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1. The first portrait of Gen. Milley, from his time as the U.S. military's top officer, was removed from the Pentagon last week on Inauguration Day less than two hours after President Trump was sworn into office.
2. The now retired Gen. Milley and other former senior Trump aides had been assigned personal security details ever since Iran vowed revenge for the killing of Qasem Soleimani in a drone strike in 2020 ordered by Trump in his first term.
3. On "Fox News Sunday," the chairman of the Senate Intelligence Committee, Tom Cotton, said he hoped President Trump would "revisit" the decision to pull the protective security details from John Bolton, Mike Pompeo, and Brian Hook who previously served under Trump.
4. A senior administration official who requested anonymity replied, "There is a new era of accountability in the Defense Department under President Trump's leadership—and that's exactly what the American people expect."
5. Gen. Milley served as chairman of the Joint Chiefs of Staff from 2019 to 2023 under both Presidents Trump and Biden.

-- Fox News

Larry Summers, a renowned economist and former Treasury Secretary, is known for his sharp intellect, incisive analysis, and a distinctive speaking style that has long been a fixture in economic and political discourse. Recently, however, some have noticed a subtle shift in his pronunciation—specifically, a tendency to pronounce second as schecond. At first glance, such a minor phonetic alteration might seem trivial, but in the case of a public intellectual whose verbal precision is integral to his professional presence, it raises intriguing linguistic and neurological questions.

From a strictly phonetic perspective, the shift from /s/ to /ʃ/ (the “sh” sound) is a change in place of articulation, moving from the alveolar ridge (just behind the upper teeth) to the postalveolar region (further back in the mouth). This is not a random transformation; it suggests an adjustment in tongue placement, airflow, or muscular coordination. Such changes can be influenced by several factors, including age-related shifts in speech production, dental alterations, or even unconscious phonetic drift. The extent to which this affects Summers’s overall speech clarity is debatable, but the specificity of this shift makes it worth examining in greater depth.

Fig. 1 Digital Library. Our color-coded QR code library with a franchize model for the digital twin will be launched in a month.

One possibility is that this is an instance of age-related phonetic drift. At 70, Summers belongs to an age group where subtle changes in articulation are not uncommon. The muscles involved in speech production may lose some of their precision, leading to slightly altered phonemes, particularly in sibilant sounds like /s/ and /ʃ/. This could be an entirely benign development, akin to how some people’s handwriting becomes looser with age or how certain vowel sounds shift subtly over time.

Another possibility involves dental or oral structure changes. The presence of dental prosthetics, missing teeth, or even changes in bite alignment can all contribute to phonetic shifts. The production of /s/ requires a precisely controlled airflow between the tongue and the upper teeth, and if this mechanism is disrupted—by, say, ill-fitting dental work or altered tongue positioning—the sound may soften or shift toward a different place of articulation, resulting in a pronunciation more akin to /ʃ/. If Summers has had recent dental adjustments, this could provide a straightforward explanation.

Fig. 2 There’s a demand for improvement, a supply of product, and agents keeping costs down through it all. However, when product-supply is manipulated to fix a price, its no different from a mob-boss fixing a fight by asking the fighter to tank. This was a fork in the road for human civilization. Our dear planet earth now becomes just but an optional resource on which we jostle for resources. By expanding to Mars, the jostle reduces for perhaps a couple of centuries of millenia. There need to be things that inspire you. Things that make you glad to wake up in the morning and say “I’m looking forward to the future.” And until then, we have gym and coffee – or perhaps gin & juice. We are going to have a golden age. One of the American values that I love is optimism. We are going to make the future good.

A more complex and potentially concerning hypothesis involves neurological factors. In cases of early-stage dysarthria—an umbrella term for motor speech disorders resulting from neurological impairment—consonant articulation may become less crisp, leading to subtle shifts in phoneme production. Conditions such as Parkinson’s disease, mild cognitive impairment, or even transient ischemic events (often called “mini-strokes”) can sometimes manifest first in speech patterns before more overt symptoms appear. However, dysarthria usually presents with a broader array of speech irregularities, including slurred or slowed speech, rather than a single phoneme shift. In the absence of additional indicators, it would be premature to attribute Summers’s pronunciation change to anything pathological.

There is also the possibility of phonetic accommodation or linguistic drift, in which speakers unconsciously adjust their speech due to social or environmental influences. Though this phenomenon is more common in younger speakers or those exposed to new dialects, even well-established speakers can exhibit subtle shifts over time, especially if they are frequently engaged in dialogue with varied linguistic communities. It is possible that Summers, knowingly or unknowingly, has adopted a slightly modified articulation pattern that has now become more prominent in his speech.

Ultimately, the significance of this phonetic shift depends on whether it remains an isolated quirk or emerges as part of a broader change in speech patterns. If it is simply a minor articulation drift, then it is a natural, inconsequential aspect of speech evolution. If it signals a more systemic shift—whether due to oral, neurological, or social factors—it may warrant closer attention. In the public realm, where oratory remains a defining feature of influence, even minute changes in speech can invite scrutiny, particularly for figures like Summers, whose words carry weight in both policy and intellectual discourse.

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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': ['Foundational', 'Grammar', 'Syntax', 'Punctuation', "Rhythm", 'Time'],  # Static
        'Voir': ['Data Flywheel'],  
        'Choisis': ['LLM', 'User'],  
        'Deviens': ['Action', 'Token', 'Rhythm.'],  
        "M'èléve": ['Victory', 'Payoff', 'NexToken', 'Time.', 'Cadence']  
    }

# Assign colors to nodes
def assign_colors():
    color_map = {
        'yellow': ['Data Flywheel'],  
        'paleturquoise': ['Time', 'User', 'Rhythm.', 'Cadence'],  
        'lightgreen': ["Rhythm", 'Token', 'Payoff', 'Time.', 'NexToken'],  
        'lightsalmon': ['Syntax', 'Punctuation', 'LLM', 'Action', 'Victory'],
    }
    return {node: color for color, nodes in color_map.items() for node in nodes}

# Define edge weights (hardcoded for editing)
def define_edges():
    return {
        ('Foundational', 'Data Flywheel'): '1/99',
        ('Grammar', 'Data Flywheel'): '5/95',
        ('Syntax', 'Data Flywheel'): '20/80',
        ('Punctuation', 'Data Flywheel'): '51/49',
        ("Rhythm", 'Data Flywheel'): '80/20',
        ('Time', 'Data Flywheel'): '95/5',
        ('Data Flywheel', 'LLM'): '20/80',
        ('Data Flywheel', 'User'): '80/20',
        ('LLM', 'Action'): '49/51',
        ('LLM', 'Token'): '80/20',
        ('LLM', 'Rhythm.'): '95/5',
        ('User', 'Action'): '5/95',
        ('User', 'Token'): '20/80',
        ('User', 'Rhythm.'): '51/49',
        ('Action', 'Victory'): '80/20',
        ('Action', 'Payoff'): '85/15',
        ('Action', 'NexToken'): '90/10',
        ('Action', 'Time.'): '95/5',
        ('Action', 'Cadence'): '99/1',
        ('Token', 'Victory'): '1/9',
        ('Token', 'Payoff'): '1/8',
        ('Token', 'NexToken'): '1/7',
        ('Token', 'Time.'): '1/6',
        ('Token', 'Cadence'): '1/5',
        ('Rhythm.', 'Victory'): '1/99',
        ('Rhythm.', 'Payoff'): '5/95',
        ('Rhythm.', 'NexToken'): '10/90',
        ('Rhythm.', 'Time.'): '15/85',
        ('Rhythm.', 'Cadence'): '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 = []
    
    # 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
    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)
    
    # 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=9, connectionstyle="arc3,rad=0.2"
    )
    nx.draw_networkx_edge_labels(G, pos, edge_labels=edges_labels, font_size=8)
    plt.title("OPRAH™", fontsize=25)
    plt.show()

# Run the visualization
visualize_nn()

Fig. 3 Grammar is the Ecosystem. Therein we have phonetics, syntax, melody, rhythm, and cadences – meaning or syntax.