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I put my coursework from SIUe's Master's in Mathematics program up on the problem sets section of this site. Five courses from 2021-2022 that formed the core of my graduate statistics work. The Courses STAT 478 - Time Series Analysis Spring 2021, Dr. Beidi ARIMA models, forecasting, spectral analysis, state space methods. The problem sets mix theoretical derivations with R implementations for temporal data. STAT 482 - Regression Analysis Fall 2022, Dr. Andrew Neath Linear models, diagnostics, variable selection, model comparison. Neath's approach put the theoretical foundations front and center, with application following from understanding. STAT 575 - Computational Statistics Summer 2021, Dr. Qiang Beidi Probably the most hands-on course of the five. Topics included: Newton-Raphson and numerical optimization Monte Carlo simulation Sampling methods (inverse transform, acceptance-rejection) Hand-coded MLE for Poisson regression Implementing these algorithms from scratch instead of calling library functions teaches you what the methods are actually doing. You hit the edge cases. You debug convergence failures. That's where the understanding comes from. STAT 579 - Discrete Multivariate Analysis Spring 2021, Dr. Andrew Neath Categorical data analysis, log-linear models, contingency tables. Both the mathematical theory and R implementations for discrete multivariate data. STAT 581 - Statistical Methods Fall 2021, Dr. Neath Experimental design, ANOVA, general linear models with practical applications. Why Share This? A few reasons. Learning resource. Worked solutions for graduate statistics are surprisingly hard to find online. If someone studying this material stumbles across these and they help, good. Personal archive. I did much of this work during cancer treatment. Keeping it organized and accessible matters to me. Reference. I still look up my own derivations and implementations when something comes up in research. Easier to find them here than to dig through old dir

When can you safely use a simpler model for a series system? I ran extensive simulation studies with likelihood ratio tests to get a quantitative answer. The Problem In series system reliability, you estimate component parameters from masked failure data. For Weibull components, that means estimating (2m) parameters: shape (k_j) and scale (\lambda_j) for each of (m) components. But what if the components have similar failure characteristics? A reduced model with homogeneous shape parameters uses only (m+1) parameters (one common (k) plus (m) scales). This roughly halves the parameter count and has a nice property: the system itself becomes Weibull-distributed. The question is when this simplification is justified. Key Findings Robustness of the Reduced Model For well-designed series systems (components with similar failure characteristics), the result is striking: The reduced homogeneous-shape model cannot be rejected even with sample sizes approaching 30,000, far larger than anything typically available in practice. With realistic sample sizes (50 to 500), the likelihood ratio test shows no evidence against the reduced model when components truly have similar shapes. This is strong justification for using the simpler model. Sharp Boundaries The paper pins down exactly how much heterogeneity it takes to trigger rejection: Shape Deviation Sample Size LRT Decision 0.25 30,000 Fail to reject 0.50 1,000+ Reject 1.0 100+ Strong reject 3.0 50+ Very strong reject Even modest deviations in a single component's shape parameter provide evidence against the reduced model. The boundaries are clean. Practical Guidance Use the reduced model when: Components come from similar manufacturing processes Historical data suggests similar wear-out patterns Sample sizes are moderate ((n < 500)) You need a quick reliability assessment Use the full model when: Components have fundamentally different failure modes (infant mortality vs wear-out) Large samples are available ((n > 1000)) Precis

The first time I encountered an EIA-96 resistor , I assumed the marking would tell me the resistance value directly. I was troubleshooting a PCB and found a resistor marked 24C . Naturally, I expected some relationship between "24" and the actual resistance. After measuring and checking the datasheet, I discovered the resistor was 17.4 kΩ . That raised an obvious question: Why doesn't the code match the resistance value? The Problem With Traditional SMD Codes Most electronics enthusiasts learn resistor markings through familiar examples: 103 = 10 kΩ 472 = 4.7 kΩ 681 = 680 Ω These markings are straightforward. The first digits are significant figures and the last digit is a multiplier. The system works well for common resistor values, especially 5% tolerance components. However, things become complicated when manufacturers need to identify large numbers of precision resistor values on extremely small packages. Enter the EIA-96 Series Precision resistors often use the E96 preferred value series. Instead of having only a handful of values per decade, the E96 series contains 96 standardized resistance values between powers of ten. Some examples include: 100 Ω 102 Ω 105 Ω 107 Ω 110 Ω 113 Ω Notice how closely spaced these values are. Trying to represent all of them with traditional three-digit markings would quickly become messy and inconsistent. A Different Approach Rather than printing the resistance value directly, EIA-96 uses an index system. Each number from 01 to 96 corresponds to one of the standard E96 values. For example: Code Base Value 01 100 24 174 68 499 96 976 A letter is then added to indicate the multiplier. So the resistor marking becomes: Number + Letter Instead of: Resistance Value Example: Decoding 24C Let's break down 24C. First, look up the base value: 24 → 174 Next, decode the multiplier letter: C → ×100 Now calculate: 174 × 100 = 17,400 Ω Final resistance: 17.4 kΩ At first glance, nothing about "24C" resembles 17.4 kΩ, but that's because the code i

Alzheimer's disease affects over 55 million people worldwide, yet the precise molecular changes happening inside individual brain cells remain poorly understood. I wanted to dig into that question - not at the tissue level, but at single-cell resolution. So I built a full scRNA-seq analysis pipeline in Python using Scanpy, working with a publicly available dataset of 63,608 nuclei from human prefrontal cortex tissue (sourced from CZ CELLxGENE). The donors spanned three Braak stages: 0 (cognitively normal), 2 (early Alzheimer's), and 6 (severe Alzheimer's). Here's what I found and how I found it. The Dataset The data came from a study on the molecular characterisation of selectively vulnerable neurons in AD. It covers the superior frontal gyrus, a prefrontal region known to be hit hard by neurodegeneration - and includes seven major brain cell types: Glutamatergic neurons GABAergic neurons Oligodendrocytes OPCs (oligodendrocyte precursor cells) Astrocytes Microglia Endothelial cells 31,997 genes. 63,608 cells. Three disease stages. A lot to work with. The Pipeline 1. Quality Control No dataset is clean out of the box. I filtered cells to keep only those with between 200 and 6,000 detected genes, and excluded anything with more than 20% mitochondrial gene content (high mitochondrial reads usually signal a dying or damaged cell). This removed around 2,809 low-quality cells. 2. Normalisation Library sizes were normalised to 10,000 counts per cell, followed by log1p transformation, standard practice that makes cells comparable regardless of how deeply they were sequenced. I then identified 5,607 highly variable genes to focus the downstream analysis. 3. Dimensionality Reduction PCA (50 components) → neighbourhood graph (10 neighbours, 20 PCs) → UMAP embedding. The UMAP is where the biology starts to become visible. All seven cell types separated into distinct clusters, with clear separation between neuronal subtypes and glial populations. 4. Differential Expression For t

Introduction In today's fast-paced business environments, data is considered the cornerstone of decision-making, policy formulation, and other organizational needs. MS Excel is a robust spreadsheet developed by Microsoft for organizing, analyzing, and visualizing data in rows and columns. In the data science and analytics domain, MS Excel is critical for analyzing and managing data to generate insights that enhance decision-making. Excel's polarity is characterized by its ease of use, flexibility, automation, and visualization. Ways Excel Is Used in Real-World Data Analysis Across the data science and analytics domain, MS Excel is frequently employed in the following ways; a) Data Cleaning and Preprocessing At the beginning of every data science and analytics project, data cleaning is required, and MS Excel is the primary tool. Typical Excel features and functions applied during data cleaning include Text to Columns, Remove Duplicates, Find and Replace, and Power Query. b) Exploratory Data Analysis Before performing data science and analytics activities, it is crucial to understand the dataset at hand, its structure, and trends. MS Excel features Pivot Tables, Pivot Charts, and Slicers that provide instant aggregation, sorting, and visualizations. c) Data Analysis and Reporting Modern organizations and businesses operate based on insights generated from data. MS Excel features such as pivot tables, charts, and conditional formatting help data analysts analyze and visualize data for clear, actionable insights that enhance decision-making. MS Excel Features or Formulas The typical MS Excel features and formulas employed in the data science and analytics domain include the following. Data Cleaning Functions Function Purpose Example Result UPPER() Converts text to uppercase =UPPER("john") JOHN LOWER() Converts text to lowercase =LOWER("JOHN") john PROPER() Capitalizes the first letter of each word =PROPER("john doe") John Doe TRIM() Removes extra spaces from text =TRIM(

A proposed $2 billion data center has become a political flashpoint in the small city of Shelbyville, Indiana. And the controversy has only grown more intense after the mayor, Scott Furgeson, was caught on camera saying of the "No Data Center" signs going up that, "I've seen a lot of these all over town, but […]

If you're building or integrating Automated Test Equipment for aerospace or defence electronics, the technical requirements go well beyond "does the test pass." You need documentation that satisfies MIL-STD, AS9100, and DO-178C auditors — and an architecture that scales from prototype to production. Here's how modern Universal ATE systems are structured for defence-grade compliance. The Core Architecture A defence-grade ATE system has four functional layers: ┌─────────────────────────────────────────┐ │ Test Executive (LabVIEW) │ ← Orchestrates all test sequences ├────────────────┬────────────────────────┤ │ Instrument │ DUT Interface │ ← Hardware layer │ Control │ (ICT/JTAG/Func) │ ├────────────────┴────────────────────────┤ │ Data Management Layer │ ← Logging, traceability, reports ├─────────────────────────────────────────┤ │ Calibration & Verification │ ← Ensures measurement accuracy └─────────────────────────────────────────┘ Test Executive Design in LabVIEW The test executive controls the sequence, manages results, and handles failures. Key design principles: Test Sequence: 1. DUT identification (serial number scan or manual entry) 2. Pre-test self-check (verify instrument calibration status) 3. ICT phase — passive component verification 4. JTAG boundary scan — IEEE 1149.1 interconnect verification 5. Power-on functional test — operational verification 6. RF/signal analysis — if applicable to DUT type 7. Report generation — automatic, timestamped 8. Pass/fail disposition record JTAG Integration via IEEE 1149.1 For high-density boards where bed-of-nails is not viable, JTAG boundary scan is implemented via a JTAG controller (e.g., XJTAG, Corelis, or ASSET InterTech) integrated into the LabVIEW environment: LabVIEW → JTAG Controller API → Scan Chain → DUT ICs The boundary scan description files (BSDL) for each IC define the test vectors. Your test executive loads BSDL files, generates scan chain topology, and runs interconnect tests automatically. Data Traceabili

Table of Contents The Integration Problem That Broke Industry 4.0 MCP: The Vertical Connection Layer How MCP Connects to Servers, Tools, and Databases MCP in Real World Industrial Automation ACP: The Horizontal Communication Layer How ACP Works Under the Hood ACP in Real World Industrial Coordination The Six Precise Differences How They Work Together: The Complete Stack Decision Framework for Industrial AI Architects 1. The Integration Problem That Broke Industry 4.0 Industry 4.0 promised connected factories, intelligent automation, and seamless data flow between machines, systems, and humans. The technology arrived. The connectivity did not. The reason is a number called N times M. An enterprise manufacturing facility might have 12 AI agents across quality, maintenance, and planning — and 28 data sources including ERP, MES, SCADA, IoT sensors, databases, CAD repositories, and supplier APIs. Without a standard protocol: 12 agents multiplied by 28 data sources equals 336 custom integrations. Each integration is bespoke code. Each breaks when either side updates. Each requires maintenance. Each represents a point of failure and a security surface that must be independently managed. IBM VP Armand Ruiz stated this precisely: "Without a common standard, every integration is costly duct tape." MCP and ACP together replace 336 pieces of duct tape with two standard protocols — one governing how agents connect to systems, one governing how agents connect to each other. The smart manufacturing market is projected to reach 374 billion dollars by 2025 at 11.8 percent CAGR. Over 50 percent of companies in industrial automation are expected to adopt MCP-based connectivity. The integration problem is not theoretical. The solution is being deployed at scale right now. 2. MCP: The Vertical Connection Layer MCP connects agents to tools and data — the vertical integration layer. It handles the connection between an AI agent and everything it needs to interact with in the external worl

Introduction A traditional database. That is what many who have not really interacted with Excel to a great extent would define it as in its most basic form. Not that they are wrong, only that is the scope their utilization of Excel covers. Mostly record keeping, basic operations, and data representation. But for those whose utilization scope of Excel is broader, we definitely know better. This underestimation of Excel is a grave mistake for anyone considering themselves as tech-oriented, especially for anyone dealing with data operations, be it simple record keeping or complex concepts involving data. What is Excel A spreadsheet program or tool that facilitates data organization, analysis, and visualization through mathematical operations, chart creation, and building financial models. Real-world application of Excel in Data Analytics Reporting and visualisation Excel facilitates data representation in the form of charts(bar charts, pie charts, line graphs) and dashboards. Businesses and organisations utilize this to get an organised, more insightful, and simplified view and report of their raw data. Financial Accounting Excel's provision for mathematical operations, functions, and formulas in analysis facilitates financial accounting. Balance sheets and income statements preparation, budgeting, and expense tracking are just some of the ways Excel can be used in accounting. Decision-Making Businesses and organisations heavily rely on analysis to support their decision-making. Excel helps in the analysis through different data metrics comparisons, e.g., sales across seasons and locations, forecasting, and tracking key performance indicators. This helps businesses make the best decisions based on the insights gathered from the analysis. Beginner Excel Features and Formulas for Data Analysis Learnt so far Sort and Filter By applying the Filter feature for each column, data in specific columns can not only be sorted from newest to oldest, but also be filtered based on

The deadline to reauthorize Section 702 of the Foreign Intelligence Surveillance Act is coming up a week from now on June 12th, and legislators seem no closer to reaching a deal. If this sounds like deja vu, it's because we've been here before. Congress reauthorized Section 702 in late April - but only for 45 […]

Even if you've done nothing wrong, it's never a good idea to hand your phone to the cops. But international travelers at American airports often have no choice - even if they're US citizens. When Minnesota labor organizer Janette Zahia Corcelius returned home from a three-week trip to Europe in late April, she was detained […]

The New York State legislature passed a one-year moratorium on new large data centers, the first statewide ban of its kind if Democratic Governor Kathy Hochul signs it into law. Lawmakers behind the bill say it's meant to give policymakers time to understand the impact of large data centers on the environment and energy prices. […]

Former DOGE members and Elon Musk allies are backing a startup aimed at using AI to apply "learnings" from DOGE to the private sector.

Just wanted to share my journey after 3.5 years of building a live web analytics tool from scratch, and without AI: WireBoard.io As a web publisher for the last 15 years, I used UA (before GA4) and then Chartbeat. I always loved real-time. At the beginning of the internet I spent my time on IRC, which was great and a huge change from newsgroups and forums. A few years later, Chartbeat was changing its customer base and focusing on big news publishers, and my workflow was about to change with it. I loved being able to watch my traffic live and spot anything unusual, day by day, compared to the same day the previous week. So I decided to replace it with my own product, but with the things their tool was missing. I spent about 8 months thinking it through and sketching different architectures to handle the load and the logic of the data processing pipeline. Then I learned to code in Go (which was surprisingly easy) and built an MVP. After some iterations, I worked on the frontend (Laravel + React) and kept improving the product based on user feedback. The features I couldn't find anywhere else: Truly real-time data via streaming instead of polling. Merging data from different websites into one chart, so I can see at a glance whether traffic is unusual "today" compared to last week. A flexible dashboard, arranged like widgets on a phone. I mostly worked on this quietly and didn't talk about it much on social media or anywhere else, since I'm a dev and not a marketer. This is my biggest project, and I've enjoyed the long journey. All of this happened before the AI era, which turned out to be good timing: I can work on the codebase knowing exactly what does what and where. Over the last 6 months I've experimented with using Claude on some parts of the project (only the frontend), but in limited areas: Performance improvements (useMemo, etc.) Security reviews (not strictly needed, but reassuring when it confirms what I expected) CMS (blog section) The tech stack: Data proc

Introduction Excel is one of the most used tools for data analysis. It allows beginners like myself to easily clean, organize, analyze and visualize data.Excel enables users to work with large datasets and extract meaningful insights without requiring advanced technical skills. What is Excel Excel is a spreadsheet that allows you to collect, organize, analyze, calculate, and visualize data efficiently.Despite the emergence of other data analysis tools like SQL and Power BI, Excel remains one of the most widely used tools for both personal and professional data management.This can be credited to its ease of access, learning, and use. Ways Excel is used in real-world data analysis This week, I had the opportunity to explore how Excel is used in real-world data analysis.I discovered that Excel is not just a basic spreadsheet tool, but a powerful application that helps make sense of data and support decision-making. Data organization and cleaning Excel is used to structure raw data, remove duplicates, and fix errors. This improves data quality, making it easier to analyze and more reliable for decision-making.This improves data quality, making it easier to analyze and more reliable for decision-making. Financial Excel is commonly used in finance to create budgets, calculate profits and losses, and monitor expenses.It helps organizations keep accurate financial records and understand their financial situation. Business decision-making Businesses use Excel to track sales, compare performance over time, and identify trends.This helps managers understand what is working well and what needs improvement. Excel features and formulas In just a week, I have learned several Excel formulas that simplify data management and make working with data more efficient. SUM function The SUM function is used to add a range of values together in Excel, making it one of the most essential tools for quick calculations.It's used to automatically add a range of numerical values together, elimina

Robot demonstrations can distort public perceptions of robotic capabilities.

On Uncanny Valley, we dive into the IPO bonanza that the top AI companies are embarking on to the point where some real estate listings are looking for not just regular old cash, but Anthropic stock.

Kevin O'Leary agreed to halve the size of his planned 40,000-acre data center in Utah amid mounting pressure from residents and activists, as reported earlier by local affiliate ABC4. The Shark Tank star sent a letter to Utah Senate President J. Stuart Adams on Thursday, saying that he will remove 19,430 acres from the project, […]

Bittensor pays roughly eight dollars in TAO token emissions for every dollar of real AI revenue that flows through the network. The exact ratio fluctuates by quarter, but the shape is durable. Q1 2026: about $328 million in annual emissions against $43 million in real AI revenue. That is 7.6 to 1. It is what the crypto-skeptical press has called "extractive by default." It is also what the crypto-friendly analysts call "the subsidy treadmill." The Bittensor engineering team is sophisticated. The subnet validators run real ML evaluation. The miners serve real inference. The revenue is real. The emissions are also real. The cause is the token model itself. One asset is asked to do two jobs that do not belong together. I want to be specific about this part, because every other decentralized AI compute network I have looked at has the same problem, and the fix is well-known. What the token does A token in a decentralized AI compute network does two structurally distinct things. The first job is utility settlement . Contributors run inference, and someone has to pay them for the compute work they did. The payment medium has to scale with usage, has to be denominated in something the contributor can spend on the network or convert to fiat, and has to remain stable enough that contributors can plan around it. This is a billing system. The second job is value capture . Early supporters, investors, and contributors take risk to bootstrap a network that does not yet exist. They have to be paid back for that risk in a way that scales with the eventual success of the network. The payment medium has to be a speculative asset that appreciates as the network grows. This is an equity instrument. A billing system and an equity instrument want opposite things. A billing system that is also a speculative asset means that contributors who get paid in it cannot help but hold a speculative position. An equity instrument that is also a billing system means that token-price volatility show

The California startup released the fourth-generation of its home assistance robot, Stretch.