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The Weather Company announced an "enhanced allergy experience" now available through its The Weather Channel app designed to help allergy sufferers better understand when their symptoms might flare up, and what's causing them. While the app already provides static pollen counts, its "Health & Wellness" section is being expanded to take into account other factors […]
The spaceman wears Prada.
Alaska's multibillion-dollar fishing industry and vulnerable coastal communities at risk.
Our ancestors' genomes were built through successive waves of gene transfers.
As summer returns, I'm again reminded of my limits as I head into the great outdoors: I can put up with a heavy, uncomfortable backpack, bug bites, mud, and even bland dehydrated food, but I will not forsake my morning brew. I've tried every imaginable coffee gadget in my half-century of camping. These range from […]
Venues hosting the 2026 World Cup must meet high standards to obtain environmental certifications, but FIFA also requires that they use natural grass, which is water-intensive to maintain.
Elon Musk is set to make hundreds of billions even as communities in Mississippi and Tennessee are fighting to stop the gas turbines powering xAI's supercomputers.
Customers have complained that a telehealth network selling compounded GLP-1s has been ripping them off—even after it had to pay $5 million to clients as part of a settlement with the US government.
For days after the stunning incident, the ADA had doubled-down on the choice.
Anthropic just released Claude Fable 5, calling it the most powerful AI model it has ever made widely available and praising its skills in biology, among others. But the model won't answer basic biology questions - the kind you'd expect a high schooler to handle. Instead, it hands off the query to the former flagship […]
With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system.
We train a model on handwritten digit classification. 99% accuracy . Then we train the same model on a new task — say, fashion item recognition. We go back and test it on digits. 34% accuracy . It has completely forgotten. Not gradually, not partially — almost entirely. What Just Happened? We trained a CNN on MNIST digits — 99.2% accuracy . After fine‑tuning on Fashion MNIST, it reached 91.1% accuracy . But when re‑evaluated on MNIST, accuracy collapsed to 33.9% . This collapse is catastrophic forgetting : the model’s weights shifted to optimize for the new task, erasing the old solution. Why did training on more data make the model worse at something it already knew? MNIST is handwritten digits (0–9). Fashion MNIST is clothing items like shirts and shoes. Both are 28×28 grayscale images, but the tasks are distinct. Why Does It Happen? The core issue is that the model relies on the same set of weights for both tasks. There is no separation or dedicated memory; every parameter is shared . When training shifts from Task A ( MNIST digits ) to Task B ( Fashion MNIST ), gradient descent simply minimizes the loss on the data it sees at that moment. It has no awareness that Task A ever existed. In the loss landscape, imagine two parabolic bowls: one for Task A and one for Task B. The optimum for Task A lies at θ A ∗ , while Task B's optimum is at θ B ∗ . As training on Task B progresses, the weights θ move towards θ B ∗ . This movement inevitably raises the loss for Task A because its minimum is left behind. The root cause is the shared weight space. Gradient descent is a stateless optimizer; it only follows the current gradient signal. Since the minima for Task A and Task B are far apart, there is no single configuration of θ that satisfies both tasks simultaneously. This is why catastrophic forgetting occurs. Weight space can be visualized as an N-dimensional space, where each axis corresponds to one parameter. Every point in this space represents a full set of wei
Problem Link - https://leetcode.com/problems/delete-node-in-a-linked-list/ This is one of those interview questions that looks impossible at first. Normally, to delete a node from a Linked List, we need access to the previous node. But in this problem, we're only given the node that needs to be deleted. No head. No previous pointer. So how do we remove it? Let's understand the trick. Problem Statement Write a function to delete a node in a singly linked list. You are not given the head of the list. Instead, you are given only the node that needs to be deleted. Example Input: 4 -> 5 -> 1 -> 9 node = 5 Output: 4 -> 1 -> 9 Initial Thought Normally we delete a node like this: prev.next = node.next But here: We don't have prev We don't have head So the usual deletion approach is impossible. Key Observation Although we cannot delete the current node directly, we can make it look like it never existed. Consider: 4 -> 5 -> 1 -> 9 We need to delete: 5 Instead of removing node 5 , copy the value of the next node into it. 4 -> 1 -> 1 -> 9 Now remove the next node. 4 -> 1 -> 9 The original value 5 has disappeared. Mission accomplished. Intuition Copy the next node's value into the current node. Skip the next node. The current node now behaves as if it was deleted. Since the problem guarantees that the given node is not the tail node, a next node will always exist. Dry Run Input 4 -> 5 -> 1 -> 9 node = 5 Current node: 5 Next node: 1 Step 1 Copy next node value. node.val = node.next.val List becomes: 4 -> 1 -> 1 -> 9 Step 2 Skip next node. node.next = node.next.next List becomes: 4 -> 1 -> 9 Done. Optimal Java Solution class Solution { public void deleteNode ( ListNode node ) { ListNode cur = node . next ; node . val = cur . val ; node . next = cur . next ; } } Even Shorter Version class Solution { public void deleteNode ( ListNode node ) { node . val = node . next . val ; node . next = node . next . next ; } } Complexity Analysis Metric Complexity Time Complexity O(1) Space Comp
I recently built GeoPrizm , a free and open-source dashboard for tracking bilateral relations through global news event signals. The idea is simple: instead of reading dozens of headlines every day and trying to guess whether a relationship is improving or worsening, can we turn public news event data into a readable trend signal? GeoPrizm is my attempt at that. Website: https://www.geoprizm.com/en GitHub: https://github.com/Haullk/relationship-temperature The problem International relations are usually discussed through headlines, speeches, official statements, and expert commentary. That is valuable, but it creates a few practical problems: It is hard to compare country pairs on the same scale. A single headline can feel more important than it really is. Readers often see conclusions before they see the underlying signals. Most non-specialists do not have time to follow every event in detail. I wanted a lightweight way to answer one question: Based on public news event signals, is this bilateral relationship trending more cooperative, neutral, or tense? Data source: GDELT GeoPrizm uses the GDELT global news event database. GDELT monitors global news coverage and converts news reports into structured event records. These records include fields such as: actor countries event date CAMEO event category GoldsteinScale value number of mentions number of articles source information For GeoPrizm, the key idea is to focus on events where two countries appear as actors, then aggregate the cooperation or conflict signals over time. From event signals to an index Each bilateral pair is converted into a 0-100 relationship index. The midpoint is 50. Above 50 means the recent signal is more cooperative or favorable. Around 50 means the signal is relatively neutral or mixed. Below 50 means the recent signal is more tense or conflict-heavy. The rough process is: Select recent GDELT events for a country pair. Keep events where both actors are present and the GoldsteinScale value is
Earlier parts examined the performance characteristics of sequential and random access under single-threaded execution, and noted in passing the destructive effect of random access on the TLB. This part devotes full attention to the TLB: what it is, why a TLB miss is more severe than a cache miss, why a page table walk constitutes one of the longest dependency chains a CPU can encounter, how huge pages fundamentally alter TLB reach, and where memory-level parallelism falters in the face of TLB misses. Page Boundaries: Where the Prefetcher Halts Part III, in its discussion of prefetchers, noted a hard constraint: a prefetcher must not cross page boundaries on its own authority. The operating system manages virtual memory in units of pages (typically 4 KB, i.e., 64 cache lines). When a program reaches the end of one page and is about to step into the next, the prefetcher cannot proceed. The reason is that the next page may not reside in physical memory (it may have been swapped out to disk), or it may be an entirely invalid virtual address — if the prefetcher were to speculatively initiate an access to the next page, it would trigger a page fault: the OS would have to suspend the process and swap the page in from disk; in the case of an invalid address, the OS would terminate the process outright. From a security standpoint, the prefetcher neither can nor is permitted to autonomously cross page boundaries without TLB approval. Hence a performance brake appears every 4 KB — even when traversing an array sequentially, after every 64 cache line accesses the prefetch pipeline must pause and await confirmation of an address translation. This is not to say that modern CPU prefetchers are completely unable to cross pages. Intel's Next Page Prefetcher and AMD's equivalent mechanism can consult the TLB when approaching a page boundary — if the address mapping for the next page is already registered in the TLB, the prefetcher receives clearance to continue prefetching across th
A recent video of a great white shark in the Mediterranean Sea offers the possibility of deriving valuable information for conservation strategies.
Cities are dynamic, not static grids, and urbanization is a "spiky," cyclical, and asynchronous process.
Five peer-reviewed papers update the design and model its expected output.
If you scrape Weibo's hot-search board you get a snapshot: ~50 trending topics, ranked, right now. That's table stakes — and on its own it's almost useless as a signal. The value isn't what is trending; it's what's moving : which topic just jumped 30 places in 20 minutes, which is decaying, which is brand-new this hour. That's velocity , and velocity is where the signal lives — for brand-crisis teams, consumer-trend desks, and anyone modelling attention in China. The catch: a single scrape can't tell you velocity. You have to diff the board against its own past, reliably, run after run. That's a stateful pipeline, and it has a few non-obvious gotchas. Here's the shape of the problem and how to handle it. Why a snapshot isn't enough Rank-right-now tells you nothing about trajectory. "#7" could be a topic on its way to #1 or one fading out of the top 50 — same row, opposite meaning. To act on a trend you need the derivative : direction, speed, and how long it's been climbing. None of that is in a single pull. The trending-delta problem Three things make "just diff the board" harder than it looks: Key by identity, not position. You can't track a topic by its rank — rank is the thing that changes. Key by the topic itself (its text/keyword) or your deltas are nonsense. State has to survive between runs. A scheduled scrape is stateless by default — each run starts cold. To compute "this rose 12 places since 30 minutes ago," you must persist the previous board and reload it next run, keyed so independent schedules don't overwrite each other. The board churns. Topics appear, peak, and fall off. You want each tagged new / rising / falling / steady / dropped , plus how long it's been on the board and its running peak — none of which exist in the raw snapshot. How to handle it (the pattern) current = pull_board () # [{topic, rank, heat}, ...] previous = load_state ( key ) # durable store that persists across runs for t in current : prev = previous . get ( t . topic ) # match o
Individual gold atoms move around to form oxidation-proof structures.