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Fully-connected

The obvious solution to the limitations imposed by shared bus communications is to fully connect each processor to all other processors via dedicated pathways. The problem is that the number of such pathways grows rapidly, N N — l)/2, where N is the number of processors. The inherent costs and complexity of such a system render it an impractical solution for large-scale parallel computing. [Pg.95]

Recurrent networks are based on the work of Hopfield and contain feedback paths. Figure 10.23 shows a single-layer fully-connected recurrent network with a delay (z l in the feedback path. [Pg.350]

Stimulated by these observations, Odelius et al. [73] performed molecular dynamic (MD) simulations of water adsorption at the surface of muscovite mica. They found that at monolayer coverage, water forms a fully connected two-dimensional hydrogen-bonded network in epitaxy with the mica lattice, which is stable at room temperature. A model of the calculated structure is shown in Figure 26. The icelike monolayer (actually a warped molecular bilayer) corresponds to what we have called phase-I. The model is in line with the observed hexagonal shape of the boundaries between phase-I and phase-II. Another result of the MD simulations is that no free OH bonds stick out of the surface and that on average the dipole moment of the water molecules points downward toward the surface, giving a ferroelectric character to the water bilayer. [Pg.274]

It is helpful in the discussion to describe silicate structures using the Q nomenclature, where Q represents [SiOJ tetrahedra and the superscript n the number of Q units in the second coordination sphere. Thus, isolated [SiO ] " are represented as Q and those fully connected to other Q units as Q. In general, minerals based on Q , Q and units are decomposed by acids. Such minerals are those containing isolated silicate ions, the orthosilicates, SiO (Q ) the pyrosilicates, Si O " (Q ) ring and chain silicates, (SiOg) (Q ). Certain sheet and three-dimensional silicates can also yield gels with acids if they contain sites vulnerable to acid attack. This occurs with aluminosilicates provided the Al/Si ratio is at least 2 3 when attack occurs at A1 sites, with scission of the network (Murata, 1943). [Pg.114]

Many gallophosphate molecular sieves are unstable to calcination in air or in the presence of moisture, which limits the utility of these materials because at room temperature (RT) the templates stuff the channels thus limiting access to the porosity. The ULM-16 structure is interesting because it is stable to 800°C under argon and up to 350-400°C in the presence of oxygen. This stability may reflect the fully connected framework of ULM-16. [Pg.243]

The frameworks of zeolites are formed by fully connected Si04 and A104 tetrahedra linked by shared oxygen atoms as shown in Figure 4.1 (top) for a Faujasite-type zeolite. Faujasite is a zeolitic mineral, which can be found in nature. Synthetic Faujasite-type zeolites are of particular importance in zeolite catalysis as we will see below. [Pg.97]

A feedforward neural network brings together several of these little processors in a layered structure (Figure 9). The network in Figure 9 is fully connected, which means that every neuron in one layer is connected to every neuron in the next layer. The first layer actually does no processing it merely distributes the inputs to a hidden layer of neurons. These neurons process the input, and then pass the result of their computation on to the output layer. If there is a second hidden layer, the process is repeated until the output layer is reached. [Pg.370]

In the above MILP-optimization problem, Euler s theorem for the generation of stable and feasible molecular structures (fully connected graphs) needs to be added as a condition in order to ensure the generation of chemically feasible molecules. This condition is mathematically formulated as,... [Pg.91]

Network A is designed to funchon as an information processor when each basic system can be seen as a node or a biochemical neuron in the network. In this network, cofactors A and B are shared by all the biochemical neurons of the network. Therefore, these biochemical neurons are fully connected to one another, and the information flows back and forth from each neuron to all others. [Pg.80]

In this network, the information proceeds from one biochemical neuron to a subsequent one and also in the opposite direchon, and this is due to cofactors A and B, which are common to all the biochenaical neurons in the network. Thus, this network is fully connected rather than being of the feedforward type exemplified by network B. [Pg.93]

Now we can look at the biochemical networks developed in this work and compare them to the recurrent networks discussed above. Network A (Section 4.2.1) and Network C (Section 4.2.3) are fully connected to one another, and the information flows back and forth from each neuron to all the others. This situation is very much hke the one described for recurrent neural networks, and in these cases, memory, which is a necessary to demonstrate computational power, is clearly incorporated in the networks. Network B (Section 4.2.2) is a feedforward network and thus appears to have no memory in this form. However, when we examine the processes taking place in the biochemical neuron more carefully, we can see that the enzymic reactions take into account the concentration of the relevant substrates present in the system. These substrates can be fed as inputs at any time t. However, part of them also remained from the reactions that took place at time t — and thus the enzymic system in every form is influenced by the processes that took place at early stages. Hence, memory is always incorporated. [Pg.132]

An ANN is a set of interconnected neurons (also termed nodes, cells, units or process elements distributed in a specific arrangement, usually termed an architecture. In general, neurons are organised in layers (see Figure 5.3).The most common neural nets, the feed-forward nets, are fully connected, i.e. each node is connected to all the nodes in the next layer. The information we want to enter in... [Pg.248]

Figure 3. Schematic representation of two different hexagonal arrangements in mesostructured inorganic / surfactant composites the hydrophobic chains are drawn as straight lines for simplicity, (a) The normal structure with a fully-connected inorganic network (dark area), (b) Inverse surfactant assemblies with single domains of the inorganic material enclosed in the centres. In the latter case the hydrophobic surfactant chains are allowed more space for their distribution, leading to a smaller d spacing. In this picture they are also interpenetrating each other. Figure 3. Schematic representation of two different hexagonal arrangements in mesostructured inorganic / surfactant composites the hydrophobic chains are drawn as straight lines for simplicity, (a) The normal structure with a fully-connected inorganic network (dark area), (b) Inverse surfactant assemblies with single domains of the inorganic material enclosed in the centres. In the latter case the hydrophobic surfactant chains are allowed more space for their distribution, leading to a smaller d spacing. In this picture they are also interpenetrating each other.
Neurological Disorders. Although there are other disorders that are suspect, but fully connected to genetic causes, (he principal connections already positively made an the muscular dystrophies. [Pg.716]

In the new model, the three-dimensional electric current path in the cell components is simulated precisely. The calculation of the electric current path inside the single cell and the calculations of chemical and thermo-fluid phenomena are connected to each other, i.e., it is a fully connected model. Using this model, it is possible to simulate the diagonal electric current in the electrolyte, and in addition, the geometry of the cell components can be considered in the calculation of the cell performance. Contact resistances among each cell component are also considered in the calculation. Here, a detailed calculation using the new model will not be mentioned. [Pg.336]

As it is mentioned before, the goal of nonlinear PCA network (NPCA) is to identify an attack. The NPCA architecture consists of five fully connected layers with 3 input nodes and 3 output units. The number of nodes in the hidden layers is shown in Figure 2. [Pg.373]

Figure 13.9 Architecture of a fully connected feed-forward network. Formal neurons are drawn as circles, and weights are represented by lines connecting the neuron layers. Fan-out neurons are drawn in white, sigmoidal neurons in black. Figure 13.9 Architecture of a fully connected feed-forward network. Formal neurons are drawn as circles, and weights are represented by lines connecting the neuron layers. Fan-out neurons are drawn in white, sigmoidal neurons in black.
The plug-socket concept described above can be used to design molecular systems that mimic the function played by a macroscopic electrical extension cable. The operation of an extension cable is more complex than that of a plug/socket system, since it involves three components that must be hold together by two connections that have to be controllable reversibly and independently in the fully connected system, an electron (or electronic energy) flow must take place between the remote donor and acceptor units (Fig. 8). [Pg.85]

Fig. 9 Chemical system for mimicking an electrical extension cable. Structural formulas of the three molecular components 72+, 8-H+ and 92+ (a), which self-assemble in solution (CH2C12 room temperature) to give the 72+d8-H+d92+ triad (b). In the fully connected system, excitation with visible light of the Ru-based unit of 72+ is followed by electron transfer to 92+, with 8-H+ playing the role of an extension cable [58]... Fig. 9 Chemical system for mimicking an electrical extension cable. Structural formulas of the three molecular components 72+, 8-H+ and 92+ (a), which self-assemble in solution (CH2C12 room temperature) to give the 72+d8-H+d92+ triad (b). In the fully connected system, excitation with visible light of the Ru-based unit of 72+ is followed by electron transfer to 92+, with 8-H+ playing the role of an extension cable [58]...
More detailed calculations of the elastic properties of model networks have confirmed Phillips model. The coordination dependence of the elastic modulus is shown in Fig. 2.12 (He and Thorpe 1985). Both the modulus Cn and the number of zero frequency vibrational modes, /, drop to zero at the critical coordination of 2.4, as predicted by Eq. (2.17). The properties are explained in terms of percolation of rigidity. The coordination of 2.4 represents the lowest network coordination for which locally rigid structmes are fully connected, so that the entire network is rigid, but only just so. The elastic modulus is therefore non-zero and continues to increase as the network becomes more connected. The four-fold amorphous silicon network is far from the critical coordination and is very rigid. [Pg.38]

Assume a porous material in which pores have a maximum connectivity Cm. In general, some pores will be lesser connected than others, then the number of bonds linking a given site to its nearest neighbouring ones, C, can vary from C = 0 (i.e., an isolated pore within the solid matrix) to C = Cm (i.e. a fully connected site). In this situation the porous space can be readily represented by an a priori regular network of sites and bonds with connectivity Cm, but... [Pg.123]


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Fully connected graph

Layered neural network fully connected

Neural network fully connected

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