Freedom in Fetters

The universe rarely moves in straight lines. Probability governs the world with an iron hand, dictating the balance of randomness and causality, yet the improbable continually asserts itself with an audacity that defies prediction. Take the numbers: 95% probability ensures control, yet it is the rogue 5%—the freak occurrence, the absurd, the impossible—that alters the course of history. Gary Skinner, the Ugandan pastor, never should have succeeded in his calling; born outside of the African continent, he had no predetermined claim to its spiritual destiny. Yet he built a sanctuary that reshaped the faith of thousands. Jules, the hitman from Pulp Fiction, should have died in that apartment, shot down in the most predictable of ways. Instead, the bullets never touched him. Donald Trump, the most polarizing political figure of the era, should have met the fate of so many leaders who walked too close to history’s fire. But he did not. The 5% ruled in each of these moments, marking the boundary between expectation and what cannot be accounted for.

Fig. 30 The Next Time Your Horse is Behaving Well, Sell it. The numbers in private equity don’t add up because its very much like a betting in a horse race. Too many entrants and exits for anyone to have a reliable dataset with which to estimate odds for any horse-jokey vs. the others for quinella, trifecta, superfecta

Every transformation of fate carries within it an element of deception. In most cases—80% of the time, perhaps—what appears to be real is merely a trick of light and narrative. Skinner’s faith-driven empire was not built on mere belief; it was constructed through strategic design, persuasion, and the ability to shape perception. Jules’ so-called divine intervention did not transform him in an instant; he carried doubt, knowing that faith is too often a convenient answer to an incomprehensible truth. Trump, having survived what should have been his final moment, immediately recognized the weight of the event. The world saw him emerge as an untouchable figure, the man who simply would not fall. Yet in the margins of that story, deception lurks. How much of what we see in these moments is orchestrated, shaped by those who understand that perception is the most valuable currency of power?

Faith is a razor-thin proposition, a 51/49 split between certainty and calling, between knowing and merely hoping that one’s steps are guided by something greater. Skinner, with all his scripture and sermons, had to walk that narrow edge, never truly knowing if his work would endure. Jules, for all his talk of quitting, faced the terrifying reality that transformation requires sacrifice. And Trump, standing on that stage in Pennsylvania, blood on his face, had to reckon with the very thing he had built his political career upon—the illusion of inevitability. Did he believe in a divine mandate at that moment? Or did he simply understand, in the most primal way, that he had become something larger than himself?

Hope, however, is not for those who hold power but for those who transform others. Skinner did not set out to build an empire; he was moved by the suffering of those he sought to serve. Jules, despite his life of violence, realized that redemption is not about saving oneself but about setting others on a different path. Trump, in contrast, does not transform others so much as he compels them to serve his vision. This is where the balance tips. Hope, in its truest form, is not about winning, but about offering someone else a way out. The 80% who receive transformation are not the ones who sought it but the ones who were given a new way to see their own lives.

Love, in the end, belongs to those who need it most. Skinner’s greatest impact was not in preaching to those who already believed but in reaching those who had nothing left. Jules did not seek love; he needed grace, and in walking away from the life he knew, he acknowledged that he was, perhaps for the first time, the beggar in the equation. Trump, for all his wealth and power, remains the most wanting of them all. He has command, he has dominance, but love—the kind that is not transactional, not dependent on influence—is the thing he lacks most. And perhaps, in the final analysis, that is the great irony of human existence: those who seem to have everything often need the one thing they cannot force into existence.

Probability may control the game, but it is the aberrations—the moments of improbability, deception, faith, transformation, and love—that define what it means to truly live.

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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': ['Fewer Men', 'Greater Honor', 'And If To Live', 'Country Loss', "If We Are Mark’d", 'To Die, We Enough'],  # Static
        'Voir': ['Information'],  
        'Choisis': ['Baseline', 'Decision'],  
        'Deviens': ['Adverse Event Markers', 'Comorbidity/ICD Code', 'Temporal Changes'],  
        "M'èléve": ['Mortality Rate', 'Organ Failure', 'Hospitalization', 'Dependency', 'Physical Frailty']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Information'],  
        'paleturquoise': ['To Die, We Enough', 'Decision', 'Temporal Changes', 'Physical Frailty'],  
        'lightgreen': ["If We Are Mark’d", 'Comorbidity/ICD Code', 'Organ Failure', 'Dependency', 'Hospitalization'],  
        'lightsalmon': ['And If To Live', 'Country Loss', 'Baseline', 'Adverse Event Markers', 'Mortality Rate'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Fewer Men', 'Information'): '1/99',
        ('Greater Honor', 'Information'): '5/95',
        ('And If To Live', 'Information'): '20/80',
        ('Country Loss', 'Information'): '51/49',
        ("If We Are Mark’d", 'Information'): '80/20',
        ('To Die, We Enough', 'Information'): '95/5',
        ('Information', 'Baseline'): '20/80',
        ('Information', 'Decision'): '80/20',
        ('Baseline', 'Adverse Event Markers'): '49/51',
        ('Baseline', 'Comorbidity/ICD Code'): '80/20',
        ('Baseline', 'Temporal Changes'): '95/5',
        ('Decision', 'Adverse Event Markers'): '5/95',
        ('Decision', 'Comorbidity/ICD Code'): '20/80',
        ('Decision', 'Temporal Changes'): '51/49',
        ('Adverse Event Markers', 'Mortality Rate'): '80/20',
        ('Adverse Event Markers', 'Organ Failure'): '85/15',
        ('Adverse Event Markers', 'Hospitalization'): '90/10',
        ('Adverse Event Markers', 'Dependency'): '95/5',
        ('Adverse Event Markers', 'Physical Frailty'): '99/1',
        ('Comorbidity/ICD Code', 'Mortality Rate'): '1/9',
        ('Comorbidity/ICD Code', 'Organ Failure'): '1/8',
        ('Comorbidity/ICD Code', 'Hospitalization'): '1/7',
        ('Comorbidity/ICD Code', 'Dependency'): '1/6',
        ('Comorbidity/ICD Code', 'Physical Frailty'): '1/5',
        ('Temporal Changes', 'Mortality Rate'): '1/99',
        ('Temporal Changes', 'Organ Failure'): '5/95',
        ('Temporal Changes', 'Hospitalization'): '10/90',
        ('Temporal Changes', 'Dependency'): '15/85',
        ('Temporal Changes', 'Physical Frailty'): '20/80'
    }

# Calculate positions for nodes
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()
    G = nx.DiGraph()
    pos = {}
    node_colors = []
    
    # Add nodes 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):
            G.add_node(node, layer=layer_name)
            pos[node] = position
            node_colors.append(colors.get(node, 'lightgray'))   
    
    # Add edges with weights
    for (source, target), weight in edges.items():
        if source in G.nodes and target in G.nodes:
            G.add_edge(source, target, weight=weight)
    
    # 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='gray',
        node_size=3000, font_size=15, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("Holy Grail", fontsize=23)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 31 From a Pianist View. Left hand voices the mode and defines harmony. Right hand voice leads freely extend and alter modal landscapes. In R&B that typically manifests as 9ths, 11ths, 13ths. Passing chords and leading notes are often chromatic in the genre. Music is evocative because it transmits information that traverses through a primeval pattern-recognizing architecture that demands a classification of what you confront as the sort for feeding & breeding or fight & flight. It’s thus a very high-risk, high-error business if successful. We may engage in pattern recognition in literature too: concluding by inspection but erroneously that his silent companion was engaged in mental composition he reflected on the pleasures derived from literature of instruction rather than of amusement as he himself had applied to the works of William Shakespeare more than once for the solution of difficult problems in imaginary or real life. Source: Ulysses