MIT教授:RLNC技術助力Web3擴展與發展

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Create a hyper-realistic 8K close-up body portrait of a female model, using the uploaded photo as the exact facial reference. Maintain 100% accuracy of the facial features — do not alter or modify any aspect of the face. Render the skin texture, lighting, and overall composition with photo-realistic detail, ensuring lifelike color tones and natural depth of field. A man with his original hair is sitting casually on a white cube, smiling warmly at the camera. He is wearing a cream-colored cable-knit sweater, blue jeans, and brown loafers. His legs are crossed, with one hand resting on his knee.
The background reveals a cozy and festive living room. A large, beautifully decorated Christmas tree with numerous warm lights and gold ornaments stands prominently behind him. Several wrapped gift boxes are visible at the base of the tree. To his left, another smaller decorated Christmas tree and a wreath on the wall further enhance the holiday atmosphere. The lighting is soft and inviting, creating a warm and welcoming scene.
Generate an ultra-realistic, highly ultra-detailed, 8k resolution with 1080x1080 pixel portrait of me using the uploaded image for reference (preserved the likeness and the original face for reference) of a captivating studio portrait of a woman with long, wavy brown hair, seated gracefully on a stool in a thoughtful pose. She is wearing a long, flowing red gown with a one-shoulder design and a thigh-high slit, exuding elegance and sophistication. Her head is slightly tilted, with one hand gently resting on her chin. One leg is slightly extended, revealing a delicate ankle bracelet. Her makeup is polished and natural, featuring defined eyebrows, subtle eyeshadow, mascara, and a berry-toned lipstick. She wears delicate earrings and a gold bracelet that add a touch of sparkle to her overall look. The lighting is soft and diffused, creating a warm and flattering effect that emphasizes her beauty and the flowing lines of the gown. The background is a plain, brown, providing a warm and neutral backdrop. *** The composition is a full shot, capturing her from head to toe, framed vertically to accentuate her height and the flowing lines of the gown. The model is positioned in the center of the frame, creating a balanced and harmonious composition. The overall style is classic, elegant, and timeless, reminiscent of portrait photography, evoking a sense of grace, sophistication, and enduring beauty.
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Use my image in Ultra-realistic, hyper-detailed, 8K cinematic portrait of a young stylish man, using the uploaded image for exact face and hairstyle.
Outfit: An oversized red knit sweater with white hearts, exactly as described in the prompt.
Pose: A hyper-realistic close-up portrait with a messy, cropped framing showing only the boy holding the book. His left hand rests on the wooden table and covers part of his cheek, with a subtle smile on his lips. His other hand holds the book titled "Something I Never Told You" with the word "YOU" written in pink, exactly as
described in the prompt. Background: Not specified.

‘擴展或失敗’:RLNC技術能推動Web3採用 — 麻省理工學院教授

根據麻省理工學院(MIT)教授及區塊鏈基礎設施開發商Optimum創始人Muriel Médard的說法,隨著在MIT進行了15年的研究,隨機線性網絡編碼(RLNC)現在已準備好在Web3行業進行商業化。

Optimum於2月28日正式亮相,作為一個去中心化的記憶體基礎設施,任何希望提升Web3可擴展性的區塊鏈均可利用。該平台利用了由Médard教授首次提出的RLNC技術。

RLNC是一項編碼突破,已在5G、衛星通訊和物聯網(IoT)行業中得到應用。

在接受Cointelegraph訪問時,Médard教授表示,RLNC就像是“將一個拼圖分解成小塊,將這些小塊混合成方程式,然後發送給你的朋友。”

“即使有幾塊丟失,你的朋友仍然可以根據他們收到的塊拼湊出整個拼圖。與其尋找特定的塊,不如尋找足夠的塊,”她說。

Médard指出,RLNC技術能幫助區塊鏈克服“可擴展性中的關鍵瓶頸”,通過“將數據編碼為數學方程式,實現更快的傳輸、減少帶寬使用、降低靈活節點的進入門檻以及更可靠的交付”。

Médard在Nancy Lynch的幫助下創立了Optimum,後者是拜占庭容錯共識的顧問和共同發明人,Médard表示這是因為“幾年來目睹了Web3的興起和成熟”。

“我們的願景是將傳統計算機記憶體(RAM)的效率帶入去中心化網絡,為Web3基礎設施的突破奠定基礎。”

“擴展或失敗”

RLNC在Web3中的潛在應用引起了不少著名支持者的關注,其中一些人作為天使投資者投資了Optimum。他們包括Polygon的聯合創始人Sandeep Nailwal、Wormhole的聯合創始人Robinson Burkey、Polychain首席技術官Abhijeet Mahagaonkar、Bitget首席執行官Gracy Chen以及DeFiance Capital的創始人兼首席執行官Arthur Cheong。

Médard教授告訴Cointelegraph,Web3需要可擴展性的突破,特別是隨著區塊鏈採用不斷增長,這是出於“支付、金融工具甚至國家政府策略的多樣化需求”。

“我們相信這一趨勢將持續下去,隨著使用量和需求的增加,區塊鏈必須擴展,否則將會失敗,”她說。

可擴展性仍然是行業最大的一個瓶頸,這個問題在比特幣和以太坊的歷史上多次出現。競爭網絡誓言要解決由於大規模消費者採用而引發的可擴展性問題,但他們的記錄並不完美。

在這樣的背景下,加密支付的格局在近年來發生了顯著變化,從代幣轉向速度更快、成本更低的穩定幣。

穩定幣已成為區塊鏈最受歡迎的應用之一,特別是在支付和跨境匯款方面。根據財富管理公司Bernstein的一份報告,Solana是穩定幣採用的領先網絡,但即使如此,它在面對日益增長的支付和匯款需求時也面臨擴展困難。

儘管Solana已與Visa和Shopify試點穩定幣支付,但Bernstein表示,尚不清楚該區塊鏈是否能在沒有大幅提升容量的情況下促進主流採用。

在這篇文章中,我們看到RLNC技術的潛力不僅在於其能夠解決可擴展性問題,還在於它能夠為Web3帶來更高的效率和可靠性。隨著區塊鏈技術的發展,如何有效地處理數據傳輸和存儲將成為未來的關鍵挑戰。這不僅僅是技術的進步,更是對整個行業未來發展的深遠影響。隨著越來越多的企業和用戶進入Web3,RLNC技術的應用將成為推動這一轉型的重要力量。

以上文章由特價GPT API KEY所翻譯及撰寫。而圖片則由FLUX根據內容自動生成。

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