标签：#Science

The cyclospora diarrhea outbreak isn't an isolated incident. It's part of a pattern of leafy greens wreaking digestive havoc.

Several federal agencies are contributing toward the interdisciplinary research efforts.

A mirror that moves mid-photon could release a shower of new photons.

In the face of backlash to concerns the AI boom will increase consumer electricity bills, the largest utility companies and data center developers in the US are now promising to do something about it. The Wall Street Journal reports that nearly 200 organizations have signed President Donald Trump's "rate payer protection pledge" that's meant to […]

The rule of three for evals says zero failures in N runs is a count, not a rate. With 0 failures in N independent runs, the exact 95% upper bound on the true failure rate is 1 - 0.05^(1/N) , which 3/N approximates. After 100 clean runs you still cannot rule out a 2.95% rate, about 1 in 34. Here is the reading that bites you. Your eval harness runs the agent 100 times, prints "0 failures," and the tile goes green. Someone screenshots it into the launch thread. The unspoken translation is "the failure rate is zero." It is not what the data says. I wrote a small script to make the gap concrete, so I ran a real gate over 200 deterministic agent runs first, counted honestly, and got the dashboard everyone trusts: gate: spend<=budget over 200 deterministic agent runs observed failures: 0 (distinct scenarios: 200) naive point rate : 0.00% binomial SE: 0.00 pp naive 95% CI : [0.00%, 0.00%] <- zero width: false certainty Look at the standard error. For a zero count the binomial SE is sqrt(0*1/200) , which is exactly 0, so the naive 95% interval collapses to [0.00%, 0.00%] . A zero-width confidence interval. The math is telling you it is completely certain, from 200 samples, that the true rate is precisely zero. That is obviously wrong, and it is the exact shape of every "all green" board I have ever trusted too much. TL;DR "0 failures in N runs" is an observed count, not a rate. The naive binomial SE of a zero count is 0, which is why a green board looks like proof and isn't. The honest number is the one-sided upper bound. With 0 failures in N runs, the 95% upper confidence limit on the true failure rate is 1 - 0.05^(1/N) . The rule of three, 3/N , approximates it and rounds the risk slightly up. At N=30 the bound is 9.50% (about 1 in 11). At N=100 it is 2.95% (1 in 34). At N=1000 it is 0.30% (1 in 334). Zero failures in 30 runs is compatible with a 1-in-11 true failure rate. To rule out a 0.1% rate at 95% you need about 2995 clean runs, not 50. "We ran it fifty times" and "

NatGeo's Pompeii: Out of Time fuses historical fact and imagination to bring city's last 24 hours to life.

A single chemical made by mole-rat queens enforces the social order.

Building a Decompiler Pipeline in Rust: Why Fission Separates NIR and HIR Decompiler output often looks simple from the outside. A binary goes in. Pseudocode comes out. But between those two points, a decompiler must recover several different kinds of information: instruction semantics register and memory effects control flow stack variables calling conventions data types expressions loops and conditionals readable source-like structure Trying to represent all of this in one intermediate representation quickly becomes difficult. While building Fission , a reverse-engineering and binary decompilation workspace written primarily in Rust, I decided to separate the decompiler pipeline into two main intermediate representations: NIR , a lower-level representation intended to preserve machine semantics HIR , a higher-level representation intended to express recovered, human-readable program structure This article explains why that separation exists, what each representation owns, and why it makes decompiler development easier to reason about. Correctness and readability want different things A decompiler has at least two responsibilities. First, it must preserve the behavior of the original machine code. Second, it must produce output that a human can understand. Those goals overlap, but they are not identical. Consider a simplified fragment of machine-level behavior: tmp0 = RAX tmp1 = tmp0 + 1 RAX = tmp1 flags = update_flags(tmp0, 1, tmp1) A human reader may prefer to see: rax ++ ; The concise form is easier to read, but it omits details that may still matter elsewhere in the pipeline. The flags update could affect a later conditional branch. The operation width may matter. The source and destination could alias. The operation may have originated from an instruction with additional side effects. If the decompiler converts everything into source-like syntax too early, it becomes easy to discard evidence. If it keeps everything at machine level until the final rendering st

Michigan has been at the epicenter of the wildfire smoke and explosive diarrhea outbreaks. The progressive Democrat talks to WIRED about how he would tackle the forces behind them if he’s elected.

How does AI make you feel? Are you excited to “vibe-code” your smart home? Or anxious about all the added pollution and billions of gallons of water used by data centers? Dig a little deeper and you’ll start to question the actual value of the GPUs that underpin all the leaps and promises of generative […]

If you have written any optimization code, you have met linear programming even if nobody called it that. "Maximize output without blowing the resource budget" is an LP problem, and the classic algorithm that solves it is the simplex method. It is worth understanding not because you will hand-code it (you'll usually call a solver), but because knowing how it moves makes you far better at modeling problems for it. Here is the algorithm stripped down to its logic. The problem shape Every LP problem has three parts: an objective function to maximize or minimize, e.g. Z = 5x1 + 4x2 a set of linear constraints, e.g. 6x1 + 4x2 <= 24, x1 + 2x2 <= 6 non-negativity: all variables >= 0 Geometrically, the constraints carve out a feasible region (a polytope). The optimum always sits at a corner of that region. The simplex method is just a smart way of hopping from corner to corner, uphill, until there is no higher corner to move to. The algorithm as pseudocode build initial tableau (add a slack variable per <= constraint) loop: compute Cj - Zj for each column if all (Cj - Zj) <= 0: break # optimal reached pivot_col = column with most positive Cj - Zj # entering variable ratios = RHS / pivot_col entries (only positive entries) pivot_row = row with smallest non-negative ratio # leaving variable pivot(pivot_row, pivot_col) # elementary row operations return solution from final tableau That's it. Four moves per iteration: score the columns, pick the entering variable, run the ratio test for the leaving variable, pivot. Repeat until the optimality condition holds. A quick worked run Take Maximize Z = 5x1 + 4x2 subject to 6x1 + 4x2 <= 24 and x1 + 2x2 <= 6. Add slack variables s1, s2, build the tableau, and iterate. The optimum lands at x1 = 3, x2 = 1.5, Z = 21. Two pivots and you're done. Simple on paper until the numbers get ugly. Where humans (and debugging) actually break The algorithm is clean. The arithmetic is not. A single wrong entry in one pivot silently corrupts every table

The de-extinction startup is looking to double or triple its previous valuation, according to the report.

Index funds are touted as one of the safest ways to invest. Rather than picking and choosing individual stocks, index funds let you bet on the market as a whole. So what happens when a company like SpaceX - a giant gamble, and, in my opinion, terribly overpriced - is fast-tracked into the Nasdaq-100? Does […]

The Trump administration is asking the Space Force to do a lot. This will require more launches.

The Trump administration is asking the Space Force to do a lot. This will require more launches.

The latest estimates reveal that the majority of greenhouse gas emissions from large-scale events such as the FIFA World Cup stem from spectators traveling to host cities.

A recent study suggests that not getting enough sleep increases the risk of weight gain, and thus of conditions like heart disease and type 2 diabetes.

Why Your WeChat Mini-Program Work Is Actually Relevant US data engineering interviews prioritize hands-on experience with data pipelines, SQL, and scalable storage. WeChat mini-programs, though often seen as front-end tools, generate and process substantial backend data. If you designed the logic behind user actions, session management, or real-time updates, you already have transferable skills. The key is to stop describing the mini-program as a "feature" and start describing it as a "data system." Recruiters don't care about WeChat's UI components; they care about how you moved, transformed, and stored data. The Core Translation: From Mini-Program to Data Engineering When rewriting your resume, map each mini-program task to a standard data engineering function: User interaction logic → Event-driven data pipeline (e.g., Kafka, AWS Kinesis, or custom queues) WeChat cloud functions → Serverless compute (e.g., AWS Lambda, Azure Functions) Database reads/writes via WeChat API → NoSQL or relational database operations (e.g., MongoDB, MySQL, DynamoDB) Session tracking or analytics → ETL pipeline extracting user behavior data into a warehouse Do not use WeChat-specific terms like "wx.request" or "Mini Program SDK" without explaining the data they handled. Replace them with equivalent English terms. A Quick Side-by-Side Translation Table WeChat Term US Data Engineering Equivalent WeChat Cloud Database Managed NoSQL database (like MongoDB Atlas) Mini Program Backend with Cloud Functions Serverless data processing (AWS Lambda) User login & session management Authentication pipeline with token storage Real-time message push Event-driven notification system Reshaping Your Bullet Points: Before and After Generic bullet points kill your chances. Here is a concrete rewrite that turns a typical WeChat mini-program description into a data engineering achievement. Before (Chinese-English hybrid, vague): "Developed a WeChat mini-program for e-commerce with 50k users. Used wx.request

Four-minute miles are almost routine. Middle schoolers are beating college runners. Inside the youth track boom that’s turning kids and teens into speed machines.

I watched my sister paste a CSV into on of our LLM overlords and ask a question then reword it and get a different number. That's the problem with letting an LLM both read your question and trust it's math. ADA splits the two jobs. Drop in a CSV or Excel file, get a dashboard, anomaly detection, a forecast, and plain-English answers, all calculated locally using pandas (locally in the sense of never leaving the server and going to the AI apis.). The LLM layer that you can use (it's optional) but that only sees column names and never your rows, and the whole thing works with zero API key if need be (not the LLM part of course). MIT licensed, solo-built, open to contributors (in fact would need help to make it move out of LLMs and have a few open issues). I wish to make it as LLM free as possible as I want to make it deterministic. Live demo: automated-data-analyst.streamlit.app Repo: https://github.com/saineshnakra/automated-data-analyst