p chart, spc
Proportion defective.
758 technical terms and definitions
Proportion defective.
Method to optimize prompt embeddings for better few-shot and zero-shot performance.
Acceptors (B) add holes.
P-values measure evidence against null hypotheses with smaller values indicating stronger evidence.
Opposite polarity configuration.
Percentile latency metrics.
Probably Approximately Correct learning provides theoretical bounds on sample complexity for learning with high probability and accuracy.
Pacemaker process sets production rhythm for entire value stream receiving customer schedule.
Pachyderm versions data with automatic lineage. Kubernetes-native pipelines.
Length and width of package.
Package costs include materials assembly and testing for packaged semiconductor.
Package decapsulation removes molding compound and heat spreaders exposing die for failure analysis.
Package decoupling capacitors mounted on substrate or package provide intermediate frequency response between on-die and board capacitors.
Physical size measurements.
Package failure analysis investigates defects in packaging materials interconnects and interfaces using cross-sectioning acoustic microscopy and X-ray.
Total package thickness.
Laser-mark chips with part number date code.
Encapsulate devices in protective material.
Dimensional specifications.
Package resonances occur when inductance and capacitance resonate creating impedance peaks.
Interconnect layer between die and PCB.
Package thermal modeling predicts temperature distributions using finite element analysis and computational fluid dynamics.
Bending due to molding stress.
Yield through packaging.
Advanced packaging connects multiple dies. Chiplets enable mix-and-match. Interposers and 3D stacking.
Packed sequences concatenate examples without padding for memory efficiency.
Prune and freeze for tasks.
Iteratively prune and train for new tasks.
Padding for equal-length sequences.
Padding masks prevent attention to padded positions during processing.
Token used to fill sequences to same length in batches.
Padding extends shorter sequences to batch length enabling parallel processing.
Efficiently manage paged KV cache.
Paged attention manages KV cache in non-contiguous memory blocks like virtual memory.
PagedAttention (vLLM) manages KV cache like virtual memory. Reduces fragmentation. Enables larger batches.
Memory-efficient attention mechanism used in vLLM for serving.
Measure node importance via random walks.
Equivariant molecular network.
Polarizable Atom Interaction Neural Network uses equivariant message passing for molecular property prediction.
Paired t-tests compare related measurements on same units.
Pairwise comparisons judge relative quality between two responses.
Compare two outputs and pick better one.
Pairwise ranking learns relative preferences between item pairs for recommendation ordering.
Learn from item comparisons.
Google's large-scale language model.
# Palo Alto & Stanford Research Park ## Palo Alto | Property | Value | |----------|-------| | **Location** | Santa Clara County, California, USA | | **Coordinates** | $37.4419° \text{ N}, 122.1430° \text{ W}$ | | **Founded** | 1894 | | **Area** | $67.2 \text{ km}^2$ $(25.95 \text{ mi}^2)$ | | **Population (2020)** | $\approx 68,572$ | | **Elevation** | $9 \text{ m}$ $(30 \text{ ft})$ | ## 1. Origin - **Name Origin:** "Palo Alto" = Spanish for "tall tree" - Refers to: *El Palo Alto* — a historic coastal redwood (*Sequoia sempervirens*) - Location: Along San Francisquito Creek - Height estimate: $h \approx 33.5 \text{ m}$ $(110 \text{ ft})$ - Age estimate: $t \approx 1,000 \text{ years}$ - **Mathematical representation of tree growth:** $$ H(t) = H_{max} \cdot \left(1 - e^{-kt}\right) $$ Where: - $H(t)$ = height at time $t$ - $H_{max}$ = maximum potential height - $k$ = growth rate constant - $t$ = time in years ## 2. Historical Timeline | Year | Event | Significance Index $S_i$ | |------|-------|--------------------------| | 1769 | Spanish expedition discovers El Palo Alto | $S_1 = 1.0$ | | 1876 | Leland Stanford purchases land | $S_2 = 2.5$ | | 1891 | Stanford University opens | $S_3 = 5.0$ | | 1894 | Palo Alto incorporated | $S_4 = 3.0$ | | 1939 | HP founded in garage | $S_5 = 8.0$ | | 1951 | Stanford Research Park established | $S_6 = 9.5$ | | 1970 | Xerox PARC established | $S_7 = 9.0$ | **Cumulative Historical Impact:** $$ I_{total} = \sum_{i=1}^{n} S_i \cdot w_i = \sum_{i=1}^{7} S_i \cdot w_i $$ ## 3. Stanford Research Park ### 3.1 Overview - **Established:** 1951 - **Area:** $A = 700 \text{ acres} = 2.83 \text{ km}^2$ - **Buildings:** $n \approx 150$ - **Companies:** $N_{companies} \approx 140$ - **Employees:** $E \approx 23,000$ ### 3.2 Key Figures - **Frederick Terman** — "Father of Silicon Valley" - Dean of Engineering, Stanford University - Concept: University-industry symbiosis - **Innovation Model:** $$ \text{Innovation Rate} = \frac{dI}{dt} = \alpha \cdot R \cdot C \cdot U $$ Where: - $R$ = Research investment (USD) - $C$ = Corporate collaboration factor - $U$ = University knowledge transfer coefficient - $\alpha$ = Regional multiplier constant ### 3.3 Notable Tenants (Historical & Current) ``` - Company Timeline & Market Cap Evolution ======================================== 1. Hewlett-Packard (1939) - Founded: Garage at 367 Addison Ave - Initial investment: $538 - Split (2015): HP Inc. + HPE 2. Varian Associates (1948) - First SRP tenant - Focus: Microwave technology 3. Xerox PARC (1970) - Inventions: GUI, Ethernet, Laser printing - Impact factor: I_PARC → ∞ 4. Tesla (HQ moved 2010) - Market Cap (peak): M ≈ $1.2 × 10¹² USD 5. VMware - Founded: 1998 - Virtualization pioneer ``` ### 3.4 Economic Impact Model **Land Lease Revenue:** $$ R_{annual} = \sum_{i=1}^{n} A_i \cdot r_i $$ Where: - $A_i$ = area leased by company $i$ (sq ft) - $r_i$ = rate per sq ft for company $i$ - $n$ = total number of tenants **Estimated Annual Revenue to Stanford:** $$ R_{Stanford} \approx 50\,\text{ million USD/year} $$ ## 4. Silicon Valley Genesis ### 4.1 The HP Garage - **Address:** 367 Addison Avenue, Palo Alto - **Designation:** California Historical Landmark #976 - **Title:** "Birthplace of Silicon Valley" **Initial Capital:** $$ C_0 = 538\,\text{ USD} \quad \text{(1939)} $$ **Inflation-adjusted (2024):** $$ C_{2024} = C_0 \cdot \left(\frac{CPI_{2024}}{CPI_{1939}}\right) \approx 11{,}800\,\text{ USD} $$ ### 4.2 Venture Capital Ecosystem **Sand Hill Road Statistics:** - **Location:** Adjacent to Stanford Research Park - **VC Firms:** $n_{VC} > 50$ - **Assets Under Management (AUM):** $$ AUM_{SandHill} \approx 100\,\text{ billion USD} $$ **Investment Return Model:** $$ IRR = \left(\frac{FV}{PV}\right)^{\frac{1}{n}} - 1 $$ Where: - $IRR$ = Internal Rate of Return - $FV$ = Future Value - $PV$ = Present Value (initial investment) - $n$ = years ## 5. Geographic & Demographic Data ### 5.1 Geographic Boundaries ``` Cardinal Boundaries =================== North: Menlo Park, East Palo Alto South: Mountain View, Los Altos East: San Francisco Bay West: Portola Valley, Los Altos Hills ``` ### 5.2 Area Calculations **Total Area:** $$ A_{total} = A_{land} + A_{water} = 66.4 + 0.8 = 67.2 \text{ km}^2 $$ **Population Density:** $$ \rho = \frac{P}{A_{land}} = \frac{68,572}{66.4} \approx 1,033 \text{ people/km}^2 $$ ### 5.3 Demographics (2020 Census) | Demographic | Percentage | Population | |-------------|------------|------------| | White | $57.5\%$ | $\approx 39,429$ | | Asian | $32.9\%$ | $\approx 22,560$ | | Hispanic/Latino | $5.9\%$ | $\approx 4,046$ | | Black/African American | $1.6\%$ | $\approx 1,097$ | | Other/Mixed | $2.1\%$ | $\approx 1,440$ | **Diversity Index (Simpson's Index):** $$ D = 1 - \sum_{i=1}^{n} p_i^2 = 1 - (0.575^2 + 0.329^2 + 0.059^2 + 0.016^2 + 0.021^2) $$ $$ D \approx 1 - 0.439 = 0.561 $$ ## 6. Economic Indicators ### 6.1 Housing Market **Median Home Price (2024):** $$ P_{median} \approx 3.5 \times 10^6\,\text{ USD} $$ **Price-to-Income Ratio:** $$ \text{PIR} = \frac{P_{median}}{I_{median}} = \frac{3,500,000}{180,000} \approx 19.4 $$ *(National average PIR $\approx 5$)* ### 6.2 Income Distribution **Median Household Income:** $$ I_{median} \approx 180{,}000\,\text{ USD/year} $$ **Gini Coefficient (Income Inequality):** $$ G_{PaloAlto} \approx 0.52 $$ $$ G = \frac{\sum_{i=1}^{n}\sum_{j=1}^{n}|x_i - x_j|}{2n^2\bar{x}} $$ ## 7. Education & Research ### 7.1 Stanford University Statistics | Metric | Value | |--------|-------| | **Founded** | 1891 | | **Endowment** | approx USD 36.3 billion | | **Students** | $\approx 17,000$ | | **Faculty** | $\approx 2,300$ | | **Nobel Laureates** | $> 85$ | ### 7.2 Research Output Model **Publications per Year:** $$ P(t) = P_0 \cdot e^{rt} $$ **Citation Impact Factor:** $$ IF = \frac{C_{t-1} + C_{t-2}}{N_{t-1} + N_{t-2}} $$ Where: - $C$ = citations received - $N$ = citable items published - $t$ = current year ## 8. Innovation Metrics ### 8.1 Patent Activity **Patents originating from Palo Alto (annual):** $$ N_{patents} \approx 2,500 \text{ patents/year} $$ **Patent Density:** $$ \rho_{patent} = \frac{N_{patents}}{P} = \frac{2,500}{68,572} \approx 0.036 \text{ patents/capita} $$ ### 8.2 Startup Formation Rate **New Startups per Year:** $$ S_{new} \approx 150-200 \text{ startups/year} $$ **Survival Rate (5-year):** $$ P(survival \geq 5) \approx 0.45 $$ **Expected Value of Startup:** $$ E[V] = \sum_{i} P(outcome_i) \cdot V_i $$ ## 9. Transportation & Infrastructure ### 9.1 Commute Statistics **Average Commute Time:** $$ \bar{t}_{commute} \approx 25 \text{ minutes} $$ **Modal Split:** | Mode | Percentage | |------|------------| | Drive alone | $58\%$ | | Carpool | $8\%$ | | Public transit | $7\%$ | | Bicycle | $8\%$ | | Walk | $6\%$ | | Work from home | $13\%$ | ### 9.2 Caltrain Service **Stations in Palo Alto:** 1. Palo Alto Station 2. California Avenue Station **Daily Ridership (pre-pandemic):** $$ R_{daily} \approx 8,000 \text{ passengers} $$ ## 10. Environmental Data ### 10.1 Climate Classification **Köppen Classification:** $Csb$ (Mediterranean) **Average Temperature:** $$ \bar{T}_{annual} = \frac{1}{12}\sum_{m=1}^{12} T_m \approx 15°C \text{ } (59°F) $$ ### 10.2 Precipitation **Annual Rainfall:** $$ P_{annual} \approx 400 \text{ mm} \text{ } (15.7 \text{ in}) $$ **Monthly Distribution:** $$ P_m = P_{annual} \cdot f_m $$ Where $f_m$ = fraction of annual precipitation in month $m$
Pandas DataFrames handle tabular data. Data manipulation, analysis. Memory-intensive for large data.
Create wide panoramic images.
Paperspace Gradient provides ML platform. Notebooks, workflows.
Aligned text in multiple languages.