搜索结果：Inspiring

Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effort toward identifying content that is inspiring, as opposed to just engaging or positive. Additionally, most research has concentrated on Western data, with little attention paid to other cultures. This work is the first to study cross-cultural inspiration through machine learning methods. We aim to identify and analyze real and AI-generated cross-cultural inspiring posts. To this end, we compile and make publicly available the InspAIred dataset, which consists of 2,000 real inspiring posts, 2,000 real non-inspiring posts, and 2,000 generated inspiring posts evenly distributed across India and the UK. The real posts are sourced from Reddit, while the generated posts are created using the GPT-4 model. Using this dataset, we conduct extensive computational linguistic analyses to (1) compare inspiring content across cultures, (2) compare AI-generated inspiring posts to real inspiring posts, and (3) determine if detection models can accurately distinguish between inspiring content across cultures and data sou

Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model "good practice" for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that "inspiration talk" is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged-novelty, authority, authenticity, and affect-and three operative metaphors that license different practices-spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.

For most of human history, we have not thought systematically about how and why we incorporate aspects of the natural world into our designs. The lack of a systematic approach has resulted in inconsistencies in motivations and methods that make it difficult to predict or evaluate the success of bio-inspired design. This mismatch between expectations and results can lead to disappointment when a reader considers a bio-inspired design to be superficial, weak, or incomplete. This is especially true in the field of Robotics, in which similarity to a biological system might be the driving motivation for construction. In an effort to assist robotics researchers justify their specific bio-inspired approach and to assist funding program managers with discerning the value of different bio-inspired approaches, here we propose a taxonomy of motivations for bio-inspired design and describe the potential significant contributions that are likely to result from different approaches.

In the first part of the talk, I review general properties of $SO(10)$-inspired leptogenesis. This high-scale leptogenesis scenario is based on the simple assumption that the neutrino Dirac mass matrix is not too different from the up quark mass matrix. After showing how this necessarily implies a production of the asymmetry from the next-to-lightest right handed neutrino decays, so-called $N_2$-leptogenesis, I discuss how this results into important testable constraints on low energy neutrino parameters. In particular inverted ordering is not viable if strict $SO(10)$-inspired conditions are assumed. This is an important test in view of the expected results from the JUNO experiment. I also discuss how a subset of the $SO(10)$-inspired leptogenesis solutions realises strong thermal leptogenesis, where the final asymmetry is independent of the initial conditions. In this case a signal might be discovered by next generation $0νββ$ decay experiments. In the second part, I present some new results from \cite{DiBari:2025zlv}, where the impact of flavour coupling on $SO(10)$-inspired leptogenesis has been studied in detail.

Anti-Ramsey theory was initiated in 1975 by Erdős, Simonovits and Sós, inspiring hundreds of publications since then. The present work is the third and last piece of our trilogy in which we introduce a far-reaching generalization via the following two functions for any graph $G$ and family ${\cal F}$ of graphs: If $K_2 \in {\cal F}$, let $f(n,G|{\cal F})$ be the smallest integer $k$ such that every edge coloring of $K_n$ with at least $k$ colors forces a copy of $G$ in which all color classes are members of ${\cal F}$. If $K_2 otin {\cal F}$, let $g(n,G|{\cal F})$ be the largest integer $k$ for which there exists an edge coloring of $K_n$ using exactly $k$ colors, such that every copy of $G$ contains an induced color class which is a member of ${\cal F}$. We develop methods suitable for deriving asymptotically tight results for the $f$-function and the $g$-function for many combinations of $G$ and ${\cal F}$. The preceding parts of the trilogy are arXiv: 2405.19812 and 2408.04257, published in Discrete Applied Math. Vol. 363 and Mathematics Vol. 12:23, respectively.

Recent large language model (LLM) research has undergone an architectural shift from encoder-decoder modeling to nowadays the dominant decoder-only modeling. This rapid transition, however, comes without a rigorous comparative analysis especially \textit{from the scaling perspective}, raising concerns that the potential of encoder-decoder models may have been overlooked. To fill this gap, we revisit encoder-decoder LLM (RedLLM), enhancing it with recent recipes from decoder-only LLM (DecLLM). We conduct a comprehensive comparison between RedLLM, pretrained with prefix language modeling (LM), and DecLLM, pretrained with causal LM, at different model scales, ranging from $\sim$150M to $\sim$8B. Using RedPajama V1 (1.6T tokens) for pretraining and FLAN for instruction tuning, our experiments show that RedLLM produces compelling scaling properties and surprisingly strong performance. While DecLLM is overall more compute-optimal during pretraining, RedLLM demonstrates comparable scaling and context length extrapolation capabilities. After instruction tuning, RedLLM achieves comparable and even better results on various downstream tasks while enjoying substantially better inference effic

The Noisy Intermediate-Scale Quantum (NISQ) era of technology in which we currently find ourselves is defined by non-universality, susceptibility to errors and noise, and a search for useful applications. While demonstrations of practical quantum advantage remain elusive in this era, it provides space to develop and analyze the advantages and limitations of systems and their ability to solve problems. In this work, we critically assess a proposed quantum algorithm for the graph isomorphism problem, implemented on a photonic quantum device. Inspired by the nature of this quantum algorithm, we formulate a necessary condition for the isomorphism of graphs encoded in Gaussian boson samplers and a classical algorithm to test for it. Our classical algorithm makes use of efficiently computable statistical properties of a quantum sampling system to show a pair of graphs fail to meet our necessary condition and thus cannot be isomorphic. We analyze our algorithm in the context of the inspiring, sampler-based quantum algorithm of Bràdler et. al., the classical color refinement algorithm, and the state-of-the-art quasi-polynomial Babai algorithm.

Metamaterials benefit from unique architected patterns to achieve lightweight with exceptional mechanical properties inaccessible to conventional materials. Typical mechanical metamaterials are inspired by crystal-like lattice structures, whose closely packed frameworks often exhibit a rigid mechanical nature. Here, we present polymer-inspired metamaterials (PIMs) by programming deformation and strengthening mechanisms that mimic the mechanical roles of key constituent elements in polymer networks. By combining metamaterial programmability with polymer-inspired structures, we design crosslinking, proto-crystalline order, and entanglement in PIMs to enable macroscale strengthening mechanisms inspired by crosslink, molecular-density, and pre-stretch strengthening in polymers, expanding the metamaterial structure-property design space. This macroscale polymer-inspired programmability also suggests that PIMs could serve as a design platform incorporating the programmability strategies to achieve desired deformation and strengthening responses, holding a potential for applications in soft robotic joints and compliant connectors.

Aquatic mammals, such as pinnipeds, utilize their whiskers to detect and discriminate objects and analyze water movements, inspiring the development of robotic whiskers for sensing contacts, surfaces, and water flows. We present the design and application of underwater whisker sensors based on Fiber Bragg Grating (FBG) technology. These passive whiskers are mounted along the robot$'$s exterior to sense its surroundings through light, non-intrusive contacts. For contact tracking, we employ a sim-to-real learning framework, which involves extensive data collection in simulation followed by a sim-to-real calibration process to transfer the model trained in simulation to the real world. Experiments with whiskers immersed in water indicate that our approach can track contact points with an accuracy of $<2$ mm, without requiring precise robot proprioception. We demonstrate that the approach also generalizes to unseen objects.

Inspiration plays an important role in design, yet its specific impact on data visualization design practice remains underexplored. This study investigates how professional visualization designers perceive and use inspiration in their practice. Through semi-structured interviews, we examine their sources of inspiration, the value they place on them, and how they navigate the balance between inspiration and imitation. Our findings reveal that designers draw from a diverse array of sources, including existing visualizations, real-world phenomena, and personal experiences. Participants describe a mix of active and passive inspiration practices, often iterating on sources to create original designs. This research offers insights into the role of inspiration in visualization practice, the need to expand visualization design theory, and the implications for the development of visualization tools that support inspiration and for training future visualization designers.

In this study, we investigate traversable wormholes inspired by the Kiselev framework, which extends classical black hole solutions by incorporating anisotropic fluids. These exotic fluids play a crucial role in cosmology, particularly in explaining phenomena such as the accelerated expansion of the universe. We generalize the Kiselev framework to static, spherically symmetric traversable wormholes and analyze their properties under two distinct models of the redshift function: a constant redshift function and one that varies inversely with the radial coordinate. We examine the energy conditions-specifically the Null Energy Condition (NEC), Weak Energy Condition (WEC), and Strong Energy Condition (SEC)-for these models, demonstrating that only certain exotic fluids can sustain the wormhole structure. Furthermore, we quantify the amount of exotic matter required to maintain these wormholes using the volume integral quantifier and compare our results with other wormhole models. Additionally, we compute the effective potential for photons in Kiselev-inspired wormholes under both redshift function models and analyze their implications for weak gravitational lensing. Our findings sugges

Online, visual artists have more places than ever to routinely share their creative work and connect with other artists. These interactions support the routine enactment of creative identity in artists and provide inspirational opportunities for artists. As creative work shifts online, interactions between artists and routines around how these artists get inspired to do creative work are mediated by and through the logics of the online platforms where they take place. In an interview study of 22 artists, this paper explores the interplay between the development of artists' creative identities and the, at times, contradictory practices they have around getting inspired. We find platforms which support the disciplined practice of creative work while supporting spontaneous moments of inspiration, play an increasing role in passive approaches to searching for inspiration, and foster numerous small community spaces for artists to negotiate their creative identities. We discuss how platforms can better support and embed mechanisms for inspiration into their infrastructures into their design and platform policy.

Graphical User Interface (or simply UI) is a primary mean of interaction between users and their devices. In this paper, we discuss three complementary Artificial Intelligence (AI) approaches for triggering the creativity of app designers and inspiring them create better and more diverse UI designs. First, designers can prompt a Large Language Model (LLM) to directly generate and adjust UIs. Second, a Vision-Language Model (VLM) enables designers to effectively search a large screenshot dataset, e.g. from apps published in app stores. Third, a Diffusion Model (DM) can be trained to specifically generate UIs as inspirational images. We present an AI-inspired design process and discuss the implications and limitations of the approaches.