Graph kernel: Kernel methods are a popular method with broad applications in data mining. In a simple way, a kernel function can be considered as a positive definite matrix that measures the similarities between each pair of input data. It the currently study, a graph kernel method, namely shortest-path kernel, developed by Borgwart and Kriegel, is used to compute the similarities between graphs. The first step of the shortest-path kernel is to transform original graphs into shortest-path graphs. A shortest-path graph has the same nodes as its original graph, and between each pair of nodes, there is an edge labeled with the shortest distance between the two nodes in the original graph. In the current study, the edge label will be referred to as the weight of the edge. This transformation can be done using any algorithm that solves the all-pairs-shortest-paths problem. In the current study, the Floyd-Warshall algorithm was used. Let G1 and G2 be two original graphs. They are transformed into shortest-path graphs S1(V1, E1) and S2(V2, E2), where V1 and V2 are the sets of nodes in S1 and S2, and E1 and E2 are the sets of edges in S1 and S2. Then a kernel function is used to calculate the similarity between G1 and G2 by comparing all pairs of edges between S1 and S2. where, kedge( ) is a kernel function for comparing two edges (including the node labels and the edge weight). Let e1 be the edge between nodes v1 and w1, and e2 be the edge between nodes v2 and w2. Then, where, knode( ) is a kernel function for comparing the labels of two nodes, and kweight( ) is a kernel function for comparing the weights of two edges. These two functions are defined as in Borgward et al.(2005): where, labels(v) returns the vector of attributes associated with node v. Note that Knode() is a Gaussian kernel function. was set to 72 by trying different values between 32 and 128 with increments of 2. where, weight(e) returns the weight of edge e. Kweight( ) is a Brownian bridge kernel that assigns the highest value to the edges that are identical in length. Constant c was set to 2 as in Borgward et al.(2005). Classification and cross-validation When the shortest-path graph kernel is used to compute similarities between graphs, the results are affected by the sizes of the graphs. Consider the case that graph G is compared with graphs Gx and Gy separately using the graph kernel: If Gx has more nodes than Gy does, then |Ex|>|Ey|, where Ex and Ey are the sets of edges in the shortest-path graphs of Gx and Gy. Therefore, the summation (i.e., SS( ) ) in K(G, Gx ) includes more items than the summation in K(G, Gy) does. Each item (i.e., kedge( )) inside the summation has a non-negative value. The consequence is that if K(G, Gx)>K(G,Gy) it may not necessary indicate that Gx is more similar to G than Gy is, in stead, it could be an artifact of the fact that Gx has more nodes than Gy. To overcome this problem, a voting strategy is developed for predicting whether a graph (or a patch) is an interface patch: Algoritm Voting_Stategy (G) Input: graph G Output: G is an interface patch or non-interface patch Let T be the set of proteins in the training setLet v be the number of votes given to “G is an interface patch” v=0 While (T is not empty) { Take one protein (P) out of T Let Gint and Gnon-int be the interface and non-interface patches from P. If K(G, Gint)>K(G,Gnon-int), then increase v by 1 } If , then G is an interface patch Else G is a non-interface patch Using this strategy, when K(G, Gint) is compared with K(G, Gnon-int), Gint and Gnon-int are guaranteed to have identical number of nodes, since they are the interface and non-interface patches extracted from the same protein (see section 2.4 for details). Each time K(G, Gint)>K(G, Gnon-int) is true, one vote is given to “G is an interface patch”. In the end G is predicted to be an interface patch if “G is an interface patch” gets more than half of the total votes, i.e.,. Leave-one-out cross-validation was performed at protein level. In one round of the experiment, the interface patch and non-interface patch of a

Chapter 2: Operating-System Structures Operating System Services User Operating System Interface System Calls Types of System Calls System Programs Operating System Design and Implementation Operating System Structure Operating System Debugging System Boot Operating System Concepts – 9th Edition 2.2 Silberschatz, Galvin and Gagne

Question #2 How is a popcorn kernel created? • • • • • • • • • It is important to know that there are many different types of corn. There can be field corn, flour corn, sweet corn, and flint corn to name a few. The scientific name for popcorn is Zea Mays Everta, and this is the only type of corn that will pop. A popcorn seed has three main components to it’s make up. The first component is known as the “endosperm” which is made of soft and hard starch granules. This helps to give energy to the living part of the kernel which is the second main component of the kernel and is referred to as a “germ”. The third component is the “pericarp” or the outside of the kernel which is made of cellulose. Plant breeders use inbreeding to make popcorn seeds. It takes years and years of inbreeding until there are select desirable traits for the popcorn seeds. When this occurs, two inbreds are crossed together which produce a hybrid. This hybrid is then planted as a popcorn seed. Much of the popcorn is grown here in the United States in states such as Iowa, Kansas, and Kentucky. Every spring popcorn seed are placed 1 ½ inches deep and are six inches apart. The seed will germinate in about seven days and will appear from the soil at around ten days. A popcorn plant will grow to about eight feet and contain long, green leaves. The plant will produce ears of corn and will catch pollen that allow the ears to produce kernels. The growing process will not cease until the entire plant is brown and dry. Where there is a black layer on the kernel, and it is hard, this means the kernel has reached it’s full maturity. This shows that the kernel does not require nourishment from the plant. A kernel needs to be harvested when it has a moisture content of 16- 20% because this moisture is what causes the kernel to pop. The ears will be collected and taken to steel cribs that have open slots to maximize drying. Ears are normally stored for eight to twelve months to ensure there has been enough drying time. The optimum moisture level of a popcorn kernel is 14%. Once the kernel is cleaned and polished, it is ready for packing.

Monolithic vs. Microckernel Design MS-DOS, UNIX, and most other OSes implement what is called a monolithic design Kernel is relatively large, contains much code File systems, device drivers, etc. are all run with full kernel privileges Hybrid/Layered systems are still largely monolithic designs! MINIX and Mach are examples of a Microkernel design Kernel is small, delegates much of core system functionality to user-space daemons Typically only the most core functionality is implemented in kernel space Process and memory management, message passing Benefits include that it is easier to extend/port a microkernel, more reliable/secure (less code is running in kernel mode) Detriments include performance overhead of user space to kernel space communication Operating System Concepts – 9th Edition 2.38 Silberschatz, Galvin and Gagne