6.1 AI / ML: add a time head to any model
Feature vector (minimal).
# Inputs: tau_hat_days, confidence in [0,1), manifest with channels {period_days}
# Output features: [tau_norm_sin, tau_norm_cos, confidence]
T = T_search # same policy as the inverse (LCM or max(periods))
x = (tau_hat_days % T) / T # normalized tau in [0,1)
two_pi = 6.283185307179586
f0 = sin(two_pi * x)
f1 = cos(two_pi * x)
f2 = confidence
feat = [f0, f1, f2]
Richer features (per cycle).
# Add per-cycle embeddings (robust across domains)
for each channel i with period P_i:
x_i = (tau_hat_days % P_i) / P_i
add sin(two_pi * x_i), cos(two_pi * x_i)
# Optional soft-mask:
multiply all 2D cycle features by confidence
Training-time tips.
- Do not backprop into SSM-Clock; treat features as read-only.
- For privacy, compute phases locally and share only
[sin, cos, confidence]. - If labels drift with diurnal/weekly bias, append these features to stabilize training.
6.2 Systems / Ops / Observability
Cron sanity & drift detection (phase residuals).
# Define expected phase windows for known jobs in the 1-day cycle
allow = 15.0 # deg
residual = angdiff( observed_phase_day , expected_phase )
if abs(residual) > allow:
alert("cron drift")
Clock skew without timestamps.
- Compare model phase from SSM-Clock with heartbeat rhythms (traffic/load).
- Monitor curvature-based confidence; sustained low values flag desynchronization.
- Use SSM-Wait to gate retries/backoffs by symbolic minutes instead of wall clock.
6.3 Wearables / chronobiology (privacy-first)
Cycles. 24 h circadian (~1.0 d), 7 d social, optional lunar (29.5306).
Signals. Activity, HRV, skin temperature → convert to phases per cycle offline.
Use.
tau_hat_days, conf = SSM-Clock(phases)
if conf < 0.5:
do_not_decide()
else:
nudge_schedule_by( phase_day , phase_week )
Privacy. No wall-time leaves the device; share only [tau_hat_days, conf] if needed.
Navigation
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