标签：#science

A critique published in Nature Wednesday calls the basic technology behind Microsoft's "breakthrough" quantum computing chip the Majorana 1 into question. Microsoft unveiled the chip in February 2025 and said it featured a brand-new technology known as a topological qubit. Topological qubits, they said, would be the "building blocks" for their future quantum computer. Microsoft […]

The small bit of air in the bottle sees oxygen and other chemicals move in and out.

This curious phenomenon was documented by the seismometer at the University of Bergen, which recorded slight vibrations whenever the national team scored a goal.

Math.random() returns a number between 0 and 1, and roughly nobody reading this could explain what happens between the call and the return. That is fine, fine right up until the output decides who gets money, and then it becomes one of the genuinely hard problems in applied software, the kind that regulated industries build entire testing labs around. Start with the thing most people get wrong: a sequence that passes for random and a fair sequence are different claims, and your users cannot tell them apart by staring at outputs. The users will never catch the difference and that is the whole problem in one sentence. This is why fairness in any real-money system, an online casino being the sharpest example, is a verification problem long before it is a math problem. Pseudorandom generators are deterministic. A PRNG eats a seed, runs it through fixed arithmetic, and spits out numbers that sail through statistical randomness tests while being completely predetermined by that seed. Mersenne Twister is the poster child: excellent distribution, used everywhere by default for years, and from a few hundred observed outputs you can reconstruct its internal state and predict the rest. For a Monte Carlo simulation, who cares! For anything where a human has a financial reason to guess your next number, you just shipped a vulnerability and called it a feature. What you want when stakes exist is a CSPRNG. The guarantee that matters: even with a long history of outputs, an attacker cannot compute the next one or recover the internal state. crypto.randomBytes() in Node. crypto.getRandomValues() in the browser. They sit one autocomplete away from the unsafe option and offer wildly different guarantees, which is exactly why this bug ships so often. The safe call and the dangerous call look like fraternal twins. ** The part players actually rely on ** Say you build it correctly: a proper CSPRNG, real entropy, no timestamp nonsense. You know it is fair but now prove it to a stranger wh

The 2026 FIFA World Cup is reshaping competitive balance in ways that traditional 32-team analysis cannot predict. With 16 groups of 3 teams instead of 8 groups of 4, the mathematical probability of upsets—and the consequences of single matches—has fundamentally shifted. Early tournament data already shows this pattern emerging. The Format Change: A Statistical Earthquake The move from 32 to 48 teams introduces a critical structural change: Format Groups Teams/Group Matches/Team Elimination Threshold 2022 (Qatar) 8 4 3 Top 2 of 4 2026 (USA/CAN/MEX) 16 3 2 Top 2 of 3 This seemingly small difference creates massive implications. In a 3-team group, each team plays only 2 matches to determine their fate . Compare this to the 2022 format where teams had 3 chances to secure advancement. The upset probability multiplier: With two fewer matches per group, variance compounds. A single bad result—or a fortunate one—carries exponentially more weight. Early Tournament Evidence: The Data Doesn't Lie Let's examine the first week of actual 2026 results: Match Expected Result Actual Result Upset Indicator Portugal 5-0 Uzbekistan Portugal W Portugal W (5-0) Expected England 0-0 Ghana England W Draw Minor Upset France 3-0 Iraq France W France W (3-0) Expected Argentina 2-0 Austria Argentina W Argentina W (2-0) Expected Norway 3-2 Senegal Senegal slight favorite Norway W Major Upset Jordan 1-2 Algeria Algeria strong favorite Competitive Closer than xG Panama 0-1 Croatia Croatia W Croatia W Expected Colombia 1-0 Congo DR Colombia W Colombia W Expected Three critical takeaways: Norway's 3-2 victory over Senegal is statistically significant. Pre-tournament models favored Senegal slightly (ranked 18th globally vs Norway's 22nd). In a 4-team group, this result matters less; in a 3-team group, Norway essentially secures qualification with one match remaining. England's 0-0 with Ghana represents draw probability explosion. With only 2 group matches, a draw consumes 50% of your advancement op

Jump Tables, TLV Footers, and the Real Cost of Reading What You Don't Need You're paying for bytes you never read. A data engineer on a busy pipeline touches dozens of Parquet files a day: schema discovery, predicate pushdown, column pruning, metadata scrapes for a data catalog sync. In each case, the application needs maybe 200 KB of context from a file that is 4 GB on disk. Without a seekable archive format and a jump table to find the right frame, your HTTP client fetches the whole thing, and your cloud egress invoice reflects every unnecessary gigabyte. This post quantifies the problem, then walks through how HuskHoard uses seekable Zstd frames, a per-volume jump table, and TLV-encoded footer metadata to make partial egress a first-class citizen across multi-volume archives — disk, cloud, and LTO tape alike. The Problem, In Dollars S3 standard egress runs $0.09/GB. GCS is $0.08/GB. Even Cloudflare R2, which is free for egress from R2 to the internet , still costs you in latency and API call count when you cannot bound the range of bytes you need. Here is a representative read pattern for a cold analytics archive: Operation Bytes Needed Bytes Fetched (naïve) Ratio Schema discovery ~50 KB (Parquet footer) 1–8 GB (full file) ~1:16,000 Single column scan ~200 MB (one column chunk) 4 GB (full row group) 1:20 Data catalog sync (1M files) ~50 GB (footers only) ~4 PB (full files) 1:80,000 Selective restore (1 row group) ~128 MB 4 GB 1:32 On 100 TB of cold Parquet data with $0.09/GB egress: Full read for schema sync : 100 TB × $0.09 = $9,216 Partial read (footers only, avg 100 KB/file, 1M files) : ~100 GB × $0.09 = $9.00 Savings per catalog sync: $9,207 — 99.9% reduction Even a conservative column-scan scenario (pulling 15% of each file's bytes) cuts a $9,216 monthly read bill to $1,382 . The ceiling on savings is determined entirely by how precisely you can address the bytes you actually need. That precision is what frames and jump tables buy you. Zstd Frames: What They

"It is an incredible exhibit and incredible sight."

As record-breaking heat crushes Europe, organizers are moving events online to avoid exposing people to dangerously high temperatures.

The biodegradable material can help improve healing after surgery—or an avocado-related accident.

Climate.us has now restored everything taken down by the government.

Algae blooms, peeling paint, and a host of fixes from hydrogen peroxide to nanobubblers have made it hard to diagnose what's wrong with the Reflecting Pool, let alone how to clean up the mess.

A newly identified species of fungus attacks the famous “zombie mushrooms” that control ants.

Current amphibian development may not have been typical of early land vertebrates.

Coherence Neuro has started testing a brain-computer interface that could one day use electrical stimulation to prevent tumors from growing.

The purpose of Starfall is to support the "transport and delivery of goods through space."

Researchers say the discovery could be a “Rosetta stone” for cosmic signals.

The Space Force wants to cut the time to field new satellites from years to weeks, days, or hours.

Seven months after introducing its $119.98 Ultra BodyScan smart scale, Wyze announced a cheaper $79.98 alternative available today that makes a few compromises to shave $40 off the price. There's no Wi-Fi, but you can sync the BodyScan's measurements to Apple Health and Google Fit by connecting to the Wyze mobile app over Bluetooth. And […]

If you're learning Data Analytics and looking to build a strong portfolio, working on real-world...

We’ve all been there: waking up feeling like a zombie despite getting eight hours of sleep. While wearables give us data, they often fail to give us foresight . What if you could predict your stress levels 24 hours in advance? 🚀 In this tutorial, we are going to tackle HRV prediction (Heart Rate Variability) using a state-of-the-art Temporal Convolutional Network (TCN) . By leveraging the Oura Ring API and deep learning, we’ll transform non-stationary biometric time series into actionable insights. Whether you're into time series forecasting or building the next big health-tech app, mastering Temporal Convolutional Networks (TCN) is a game-changer for handling long-term dependencies without the vanishing gradient headaches of traditional RNNs. For those looking for more production-ready examples and advanced biometric signal processing patterns, I highly recommend checking out the deep-dives at WellAlly Blog , which served as a major inspiration for this architecture. The Architecture: Why TCN? Traditional LSTMs are great, but they process data sequentially, making them slow and prone to memory loss over long sequences. TCNs, however, use Dilated Causal Convolutions , allowing the model to look back exponentially further into the past with fewer layers. Data Flow Overview graph TD A[Oura Cloud API] -->|Raw JSON| B(Pandas Preprocessing) B -->|Cleaned HRV/Activity| C{Feature Engineering} C -->|Sliding Windows| D[TCN Model Training] D -->|Dilated Convolutions| E[Stress Trend Prediction] E -->|24h Forecast| F[Dashboard/Alerts] style D fill:#f9f,stroke:#333,stroke-width:2px Prerequisites To follow along, you'll need: Tech Stack : Python, TensorFlow/Keras, Pandas, Scikit-learn. Data : An Oura Cloud Personal Access Token (or use the mock data generator provided). Difficulty : Advanced (Buckle up! 🏎️). Step 1: Fetching Biometric Data First, we need to pull our "Readiness" and "Sleep" data. Oura provides high-resolution HRV samples (usually 5-minute intervals during sleep).