Veiled Resentment

The formula w = 1/(1 + X/Y) encapsulates an essential tension: the balance of signal to noise, molecule to epitope, or, in a broader sense, clarity to obfuscation. When X/Y trends high—when noise dominates—the weight collapses toward insignificance. But when the denominator shrinks, when the signal asserts itself with dominance, w approaches unity. This equation governs immunological interactions, yet its shadow stretches across history, threading through power struggles, deception, and the self-justifying machinations of ambition. In Shakespeare’s Richard III, the titular king emerges as the ultimate manipulator of this ratio. His signal—his will to power—asserts itself not through rightful succession but through the systematic elimination of barriers. The legitimate heirs, reduced to noise, are erased with calculated precision, leaving behind only the stark clarity of Richard’s self-assertion. Who of York’s heirs is alive but we? The question is rhetorical; the answer is the result of an equation Richard has already balanced in his favor.

Fig. 26 Uddin: a Kindred Spirit. Our gh-pages based ecosystem integration & navigation (EIN) framework is a competitive solution to a diagnosis we reached independently of Uddin. Source: Draft Complaint

Warning

Richard iii!!!

Richard III’s ascent is not a tale of raw power but of veiled resentment—a resentment that transforms into action, slicing through dynastic legitimacy like an immune response eliminating foreign bodies. The neural network-like structure derived from biological models mirrors this pattern, its layers separating perception from agency, strategy from execution. Within this framework, nodes like The Great York, 5% or York’s Heirs Alive, 50% symbolize statistical probabilities, weights in a system of inevitable elimination. The graph’s directed edges—“1/99” from one node, “95/5” from another—capture the odds of survival or extinction, much like Richard’s calculated odds of seizing the throne. His manipulation of succession operates under the same logic as an immune system’s differentiation between self and non-self, where the weakly weighted epitope is discarded, and the dominant molecule persists. Shakespeare, through poetic compression, constructs Richard as both an antigen and an immune response, an agent of destruction cloaked in necessity. His reign, like an unstable biological equilibrium, is ultimately unsustainable. His grip on power is proportional to how much he has excised—when nothing remains to cut away, he himself is discarded.

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import numpy as np
import matplotlib.pyplot as plt
import networkx as nx

# Define the neural network layers
def define_layers():
    return {
        'Suis': ['The Great York,  5%', 'Peptidoglycans, Lipoteichoics', 'Lipopolysaccharide', 'N-Formylmethionine', "Glucans, Chitin", 'Specific Antigens'],
        'Voir': ['Empire Unpossesed, 20%'],  
        'Choisis': ['Yorks Heirs Alive, 50%', 'King of England'],  
        'Deviens': ['Sword Unswayed', 'Chair Empty', 'King Dead, 20%'],  
        "M'èléve": ['Why Then at Sea?', 'Platelet System', 'Granulocyte System', 'Innate Lymphoid Cells, 5%', 'Adaptive Lymphoid Cells']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Empire Unpossesed, 20%'],  
        'paleturquoise': ['Specific Antigens', 'King of England', 'King Dead, 20%', 'Adaptive Lymphoid Cells'],  
        'lightgreen': ["Glucans, Chitin", 'Chair Empty', 'Platelet System', 'Innate Lymphoid Cells, 5%', 'Granulocyte System'],  
        'lightsalmon': ['Lipopolysaccharide', 'N-Formylmethionine', 'Yorks Heirs Alive, 50%', 'Sword Unswayed', 'Why Then at Sea?'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights
def define_edges():
    return {
        ('The Great York,  5%', 'Empire Unpossesed, 20%'): '1/99',
        ('Peptidoglycans, Lipoteichoics', 'Empire Unpossesed, 20%'): '5/95',
        ('Lipopolysaccharide', 'Empire Unpossesed, 20%'): '20/80',
        ('N-Formylmethionine', 'Empire Unpossesed, 20%'): '51/49',
        ("Glucans, Chitin", 'Empire Unpossesed, 20%'): '80/20',
        ('Specific Antigens', 'Empire Unpossesed, 20%'): '95/5',
        ('Empire Unpossesed, 20%', 'Yorks Heirs Alive, 50%'): '20/80',
        ('Empire Unpossesed, 20%', 'King of England'): '80/20',
        ('Yorks Heirs Alive, 50%', 'Sword Unswayed'): '49/51',
        ('Yorks Heirs Alive, 50%', 'Chair Empty'): '80/20',
        ('Yorks Heirs Alive, 50%', 'King Dead, 20%'): '95/5',
        ('King of England', 'Sword Unswayed'): '5/95',
        ('King of England', 'Chair Empty'): '20/80',
        ('King of England', 'King Dead, 20%'): '51/49',
        ('Sword Unswayed', 'Why Then at Sea?'): '80/20',
        ('Sword Unswayed', 'Platelet System'): '85/15',
        ('Sword Unswayed', 'Granulocyte System'): '90/10',
        ('Sword Unswayed', 'Innate Lymphoid Cells, 5%'): '95/5',
        ('Sword Unswayed', 'Adaptive Lymphoid Cells'): '99/1',
        ('Chair Empty', 'Why Then at Sea?'): '1/9',
        ('Chair Empty', 'Platelet System'): '1/8',
        ('Chair Empty', 'Granulocyte System'): '1/7',
        ('Chair Empty', 'Innate Lymphoid Cells, 5%'): '1/6',
        ('Chair Empty', 'Adaptive Lymphoid Cells'): '1/5',
        ('King Dead, 20%', 'Why Then at Sea?'): '1/99',
        ('King Dead, 20%', 'Platelet System'): '5/95',
        ('King Dead, 20%', 'Granulocyte System'): '10/90',
        ('King Dead, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
        ('King Dead, 20%', 'Adaptive Lymphoid Cells'): '20/80'
    }

# Define edges to be highlighted in black
def define_black_edges():
    return {
        ('King Dead, 20%', 'Why Then at Sea?'): '1/99',
        ('King Dead, 20%', 'Platelet System'): '5/95',
        ('King Dead, 20%', 'Granulocyte System'): '10/90',
        ('King Dead, 20%', 'Innate Lymphoid Cells, 5%'): '15/85',
        ('King Dead, 20%', 'Adaptive Lymphoid Cells'): '20/80'
    }

# Calculate node positions
def calculate_positions(layer, x_offset):
    y_positions = np.linspace(-len(layer) / 2, len(layer) / 2, len(layer))
    return [(x_offset, y) for y in y_positions]

# Create and visualize the neural network graph
def visualize_nn():
    layers = define_layers()
    colors = assign_colors()
    edges = define_edges()
    black_edges = define_black_edges()
    
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Create mapping from original node names to numbered labels
    mapping = {}
    counter = 1
    for layer in layers.values():
        for node in layer:
            mapping[node] = f"{counter}. {node}"
            counter += 1
            
    # Add nodes with new numbered labels and assign positions
    for i, (layer_name, nodes) in enumerate(layers.items()):
        positions = calculate_positions(nodes, x_offset=i * 2)
        for node, position in zip(nodes, positions):
            new_node = mapping[node]
            G.add_node(new_node, layer=layer_name)
            pos[new_node] = position
            node_colors.append(colors.get(node, 'lightgray'))
    
    # Add edges with updated node labels
    edge_colors = []
    for (source, target), weight in edges.items():
        if source in mapping and target in mapping:
            new_source = mapping[source]
            new_target = mapping[target]
            G.add_edge(new_source, new_target, weight=weight)
            edge_colors.append('black' if (source, target) in black_edges else 'lightgrey')
    
    # Draw the graph
    plt.figure(figsize=(12, 8))
    edges_labels = {(u, v): d["weight"] for u, v, d in G.edges(data=True)}
    
    nx.draw(
        G, pos, with_labels=True, node_color=node_colors, edge_color=edge_colors,
        node_size=3000, font_size=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™: Richard III", fontsize=18)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 27 Is the chair empty? Is the sword unswayed? Is the King dead? The empire unpossessed? What heir of York is there alive but we? And who is England’s King but great York’s heir? Then tell me, what makes he upon the seas?