Good afternoon everyone, am I audible? So, my name is Ritinder. So, that is my role essentially. I lead this project leap in a start up sort of environment. So, this is about logic engines for accelerated processing. So, I will be talking a little bit about that in the context of big data. So, just to you know get some interest in this, you might have heard that about an year back Microsoft announced that they have done this experiment for their Bing search engine where they would use these devices called FPGAs for accelerating the page rank algorithm used for Bing. So, then you know Intel announced that it is coming out with a Xeon chip with an FPGA device within the same package. And just about last month Intel announced that it is buying this company called Altera which makes FPGAs for almost 17 billion dollars. So, what is going on here? Intel invests billions of dollars in making these microprocessors. It is very successful there. So, why all of a sudden does it need to get into this FPGA stuff? What are FPGAs and how are they useful? So, to get some idea of what is going on before taking a look at FPGAs, let us take a look at a microprocessor right, the kind of stuff that Intel makes. So, before that I will just give you an outline. So, I will start by giving a mini tutorial of what FPGAs are. Then we will talk a little bit about you know it is a short talk.  Then I will give a more concrete example which is something that we have been working on where I will show what kind of speed ups one can obtain using these FPGA devices and doing the computation hardware compared to software. So, moving to what a microprocessor what a modern microprocessor looks like right. So, this is a die of a AMD bulldozer but Intel chips also looks similar. So, if you will notice the first thing that leaps out at you is that about half of the you know die area is taken by the cache right. This is the L2 cache and if you look closely you can see the L1 L2 caches. So, why is it if you look at you know the history of die photographs you will see that the cache size goes on increasing and the reason is something called the von Neumann bottleneck right. So, what is essentially happening is that the speed at which your main memory is in a the speed of main memory DRAM chips is not increasing as fast as that of the microprocessor. So, you need this paraphernalia of the cache hierarchy, branch prediction buffers and all sorts of things just to keep the parts of the chip that do useful work busy. So, what are the parts of the chip that do useful work? So, one is the floating point unit that you can see below here right and the other is the arithmetic and logic unit.  So, if you consider these as the useful units one can make the case that barely around 10 percent of the die and consequently so many of the transistors are only doing quote useful work right the rest of the stuff is just to keep that part busy right. So, that does not seem like a very efficient way of doing things. So, you know what I am doing here is I am highlighting one of the significant inefficiencies in the way that microprocessor does computation and this comment is valid not only for big data, but in general right for whatever computation one does it is only around you know 10 percent of the area that is actually doing the useful work. So, coming specifically to big data there is a significant number of big data applications if not the majority of them that are streaming applications in the sense that you have lots and lots of data that you feed through and each data item is typically process just once right. So, search is a very good example of that you look at each byte of your data just once in general. So, such applications actually the cache itself is not very useful right. In fact, it is almost useless I would go as far as saying right. So, and there are many big data applications which do not have much floating points. So, you know the point is that for big data applications at least some big data applications a microprocessor is a significantly inefficient way of doing computation and that trend seems to be exacerbating. So, what does an FPG offer us instead? So, this I am just giving you so we will get into the details, but what I am trying to show here is you know given intuitive idea of what an FPG structure looks like. So, there is a you know huge chip and you can configure the what each of these logic elements does and you can get you know 10 to 100 times performance. So, the marketing slogan is hardware like speeds with software like flexibility. So, just to give a taste of it we look at the various building blocks. So, this is the key building block of an FPGA it is called a lookup table. So, it is essentially a 8 is to 1 MUX and at the data inputs I have connected SRAM cells right or you can think of these as flip flops and so supposing I write into these flip flops right I write these values then the only time when I will get a so and I give 3 inputs there right. So, these are 3 inputs this is the output.  So, essentially what I have done is I have configured a 3 input AND gate correct and if I made this 1 0 and put 1 bits all here I will have a 3 input OR gate right. So, that is the essence of FPGA logic configuration. So, by appropriately writing a configuration bits into SRAM cells you can make whatever logic gates one wants right and in current FPGA is the size of the MUX is 64 is to 1 right. So, you can make 6 is to 1 gates yeah. So, that is a 3 input AND gate there. The other element are switches. So, here again you have a couple of MUXes.  So, there are millions of such switches in an FPGA and yeah. So, writing these bits you can make connectivity ok. So, the third block is you know you just use the lookup table and you need sequential logic also. So, you add a flip flop there and the output you can take either via the flip flop or directly controlled by a configuration bit. So, one has a sequential logic also. So, this unit is called a logic block right. So, now coming back to the layout. So, an FPGA consists of a 2D array of these logic blocks and these are embedded in an interconnection network lots and lots of wires with lots and lots of switches in those switch matrix matrices. So, one can you know by writing appropriate configuration bits make it do whatever the application requires and this will be much faster right because it is low logic level. Yeah. So, it is surrounded by IO blocks and now I will give you some idea what the size of this thing is like. So, what I have shown here is a 3 cross 3 array. A modern FPGA has about a million such logic blocks. So, you can imagine this as a 3000 cross 3000 array right of logic blocks and sprinkled among these logic blocks you also have RAM blocks. Each of these RAM blocks can contain 32 k memory. So, you can make really fast finite state machines and stuff. These memory blocks are dual ported also and you have got these DSP units. So, there are these multiplier adder accumulator units. So, on one of the top FPGAs it does each of these does 25 cross 18 bit multiplies and there are few thousand of them on the device. So, essentially FPGAs are quite powerful beasts. So, long as one knows how to program them. So, let us look at programming them. So, essentially the process is the same as programming an ASIC or designing for an ASIC right. So, you start with the logic design in HDL, synthesize it, get the net list, do place and route. Then the last step is instead of getting you know fabrication mass as you would for an ASIC you get these configuration bits that you put on an FPG. So, time is from few minutes to several hours and just to give you an idea. So, HDL you know you will specify a full adder as it is shown here right in very log. So, your synthesis will convert into gate level net list right. This will give you the adder output, this will give you the carry out. So, when you do place and route you will get these configuration bits which is a bit sequence of the configuration bits. And finally you can you know configure it on your lookup tables. So, actually these two lookup tables if you look at the contents carefully do implement these two logic structures right. So, that is essentially how you program an FPGA. So, now moving on to big data I incited your curiosity by talking about Intel and Microsoft. So, let's talk about them. So, okay first of all let's look at how you would use an FPGA right. So, typically it comes on a PCI express board that slots into a standard slot in your motherboard. And via IO hub chipset it talks to the CPU which is connected to the DRAM. So, this works this is very efficient and the bandwidth is not a problem at all. If you look at the PCI express standard you got lots and lots of bandwidth right. So, this is the way currently things work and they work pretty well. What Intel is trying to do is the next step essentially they have announced that they are going to come up with a single package with the Xeon and the FPGA connected with the Intel Quickpath Interconnect. So, and this FPGA will have complete access to the entire Xeon memory hierarchy right. So, they have announced it they have released some boards to the FPGA R&D community and if things go well next year one is expecting to get you know some commercially available products based on this. Sorry, sure. As far as I understand they are both the same AMD came out with Hyper Transport first and then Intel used you know the engineers who had worked on DECAlpha and that group in Massachusetts I think they developed Quickpath. So, essentially both are point to point links for high speed bandwidth. So, Intel claims that they get 2x performance for this Xeon FPGA combo just because they are using Quickpath here. Yeah, so this reduces latency and really simplifies deployment right. You don't need to have a PCI express board. So, moving on to what Microsoft is doing I'll summarize it here. So, essentially they have also developed a mini board right on which they put an FPGA with around 8 GB of RAM and what they do is they use a rack of 48 servers. So, these are your standard 1U servers and on each of these servers they'll put one of these tiny daughter cards and they connect these in a 6 cross 8 2D array. They use high speed point to point SAS links each I think is around 10 GBPS to connect a pair of FPGAs and they've deployed this, they've done an experiment on around 1600 servers and what they were able to show is throughput doubling with only modest increases in total cost of ownership and power. So, they've been effective in demonstrating that for data center needs of high performance, low power, flexibility and low cost FPGAs can play a effective role. So, from what one hears Microsoft is now expanding these efforts. So, now I'll try to give you a brief idea of the power of these devices going back to a little bit of theory. So, you look at a regular expression right. So, this one says that it will match any string which is a string sequence of A's and B's and the last two characters C and D right. So, this is one example. So, one can you know convert this into a NFA right and so, I'll just show you the power of FPGAs by showing how easy it is to map this kind of logic structure directly onto the FPGA device right. So, all you have to do is you have to replace each of the states by a flip flop and you just put in these AND gates for the transitioning and you get a structure that will do the matching. By the way, I have put these colored you know things I didn't want to clutter the presentation. So, these are links. So, these contain papers and stuff in which you know you can find more references. So, you can download and click and you can get more details. So, I'll just skip this I'm running out of time. So, I'll just talk about little bit about what we have done. We have developed this XML processing solution. So, this uses something called tree automata which are better than pushdown automata and we use this board and I'll just skip the processing. So, this is a schema validation example and we compared it against you know software the fastest we could find running on an Intel Xeon and this is the result that we get. So, X axis shows the file size is from 64 K to 4 megabytes. Y axis shows the throughput. So, software is around 300 odd megabits per second and hardware goes up to around 3 plus gigabits per second. So, we are getting around a 10 X speed up using these FPGA devices. So, we are utilizing only so much resources on the FPGA and this is the current FPGA we are using and you get FPGA is as big as this. So, to conclude you can get dramatically more efficient computation but one needs certain amount of expertise in the area at present to leverage that but devices and tools are improving and these devices have the potential to you know be at least one of the workhorses for big data processing. I'm sorry about going over time. Thanks. Any questions?


The so-called French paradox is a term coined in the 80s by three Frenchmen to explain a curious finding. If you chart death from heart attack versus the amount of saturated fat in cholesterol countries consume, there appears to be a straight line. The more animal foods populations eat, the higher their death rates appear to be. Conversely, maybe if we got meat, egg, and dairy intake low enough, we could bring coronary death rates down towards zero. But two countries fell outside the line. Finland seemed to be doing worse than expected, and France appeared to be doing better, hence the paradox. How could France have similar saturated fat in cholesterol intake as Finland, but five times fewer fatal heart attacks? Everyone had their pet theories. Was it the whining? Was it the dining? Yes, animal foods were associated with coronary heart disease mortality, but plant foods appeared protected. So maybe the fact that the French reading four times as many vegetables helps account for their lower death rates. But it turns out there's apparently no paradox at all. As Mary Nessel stutely pointed out, the French had just recently started eating so unhealthy and chronic diseases take decades to develop.  It's like we all started smoking today and found no measurable increase in lung cancer tomorrow. It wouldn't mean smoking doesn't cause lung cancer, it just takes a while. So what happens when you actually run the numbers? If you compare coronary death rates to the amount of animal found cholesterol levels at the time, France does seem unusually protected. And if you compare death rates to what they were eating two decades before, they're still pretty far off the line. Ah, but it turns out French physicians under-report a schemic heart disease deaths on death certificates by as much as 20% according to a World Health Organization investigation.


Using simulations of motion in an asymmetric potential, we've seen that anharmonic oscillations are characterized by two properties. First, the average distance between atoms increases with energy, explaining the phenomenon of thermal expansion. Second, as total energy increases, the fraction due to average potential energy decreases, while the fraction due to average potential energy increases. Therefore, at a given temperature, the total energy of the anharmonic oscillator is greater than the total energy of a harmonic oscillator. They have the same average kinetic energy, but the anharmonic oscillator has more average potential energy. We can use statistical mechanics to calculate the heat capacity of our anharmonic oscillator. Following the statistical mechanics video of this series, we write the total energy as a function of momentum and position. E equals p squared over two plus x squared over two minus bx cubed over three. For simplicity, we are taking the mass and spring constant to be one. We know that the probability that momentum and position will take on particular values at a particular temperature is given by a decreasing exponential function of energy. We write f of p x equals one over z e to the minus p squared plus x squared over two kt times e to the bx cubed over three kt. If b is very small, then for the x values of interest, the exponent of the last factor is very small. Then we can use the approximation e to the u equals one plus u plus one half u squared to write the last factor as a sum of three terms. The value of the partition function z is that which makes the probability summed or integrated over all p and x values equal to one. We find z equals two pi kt plus five pi b squared over three times quantity kt squared. Then we calculate the expected value of total energy as the integral over all p and x of f of px times e of px. To second order in temperature, we obtain kt plus five b squared over six times quantity kt squared, replacing kt with two times kinetic energy and adding a plot of this function to our previous graph. We see that it matches our experimental values quite well, except at very large energy. Those double integrals are a bit intimidating. However, all the calculations can be done with a computer algebra tool. This page shows how using the open source tool maxima. Given the average energy of a single oscillator, the molar heat capacity is Avogadro's number about six times ten to the twenty-three times the rate of change of energy with respect to temperature. This works out to the gas constant r times one plus five b squared over three times kt. Our prediction is that heat capacity should increase above the classical value r linearly with temperature. Here are the molar heat capacities of lead and aluminum as a function of temperature from roughly room temperature at 300 Kelvin up to 600 Kelvin. The lead curve is quite linear. At higher temperatures, the aluminum curve is also. The dotted horizontal line is the doolong peti value. The Dubai temperatures are 87 Kelvin for lead and 390 Kelvin for aluminum. This allows us to understand why the aluminum plot is curved near room temperature while the lead plot is not. 300 Kelvin is well above the Dubai temperature of lead, so its vibrational degrees of freedom are completely thawed and heat capacity follows the classical model with the anharmonic linear correction. But 300 Kelvin is below the Dubai temperature of aluminum, so its vibrational degrees of freedom are not completely thawed out. Only as temperature exceeds the Dubai temperature does the graph transition to the linear behavior we have predicted. We now have a complete explanation for the heat capacities of solids and gases. The classical prediction is the gas constant over two times the number of quadratic degrees of freedom. The horizontal orange lines in these graphs. Values below this can be understood as due to quantum mechanical freezing of vibrational and possibly rotational degrees of freedom. Values above this can be understood as due to the effects of anharmonic oscillation. Let's return to the central idea of the third law, entropy at absolute zero temperature. According to Boltzmann's principle, the entropy of a system is Boltzmann's constant times the natural logarithm of gamma, where gamma is the number of ways to arrange the system. So the third law is fundamentally a statement about the number of ways to arrange a system at absolute zero temperature. Thinking quantum mechanically, a system has various possible energy states. At absolute zero, it must be in its ground state, the state of lowest energy, since there is no thermal energy available to excite higher energy levels. Let's denote entropy at absolute zero by S0. If S0 equals zero, then gamma must equal one. There is only a single way to arrange the system, and we say the ground state is non-degenerate. Generally speaking, perfect elemental crystals, crystals of a single element with no crystal defects, have non-degenerate ground states. Other substances may or may not have degenerate ground states. This means that gamma equals an integer GGS greater than one, the degeneracy of the ground state. Then S0 is greater than zero. A classic example of non-zero entropy at absolute zero is the so-called residual entropy of ice. In the chemistry video of the quantum mechanics series, we discussed how the four highest energy level electron orbitals of oxygen tend to form a tetrahedral structure. Two of these orbitals are filled with two electrons, while the other two contain only one electron. The unfilled orbitals strongly bond to hydrogen atoms, creating the water molecule. In the common form of ice, the oxygen atoms form a crystal lattice based on this tetrahedral geometry. In between each pair of oxygen atoms lies a hydrogen atom. If these were positioned symmetrically, as shown here, there would be no degeneracy in the ice crystal. Instead, each hydrogen atom is chemically bonded to a single oxygen atom. The result is that two of the four tetrahedral directions will be occupied by chemical bonds with hydrogen atoms. The other two directions will form weaker so-called hydrogen bonds with an adjacent hydrogen atom, which itself is chemically bonded to a neighboring oxygen atom. The ground state degeneracy of ice arises from the different ways these two chemical and two hydrogen bonds can be oriented. Here are the six ways that two hydrogen atoms can be chemically bonded to an oxygen atom. Notice that for any one of the tetrahedral directions, a blue bar, half of the arrangements have a chemically bonded hydrogen atom and half do not. If this was our only consideration, then each water molecule in the crystal would have six possible arrangements. Therefore, a crystal with n molecules would have a degeneracy of six to the n. But the arrangement of each molecule has to be consistent with the arrangements of the neighboring molecules. If we place a chemical bond on one of the tetrahedral directions, the probability that the neighboring molecule will not also have a chemical bond in this direction is one-half. For two chemical bonds, therefore, the probability of being compatible with two neighbors is one-half times one-half equals one-fourth. Therefore, the average number of arrangements for each molecule is six times one-fourth equals six-fourths. So the number of ways to arrange n molecules of water in an ice crystal should be gamma equals six-fourths to the n, which is three-halves to the n, and S0 should equal K log three-halves to the n, which can be written Kn log three-halves. If n is Avogadro's number, then Kn is the gas constant R. In 1935, Linus Pauling used this logic to argue that the residual entropy of ice at absolute zero should be R log three-halves, equal to 3.37 joules per mole Kelvin. In 1936, Geo and Stout used measurements and calculations to establish the actual residual entropy as 3.4 joules per mole Kelvin, in excellent agreement with Pauling's prediction. Let's finish by attempting a rigorous formulation of the third law of thermodynamics. The origin of the third law was the heat theorem presented by Walter Nernst in 1906. We will express this as follows. The entropy change in an isothermal process approaches zero, as the temperature does. Mathematically, the limit as temperature approaches zero, of the entropy change of any process is zero. Suppose we represent the thermodynamic state of a system on an ST diagram, entropy versus temperature. Suppose u is some parameter, such as pressure or volume, or even say, magnetic field strength. Then u equals u1 forms a curve on the ST diagram, and u equals u2 forms another curve. At a given temperature, an isothermal process in which u changes from u1 to u2, while t remains constant, is represented by a vertical line between these two curves. The vertical distance between the curves is delta S, the change in entropy due to the process. The heat theorem implies that these curves get closer together as temperature decreases, so the entropy change decreases. Ultimately, the curves converge to the same value, S0, at t equals zero. This is the entropy at absolute zero, which is independent of the value of the parameter u. Moreover, the slope of the S versus t curve, dSTt, is equal to heat capacity over temperature. According to the Einstein and Debye models, this goes to zero as the temperature does. So the two curves not only converge to the same value, they converge to the same zero slope. Now, imagine the following strategy for cooling a system to absolute zero. The parameter u can represent any physical quantity, but as a specific example, assume u is volume, and u1 is greater than u2. We start with the system at volume u1, and in contact with a heat reservoir at some temperature. Then we compress the system to volume u2. The work done in the system will be converted to heat. To maintain a constant temperature, this heat will be transferred to the reservoir, which will lower the entropy of the system. Then, we insulate the system and allow it to expand back to volume u1. During the expansion, the system will do work on its environment. But this will lower its internal energy and enhance its temperature. Because no heat is transferred, the entropy will remain constant. Now, we repeat this process, starting at this lower temperature. Because the u1 and u2 curves converge, these steps will get ever smaller, and no finite number of such steps will ever get us to absolute zero. This type of reasoning led Nernst in 1912 to formulate the unattainability principle. No process can reach absolute zero temperature in a finite number of steps and in a finite amount of time. The heat theorem implies the following form of the third law, as stated by Charles Kittle in his textbook, Thermophysics. The entropy of a system has the property that S goes to S0 as T goes to zero, where S0 is a constant independent of the external parameters which act on the system. A special case was stated by Max Planck. For a perfect elemental crystal, the limit as T goes to zero of the entropy is zero. And this is true for any finite value of an external parameter u. In video five of this series, we derive the Sacher-Tetrot equation, which gives the absolute entropy of a monatomic gas. Experimentally, absolute entropy is determined by starting with the assumption of zero entropy at zero temperature, consistent with Planck's statement of the third law. Then experimental entropy increases or sum from zero to the temperature of interest. The excellent agreement between experiment and the predictions of the Sacher-Tetrot equation confirm the validity of the assumption of zero entropy at zero temperature. Planck's formulation of the third law, therefore, is firmly established experimentally. However, it is not a general statement, but only applicable to the special case of a perfect elemental crystal. In the early 1900s, Nernst, Einstein, and others vigorously argued the foundations of what we now call the third law. Einstein questioned if the concept of an isothermal process at absolute zero is possible, even in principle. He agreed that the heat theorem implies the entertainability principle, but he denied the converse that the entertainability principle implies the heat theorem. Einstein also argued that the entropy at absolute zero could not, in general, be a constant independent of any external parameters. Let's look at a cartoonishly simple thought experiment. Suppose we have N atoms. Half are of one type, with a larger mass pictured as a solid. The other half are of another type, with a smaller mass pictured as cross-hatched. Suppose the atoms do not form chemical bonds, but act like loose balls. Alternately, suppose both types have identical chemical properties. In either case, they will not sort themselves by some chemical process. Let our external parameter be a downward-pointing gravitational field. The system is held at a temperature high enough that individual atoms can diffuse throughout the system. The atoms will tend to separate with the heavier toward the bottom and the lighter toward the top. Then, assume the temperature is lowered very slowly to absolute zero, so the structure is frozen in place. The system will freeze in its lowest energy state, one in which all heavy atoms are on the bottom and all light atoms are on the top. Since each atom is indistinguishable from the others of the same type, there is only gamma equals one way to arrange this system. Now, repeat the process with the external parameter set to zero. No gravitational field is present. The system will freeze into an arrangement in which the atoms are randomly positioned. For large n, the number of possible arrangements is gamma approximately equal to two to the n. So, the entropy of this system at absolute zero depends on the value of the external parameter, the gravitational field. With a gravitational field, the entropy is zero. Without a gravitational field, the entropy is approximately kn log two. Therefore, Einstein argued, although using more sophisticated thought experiments, the heat theorem cannot be true in all conceivable cases. Indeed, it is stated by a recent paper in Nature Communications titled, A General Derivation and Quantification of the Third Law of Thermodynamics. One can easily find families of Hamiltonians, which satisfy or violate the heat theorem. So, the heat theorem and the associated statement of the third law is given by Kittle. Cannot be true in general, although they may be in situations actually observed to date in the laboratory. This has led some authors to argue that the third law is a statement about the kinds of systems actually encountered in Nature, excluding systems which are theoretically possible, but not actually observed. In this view, the third law, unlike the first and second laws, cannot be established from first principles. It does not have a solid theoretical basis. In the Reference Nature Communications paper, the authors use quantum information theory to argue that, even though the heat theorem is not absolutely valid, the entertainability principle is. When analyzed quantum mechanically, the entertainability principle does not depend on the heat theorem for its validity, as Nernst had argued. Consistent with this, to date, no experiment has reached absolute zero. And no one has yet put forth a sound theoretical strategy for doing so. As a result, the entertainability principle stands as the most widely accepted general formulation of the third law. In light of these considerations, a reasonable conclusion, given in a paper on the early history of the heat theorem and third law, is that the third law is of a different, less fundamental, and more experimental character than the other laws of thermodynamics.


Good morning, good afternoon, or good evening. You can go to Lovie in France, but you cannot become a Frenchman. You can go to Lovie in Germany or Turkey or Japan, but you cannot become a German or Turk or Japanese. But anyone from any corner of the earth can come to Lovie America and become an American. Welcome back to A Nation of Immigrants, a bi-weekly interview program featuring the lives of immigrants, knowledge, diversity, and inclusion created by Sinh-Tang Hawaii and Kingsfield Law Office. We invite renowned immigrants to discuss their life stories, immigration adventures, and their contributions to cultural diversity. Today's guest is Professor Liu An-Ting, professor and founding director of the Immigration and Deportation Defense Clinic and Dings Circle Scholar at the University of San Francisco School of Law. Thank you so much to be on the show, Professor Hing. We are very honored to have you. Thank you for the invitation. I really appreciate it. Yeah, it's from your name, and I happen to search your last name in Chinese language and it appears your last name is Deng. It's a quite popular Chinese last name and you are the professor of law and immigration studies at the University of San Francisco and professor of law and Asian-American studies Emeritus at UC Davis. You previously on the law faculties at Stanford University and Golden Gate University and you founded the Immigration Legal Resources Center in San Francisco and direct the Immigration Deportation Defense Clinic. And you have a tremendous scholarship, you have published many books, and also you teach immigration law and policy, migration studies, rep, rebellious, lowering, that's very, very intriguing course and evidence. And your most recent book is a humanizing immigration, how to transform our resist and unjust system that's very, you know, harsh criticism of our immigration policy, but you have co-consult in the United States Supreme Court, asylum, president setting case INS versus Cadoza, Paseca. That's an extremely impressive resume, Professor Hing, and I have hundreds of questions I would like to ask you, but let's start with your cultural identity.  That's correct. My father was born in mainland China, in Guangzhou province, actually near Poisan, but actually were from Kuipeng, and my mother is actually curious of the situation. She was actually born in Scranton, Pennsylvania in 1901, but then when she was two or three years old, her mother took her back to China to care for the grandmother. And so my mother really, she was born in the United States, but she didn't re-immigrate until after she married my father in the 1920s. Didn't she have the birth right to be a U.S. citizen? Should she just apply? That's a very good question. And the answer is actually no, because there was a period of... Your question is well-founded, because usually when you're born in the United States, you are a U.S. citizen, and there was a U.S. citizen because she was born in Pennsylvania. But there was also a law when she got married to my father in 1921. There was a law in the United States that if a citizen woman married a foreign male, the U.S. citizen woman would lose her U.S. citizenship. That applied to all women who were U.S. citizens who married foreign nationals, not just Chinese. So technically, she lost her U.S. citizenship when she married a foreign national. Unbelievable. So because this law, you mentioned, this law applied to all female U.S. citizens. So I assume that was not a Chinese Exclusion Act related. No, it was not. It was a very gender-sexist law in part of the United States. The belief was that, well, if you're a woman and you marry somebody, well, then you've got to follow your husband. And so if he's a citizen of Germany or Mexico, well, then you should go with your husband. You're no longer a U.S. citizen. Interesting. I have to say that it appears our immigration system was not only resist, but also sexist. Sexist, at least for a certain period of time. Very much so. So when your parents got married? They got married in 1921 in China. My father had immigrated to the United States about 1915. Incidentally, with false papers, we can come back to that if you want. Paper songs, OK. And so he made a false claim to U.S. citizenship. But after he lived here for about five years, he decided to go back to China to find a bride, which was common. And because there were not many women, not many Chinese women in the United States, a bachelor society. And so he went back to China and through a marriage broker, a matchmaker. He met my mother and they got married in 1921 in China. In Guangzhou? Yes, Guangzhou. The 21, that was the warlord period, wasn't it? China was not in very good shape, but relatively it's better than the late Qing Dynasty. And you were born in the 40s, I believe? Yes, so I'm the youngest of... Hold on to your seat, OK? I'm the youngest of 10 children. Wow. Yeah, I was born in 1949, but my oldest sister who has passed away, she was actually born in 1926. So there's a wide span of my brothers and sisters in terms of age. Well, happy for you, you have a very big family. I only have one sister and I hope I had more. I really would love to have a brother. But anyway, so all your siblings were born in the United States? From Chinese? No, actually, in the early 1930s, before I was born, my parents, my mother, went back to China to take care of her mother. Oh, you see. So she took the first four children with her. And then my father went to visit and my mother got pregnant. And so actually the fifth child in the family was actually born in China. But every... and then they returned because of World War II. As you know, Japan started bombing China. And so they fled, I think the date was 1938, that they fled China on a boat to come back to the United States. Well, glad they came back. It's 38, it was a very horrendous years in China. And you were born in the United States and you were educated in the United States. And your work and your study focused on your scholarship, focused on immigration policy and the race relations. So obviously we can understand that as a second generation immigrant, you have some interest in immigration policy and also because of your parents were first generation immigrants. And my impression is that very few scholars really study the interdisciplinary arena of immigration and race relations. How do you see these two issues? They are vitally important. They are two of them. I think they are probably two most important issues in the United States right now. But how do you see the two issues intersecting and what impact do they have on our society? Yeah, that's a very good question. The way I see it is that immigration law unfortunately has a lot of racist provisions that were part of the law. And you already alluded to one. The first one was the Chinese Exclusion Act in 1882. It's specifically racist. And so I could see the effect of that in my own family and our relatives, of course, because of paper stunts and wrong different surnames and that type of thing. But I also saw that where I grew up. I actually grew up in Arizona in a small copper mining town. And many of my friends and relatives, many of my friends growing up, my classmates, were of Mexican descent. And so looking back on that, I believe that some of those families were undocumented. And then when I started practicing immigration law, I realized that at that time in the 1970s, much of the focus for deportation was directed at Mexicans. And so it was very the enforcement priorities back then were focused on the deportation of Mexicans and the Mexico-U.S. border back then in the 1970s. And anyone who's paying attention to immigration law today, we know that the same border is a big challenge. It's not so much Mexicans anymore, but it's other Central Americans and people from all over the world. So my answer is that it's very clear that the laws and the enforcement priorities have picked on certain immigrants of color. And so that's the relationship between the law and racism. Thank you very much for your explanation, Professor. And this makes perfect sense to me. As an immigrant, I studied law because I've studied civil rights. So race theory, critical race theory and a civil rights moment in the United States. And I can intuitively feel that there is a connection and I couldn't as articulate as you just said, I really appreciate that. As lawyers and a law professor, we probably all very much following what's happening with the United States Supreme Court. So I was very impressed to know that you co-counsel in the NS versus Calzada for a second case. And that case was about asylum, I believe, and well-founded fear. And could you just tell our audience that how did you get involved in the case? And so some in their working of the co-counseling in a Supreme Court case. And what impact did this case had on immigration law and protection of asylum seekers in the States? Yeah, thank you for the question. Coroza Fonseca is the major Supreme Court case where the court determined what is the burden of proof when somebody applies for asylum? In other words, how much proof do they need to introduce to be granted asylum? And the term that is used in the immigration laws is do you have a well-founded fear of persecution? Nobody knew what that meant. And so the case was being handled by a friend of mine who volunteered to represent this woman from Nicaragua who wanted to apply for asylum. And it was very soon after the 1980 Refugee Act was passed in Congress. And that's when this term well-founded fear of persecution was made part of the immigration law. So it's the first case that made it to the Supreme Court. And this woman, Dana Marks, who later became an immigration judge, this was her first immigration case. She was volunteering. So she asked me to help her. And so the two of us and we recruited two of our other very smart friends to write the brief because we won. I argued the case at the Ninth Circuit Court of Appeals. And won. But then the government appealed to the Supreme Court. And of course, Dana wanted to argue the case at the Supreme Court. And I didn't get a chance to argue the case, but I helped to write the brief. And so the importance of the case is that this is unbelievable, is hard for people to understand that what well-founded fear of persecution, the Supreme Court said, it should be a humanitarian interpretation. It's not like a regular court case. And again, I'm going to be technical very quickly here. But lawyers are familiar with two standard types of burden of proof. One is a preponderance of the evidence. And the other is beyond a reasonable doubt. And your listeners all know beyond a reasonable doubt, that's in a criminal case. And so that requires a lot of evidence for the government to prove that somebody is a guilty of a crime. Preponderance is usually what's used in a civil case. Like if a car accident or a contract dispute, you have to show by a preponderance. And again, without getting too technical, usually preponderance means you have to show like 51% likelihoods, more likely than not, that the red car ran the red light, or that the defendant breached the contract. You have to show 51% proof that the contract was broken. Well, the Supreme Court said, neither one of those apply. It should be more generous. And the Supreme Court said, it should be only 10% likelihood that if there's a reasonable possibility of persecution, then the person should be granted a silent. And they define that as if there's just a one in 10 chance that you're going to be persecuted, it should be granted. And so that's the law today. Now I think many judges in the government, they usually require too much evidence, but that law has never been changed. The Supreme Court has never reversed its decision. So, you know, being part of the litigation team, we're very proud of that decision, because it means that you're supposed to give the benefit of a doubt to the applicant. And that makes sense, right? Because if you're going to make a mistake in an asylum case, then the mistake should be let the person stay, even if they're not going to be persecuted. That should be the mistake. The mistake should not be deport somebody, even somebody who is going to be persecuted and they might get killed, right? You should err on the side of being careful and being beneficial and humanitarian to the asylum applicant. Well, you should be very proud. We are very proud for you because this case, and your participant and co-consult, it's a Supreme Court president potentially saved a thousand or thousand people's lives. And fortunately, that was run with court, wasn't it? In the 1980s. Yeah, it was. And we got, believe it or not, we got six to three, including Scalia. Scalia joined the majority. He was in a concurrence with the majority. Okay, the majority was authored by Justice Stevens, believe it. Stevens, yeah. We're very proud of that, you're right. Oh, your brief must be so well-crafted and Scalia liked it. He liked the well-crafted, well-reasoned legal argument. And very happy for you. And I'm not sure this court, this current Roberts Court, would have the same outcome. In the run-with court, even they have so many controversies, but the justices, they are more of them or were very serious about the law. They really look at the law, and you just articulated so compellingly and exactly why they should be in your favor. Because what is at stake here? Moving fast forward to today, and you just released a book, Humanizing Immigration. You argued that immigrant and refugee rights are part of the fight for racial justice. You have already elaborated that during our previous questioning, question and answer. And in this book, you offer a humanitarian approach to reform and abolition of the immigration system. So that's quite a shocking word. Could you elaborate the word abolition? And are you arguing the same as defund the police and as you're arguing abolishing the immigration system? That's a very, very good question. Am I equating abolish ICE with defund the police? And honestly, I am. And I think we need to tear down the system and start all over again. I know that there needs to be immigration laws. I understand that. But as I said at the beginning, the way the law has been enforced and what is contained in the laws, it's been racial. And let me give you an example. In particular, the Kurdish Asian immigrants. Right now, there's a backlog for people who want to immigrate from China. People want to immigrate from the Philippines, from India, from South Korea. There's backlogs. And the reason why there are backlogs is that they treat the immigration laws, treat all those countries the same in terms of numerical limitations about 26,000 visas each year. It shouldn't be that way. It was started in a racist way. Back when the Chinese Exclusion Law passed in 1882, that was soon followed by restrictions on Japanese immigrants through an international agreement. Then there was something called the Asia-Pacific Triangle that excluded other Asians. And then finally, it made it clear in the 1920s that Asians were not even eligible to apply for naturalization. So immigrants couldn't move from Asia, couldn't get naturalized. And so that's what the system was based on. And so that's why there's such a long backlog in some situations, 15 to 20 years. And so that's why, to me, you have to totally redo the visa system so that do it on a first come, first serve basis. People apply. And I know there has to be a numerical limitation, but it should go by demand. If there's high demand from Asian country, well, then they should get more visas. We should go by demand. That wasn't my idea. That idea, believe it or not, was John Kennedy's idea. Oh yeah, of course. The president, he was very egalitarian and he thought there should be a first come, first serve system. He was actually embarrassed by the Chinese exclusion law. And it's too bad that his vision never got instituted. He was assassinated before he could push through his vision. So that's where I'm borrowing that idea from, redo the system. So he would have said the same thing. He would say abolish the visa system and do it over. Well, that's quite a proposal. I just hope that some radical changes, some major changes will come to immigration because at this time it appears that everybody know the immigration system is not working and there are major flaws in the system, but it's just too big to transform is to change it. And let's hope that your scholarship, your advocacy, and your colleagues, and we all work together to appeal to the elected officials. Then they are finally going to reach the conclusion that something must be done. Otherwise, the consequences are going to hurt our economy and hurt us as a nation of immigrants. We normally have two generic questions for our distinguished guest. And we have like five minutes left, but I do want to ask you these two questions, Professor. The first question is about time travel. Time travel permits. And if you have the opportunity to offer advice to your younger self in your 20s, what advice would you share? Well, you know, I have been very happy and blessed in my life. And I think my main advice actually doesn't have much to do with the professional side of my life, but it's to make sure that your family is your most important priority. Because at the end of the day, no matter how much money you make or how many books you may write, the most important thing is family because you don't want to have a lonely existence. You want to have good friends and family. And so I would tell my younger self to make sure to take the time to love your family and enjoy your time with them because that's much, much more important than fame or fortune. Thank you so much. That's very good advice. And if I were in my 20s, I would definitely listen to your advice. That will be the same advice I would give to myself in a younger self. I have a much smaller family than yours, but I totally understand the importance and the ultimate importance of family and friendship played in they are playing much larger roles than work, I would say, and everything else. Last question, Professor Hing. Are there any specific books or movies have deeply moved you, impressed you, and you would like to recommend to our audience? Feel free to recommend your own books. I'm going to recommend to the audience a new book, Humanizing Immigration. Thank you for recommending that. One of the books that I recently read, even though it's not a new book, I think it was written about seven years ago, six, seven years ago, it's a book by another Asian American woman, although her name is Duckworth, Angela Duckworth. Duckworth, okay? Yeah, she's a psychologist, and she wrote a book called GRIPT, G-R-I-T. What's important about that book is that she writes very convincing that it's not so much about talent or whether or not you have natural ability. And I think Asians are going to like what I'm going to say. It's about hard work. It's about passion and work. Now, again, I'm reminding people, the family is important. But if you put... It's not that... You're always going to meet somebody smarter than you, but one thing that you can always do is you can work harder. And she measured the fact that the people who worked the hardest, no matter what talent level they had, they actually accomplished the most. And so I would say don't give up. Keep your nose through the grindstone and you will be rewarded. Fantastic advice. 10,000 hours. I always remember that. Malcolm Gladwell in his book, Tipping Point basically argued that no matter which area, which discipline you have to devote 10,000 hours of your life, put into it and you will accomplish. Well, it is a fantastic experience to meet you, Professor Hin, and to ask you all these questions. I'm extremely grateful for your scholarship and a contribution to Immigration Law. Thank you again to be our guest. We look forward to continue to radio scholarship and hopefully we can invite you back on the show to discuss your next book and the next case. Thank you, Professor Hin. Thank you very much. Thank you. Aloha. Aloha. Thank you so much for watching Think Tech Hawaii. If you like what we do, please like us and click the subscribe button on YouTube and the follow button on Vimeo. You can also follow us on Facebook, Instagram and LinkedIn and donate to us at thinktechhawaii.com. Mahalo.