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The legacy of Dongju Yun (1917.12.30 1945.02.16), a renowned Korean poet and independence activist, has inspired many across generations. With a life marked by profound introspection and unwavering integrity, his works continue to resonate deeply. In homage to his memory and contributions, the Poet smart pen design project was born, a tribute to Yuns enduring spirit and the value of thoughtful creativity in everyday life.Poet is more than just a tool for writing and drawing; its a medium to instill meaning into everyday moments. The design team aimed to embody the warmth and respect symbolized by Dongju Yuns life, encouraging users to pause and appreciate the significance of time and expression. The pen represents a blend of artistry and utility, mirroring the poets ability to weave profound reflections into simple, everyday language.Designers: NARA LEE,junsu lee, andJiung YunAlthough Dongju Yun did not actively participate in Koreas independence movement, he grappled with deep shame and chose to channel his feelings into poignant self-reflection. His consistent use of the Korean language in all his works, from drafts to published poetry collections, highlights his devotion to his cultural identity. This unwavering integrity influenced the Poet design. The handle of the pen, a part held closely by the users hand, is crafted with a thicker and sturdier CMF (color, material, and finish) than the upper sections. This deliberate contrast emphasizes strength and resilience, aligning with the poets enduring legacy.The Poet pen draws inspiration from five of Dongju Yuns most iconic works. Each pens name, color, and design motifs are based on symbolic elements from these poems. Visualizations of these motifs are engraved on the charger and pen point, subtly embedding the poets essence into every interaction. Through this thoughtful integration of color and imagery, Poet becomes a tribute to Yuns artistry.The design of Poet embodies the softness and simplicity characteristic of Dongju Yuns poetic style. Gentle curves dominate the pens form, mirroring his use of everyday language to create profound expressions. A nod to the classic ink bottle, the pens refill mechanism is designed to resemble smooth, natural curves, further enhancing the tactile and visual experience.One of the standout features of Poet is its innovative charger design. The chargers base includes a slanted cut that allows the pen cap to magnetically attach during charging, addressing a common issue of misplaced caps. This thoughtful addition integrates practicality with aesthetics, enhancing the overall user experience.By reinterpreting traditional forms and embedding symbolic details, the Poet smart pen becomes more than just a writing instrument. Its a celebration of Dongju Yuns life, a reflection of his poetic elegance, and a tool that inspires users to find meaning in every moment. Whether in the act of writing, sketching, or simply holding the pen, Poet invites us to pause and connect with the timeless values of integrity, creativity, and cultural pride.The post This Pen Honors Dongju Yuns Legacy with Artistic Design and Thoughtful Innovation first appeared on Yanko Design.

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John Henry Turpin (18761962) was a sailor in the United States Navy in the late 19th and early 20th centuries. He was one of the first African-American chief petty officers in the U.S. Navy, becoming a chief gunner's mate on the cruiser Marblehead in 1917. He was transferred to the Fleet Reserve in 1919 and retired in 1925. He is also notable for surviving the catastrophic explosions of two U.S. Navy ships: USSMaine in 1898, and USSBennington in 1905.Photograph credit: unknown photographer; restored by Adam CuerdenRecently featured: Tocopilla railwayColias croceusMichael William BalfeArchiveMore featured pictures

January 12: Zanzibar Revolution Day in Tanzania (1964)Comet McNaught1659 The fort at Allahabad was surrendered to the forces of Mughal emperor Aurangzeb.1879 Anglo-Zulu War: Natal Native Contingent and British troops defeated Zulu forces in the Action at Sihayo's Kraal.1899 During a storm, the crew of Lynmouth Lifeboat Station transported their 10-ton lifeboat 15mi (24km) overland in order to rescue a damaged schooner.1967 Seventy-three-year-old psychology professor James Bedford became the first person to be cryonically preserved with intent of future resuscitation.2007 Comet McNaught (pictured) reached perihelion, becoming the brightest comet in over 40years, with an apparent magnitude of 5.5.John Singer Sargent (b.1856)Laura Adams Armer (b.1874)Princess Patricia of Connaught (d.1974)More anniversaries: January 11January 12January 13ArchiveBy emailList of days of the yearAbout

Multi-hop queries have always given LLM agents a hard time with their solutions, necessitating multiple reasoning steps and information from different sources. They are crucial for analyzing a models comprehension, reasoning, and function-calling capabilities. At this time when new large models are booming every other day with claims of unparalleled capabilities, multi-hop tools realistically assess them by bestowing with a complex query, which the model needs to decompose into atomic parts and iteratively solve by invoking and utilizing appropriate tools. Furthermore, multi-hop tool evaluation has emerged as pivotal for advancing models toward generalized intelligence.Existing works in this field fall short of offering a reliable evaluation method. Methods proposed until now have relied on tool-driven data construction methods where queries are simulated for a given collection of tools. This shortfall points out the loophole in ensuring the interdependence of collected tools and assessing the multi-hop reasoning. Additionally, the absence of verifiable answers introduces model bias and evaluation errors. This article discusses the latest research that presents a reliable method to honestly assess the multi-hop capabilities of a large language model.Fudan University and ByteDance researchers presented ToolHop, a dataset designed explicitly for multi-hop tool evaluation with 995 rigorously designed user queries and 3,912 associated tools. Toolhop claims to solve all the aforementioned problems through diverse queries, locally executable tools, meaningful interdependencies, detailed feedback, and verifiable answers. The authors propose a novel query-driven data construction approach that could expand a single multi-hop query into a comprehensive multi-hop tool use test case.The proposed novel scheme comprises three key stages: tool creation, document refinement, and code generation.Tool Creation: A preliminary set of tool documents is created per the user-provided multi-hop query. The document is designed to keep it interdependent and relevant by resolving queries into atomic parts and individually handling each. This way, the document captures the essence of the query and structures itself to generate similar queries, ensuring modularity and cohesion.Document Refinement: The prepared tool document undergoes comprehensive filtering to support the evaluation of models in complex multi-hop scenarios. Here, new features like result filtering and customizable formats are introduced to expand functionality while maintaining originality. Parallelly, the number of parameters is increased, and their types are optimized.Code Generation: At this stage, locally executable functions are generated by the prepared tool. Through these functions, tools are externally invoked, enabling seamless multi-turn interactions between the model and tools.The research team implemented the approach with the queries drawn from the MoreHopQA dataset. Further, to ensure the evaluation with ToolHop, a rigorous five-dimensional analysis was done. ToolHop was then evaluated on fourteen LLMs from five families, including open and closed-sourced models. The evaluation method was so designed that answer correctness and minimized invocation errors were ensured. The authors observed that using tools increased the models performance by up to 12 % on average and by up to 23 % for GPT models. The best-performing model could achieve 49.04% answer correctness even after the increase. Also, despite using tools in response to multi-hop queries, models hallucinated around 10% of the time.Conclusion:This paper presents a comprehensive dataset for solving multi-hop queries using specially designed queries and tools. The main finding from the experiments was that while LLMs have significantly enhanced their ability to solve complex multi-shop queries with the use of tools, their multi-shop tool use capabilities still leave considerable room for improvement.Check out the Paper. All credit for this research goes to the researchers of this project. Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our60k+ ML SubReddit. Adeeba Alam Ansari+ postsAdeeba Alam Ansari is currently pursuing her Dual Degree at the Indian Institute of Technology (IIT) Kharagpur, earning a B.Tech in Industrial Engineering and an M.Tech in Financial Engineering. With a keen interest in machine learning and artificial intelligence, she is an avid reader and an inquisitive individual. Adeeba firmly believes in the power of technology to empower society and promote welfare through innovative solutions driven by empathy and a deep understanding of real-world challenges. [Recommended Read] Nebius AI Studio expands with vision models, new language models, embeddings and LoRA (Promoted)

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