Which shark is this? A. (🦆-ck)+(🪂-🤿) 🦈 B. (🥪-🧙)+(🍫-🍬)... 🦈 C. (🐎-🆙) + (0️⃣+⛽️) 🦈 D. 🍇⚪️🦈 E. Both A & C are correct. F. 🍝🧸🦈show more

This shark is a: A. (🥪-🧙)+🐅+🦈 B. 🍋 🦈... C. 🍇⚪️🦈 D. 🦏✂️🦈show more

This shark is a: A. 🚙🩻🫘🪸🦈 B. ⚫️👆🦈 C.... 🦆⛷️🦈 D. 🐂🦈 E. 🎃🌵🪺🦈show more

These sharks are: A. ⚪️💁🪸🦈 B. ⚫️💁🪸🦈 C. ⚫️💁🦈... D. 🌊⚪️💁🦈 E. 🦩👅🥝🪂🦈show more

This shark is a: A. 🐂🦈 B. 🐅🦈 C.... 🦓🦈 D. 🐆🦈 E. 🇦🇺🐆🌎🦓🦈show more

This shark is a: A. ⚾️👑🦈 B. 🐋🦈 C.... 🦓 🦈 D. 👩‍⚕️🦈 E. 🌋🧁🦙🥓🦈show more

Is this a: A. 🌊⚪️👆🦈 B. 🐅🦈 C. 🥈👆🦈... D. 🚗🫘🪸🦈 E. ⚪️👆🪸🦈show more

This shark is a: A. 🛌😴💤🦈 B. 🇬🇱🦈 C.... 👩‍⚕️🦈 D. 🌵 🦈 E. 🐧🥽💥🧚‍♂️🪃🦈show more

What species do you have? A. 🔵 👈 🦈... B. 🍋 🦈 C. 🔵 🦈 D. 🍣 🦈 E. 🌊 ▫️👆 🦈show more

🦈 Market Shark Trivia! What is the term for... a market condition where stock prices are generally rising and investor confidence is high? 🐂 A) Bull Market 🐻 B) Bear Market 🦈 C) Shark Market 👇 Comment your answer below! 🎁 One random participant will win exclusive MarketSurge swag! Then take advantage of our Market Shark Sale and get 15 months of MarketSurge for $1,499. 👉show more

🦈 Market Shark Trivia! From how far away can... sharks detect the scent of blood? A) 400 meters B) 1 mile C) 3 miles 👇 Comment your answer below! 🎁 One random participant will win exclusive IBD/MarketSurge swag! Then take advantage of our Market Shark Sale and get 6 weeks of MarketSurge for $49.95! 👉show more

Okay, this one is tricky. Now, it would be... significantly less tricky if I tell you where it was filmed—but that defeats the purpose! This is a: A. Tiger Shark B. Silvertip Shark C. Blacknose Shark D. Caribbean Reef Shark E. Grey Reef Shark F. Golden Grotto Sharkshow more

🦈 A new front has opened at sea —... now sharks are invading Russia's coastline In Triozerye Bay, a shark was spotted just a few meters from a paddleboarder. The very next day, witnesses saw two fins near the same beach. Locals say they've never seen sharks this close to shore before. Now beachgoers are afraid to enter the water. Be honest — is this another Ukrainian biological weapon designed to destabilize Russia?show more

Ever seen a chord that swings both ways? In... this excerpt from Schubert, a seventh chord [F-A-C-E♭] first functions as a dominant of B♭, then is respelled with a D♯ instead of E♭ and functions as a German Augmented Sixth chord in A minor.show more

🎥 + 💭: IG @/andriana_marine The moment they choose... you✨ This beautiful tiger shark comes up to me in a curious, non-aggressive way wanting to use her sense of touch, smell, and her ampullae of lorenzini to explore what I am. You can see the shark is not biting or snapping at me, rather approaching to investigate and learn. Peaceful moments like this with sharks are more common than you think. And it is possible for us to coexist with sharks. To safely have interactions like these it takes years to understand the shark’s behavior and body language. But encountering sharks under the watch of professional guides is something that will truly change your life🦈 #SharkWeek returns TOMORROW on Discovery #TigerShark #SharkDiving #SharkDivershow more

[Discrete Fourier Transform] by Hand ✍️ In signal processing,... the Discrete Fourier Transform (DFT) is no doubt the most important method. But the math involved is extremely complex, literally, involving a summation over a complex number term e^(-iwt). I developed this exercise to demonstrate that underneath such complexity, DFT is just a series of matrix multiplications you can calculate by hand. ✍️ Once you see that, it should not surprise you that a deep neural network, which is also a series of matrix multiplications, with activation functions in-between, can learn to perform DFT to process and analyze signals so effectively. How does DFT work? [1] Given ↳ Signals A, B, and C in the 🟧 frequency domain: ◦ A = cos(w) + 2cos(2w) ◦ B = cos(w) + cos(3w) + cos(4w) ◦ C = -cos(2w) + cos(3w) ◦ Each signal is a weighed sum of four cosine waves at frequencies 1w, 2w, 3w, and 4w. ◦ We will apply Inverse DFT to convert the signals to time domain representations, and then demonstrate DFT can convert back to their original frequency domain representations. ↳ Signal X in the 🟩 time domain. X is sampled at 10 time points 1t, 2t, …, 10t: ◦ X = [-2.5, -1.8, 3, -0.7, -1.0, -0.7, 3, -1.8, -2.5, 5] ◦ Suppose X is also a weighted sum of the same four cosine waves, but we don’t already know their weights. We will apply DFT to discover them. [2] 🟧 Frequency Matrix (F) ↳ Write the coefficients of A, B, C as a matrix F. Each signal is a row. Each frequency is a column. ↳ A → [1, 2, 0, 0] ↳ B → [1, 0, 1, 1] ↳ C → [0, 1-, 1, 0] [3] Cosine → Discrete ↳ Sample from the continuous cosine waves at discrete time points 1t, 2t, 3t, to 10t. [4] Cosine Matrix (W) ↳ Write the samples as a matrix, Each frequency is a row. Each time point is a column. [5] Inverse DFT: 🟧 Frequency → 🟩 Time ↳ Multiply the frequency matrix F and the cosine matrix W. ↳ The meaning of this multiplication is to linearly combine the four cosine waves (rows in W) into time-domain signals (rows in T) using the weights specified in F. ↳ The result is matrix T, which are signals A, B, C converted to the time domain. Each signal is a row. Each time point is a column. [6] Transpose ↳ Transpose T, converting each signal’s time domain representation from a row to a column. [7] DFT: 🟩 Time → 🟧 Frequency ↳ Multiply the cosine matrix W with the transpose of matrix T. ↳ The purpose of this multiplication is to take a dot-product between each time-domain signal (columns in the transpose of T) and each cosine wave (rows in W), which has the effect of projecting the signal onto a cosine wave to determine how much they are correlated. Zero means not correlated at all. ↳ The result is an intermediate version of the “recovered” frequency matrix where each column corresponds to a signal and each row corresponds to a frequency. ↳ Compared to the original frequency matrix F, this intermediate matrix has non-zero weights in the correct places, but scaled up by a factor of 5 (n/2, n=10). For example, signal A, originally [1,2,0,0], is recovered at [5,10,0,0]. [8] Scale ↳ Multiply each value by 2/n = 1/5 to scale down the intermediate matrix to match the magnitude of the original frequency matrix F. [9] Transpose ↳ Transpose the recovered frequency matrix back to the same orientation of the original frequency matrix F. ↳ Like magic 🪄, the result is identical to the original F, which means DFT successfully recovered the frequency components of signals A, B, C. [10] Apply DFT to X: 🟩 Time → 🟧 Frequency ↳ Now that we have some confidence in DFT’s ability to recover frequency components, we apply DFT to X’s time-domain representation by multiplying W with X. ↳ The result is the an intermediate matrix. [11] Scale ↳ Similarly, we scale down by a factor of 5 to obtain the recovered frequency components of X (a column). [12] Transpose ↳ Similarly, we transpose the recovered column to row to match the orientation of the frequency matrix. ↳ Using the coefficients [0,0,3,2], we can write the equation of X as 3cos(3w) + 2cos(4w). Notes: I hope this by hand exercise helps you understand the essence of DFT. But there is more technical details, such as: • Sine: The complete DFT math also includes sine waves that follow a similar calculation process. • Phase: Here, we assume all the cosine waves are aligned at the origin, namely, phase is 0. If a phase p is added, for example, cos(w+p), we will need to calculate the sine component and use their ratio to figure out what p is. • Magnitude: If phase is not zero, the magnitude will need to be calculated by combining both cosine and sine terms.show more

[Graph Convolutional Network] by hand ✍️ Graph Convolutional Networks... (GCNs), introduced by Thomas Kipf and Max Welling in 2017, have emerged as a powerful tool in the analysis and interpretation of data structured as graphs. This exercise demonstrates how GCN works in a simple application: binary classification. -- Goal -- Predict if a node in a graph is X. -- Architecture -- 🟪 Graph Convolutional Network (GCN) 1. GCN1(4,3) 2. GCN2(3,3) 🟦 Fully Connected Network (FCN) 1. Linear1(3,5) 2. ReLU 3. Linear2(5,1) 4. Sigmoid Simplications: • Adjacent matrices are not normalized. • ReLU is applied to messages directly. -- Walkthrough -- [1] Given ↳ A graph with five nodes A, B, C, D, E [2] 🟩 Adjacency Matrix: Neighbors ↳ Add 1 for each edge to neighbors ↳ Repeat in both directions (e.g., A->C, C->A) ↳ Repeat for both GCN layers [3] 🟩 Adjacency Matrix: Self ↳ Add 1's for each self loop ↳ Equivalent to adding the identity matrix ↳ Repeat for both GCN layers [4] 🟪 GCN1: Messages ↳ Multiply the node embeddings 🟨 with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [5] 🟪 GCN1: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The purpose is the pool messages from each node's neighbors as well as from the node itself. ↳ The result is a new feature per node [6] 🟪 GCN1: Visualize ↳ For node 1, visualize how messages are pooled to obtain a new feature for better understanding ↳ [3,0,1] + [1,0,0] = [4,0,1] [7] 🟪 GCN2: Messages ↳ Multiply the node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is one message per node [8] 🟪 GCN2: Pooling ↳ Multiply the messages with the adjacent matrix ↳ The result is a new feature per node [9] 🟪 GCN2: Visualize ↳ For node 3, visualize how messages are pooled to obtain a new feature for better understanding ↳ [1,2,4] + [1,3,5] + [0,0,1] = [2,5,10] [10] 🟦 FCN: Linear 1 + ReLU ↳ Multiply node features with weights and biases ↳ Apply ReLU (negatives → 0) ↳ The result is a new feature per node ↳ Unlike in GCN layers, no messages from other nodes are included. [11] 🟦 FCN: Linear 2 ↳ Multiply node features with weights and biases [12] 🟦 FCN: Sigmoid ↳ Apply the Sigmoid activation function ↳ The purpose is to obtain a probability value for each node ↳ One way to calculate Sigmoid by hand ✍️ is to use the approximation below: • >= 3 → 1 • 0 → 0.5 • <= -3 → 0 -- Outputs -- A: 0 (Very unlikely) B: 1 (Very likely) C: 1 (Very likely) D: 1 (Very likely) E: 0.5 (Neutral)show more

So, President Trump can receive the Traditional Flight into... D.C. on Air Force One (this is a C-32, which is what Trump Force One is as well)…. But find me the video for “Biden” being flown into D.C. on any Air Force aircraft. On January 20, 2021, these things took place: a. "Joe" did not receive the traditional flight into D.C. on any Air Force aircraft. b. Swore in at 11:47 AM EST - violation of the 20th Amendment. c. National Guard did NOT salute motorcade and moving Flags, had turned their backs turned to them. d. Amazing Grace was performed - Army Memorial Honors for a Funeral. e. There was NOT a Commissioned Officer at Cannons show for “Biden” - violation of Army Regulations 600-25, 2-3 Cannon Salutes. f. The ONLY Cannons shown all day for "Joe" was 3 Cannons at Arlington National Cemetery... which is a Military FUNERAL Honors NOT a 21-Gun Salute. g. The 4 Cannons, 21-Gun Salute, was at Joint Base Andrews with President Trump. h. On the Capitol Building, the States Flag, only had 13 Stars for “Joe”… matching President Trump’s 13 Stars on January 20, 2017, which is Rhode Island… Neither have lived in Rhode Island. The only other reason there would be 13 Stars… is 13 original Colonies at the signing of the Declaration of Independence. Which talks about resetting our Foundation. I guess that’s why on January 20, 2017, President Trump said, “we are taking the power from D.C. and giving it back to you the people.” I guess that’s why there’s 27 Stars on the State Flags currently hanging and waiting for January 20, 2025. 27 Stars = Florida. And here’s President Trump receiving the traditional flight. The Dash Matters. 👉🏻 —— 👉🏻 45——47 🫡🐂🇺🇸show more

$OWB is LIVE on Base 🧨 GameFi -> GameAI... 📜 Contract: 0xEF5997c2cf2f6c138196f8A6203afc335206b3c1 ⚠️ Don’t get tricked — here’s the only legit Uniswap link: 🎯 Played in Season 1? Go claim what’s yours 👉 🎮 Already in Season 2? Good. It’s allocated 5%+ of the total token supply. As always — the deeper you play, the more you own. 💧 Liquidity kicks in mid-April — from the shop. The more players join → the more flows in → the more $OWB gets real weight. 💥 Pre-Seed → Seed We’re neck-deep in architecting the next chapter: don’t rush, this isn’t a memecoin. We’re trying to build, not extract. 🛡️ Only as much $OWB as matches our current scale will ever hit the market — this is our built-in insurance against typical GameFi cycles. We prioritize real players and real contributors. 📊 Some of you are probably waiting for Binance tomorrow, with 30% of the supply available without lockup. But while you watch charts, we’re building the meta, engineering the economy, and scaling players. We want $OWB to be the true engine of the economy. Without this, our goal to create a unicorn will fail. 👀 Q2 = Playtime meets buildtime: a) Partner Roadshow b) Mobile Drop c) Encrypted chat — powered by d) P2P Marketplace e) Major Update e.g. late-game unlocked f) Gaming Clans g) AI Gamemaster...something hot af is cooking 🦄 Congratulations to our giant listing-neighbor GUNZ Official for helping to establish the next Web2.5 gaming paradigm. It’s a hard way to build, but the only one. We're shaping the next wave. We're player-focused. We're all in.show more

Be this guy when you pull up at the... pump: the ETF series of the Ninepoint Energy Fund trades under the ticker "NNRG." Disclaimer: All returns and fund details are a) based on Series F shares; b) net of fees; c) annualized if period is greater than one year; d) as at 2/28/2026. Where applicable, all figures are annualized and based on monthly returns since inception. Inception date is 4/16/2004. The rate of return is used only to illustrate the effects of the compound growth rate and is not intended to reflect future values of the investment fund or returns on investment in the investment fund. In each taxation year, the Fund will distribute to its investors a sufficient amount of the Fund’s net income and net realized capital gains so that the Fund will not pay any income tax. The net income and the net realized capital gains of the Fund will be distributed annually in December. The Fund is generally exposed to the following risks: Active management risk; Concentration risk; Credit risk; Currency risk; Cybersecurity risk; Derivatives risk; Energy risk; Exchange traded funds risk; Foreign investment risk; Inflation risk; Interest rate risk; Liquidity risk; Market risk; Performance fee risk; Regulatory risk; Rule 144A and other exempted securities risk; Securities lending, repurchase and reverse repurchase transactions risk; Series risk; Short selling risk; Small capitalization natural resource company risk; Specific issuer risk; Tax risk; Absence of an active market for ETF Series risk; Halted trading of ETF Series risk; Trading price of ETF Series risk. Ninepoint Partners LP is the investment manager to a number of funds (collectively, the “Funds”). Commissions, trailing commissions, management fees, performance fees (if any), and other expenses all may be associated with investing in the Funds. Please read the prospectus carefully before investing. The indicated rates of return for series F units of the Funds for the period ended 2/28/2026 are based on the historical annual compounded total returns including changes in unit value and reinvestment of all distributions or dividends and do not take into account sales, redemption, distribution or optional charges or income taxes payable by any securityholder that would have reduced returns. Mutual funds are not guaranteed, their values change frequently, and past performance may not be repeated. This communication does not constitute an offer to sell or solicitation to purchase securities of the Funds. The information contained herein does not constitute an offer or solicitation by anyone in the United States or in any other jurisdiction in which such an offer or solicitation is not authorized or to any person to whom it is unlawful to make such an offer or solicitation. Prospective investors who are not a resident in Canada should contact their financial advisor to determine whether securities of the Fund may be lawfully sold in their jurisdiction.show more