the infection because we are inserting a foreign material inside the knee joint. The risk of the infection is between 0.5 to 0.8 percent. And the second thing is the ligament injury, which leads to instability after the surgery. This we can easily tackle with the meticulous clinical examination before the surgery. And we do multiple stability testing during the surgery so that the ligament injury, which happens inadvertently, can be avoided. And the third thing is the risk of what we call as a DVT, which is nothing but blood clotting in the lower leg veins, which we address by early mobilization of the patient.


Hi everyone, Joe for jaspyscasebreaks.com coming at you with a quick pack break of 2020 prism football hobby edition random team number four Just a quick little pack break no vet commonship then at the end of the break will re-randomize everybody's names and the top nine names We'll get into that 2017 prism blaster break. We can go Mahomes rookie hunting, which would be pretty awesome I think from that blaster case that we opened. I don't think we've seen a Mahomes rookie yet But the break first big thanks to all of these people for getting into the action. Thank you very much all 32 are in Let's roll it. Let's randomize it one and a three four times for each list one two three and four After four times, we've got Steve down to Derek and now one and the three four times for the teams one two Three and four fourth and final time. We got the Jaguars down to the Jets All right, there's the first half of the list right here. We got guy with the chargers Lonnie with the dolphins You know where since where did I lose Cincinnati? Oh, there's since Travis with Cincinnati. There you go. All right Now let's just print and rip No trade windows in these filler breaks always look out for these filler breaks ladies and gentlemen If you see anything sold out just Just quickly double-check to make sure there's no No, like a little pack break or a random number block randomizer that we need to accomplish before that. Oh, let's turn on my camera That is our first break of the day It's angled enough We'll just roll with it. Thanks everyone We've got oh, I thought that was a different. I saw a quarterback. I saw a J Thought it's gonna be Justin, but no, it's Jacob Eason Colts rookie card going a guy and then we got the Jordan Brooks Seattle Seahawks rookie card going to Karen. All right, nothing too crazy in this pack But the whole point of this is To see who's gonna win those spots in 2017 Panini prism football blaster edition Mahomes rookie card hunting All right, so different dice roll. Let's roll it. Let's randomize it five and a four nine times So it's gonna be top nine after nine good luck one two Three and ninth and final time So from ten down to 32 my apologies, but we did post another pack So there is another shot of getting into that and go Mahomes hunting top nine Congrats Alan Derek Travis guy Sunil Derek James Colton and Oliver Smith Top nine right here. Congrats to you You've got a shot at getting randomized the chiefs and you got a shot at a rookie Mahomes. There you have it I'm Joe for jazby's case breaks calm. We'll see you that for that break next time. Bye. Bye


So the next speaker, the last speaker of the session is Jeremiah Blockey from Purdue. Do I need to share the screen? Okay. Yeah. Okay. All right. Thanks. So excited to talk to you today about the multi-user security of short snore signatures with and without pre-processing. This is joint worth with Sunghoon Lee also at Purdue University. Okay. So digital signatures are ubiquitous in cryptography. They have many applications and use cases, message, document authentication, authenticating blockchain transactions, use for certificate authorities, electronic signatures, so on and so forth. And there's a lot of properties that we'd like to have for an efficient digital signature scheme. We'd like them to be short. We'd like to have efficient signing and verification algorithms. And we'd also like them to achieve K-bit security, which means informally, an attacker running in time T wins the signature forgery game with probability at most T over 2 to the K. Finally, we'd like to ensure that the digital signature scheme is even secure if the attacker can run a pre-processing attack in which they perform some pre-processing attack in hopes to decrease the online attack time. All right. So let's look at some options for signature schemes. We have RSA full domain hash. The signature link for RSA full domain hash is Omega K. So it's super linear. Elliptic curve DSA and Schnorr signatures get that down to 4K bits. And interestingly, in Schnorr's original paper, there was a proposal for shortening the length of the signature to 3K bits. The key question, though, is by shortening the signatures, does this adversely impact security? So are short Snorr signatures still secure? Do they remain secure even against a pre-processing attacker? Finally, there's also BLS signatures. These are 2K bit signatures. So even shorter than short Snorr signatures. The disadvantage, though, is that they require bilinear pairings. So shorter signatures, but higher computational overhead. And then finally, just for the sake of completeness, we can also go to IO-based signature constructions. We can get K-bit signatures. But I stress that this is purely theoretical construction. We don't have practical instantiations of IO. So we're not going to consider that for our purposes. OK. So to briefly summarize our results, a key question. Are short Snorr signatures still secure? Short answer, yes. Short Snorr signatures still provide K-bit of multi-user security. So no concrete security loss for truncating the hash output. Our proof is in the random oracle model and generic group model. So we do make every possible idealized assumption. But finding a tight security proof in a non-ideal setting will leave that as an open question. So another key question, are short Snorr signatures secure against preprocessing attackers? So here we consider the problem and we give two different answers. So the first answer is no. Short Snorr signature schemes are not secure against preprocessing attacks. In fact, regular Snorr signature schemes are not secure against preprocessing attacks. However, if we modify the Snorr signature scheme a little bit by key prefixing, we obtain a signature scheme that's actually secure against preprocessing attacks. With a slight caveat that we need to increase the size of our group just a little bit to achieve K-bit security. OK. So let's look at the standard Snorr signature scheme. So here our secret key is just a random integer between 1 and P. Here P is the size of our group. Our public key is just G, our generator of the cyclic group raised to the power sk. And to sign, all we're going to do is we're going to pick a random nonce R. We're going to compute G to the R. And we're going to compute an integer E by hashing I and the message M that we want to sign. Then finally, we're going to compute S by taking R and adding our secret key multiplied by this value E. And our signature will just be S and E. Now notice that S and E are both 2K bit integers. So our total signature length here is 4K bits. Verification works in the opposite direction given our message M, S and E. We can compute G to the S times PK to the minus E. If the signature was generated honestly, then what we're going to get back is just G to the R. And in this case, we expect that hash of R concatenated with M is going to give us back E. So that'll just be the check that we performed during verification. Okay, so public parameters here. We've got a group and a hash function which maps messages to elements in ZP. So the short snore signature scheme is essentially the same thing with a very small modification. Here we're just going to change the hash function so that it outputs a random integer between 1 and 2 to the K. So we've shrunk it from 2K bits to K bits. That's the only change. And making this change, we obtain the short snore signature scheme. Okay, so we're going to analyze the short snore signature scheme in the generic group model. The generic group model was introduced by Shoop in 97 to model generic attacks on discrete log type problems. Here we're modeling any attack which doesn't exploit specific structure of our cyclic group. So with lots of generality, we're just going to assume our group is ZP. And we're going to assume that the attacker accesses this group through specific oracles. So we're going to assume that we have a function tau. This is a random injective function which maps group elements to binary strings or their handles. And then we have a multiplication oracle which takes as input the handles for two group elements X and Y and outputs the handle for the new group element X plus Y. So yeah, input handle for group elements X and Y, output handle for X plus Y. We'll also consider a couple other oracles. Inverse computes the inverse of an item X or power computes X to the power N. So input here is the handle for a group element X and an integer N. Output is the handle for the group element N times X mod P. Okay, so sample result in the generic group model. We can show that any attacker making T queries to the generic group oracles solves a discrete log problem with probability at most T squared over 2 to the 2K. Just a simple bound to prove in the generic group model. Now in our setting, we're also interested in looking at pre-processing attackers. So here the attacker can be divided into two parts. There's the offline attacker who takes as input the secret random encoding function tau which maps group elements to their binary strings or handles and outputs an S bit hint, sigma. So in this case, there's no bound on the running time of the offline attacker. He can run as long as he wants. The only constraint is the size of the hint that the offline attacker outputs. Then we have an online attacker that can use this hint during its attack. Of course, the online attacker has a bounded running time, so it's bounded in the total amount of computation it can perform, total number of oracle queries it can make, et cetera. So motivation for looking at pre-processing attackers here. Well, in real world crypto systems, there's a handful of groups that tend to be used. So an attacker would be highly motivated to perform a offline phase attack where they do some pre-processing on one of these groups in the hope of speeding up their online attack. So here's an example result in the generic group with pre-processing setting. So Corrigan Gibbs and Dimitri Kogan prove that any pre-processing attacker making, I guess, running in time t using an S bit hint solves the discrete log problem with probability at most s times t squared over 2 to the 2k. So that's a bound but that's known for the pre-processing setting. Okay, and in our setting, we're interested in looking at the generic group model and random oracle model with pre-processing. So here the offline attacker is given the encoding of the generic group. They're also given oracle access to our random oracle h and they can run for a while in output in S bit hint. Now I should stress here that for technical reasons we're going to assume that the offline attacker is bounded in the number of queries that it can make to the random oracle but the bound will typically be a very large bound. So for example, the offline attacker can make, let's say, 2 to the 3k queries to the random oracle before outputting the hint. Okay, and then of course the online attacker can use this hint but has bounded running time in terms of number of queries to the generic group oracle or generic group oracles or random oracle. Okay, so now we're interested in analyzing the multi-user security of Schnorr signatures. So in the multi-user security game the challenger generates our public keys, sends them to the attacker. The attacker can submit queries to the challenger and basically each query is a request to sign a message M with the key of the ith user and the challenger will respond with the corresponding signature. We can continue querying the assigning oracle and eventually the attacker has to output a forgery for one of the user's signatures for some message M' of the attacker's choosing. The attacker wins if the signature is valid, if it verifies and if the value I' M' is rushed. So in other words, we haven't submitted this query to the challenger to get a signature in the past. Okay, now in the preprocessing setting the game is exactly the same except now the online attacker is additionally given this S-bit hint which was generated by the preprocessing attacker. Okay, so our results. Informally we prove that any attacker making at most queue oracle queries in the random oracle and generic group model wins the multi-user signature forgery game against the short snore signature scheme with probability at most q plus n over 2 to the k. So I wanna pause here to stress that this is stronger than the naive bound which would be q times n over 2 to the k. So just as an example, if k was 112, n was 2 to the 32, q was 2 to the 80, the naive bound would actually give us nothing. It would just tell us that the attacker's probability of forging a signature is one. Whereas our bound would give us, you know, the attacker's probability is at most 2 to the minus 32. So I should note here that Kiltz et al proved a similar bound for regular snore signatures and the authors later let us know through personal communication that their analysis actually extends to the short snore signature scheme even though that's not mentioned in their paper explicitly. So however the generic group model used in their analysis is not equivalent to Schup's generic group model and in particular it's not suitable for analyzing pre-processing attacks. Okay, so for pre-processing attacks we prove the following bound. Let's suppose that our pre-processing attacker makes at most q pre-random oracle queries during its pre-processing phase and then outputs an s-bit hint which can be used by the online attacker. If the online attacker makes at most q on oracle queries during the online phase, then this attacker can win the multi-user signature forgery game against key prefixed short snore signatures with probability at most and then we've got this complicated looking expression here. So first comment here is what is key pre-fixing? Key pre-fixing, we're just going to modify the snore signature scheme by injecting the public key whenever we query the random oracle. So instead of computing e as tau of r concatenated with m we're just going to prepend the public key for the user. So that's the only change we're making. Now you might wonder why is key pre-fixing necessary? Well, actually there's a trivial pre-processing attack if the attacker can generate equal zero signatures. So in other words, let's suppose the pre-processing attacker outputs a hint, r, m such that hash of tau of r concatenated with m is zero. Okay, so if we're not doing key prefixing, now notice that r, zero is actually guaranteed to be a valid snore signature for m under any public key that we might generate. So this is kind of a master signature that works under any public key. So trivially, snore signatures are broken under preprocessing attacks. But if we make this modification and use key prefixing, then we do get security. Now I should note that several standardized implementations of snore signatures explicitly disallow equal zero signatures, so that rules out the attack that I just described in the slide. So actually this is an open research challenge is to quantify the security or insecurity of snore signatures against preprocessing attacks when equal zero signatures are explicitly disallowed but when we're not using key prefixing. So that could be an interesting question to look into. Okay, so let me just comment a little bit about the concrete security bound here. So here we want to achieve k-bit security. We're going to assume that the preprocessing attacker can make, let's say, two to the 3k queries to our random oracle. That's a lot of queries in the preprocessing phase. So for example, if k is 128, that's two to the 384 queries that we're allowing the attacker to make, but the attacker is limited in that they can only output an s-bit hint at the end of the day. Now to achieve k-bit security here, the middle term becomes a little bit problematic. So to achieve k-bit security, we actually have to increase our group size a little bit. So instead of p as two to the 2k, we need to set it to be two to the 2k times sn times log p. But we can still keep our shorter k-bit hash outputs. Okay? And in this case, our signature length is k plus log base 2 of p, which works out to be 3k plus log of s plus log of n. Here, n is the number of users that we're considering in the multi-user security game. S is the size of the hint that the attacker outputs. So for example, if the attacker can output a two to the k over 2-bit hint and n is 2 to the k over 4, then our signature length is roughly 3.75 k-bits, which is still shorter than the original Schnorr signature scheme. Okay. So I'm not going to go through the whole proof, but I wanted to just give you a few key technical ideas for details, see the paper. So we're using compression arguments all over the place in the paper. The basic idea is that if the pre-processing attacker wins the multi-user signature forgery game with larger than expected probability, we can either compress the generic group mapping tau or the random oracle h. Now, of course, information theory tells us that a random oracle h and this injective mapping tau should both be incompressible, so that would yield a contradiction. So we really kind of split our analysis into two cases. Case one, we can use the attacker to find a non-trivial linear relationship. So we've got secret keys x1 to xk. If the attacker can find some vector a and some integer b such that a.x is equal to b, the attacker has found a non-trivial linear relationship. This can be used to compress the generic group mapping tau. So that will be one case where we'll compress the group mapping tau. The other case is that the attacker somehow manages to successfully forge signatures without uncovering non-trivial linear relationships. And in this case, we argue that we can use the signature forgery attacker to compress the random oracle h. Okay, so the security reduction, we're going to use the signature attacker to win a game which we call the multi-user bridge game. So here we've got inputs tau x1 through tau xn. These correspond to the public signing keys in our signature scheme. And what we're going to do is we're going to simulate our signature attacker. And whenever the signature attacker queries are signing oracle, we're actually going to program the random oracle to allow us to forge signatures. Okay? And what we managed to prove on the one hand is that the probability the attacker wins the bridge game is greater than or equal to the probability our signature attacker wins the signature forgery game minus these big O terms shown here. And the analysis of the middle terms involves a compression argument on the random oracle. I'm not going to go into it if you're interested to see details in the paper. But on the other hand, what we can prove is that the probability the attacker wins the bridge game is at most this. Snq squared log p over p. So here s is the size of the hint, n is the number of users, q is the number of queries the attacker makes in the online phase. And the proof idea here is just a compression argument with the generic group model. Okay. So let's introduce this bridge game. And to introduce the bridge game, we need to try to maintain an invariant while we simulate our signature forgery attacker. The invariant we're going to maintain is that every output of a generic group query can be explained. So we want to maintain a list of tuples of the form tau y, a. Okay, so this should be b here. So we want to basically be able to explain that this is equal to b plus a.x, where x is the x1 to xn, these are the unknown secret keys. So initially our list is just going to contain entries for the generator, which is tau 1. Here b is 1, a is 0. And then we'll have entries for tau of x1 through tau of xn. Here b is 0 and our vector here is u1, u2, u3, just the unit vectors with a 1 in the i-th coordinate. Okay, and we want to maintain this invariant. So let's suppose the attacker queries the multiplication oracle with tau of x1 and tau of x2. Well, that's going to generate a new element, tau of x1 plus x2. So we can update our table with the explanation here. So this element is just, this vector a would just be u1 plus u2 and b would just be 0. If we multiplied x1 by tau of 1, we're going to get the same vector a, so a would be u1 and b would be 1. And similarly, right, for other generic group oracles, we can update this list. Now we might run into a problem here and this problem that we encounter with the attacker queries the, queries the generic group oracle with an element eta, where eta is just some random binary string that we haven't seen before. So this could throw us off and prevent us from maintaining our invariant. So the question is if the attacker makes such a query, how do we update our list l? We can't simply find b in a vector to update the list. To address this challenge, we're going to introduce what we call the restricted discrete log oracle. So we're actually going to give the attacker access to an oracle which solved the discrete log problem. Now of course if we just gave the attacker access to this oracle without any restrictions, this would make breaking security trivial. So we're going to restrict the attacker's access to this oracle. And in particular, we're only going to allow the attacker queries to the restricted discrete log oracle if the input is fresh. So in other words, if the input doesn't already occur in our list l, then the attacker can query this restricted discrete log oracle. So for example, the attacker can't query the oracle to find one of the secret keys. They can't do tricks like create linear combinations of the secret keys and then query the discrete log oracle. But if they pick a fresh binary string, they can query the discrete log oracle to get the discrete log of this new group element. Okay, so now that we have this restricted discrete log oracle, we can use it to maintain our invariant and update our list l. Now, as we're updating this list l, we'll say that the bridge event occurs if we find a non-trivial linear dependence. So in other words, if we end up adding the same group element to our list twice in two different explanations. So suppose we add a group element eta to our list twice, once with a vector a1 b1 and the second time with a vector a2 b2, where the explanations are distinct. In this case, we find a non-trivial linear dependence which we can use to compress the generic group oracle. So just as a simple example, suppose a queries the multiplication oracle on tau of x1 and tau of x2, which is this particular binary string. Then suppose they query the generic group oracle with tau of 1 and tau of x5. And let's suppose that we got a collision. In this case, what we learn is that x1 plus x2 is equal to 1 plus x5. That gives us a non-trivial linear dependence which allows us to compress the generic group oracle. Okay, good. So we'll call this a bridge event that the probability the attacker can cause the bridge event to occur is upper bounded as follows. Okay. So sweeping a lot of technical details under the rug here, but if you're interested, see the paper. So in conclusion, what we proved, we proved that the Schwarz-Nor signature scheme gives us three k-bit signatures with k-bits of security. And essentially all we do is just truncate the hash output. So we proved that truncation of the hash output does not adversely impact any of the concrete security guarantees even with multi-user security. We also looked at key prefixed Schwarz signatures games versus pre-processing attackers. We showed that key prefixing is necessary and we showed that we can continue to truncate the hash outputs without concrete security loss. However, we do need to increase our group by a multiplicative factor roughly n times s to achieve k-bit security. And then there's an open question which is to understand the security or insecurity of key non-key prefixed Schwarz signatures when we explicitly disallow equal zero signatures. I should also mention that our compression arguments also hold for other signature schemes like Chom Peterson or Cat Swing signatures for details, see the paper. All right, so thanks for listening and I'm happy to take any questions. Thanks, Jeremiah. Questions? I think Stefan also has a question. Big one. I don't know if you can hear me. Maybe you said something but I missed it. Can you say something about the tightness of the n times s? So the s is clear that it's tight but the n times s? Yeah, so that's a good question. I'd have to... I want to do some offline pre-processing to offer definitive response. The s is definitely required. I think there might be some possibility of removing the n term but I'd have to think more about it. Thanks. Yeah. For the term... Jeremiah, could you repeat the question? Okay, so the question is for the term with random oracle queries is our pre-processing bound tight. Well, because the random oracle is only outputting k-bit hashes, we can't get anything better than k-bit security. There's just no hope to get anything better. So, yeah, in that sense we can't tighten the bounds. One place where one might be able to tighten the bounds is in terms of the number of queries that the offline attacker is allowed to make. So we do restrict the offline attacker to make two to the three k queries and which is a very large number of queries but maybe one could remove this restriction. We needed to make that restriction for some technical reasons but it might be possible to get rid of it. Any more questions? If not then let's thank the speaker again and we have now lunch and we meet at 20 past before the next session.


Welcome, folks. This is Tom O'Brien of TFNN. We go five days a week. We go seven hours a day. We go 24 hours a day on the internet at tfnn.com. Always remember, folks, whatever you think about, you bring about whatever you focus on, grows up, everyone's having a great day, safe day. It's making a great night, folks, and we're kicking into 2022, and let's make it a great new year. To master the love, you have to practice love. The art of relationship is a whole mastery, and the only way to reach mastery is with practice. To master relationship is therefore about action. It's not about attaining knowledge. Make it a wise, let's take a look at it out here. We have the Dow Industries Up 247, NASDAQ Up 181, S&P's Up 52, Gold Contract Down $2.20, Traded $1,809. We have Silver Up 4 cents, $22.97 an ounce, LightSuite Crude Up $1.77, $75.56 a barrel, Notes and Bonds. The 10-year note, Up 1 tick trade in $130.18, The 30-year up 11 at $160.26 in $KingDollar. $KingDollar's Up 74, 6th trade in $9609, The Euro's $113, The Yen is at $114.89, The British Pound is at $134 to $1 at U.S. Dollar. iPhone numbers 877, 9276648, Let's call folks. One note's going on in your world. And the world of the S&P's, let's take a look at them. What do you have? Well, bottom line folks, we are breaking top side. I don't expect we're going to have volume all week. We didn't have it last week, but guess what? Price, they are going to move this price. And it looks to me like, the spy right now is trading $476. You got over the highs. The last high was $473.54. The closing high happened to be $467. So check that out. I mean, that's what they gave it up. This market doesn't want to give up price right now. Not even close. Now the way that this is set up is saying that the spy can go to 500. And I suspect you get a break like this and we're only on Monday and it's a full week. This can run all week. That's where this thing is shaking out folks. So this is phenomenal. There's no doubt. NDX100, what do we got in the NDX100? The bottom line, the three cues, same setup. You can have another contraction of volume out here. That being said, guess what? The closing high inside the cues is a 403.99. Well, we hit 403.28 today. The all-time high is 408.71. It's going to go for it. If the S&P is going to go up that high, the bottom line, the cues, I suspect they're going to go right for this 408 number. Small caps, different ball game in the small caps. No doubt about it. We take a look at these small caps and we have what the small caps, they're higher today but they're a lot lower than where the highs are. That's for sure. And the IWM, the high is 241. You're at 223. That is approaching the swing. I suspect it'll get up to the swing. It looks like the IWM will probably get somewhere to about 228 or 223. Now, what's going to be cool is this. So on the S&P, I did the price projection. Let me put it back up again. So the way I come up with this 450 is that when you take the, there it is right there. So you take the top of the consolidation. You take the bottom of the consolidation broke top side and the bottom line is the approximately 25 points that consolidation. So you just add 25 until the 475, there's 500. So we'll see where this baby wants to go. Inside the NDX100, the strength versus the weakness out here today. You have AMD up 5.5%. You get, what's this, Fortnite. That's interesting, Fortnite. That's up 4.5%. You got Clack up 4%. You got NVIDIA up 4%. You're taking away fit. Peloton is down 4%. You get Zoom off 2.3%. Pfizer's off 1.5% and Pandora's on 1.2%. Gold, we all look at the gold contract out here. You got a sideways move with gold today. It hit a low of 1803. We're at 1809 and you get a sideways move. That's the bottom line. We need some volume of gold.  What we had happen there at that particular point is that we went up there with 157,000 contracts. And today we've done only $76. Now, we're going to have lighter volume the whole week. Bottom line, we'll see whether gold can get any juice underneath it. If we go take a look at the dollar, what it looks like in the dollar is this. The dollar is set up basically to try to get into the lower end of this consolidation, which is the 95, 516. You can see when you take a look at this, the dollar's been sideways going all the way back after it reached that high on the 24th of November. So we're basically 21 trading days later and you're kind of at the same spot. Some of the higher volume equities out here today, we take a look at, you had, that's interesting. Okay, so we got, let me put the S&P up there. Let's do it this way. There we go. Okay, so when you get Apple up 340, we have NVIDIA up 1224, AMC's up 15, Tesla's up 33. If we go take a look at Apple, what you're going to see with Apple as the market, okay, the bottom line is that we, it is moving higher with volume contracting in a monster way. Last high up here with an Apple was 153 million shares. Right now you're going up there with 55 million, but I expect that's going to hit it. What we're talking about here is 182.13. Facebook, Facebook caught a bit out here today and I suspect if you heard Tommy Scho the small one, he actually got an Oculus too. Also, he's talking about how amazing the thing is.  And it looks like a lot of other people actually got that Oculus too and realized just how powerful this thing is. So we'd have a Facebook today that's up 1083. You're breaking the swing. The swing is 22 million shares. You do have 13. I suspect this is going to go to the top of the range here, which is the 353. And we'll see whether you get any more juice on it. With Facebook, next time they come up with the numbers, it's going to be the 27th of January. They are going to be looking to bring to the top line 33.4 billion and to the bottom line $3.85. So we'll see how that baby's going to shake out. We take a look at the silver market. What do you have inside the silver market out here? Silver right now is trading up 13 cents. It's not bad. Okay, so we get 36,000 contracts. You know, silver came off the lows. I mean, it came down to the swing point here of September 20th, rejected lower price. It's building some costs for higher price. Stay right there, folks, coming right back. Our phone number is<PHONE_NUMBER>. We have the Dow Industrial's up 254. And as except 183, SAPs are up 53. We'll come right back.


Good afternoon everyone, I'm Rebecca Dorge, I'm the Glenda Ries Dean of the Mellon College of Science. My office is just across the foyer. Thank you for coming today and today we're celebrating the digitization of the Mellon Institute Industrial Research Archives. I'd like to thank the Dean of the University Libraries, Keith Webster, sitting in the front row, for inviting me to speak today. Keith and his team have completed an incredible project, namely digitizing the records from the Mellon Institute. Today when most people think of the Mellon Institute, they think of this building as an impressive facade where Batman died, and they remember the large columns out front and I know that there are 52 freestanding columns out front. No matter how grand or impressive the exterior of the building is, it is far surpassed by the caliber of the work that has been done and is still going on in this building today. Prior to the merger of Carnegie Mellon to form, prior to the merger of with Carnegie Tech to form Carnegie Mellon, the Mellon Institute was home to fellows who conducted groundbreaking industrial research, and this research led to the creation of many companies and products that are still in existence today. Today's research in the Mellon Institute building is revolutionary science. It's biology and chemistry and neuroscience. Just as the work from the Mellon Institute many, many years ago, the work being done here today is dramatically impacting the future and will continue for many years. The Mellon Institute is a vital part of the Carnegie Mellon University history. It's a vital part of my life every day. By digitizing the institute's records, the CMU libraries have done a great service for everyone. They have not only preserved the important part of our history, but they've made that history available to all who are interested. So today, I'm happy to introduce our speaker, Ellen Sparrow. Ellen is a lecturer and a researcher from MIT, the Massachusetts Institute of Technology. She studies narratives of progress, academic entrepreneurship, and innovation ecosystems. She is also a social entrepreneur, co-founder and chief curriculum officer of station one, a nonprofit organization focused on socially directed science and technology. Elaine knows firsthand about how interesting and valuable records in the Mellon Institute are. While she was completing her dissertation on textile science, her advisor Walter Friedman suggested that she take a look at Mellon Institute to find more information out about chemical research. Several years ago, and we won't say how long because it wasn't that long, she explored the Mellon Institute collection and was struck by how overwhelming and interesting the records were. She sifted through the material and learned a great deal about the work that was done and the people who did the work in the Mellon Institute so many years ago. I'm excited to hear what Ellen has to say and the panel that will follow her talk and they know that they have a 10-minute limit because I talked to them about that. Without further delay, please join me in welcoming Ellen to the podium. Thank you so much for this invitation to join to celebrate and reflect on the history and legacy of the Mellon Institute. We might think about history as where we've been and legacies about how we might mobilize that history going forward. Many of you in this room bring your own history to the collective of the Mellon Institute, its people, its problem-solving ethos, and scientific and technological impact that this work has catalyzed across industrial sectors and scientific disciplines in the United States and internationally. As Dean George mentioned, I've studied the nascent stage of the Mellon Institute as a historian, but also the spirit of the Mellon Institute founders and envisioning new relationships with academic science and industrial and societal impact through collaboration also influences my work as an academic entrepreneur. Co-founding a nonprofit educational institution, station one, which she mentioned, is dedicated to socially directed science and technology with core principles of equity inclusion and inquiry-based learning. My work is about processes of envisioning and creation, what we might consider today as entrepreneurial thinking, how these processes are manifested through the institution's exhibits, buildings, and narratives about what our world is and what it might be through science and technology. And in many ways the task of a historian is to bring many of our unanswered questions of our present to the past as a place to think with history, not as a singular thing, just as an institution, even one with giant stalwart columns and sometimes concealing the fervor of scientific innovation, is a confluence of multiple parts. We may think of an engine or an orchestra, a tapestry, a city, or a living archive with many moving pieces that come together to produce something complex and dynamic. And so it's with curiosity and the importance of understanding context that we might ask, how did we get here in this particular organizational structure or a set of values or incentives? We may wonder about this often tension between fundamental science and the social impact of technologies out in the broader world. Going a bit farther still our task might even be to question those categories and to think about knowledge production as a gradient or overlapping spheres rather than a contest between rigid binaries. The practice of history is not just trying to accumulate a list of all the things that have happened in the past. It's about the questions that we bring to this distant and in many ways not so distant place, people, situations, their configurations and their decision-making. We might ask, are there particular types of institutional or industrial settings that are better poised to innovate than others? What are the institutional structures and organizational relationships that are necessary to catalyze the kind of change to be beneficial to society? What kinds of relationships are necessary to foster innovation? What do we mean by innovation? And how does discoveries become inventions and how do they transition from the laboratory into a broader societal landscape? A lot of questions are not only rooted in understanding processes of change over time, but also the context of networks, social and economic structures that underpin these developments and the narratives of progress that they represent. What Robert Kennedy Duncan may have described as, quote, the glorious interest that attaches to the doing of real things. So how might we tell a history of the Mellon Institute with this kind of multiplicity? It could be a story of innovation. Innovation is both a product and process of change. It transforms what might have been only possible into the pervasive or even prosaic through the scaling up of invention, a process that requires more than just technology per se, but the coordination of organizations and the infrastructure. Successful innovation goes beyond just turning dreams into reality. It is also a process by which ideas are transported from the cutting edge into the fully absorbed and thus remarkably invisible landscape of the ordinary world. Perhaps innovation is most widely defined through its tangible products, patents, inventions and other associated consumables or even the new industries and sub-disciplines that emerge from industrial science, topics that the members of our panel know very, very well. However, it's equally important to also inquire about the human systems of organization and knowledge creation that are embedded in a technological landscape of things and ideas. To look for the often invisible labor and infrastructures, both of people and things that are necessary to maintain institutions and their systems. So just as much as we're interested in the science and technology that arise from the fellowships, the influence of the leaders, both in the US and abroad, we should also look to these systems of information organization, logistics and communications, that in many ways give us the traces of the institutional history that we're able to study and are now able to study more widely through the digitization effort. We could also tell this story as one of pragmatism and entrepreneurship. Indeed, when Robert Kennedy Duncan arrived in Pittsburgh to translate this system of industrial fellowship that he had begun in Kansas, in the beginning of the 20th century, he worked out of his own attic with one employee, Lois Whittle, who would spend her entire 45-year career managing the administrative side of the Institute. Of course, we know that it eventually grew far beyond that single room. We know that this mainly postgraduate research center, a so-called quote, Armory of Applied Science, was founded in Pittsburgh in 1913. It played a foundational role in shaping the emerging American R&D sector. Within its first 25 years of operation alone, the Mell Institute had already served 3,600 companies from a wide variety of industrial sectors, either as firms or members of trade organizations, an unprecedented number at the time. Out of these collaborations came 500 novel processes and products, and from then 10 new industries were created. Over the quarter of a century that followed, the Mell Institute continued to shape the landscape of R&D as a collaborative fellowship projects came and grew into in-house corporate laboratories. Some large corporations had their own research divisions from early on, yet the majority of smaller companies began their research activities through collaborations with institutes and universities similar to the Mell Institute fellowship model. By that time that Mell Institute merged with the Carnegie Institute of Technology to form the Carnegie Mellon University that we know today in 1967, laboratories and large corporations had become more of the norm rather than the exception. By the 1960s, Mellon's researchers had contributed more than 4,700 papers and more than 1,600 patents to an academic research ecosystem fueled by substantial increases in government funding that began to shift away from applied and towards more fundamental science. There was so much going on and so many overlapping kinds of activities, but it's just to give a sense of scale that was quite large. Indeed, perhaps the end of the Mell Institute for industrial research as an independent entity in 1967, after successfully promoting the growth of industrial science, should also be read as a signifier of its success. At the intersection of science and industry, the Mellon Institute could find productive synergy in the attempt to make productive order out of a messy world. To find predictability in times of uncertainty and to rest efficiency from an excess of waste. We might also tell a story of the Mellon Institute about stories themselves. That's what the editor of chemical and metallurgical engineering, Sydney Kirkpatrick did in 1937 when asked to reflect on the legacy of Robert Kennedy Duncan to commemorate the opening of this new building, which is where we're sitting today. He said, in this story, the moral must come first. If you have a big idea, bigger perhaps than you can master alone, write a book about it. The chances are that somebody will read your book and if sufficiently impressed may do something about it. And that's exactly what Robert Kennedy Duncan had done. Not only did the Mellon Institute contribute to the field of industrial research through its scientific work, but also through popular writing aimed at a broad audience. In 1907, Duncan posed a deceptively simple question, quote, how can we utilize modern knowledge? For Duncan, who was then a chemistry professor at the University of Kansas and a prolific writer on science for popular audiences, the answer to this question called for an increase in role for science as a productive agent of efficiency and coordination and as a means to what he called, quote, an era of gracious living. Duncan's belief in applied science as a necessary and indeed urgent tool for progress echoed throughout his public writing in popular magazines such as Harper's Monthly and in three books, which he fervently advocated for the application of academic research to industrial problems. Duncan's work served as a powerful catalyst that caught the attention and garnered support from many industrial leaders, sparked the development of industrial fellowship programs and the creation of a model for academic industrial cooperation that would serve for the foundation of the Mellon Institute for Industrial Research. Mellon Institute leaders not only produced a wealth of papers and patents, but also powerful narratives of progress. These narratives served to both bolster the prestige of industrial science, this new type of work with their vision for an enhanced quality of life through applied science. Quote, problems having obvious and apparent answers have all been solved. Duncan wrote in his popular book, The Chemistry of Commerce in 1907. Today in 2019, amidst growing understanding of our interconnected global challenges, the health of our planet, geopolitical tensions, growing inequity, and the promise and peril of our emerging technologies, this sentiment is just as salient. So it's in this spirit today that I'm here in our exhibit and with our distinguished panelists to think about the living history of the Mellon Institute, its place in Carnegie Mellon University, and the questions that we can ask of this usable past to shape the future of the systems of science and technology, research and education that we choose to create out of this legacy. It's my pleasure to invite our distinguished panelists who I'll introduce together and then each will contribute brief reflections on their own extensive experiences working at the Mellon Institute. During our meetings preparing for this session with Emily Davis, who we owe a debt of gratitude for this event today, I learned so much from our panelists and hope that this can be the beginning of many more conversations both here in a formal setting and also in the reception where we'll have a lot more chance to chat informally. So Brian Zandy, after graduating from Indiana University of Pennsylvania in 1980, was hired as a technologist at Carnegie Mellon Research Institute where he worked on the St. Joe Minerals Fellowship investigating lead acid battery production variables. Once the St. Joe Minerals Fellowship ended in 1983, he worked as a scientist at the Materials Characterization Center at CMRI using X-ray defractometry and scanning electron microscopy to solve problems for industrial clients. In 1995, he went to work as a scientist for Advanced Materials Corporation until retiring in 2017. Guy Berry is an emeritus university professor of chemistry and is widely recognized as a leader in rheology and light scattering of polymers. Rheology, a branch of mechanics, is the study of those properties of materials that determine their response to mechanical force. Berry began his career in polymer science as an undergraduate laboratory assistant at the University of Michigan. After earning his doctoral degree in 1960, he accepted an appointment as a fellow at Mellon Institute to work on light scattering studies of dilute solutions of polymers. He became a senior fellow of the Mellon Institute in 1965 and joined the faculty of Carnegie Mellon when the Mellon Institute merged with Carnegie Institute of Technology in 1967. He was promoted to professor in 1973 and became a university professor in 2001. Alberto Guzman, former associate director of the Carnegie Mellon Research Institute, first came to the Mellon Institute as a research fellow where Essex International Corporation and United Technologies Corporation sponsored his work on innovative technologies for electric power and communication lines as well as the automotive industry. At CMRI, Guzman led a 45-member scientific staff and applied advanced technologies. He created a research team to develop intelligent instruments by merging gas-sensor technologies with micro computers. Guzman and his research team also conducted sensor research that has had impact both in the US and internationally through the creation of new products and processes, including the development of a powerful, accurate and transportable optical methane detector the gas industry now uses to detect natural gas leaks. Guzman continues to have a long career impacting sectors across the work ranging from material science, the development of sensors and the application of microwave processes to the treatment of industrial waste and the refining of heavy petroleum crudes. So you'll see that our panel has so much experience to share from their times here in transition in the contemporary Mellon Institute and I'm going to start with a few questions just to get some glimpses of their lives and experiences and then I will invite everyone to join in a rich informal discussion afterwards. So beginning with Brian, what was it like to begin your career at the Mellon Institute during this transition period? Well, I had just graduated college from IUP in May of 1980. My interview here was April of 1980, right before that. Just coming into this building, I was overwhelmed. I grew up in a small town, maybe 8,000 people to Catholic school, high school that had no money for chemistry labs and yet I made it through college, got my BS in chemistry and I was fortunate enough to have an interview here with Dr. Tony Parker who was a senior fellow with the St. Joe Minerals Fellowship. That was in April. He hired me. I started June 16th, 1980. I remember it very well to this day. I didn't have my parking card yet. I had to park in front on Fifth Avenue. I think an hour was a quarter, if you can believe that. It's what, now five cents for 12 minutes or something? I don't know, 25 cents. Anyways, I was still just getting my feet wet. As a scientist, chemist, yeah, I'd done undergraduate research. It just wasn't the same. Dr. Parker started me at a very low salary compared to several of my colleagues who graduated with me. He said, Brian, if you work out, we'll make arrangements for you. Well, within the first three months of my employment, I was interacting with the engineers at St. Joe Minerals Research Center. The Corporate Research Center was out in Beaver County at Monaca. St. Joe had a zinc smelter out there, but they also built the Corporate Research Center out there as well. They had just had a big bonanza as far as the research goes, as far as they had developed the alloy that led to the maintenance-free lead acid battery. You didn't have to add water anymore, and that sent the battery market through the roof. St. Joe developed a lot of money from the lead mining that they were doing, and the lead was their main thing there. So they were able to invest in the fellowship. Dr. Parker had never really had a full-time staff member. He had hired part-time people through the years to help him when he was the busiest. These people came from the staff of the CMRI building. I was full-time and immediately integrated myself into the research center, the industrial side at St. Joe in Monaca, and also the academic side here at Carnegie Mellon. It was just kind of there for me to do. So I did it.  I was trying to make a career in science. So, yeah, I did whatever I could, and I would talk with the engineers at St. Joe. They had these exploratory programs that they put money into, and they used professors at CMU to help develop these programs, and I was kind of the interface, along with Dr. Parker. But he realized, eventually, that he could let me do a lot of it on my own. I eventually spent one day of a week out at Monaca, just doing research in their battery lab, talking with the engineers. They had programs with faculty in the metallurgy department at Wean Hall at CMU. I helped facilitate some of their data-taking and going over there to campus because that project was behind. Within the first six months, I think I met with Dr. Barry, along with my boss, Tony Parker. He doesn't remember, and that's excusable, but we had a problem with our rheology of our slurry solutions that we were working with as our research. So Dr. Parker said, let's go talk to Dr. Barry, and we did. And I think in the next six months, within that first six months, we had a project with Dr. Guzman's group. Dr. Guzman had acquired an implantation device. The St. Joe engineers were interested in what would happen if you implanted certain ions into the surface of lead alloys. How would that affect corrosion, the properties that are important in battery manufacture? Geez, I was involved in all of that. And I was still kind of getting into everything as far as the science. I did have daily routine, so to speak, and it was the kind of stuff that I thought, graduating as a chemist, that I would have to do for the rest of my life. Go into a lab, do two or three tests every day, the same thing, and that'd be done with it. Go home. The chemists I knew growing up, that's what they did. They worked for Kendall Refinery in my hometown, Bradford. And they went in, did routine tests, and went home for the day, played with their kids, you know, that kind of thing. I kind of thought that's what I was getting into. When I came to Mellon Institute, and I walked through those front doors with the marble and the granite and the quotes on the walls and everything, it was just amazing. And it just ballooned off after that, as far as developing a career in such a way. And then as the lead market fell out, people started recycling their old batteries, so the secondary lead market grew, the primary lead market, which is St. Joe's bread and butter, didn't. We lost funding. Tony Parker went on to teach at local College of La Roche or somewhere. I hooked on with the materials characterization center here, and I was able to use the skills I developed with St. Joe, as far as interacting with the engineers in the private sector and industry to do problem solving for them here at the university. Because the CMU people were nice enough to set up our group with some electron microscopes, high end x-ray equipment. People back then were sending me a vial of powder with the simple instruction, tell me what it is. If they found out what it was, then they could figure out maybe where it was coming from, because it was appearing in their process where they didn't want it to. I did that for about 10 years. It was one of the best parts of my job, career that I've ever had. I worked in the sciences for about 40 years, eventually starting with advanced materials characterization, where we were doing research in magnetic materials. And then I get into my routine, where I was doing the same thing all the time. But for the first 15 years, I was here at Mellon Institute. I had a great time. I remember so much of this stuff off the top of my head, because I know. I experienced it, and it was just an amazing thing for someone from a small town in Bradford. Just, you know, to do that, and to be here today to talk to you about it, I'm over the moon, as they say. Am I up yet? Thank you. We'll look over the moon to have you as well. Oh, great. Thanks. Thank you. So shifting gears, maybe a little bit more to think about what it was like during the merger. Guy, might you share some thoughts with us? Well, many things come to mind. One thing comes to mind immediately. Robert Kennedy Duncan's portrait is on the back wall. So when you go out, you can take a look at what he looked like at a stage in his life when he was writing that book or working here. Not in his building, but at Mellon. I had come to Mellon to work for three years. When I walked in the door and met the people and why I came, actually, it was like walking into a textbook in my field. Paul Flory, who later became a Nobel Laureate. Ed Casasa, who was in life-scattering like I, and Herschel Markowitz, also in biology like I, had come to Mellon before the 57 change. The 57 change brought a lot of change even before the merger. The Mellons had always had a kind of a funding for some independent research since the 20s. Not very much, a few projects. Maybe they were making too much money off of you guys. I don't know. But in 57, in 56, the Mellons could see that World War Two had changed the way research was being done in industry. And they began to worry that the model they had developed so successfully during the earlier years wouldn't any longer work. They had a committee looked at things and they said, well, maybe you should set up an independent research program with some endowment monies to work with federal monies or state or whatever, but not with sponsored research monies, not with industries. So that those groups were not allowed to go out and work with industry directly. Of course, we could send results to industry and discuss with industry what kinds of problems there were. We couldn't take any money. That was done. And they hired Paul Florey as the director of research. And Paul did not want to do any administration work. So they hired a trustee, member of the Board of Trustees named Matthew Ridgway, a general, to come in and do that kind of work. So we had a dual leadership in the in the student at that time. They hired Paul Florey, who then hired eight staff fellows, one for each of several or eight, I would guess to say, areas of research. My area was one of those polymers, metallurgy was another one. And like on on down the line. That was a structure. And that worked for a while. Paul Florey had thought he understood something about the funding of that long term, which the melons didn't think they had said. And eventually Paul left. He went out to Stanford and finished his career very effectively, again, getting a Nobel Prize for his work, much of it done here, but not all of it. Some was done before he got here. A lot of it was done before he got here. For me, I had become a senior fellow, which was a kind of a appointment with unlimited tenure, like a faculty professor at the university. In 65, I think is what you said. And the that meant I had responsibility for a few people working under me, had to help get the funding for that by writing proposals just like you do in any faculty member of any university. And knew nothing about the impending landslide that was going to hit me when the merger took place. The merger was arranged by the Mr. Mellon and the Board of Trustees, working with the presidents of the two institutions, Stevers for CIT and Cross for Mellon Institute. People, even the staff fellows right under Paul Cross knew nothing about the merger. I personally learned about it when I was in Japan when I visiting scientists who had worked with me here at Mellon, asked me what I thought about the merger. I says, what merger? I said, Well, Mellon Institute's merge was CIT. I said, Well, you've got to have something wrong. If we were going to merge with anybody, it would be pit because that's, you know, that's nearby. And that's where we came from. But he was right. So I came home back to Mellon and things were changing. There were 29 people from the Mellon Institute who had an appointment level that Meredith put them into the new university faculty in various departments. And that was the rub. We weren't a group that was spread over many, many areas. A lot of it was in chemistry, some in biology, some in metallurgy. So there were just two or three places they could go. At that time, at that time, CIT did not have a modern bioscience department. They had a biology department. So these no one wanted to go there particularly. A few went into metallurgy. The only member of these staff fellows that's still alive is Tim Masowski, who's over in metallurgy. And all the rest have just been going for several years now, most of them, including Paul Flory. Chemistry got the bulk of them. And chemistry became the size of a department that would be appropriate for a major state university with lots of students and lots of space. It was not appropriate for the program that had been existing in Carnegie Mellon or at CIT. And it wasn't clear to anybody how I was going to work in Mellon, CMU. Well, of course, the worst part of that was for the youngest members of the chemistry department who had not yet received unlimited tenure. Several of those decided it was not a good idea to stay here anymore. They moved on to other places where they didn't have to fight quite as heavy a level of senior people in front of them. Some of the senior people moved into the new biological sciences department when it was created. And of course, that helped a lot in two different directions. It meant that CMU would now have a department that was more modern and working in areas of independent from the chemistry department for sure. And it has become a very strong department for the university. Chemistry then had a more reasonable size. And I, myself and my Polymer Science colleagues, four of them, had not gone into chemistry at the first movement. We had decided we wanted to stay together and the university had several of these misfits didn't know what to do with this. So they created a center, a center for special studies they called it. I think they probably called it something else in private, but that's what they called it. Well, I was a member of that department, that center, and I went to Japan again and was talking with someone else. And when I came back home, I was told I was in the department of chemistry. Dick Sartre wanted to get rid of the center and resolve that issue. So he put some here and some there and the four of us went into chemistry. Tom Fox, our staff fellow, was really not doing anything in chemistry. He was working with the governor as governor of science advisors. So he was hardly even not here all that often. Ed Casasa and I went there with Heschel Markowitz. Ed was the only one of us, I think that was delighted with that. He was a chemist. Herschel and I were not chemists. I was trained. Well, I would say I went to university as a chemical engineer. I wouldn't say I was trained as one because about halfway through I decided I didn't like chemical engineering. And I started doing applied chemistry, physical chemistry at that point, which a lot of chemical engineers do. Herschel Markowitz had come here in 49 and began rheology early on. And he was more physics than anything, as I was actually. But we were all in chemistry. So we all decided to buckle down into our jobs. So we tried to create a polymer program in chemistry. And I see I have reached nine minutes, which probably means I should stop pretty quickly. The only thing I would have to say on top of that was it's been a like I was already been commented on. It's been an interesting career. It's been a lot of things that have been very good. Some things I wouldn't want to repeat. But you know, that's what life is like. And that's what life was like here. Thank you so much. So there's a lot of lore of mystery about what is behind these columns. Could you give us a glimpse into what it was like to lead and to do research during this important period? And were many of the things that you did were by necessity confidential? Thank you very much. I'm trying to see if I can summarize 30 years of life at the Institute in 10 minutes. And that is a very hard task. I remember having a senior vice president of research of a company that we were doing research in a consortium with other companies, it was a petroleum company based in Canada. And he was presenting a lot of ideas and see if we can really do what they normally do in petroleum processing in 10 steps, one single step. And I said, John, why do you want to torture me?  You are asking me to walk on water. And let me tell you, I'm not Jesus. So you always get this challenge because you have unique expertise, a team of people that have been highly educated. At the same point, I have about 50 PhD in my group. And how do you excel in a way that you can attract funding from different organizations? The Melon Institute for years, they have these fellowships that were supported by corporations. They are senior fellow and the fellows working in a subject of interest to the company. There was a long fellowship here that lasted, I don't know how many years, supported by the Melon Foundation on the preservation of artist materials. How do you preserve the treasure of arts that in the particular case were the Melon Collection that is in the National Gallery in Washington, D.C. That was a senior fellow, Bob Feller, Bob Feller. He did such a good work. I remember being in Paris one day, went to a museum in Forze, and I was going to have lunch. And the cafeteria was full. There was no empty seat, except one table. So I came with my tray, sat down as the lady that was there, may I sit here. And she says, of course, please sit down. She was from Vienna. And she asked me, where are you from? I said, from Pittsburgh, Pennsylvania. And she said, ah, Bob Feller. I said, how do you know that? Oh, he's an authority, his work known for his work in the preservation of artist materials. The problem that we have is that you can do research as long as you have money to do it. And funding has become very competitive.  And that puts you always in high gear. It's a lot of fun to do very interesting work. But in order to do the work, you have to have the money. So after my years being a postdoctoral senior research engineer at CIT, I said, where am I going? And I look at various opportunities I could have gone to GES, Connecta, the research laboratories, some place in California that looked like a high tech. It was not yet called the Silicon Valley. But I look into the Melon Institute, which recently has merged with Carnegie Tech.  And these people created a very large fellowship here at the Melon Institute because they wanted us to join the group. But they wanted a separate group from their very large, very well known research center in Hartford, Connecticut. But they wanted somebody that was away from the manufacturing. Remember, they were making helicopters.  So they wanted a group that was isolated from the manufacturing area. But forward-looking, thinking about what technologies are coming and how do we prepare ourselves to be involved in these technologies. You are being paid to think. You are being paid to develop ideas. Look at the trends. What is coming? So for a few years, we were here at this building. And we had an automatic group that was looking into the use of microelectronics into the automobiles to control the engine. And our group developed the first two prototypes in conjunction with Ford Motor Company. And then we provided them with a series of units that we installed in the Lincoln Continental Mark IV. So think about innovation. Nobody was doing this kind of work. We did that. I was very proud of working with this group. I was a scientific advisor within the group. But the United Technology, one day, said we are going away. We are canceling the fellowship. And that was the time when Carnegie Mellon had been running short of money. There was a lot of cleanup by President Sire. And United Technology was not too happy with the services that were provided. So they decided to move out. What happened with us, indeed, Carnegie Mellon had no chance to offer us jobs. But I was offered a job with United Technologies in a group that was installed here in Pittsburgh that was doing all kinds of microelectronics work. And as a consequence of that, we were able to produce dedicated chips to put into the microcomputer that was installing the automobiles. It was a very interesting period because I was interacting a lot with the people at Hartford, where the research center was developing their own research work. But at the time, we were talking about 1973-1974, President Sire has hired a new president of the Mellon Institute. That was Dr. Ted Herman. He also came from Kansas. And Dr. Herman came to visit our facility that was in RIDC in Blonox. And after the visit, he said, I'm going to talk to President Sire. I want you back at CMU. I said, well, that depends what you can offer me. And indeed, later on, I received a letter from Dick Sire saying, Alberto, we would like to offer you a position back at Carnegie Mellon. You would be doing this kind of work. You are going to all the seniority, getting back to all the seniority, and so on and so forth. So I returned to Carnegie Mellon in February. No, that was July of 1978. And that was a period of changes. The old fellowship idea was kind of a phase out in terms of, as a modus operandi of this institute. But a new way of doing research was get counter-research with industrial responses, federal government, or any group that is interested in spending money with ideas that you can offer, solutions to their problems, new technologies. And so it's a phase of evolution that really created a new approach. And we put together new groups at the Carnegie Mellon Research Institute. There was a group in computer engineering, a group in rail transportation looking into high-speed rails. My group that was involved in advanced devices and materials. And there was a battery group also. So we started to work in a different way. We were looking for sources of money, including venture capital. That was the name of the game. People that had money that we are trying to sponsor work. So at the time when we started looking at who we can work with, we traveled quite a lot with State Herman. We went to what was later called the Silicon Valley. We met with three guys working in a garage operation. One of them was Dr. Robert Noyce, founder of Intel Corporation. So we had tremendous opportunity to get in the ballgame of new technologies. And that was a very fruitful time for us to develop ideas in conjunction with some companies who are interested in the capabilities that we have developed at the Carnegie Mellon Research Institute. And so it was a golden period of R&D. On the other hand, there were people that were being phase out of their place of work, including the Westinghouse Corporation Research Center in Churchill. And many people wanted to move into the Mellon Research Institute. They came to see me and I got about 10 top scientists from the Westinghouse laboratory. We also had the change because a change of location because we moved from here, from this building, into the newly created Technology Center on Second Avenue by the river. The government of Pennsylvania provided money to the University of Pittsburgh and Carnegie Mellon to create two anchors in that old steel mill land. One was Pitt, the other one was CMU. And when I said CMU, I meant Carnegie Mellon Research Institute. We moved in 1985 with beautiful facilities, state-of-the-art, everything was working very fine. Some of the work that I was able to capture was a major contract with the Department of Transportation. The Department of Transportation asked me to do some work for them and for $50,000 we delivered some product that was worth perhaps millions of dollars, bearing the potential legal problems which also is a problem. Anyway, but this contract I got for about $7 million for three years was a very strong competition between our institution, the Carnegie Mellon Research Institute and Battalion Institute in Columbus, Ohio. And we competed and we won the award. That opened the door to me and to my group to look into what is going to be now the future and the future is now here, intelligent automobiles, intelligent highways. The work that we did for the Department of Transportation was an evaluation of technologies that have been developed for other applications, particularly the military, where the Pentagon will pay for a gadget $10,000 and dealing with automotive industries that won the same gadget for $1. How do you do that? How do you, you know, have to deal with mass production and make the numbers and so on and so forth. But some of the things that you see now in the new cars, the anti-collision devices, the lane departure alert system, the autonomous driving, we set the basis. I got after three years another $7 million and we continue working and we develop our own systems. The idea here is how you can make use of the knowledge to transform something that is potentially good into a product that somebody can use it. And out of this product comes the potential of creating a spin-off companies. And Carnegie Mellon as well as the University of Pittsburgh have been very good in spinning off companies. We spun off four companies. We did some work for the Department of Defense, developed a very important camera for target detection identification. The camera was tested and it was a success. We created the company, the company did some work and finally the company was acquired by another company that was in Rochester, New York and then finally acquired by a Canadian company. So the idea of entrepreneurship, very valuable for the creation of jobs. That's where we were. How we can open opportunities for people to create new companies where the public is going to benefit, the investors in the company are going to benefit and of course the owners and the people that work in the company will benefit. I was trying to do something else before my retirement and that was the creation of an operation where we could retain the talent that we get out of our schools at Carnegie Mellon rather than the people leaving Pittsburgh and going to some other places generate a center where the newly graduated people can get some seed money to develop the idea, continue the development of their doctoral thesis into something that eventually can be a product, can be manufactured. The retention of talent for the area is very critical and that's something that I was working on and finally one day I said hey Alberto, why, what is the difference between you and the guy in the corner in downtown Pittsburgh who's a little kind of coke and a few coins saying would you spare a few change with me and Alberto was a big bucket to the Pentagon taking it and saying would you spare a few million with me. It's a very demanding process with you in high year you are working very hard every day even at night and finally I said it's time to retire so in 2001 I retired and then I entered in doing consulting and after my retirement I was working more than before in consulting that's a time when I said no more but anyway I'm still linked to Carnegie Mellon I'm teaching at the Osher program the Long Learning Institute and I have given lectures on energy and the environment, green sources of energy and sing of interest at this time when we see a lot of serious problems for the future and the health of the world that's my story. Thank you so much so I think one connecting theme is really so much of this energy and the multiplicity of history in this place that we can continue to explore both in the living history and in the projects in what's preserved in the archives so now I would like to invite the dean of the libraries to give some remarks before we recess to talking informally together. Thank you. Thank you Ellen good afternoon ladies and gentlemen I'm going to try and cope with this microphone that I think has got a some sort of tachycardia. I'm Keith Webster, dean of University Libraries. I'll be brief you'll be pleased to hear. First thing I would like to thank Ellen and Guy, Alberto and Brian for their fantastic participation in this afternoon's event. Please give them a warm round of applause. I was going to thank Rebecca as well but I think she left with her sick dog. Maybe she left the dog behind I'm not sure. I'd also like to acknowledge my predecessor Gloriana St. Clair Dean Emerita of University Libraries. Welcome Gloriana. I'm delighted Guy to learn that university communications has improved fastly since the day of the merger. Everyone knows everything that's going on all the time these days. Well I wouldn't say that. I really enjoyed hearing you know this thread about the role of Mellon Institute and today Carnegie Mellon in transforming research that is good into things that are useful and the potential to create companies that build a drawcard in Pittsburgh one that attracts and retains the very best talent. That is very much at the heart of the university's role today. Further thanks to the American Chemical Society who provided financial support for this event as well as the Mellon College of Science for their financial support. We've already heard Emily Davis being mentioned. Emily thank you for your work in creating the exhibition in building the digital archive that I'll mention in a moment. Colleagues from the Libraries Digitization Projects Division I see Ann Marie at the back. I don't know if any of your team are here. You came alone. The rest are digitizing a number of my colleagues also who made this event possible. Andy, Heidi, Shannon. I'm grateful to them. I really do feel that this event also symbolizes something important about the library. I spend a lot of time talking about the evolving scholarly record which is a a kind of clunky metaphor to talk about how today we see scientific research produce a variety of digital outputs data algorithms observations as well as papers reports and other things and this collectively reflects a body of work that is amenable in a digital world to capture and to reuse and to verification and so on but it also I hope if we sort out the digital preservation bit builds a record that tomorrow's historians will be able to refer to to understand the evolution of research and disciplinary practice in today's world and just as we hope we have that future aspiration today we can look back on the evolution of this institute and our university through the records that our libraries and archives are building up to tell the story of not just the evolution of an institution but given the distinctive research at this place the evolution of scientific disciplines during the 20th and the early part of the 21st century with that in mind I did want to mention the work that Emily has done to organize 347 boxes of records and I'm told they're not like the small boxes you might have at home but big boxes a tremendous amount of stuff there the records of the Mellon Institute of Industrial Research two bodies of that material have been digitized by Ann Marie and her team representing 26,800 pages and your reports of the Mellon Institute from 1914 to 1967 and the institute's newsletters from Toot Toot which was the first title in 1960-1917 through various different titles up to Mellon columns in 1975 and through to 1980 and to gather these tell the story of that evolution that I mentioned they have been OCR'd for those who care about these things so that the text is searchable and for people that can be cuttable and pasteable into slides for presentations so do look at the website there's a link to that from the library's homepage library.cmu.edu I would be told off if I didn't remind you to fill out your survey card if your Facebook plugins haven't filled it out for you already the one thing we don't have here is a sign pointing to the exhibition but please do take time to look at it it's really fantastic I had a good chance to look at it this morning it is through there on my right and take a right please do take time to look at that take time to talk to our panelists take time to talk to Ellen take time to enjoy the refreshments enjoy the rest of the event thank you


Does anybody remember her looking like this game ranks?   I'm out


The addition of inorganic spherical nanoparticles to polymers allows for modification of physical properties and implementation of new features in the polymer matrix, while also providing potential applications in energy storage systems. This article was authored by Thomas Hanaman and Dorothy Vengasabo.