Hasil untuk "Style. Composition. Rhetoric"

Raymie E. McKerrow

Taieesa Peshkovsky, Stefan Andreas Schmid, David Taylor et al.

Concentration gradients at the microscale play a central role in many physical, chemical, and biological systems, yet their quantitative visualization remains challenging due to the limited optical contrast associated with changes in concentration. Here, we present RIO (the Refractive Index Observer), a label-free interferometric tool for quantitative imaging of refractive index, and thus concentration fields in microfluidic systems. Implemented using a Fabry-Pérot microfluidic chip mounted on a standard optical microscope, RIO achieves a per-pixel refractive index precision on the order of $1\times 10^{-5}$ refractive index units (RIU) using a standard CMOS camera, enabling high sensitivity two-dimensional chemical imaging. We Characterize the refractive index resolution and spatiotemporal performance of the instrument and demonstrate its capabilities by measuring concentration gradients of dissolved NaCl in a co-laminar flow. RIO provides an accessible, label-free platform for quantitative studies of microscale concentration fields in systems where molecular labeling is undesirable or impractical, and enables investigations of a broad range of out-of-equilibrium phenomena, from polymerization and enzymatic reactions to cell signaling and electrochemical processes.

Alessandro Cecchi

L’articolo tratta il problema della veridicità del film considerando il contributo della musica come componente del suono cinematografico. A tal fine vengono discusse preliminarmente due questioni teorico-metodologiche: la relazione tra fiction e documentario, guardando alle teorie del genere per svincolare la veridicità dal riferimento privilegiato alla nonfiction, e la relazione tra musica e veridicità, intendendo quest’ultima come proprietà emergente del film, in linea con le recenti applicazioni della teoria del bricolage alla produzione cinematografica. Queste riflessioni forniscono gli strumenti per studiare il contributo della musica in film di fiction con pretese di veridicità quali sono i film italiani d’inchiesta, storici e politici del periodo 1950-1975, accomunati da pratiche produttive assimilabili e scelte musicali inconsuete, che concorrono alla veridizione.

Carla Fernández-Rico, Robert W. Style, Stefanie Heyden et al.

Elastic MicroPhase Separation (EMPS) provides a simple route to create soft materials with homogeneous microstructures by leveraging the supersaturation of crosslinked polymer networks with liquids. At low supersaturation, network elasticity stabilizes a uniform mixture, but beyond a critical threshold, metastable microphase-separated domains emerge. While previous theories have focused on describing qualitative features about the size and morphology of these domains, they do not make quantitative predictions about EMPS phase diagrams. In this work, we extend Flory-Huggins theory to quantitatively capture EMPS phase diagrams by incorporating strain-stiffening effects. This model requires no fitting parameters and relies solely on independently measured solubility parameters and large-deformation mechanical responses. Our results reveal that strain-stiffening enables metastable microphase separation within the swelling equilibrium state and why the microstructures can range from discrete droplets to bicontinuous networks. This works highlights the critical role of nonlinear elasticity in controlling phase-separated morphologies in polymer gels.

Ondřej Pešek

The text examines the relationship between ancient rhetoric and certain key principles of contemporary text linguistics (textual plans, textual sequences). Considering the classical notions of inventio and dispositio from an etiological perspective, we explore the analogies between ancient and modern conceptions of argumentative, narrative, and descriptive structures. The article emphasises that, despite the necessary epistemological differences, classical rhetoric remains an essential source for understanding the foundations of contemporary linguistic theories.

Alberto Comparini

Rencesione di Pierliugi Pellini, Tre grandi critici. Luigi Blasucci, Remo Ceserani, Francesco Orlando. Siké, 2023.

Anuj Gupta, A. McNair

In this paper, we demonstrate how studying the rhetorics of ChatGPT prompt writing on social media can promote critical AI literacies. Prompt writing is the process of writing instructions for generative AI tools like ChatGPT to elicit desired outputs and there has been an upsurge of conversations about it on social media. To study this rhetorical activity, we build on four overlapping traditions of digital writing research in computers and composition that inform how we frame literacies, how we study social media rhetorics, how we engage iteratively and reflexively with methodologies and technologies, and how we blend computational methods with qualitative methods. Drawing on these four traditions, our paper shows our iterative research process through which we gathered and analyzed a dataset of 32,000 posts (formerly known as tweets) from X (formerly Twitter) about prompt writing posted between November 2022 to May 2023. We present five themes about these emerging AI literacy practices: (1) areas of communication impacted by prompt writing, (2) micro-literacy resources shared for prompt writing, (3) market rhetoric shaping prompt writing, (4) rhetorical characteristics of prompts, and (5) definitions of prompt writing. In discussing these themes and our methodologies, we highlight takeaways for digital writing teachers and researchers who are teaching and analyzing critical AI literacies.

Mais T. Al-Khateeb

Mais T. Al-Khateeb is Assistant Professor of English at Florida State University. Her research engages 20th and 21st centuries contemporary rhetorical theory from a transnational feminist perspective with a focus on refugees, their embodiments, and their mobilities. Al-Khateeb’s in-progress monograph traces refugee screening rhetorics to examine how they materialize and shape refugee encounters in local and global contexts. Other research interests include disability studies, feminist studies of science and technology, posthumanism, and new materialism. Al-Khateeb’s published and forthcoming work appears in Rhetoric Society Quarterly and edited collections Abstract: This essay brings together transnational feminist rhetorical studies and critical conversations in care with scholarship in the rhetoric of health and medicine (RHM) and technical and professional communication (TPC) to propose a methodological framework for reading and reimagining cultural interventions in transnational health contexts. This framework, what I term unexceptional logics of care, centers analyses of globalized power to interrogate the logics underlying the composition of cultural interventions intended to support refugees and health providers in health contexts. Using this framework to analyze the Centers for Disease Control and Prevention’s (CDC) 2014-2017 “Refugee Health Profiles,” I demonstrate how and why cultural interventions can become rhetorically entangled with logics of US exceptionalism that can limit the imaginaries of caregivers and foreclose possibilities for responsive care encounters. The analysis highlights three central logics (comparison, (re)victimization, and recognition of evidence) to consider in the construction of cultural interventions to challenge “non-performative” and/or violent forms of care in refugee health contexts.

Lei Hu, Zihao Zhang, Yongjing Ye et al.

3D Human motion style transfer is a fundamental problem in computer graphic and animation processing. Existing AdaIN- based methods necessitate datasets with balanced style distribution and content/style labels to train the clustered latent space. However, we may encounter a single unseen style example in practical scenarios, but not in sufficient quantity to constitute a style cluster for AdaIN-based methods. Therefore, in this paper, we propose a novel two-stage framework for few-shot style transfer learning based on the diffusion model. Specifically, in the first stage, we pre-train a diffusion-based text-to-motion model as a generative prior so that it can cope with various content motion inputs. In the second stage, based on the single style example, we fine-tune the pre-trained diffusion model in a few-shot manner to make it capable of style transfer. The key idea is regarding the reverse process of diffusion as a motion-style translation process since the motion styles can be viewed as special motion variations. During the fine-tuning for style transfer, a simple yet effective semantic-guided style transfer loss coordinated with style example reconstruction loss is introduced to supervise the style transfer in CLIP semantic space. The qualitative and quantitative evaluations demonstrate that our method can achieve state-of-the-art performance and has practical applications.

Zhiyu Xie, Yuqing Zhang, Xiangjun Tang et al.

Style-guided texture generation aims to generate a texture that is harmonious with both the style of the reference image and the geometry of the input mesh, given a reference style image and a 3D mesh with its text description. Although diffusion-based 3D texture generation methods, such as distillation sampling, have numerous promising applications in stylized games and films, it requires addressing two challenges: 1) decouple style and content completely from the reference image for 3D models, and 2) align the generated texture with the color tone, style of the reference image, and the given text prompt. To this end, we introduce StyleTex, an innovative diffusion-model-based framework for creating stylized textures for 3D models. Our key insight is to decouple style information from the reference image while disregarding content in diffusion-based distillation sampling. Specifically, given a reference image, we first decompose its style feature from the image CLIP embedding by subtracting the embedding's orthogonal projection in the direction of the content feature, which is represented by a text CLIP embedding. Our novel approach to disentangling the reference image's style and content information allows us to generate distinct style and content features. We then inject the style feature into the cross-attention mechanism to incorporate it into the generation process, while utilizing the content feature as a negative prompt to further dissociate content information. Finally, we incorporate these strategies into StyleTex to obtain stylized textures. The resulting textures generated by StyleTex retain the style of the reference image, while also aligning with the text prompts and intrinsic details of the given 3D mesh. Quantitative and qualitative experiments show that our method outperforms existing baseline methods by a significant margin.

Yu-Wen Pao, An-Jie Li

In this work, we propose a novel pipeline that combines AdaIN and NeRF for the task of stylized Novel View Synthesis. Compared to previous works, we make the following contributions: 1) We simplify the pipeline. 2) We extend the capabilities of model to handle the multi-style task. 3) We modify the model architecture to perform well on styles with strong brush strokes. 4) We implement style interpolation on the multi-style model, allowing us to control the style between any two styles and the style intensity between the stylized output and the original scene, providing better control over the stylization strength.

Ada Hubrig

Drawing on disability studies analysis of institutional narratives of disability by composition and rhetoric scholars, this article theorizes “favor access.” Favor access gestures toward inclusion, but is steeped in the capitalist, colonialist logic of academic institutions in service of ultimately extractive, dehumanizing agendas. Instead of favor access, the article points to collective access as articulated by disability justice activists. As opposed to favor access, collective access rejects institutional logics and values community and collaboration rather than academia’s emphasis on individualism and competition. This article considers sites where collective access is happening in composition classrooms and in the field of composition and rhetoric.

Guoqing Luo, Yu Tong Han, Lili Mou et al.

Prompting approaches have been recently explored in text style transfer, where a textual prompt is used to query a pretrained language model to generate style-transferred texts word by word in an autoregressive manner. However, such a generation process is less controllable and early prediction errors may affect future word predictions. In this paper, we present a prompt-based editing approach for text style transfer. Specifically, we prompt a pretrained language model for style classification and use the classification probability to compute a style score. Then, we perform discrete search with word-level editing to maximize a comprehensive scoring function for the style-transfer task. In this way, we transform a prompt-based generation problem into a classification one, which is a training-free process and more controllable than the autoregressive generation of sentences. In our experiments, we performed both automatic and human evaluation on three style-transfer benchmark datasets, and show that our approach largely outperforms the state-of-the-art systems that have 20 times more parameters. Additional empirical analyses further demonstrate the effectiveness of our approach.

Aleem Khan, Andrew Wang, Sophia Hager et al.

Prior work in style-controlled text generation has focused on tasks such as emulating the style of prolific literary authors, producing formal or informal text, and mitigating toxicity of generated text. Plentiful demonstrations of these styles are available, and as a result modern language models are often able to emulate them, either via prompting or discriminative control. However, in applications such as writing assistants, it is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample. For example, someone writing in a particular dialect may prefer writing suggestions that retain the same dialect. We find that instruction-tuned language models can struggle to reproduce author-specific style demonstrated in a prompt. Instead, we propose to guide a language model to generate text in a target style using contrastively-trained representations that capture stylometric features. Our approach (StyleMC) combines an author-adapted language model with sequence-level inference to improve stylistic consistency, and is found to be effective in a variety of conditions, including unconditional generation and style transfer. Additionally, we find that the proposed approach can serve as an effective anonymization method, by editing a document to mask authorship while preserving the original meaning

Chanda Grover Kamra, Indra Deep Mastan, Debayan Gupta

CLIPStyler demonstrated image style transfer with realistic textures using only a style text description (instead of requiring a reference style image). However, the ground semantics of objects in the style transfer output is lost due to style spill-over on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIPStyler (Sem-CS), that performs semantic style transfer. Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description. The semantic style transfer is achieved using global foreground loss (for salient objects) and global background loss (for non-salient objects). Our empirical results, including DISTS, NIMA and user study scores, show that our proposed framework yields superior qualitative and quantitative performance. Our code is available at github.com/chandagrover/sem-cs.

Christèle Couleau

In his long correspondence with Louise Colet, Flaubert establishes a double ethos of lover and author. These two facets are linked, around the virility key issue: they highlight the same claim to singularity; they enable the lover to slide from self-justification to a discourse of mastery; and the gender difference provides stereotypical structures likely to consolidate his authority, both in relation to his partner and to the contemporary literary field. Ethos is constructed dynamically and differentially, in a process of negotiation that closely involves his partner.

Martina Bortignon

Este artículo se centra en una selección de obras narrativa y poéticas del chileno Manuel Rojas y del estadounidense Gary Snyder, ambos autores montañistas y acomunados por la inquietud por plasmar, en el lenguaje literario, el adentrarse del ser humano en el ambiente salvaje de la montaña y su sumirse en un estado de alerta perceptiva y contemplación lúcida frente a escalas dispares que allí se evidencian. Enmarcándose en la teoría de las escalas (DiCaglio, Jue, Horton, Cueto) y en el concepto de aventura (Agamben), la hipótesis postula que las obras registran cómo la experiencia humana de la escalaridad en el medio montano produce una expansión de la conciencia, derivando, según el caso, en una desposesión del yo o en una intuición ofuscada del peligro mortal que se propone como exemplum al lector, así como en una modificación del lenguaje literario mismo que acompaña y hace posible la aventura.

Olivia Lewi

This article proposes to shed light on the relationship between style, ethos and auctoriality from a discourse analysis perspective, through a corpus belonging to the testimonial genre. The corpus constructed for this study is made up of four “ordinary” testimonies borrowed from a larger corpus made up of typescripts or manuscripts of Holocaust survivors deposited at the Centre de Documentation Juive Contemporaine (CDJC) of the Mémorial de la Shoah in Paris. We will show that their testimonies invite us to question the collective norm of an era insofar as they bring out tensions between practices, collective models of writing and a search for singularity. The incessant adaptation to the socio-discursive expectations that constitute the norms of testimonial discourse is not, however, synonymous with an absence of style if we understand it as the distinctive value of a discourse that conforms to generic and discursive patterns. We will analyze here the “capture” of the Yizher-Biher model, a discursive phenomenon that informs us about the mechanism of construction of a collective ethos. These “memory books” are presented as traces of belonging to a specific community. The discursive analysis makes it possible to reread certain testimonies in the light of a singular-collective discursive pattern and, from an ethical point of view, to re-anchor these testimonies in their cultural roots.

Pengyu Cheng, Ruineng Li

We introduce replacing language model (RLM), a sequence-to-sequence language modeling framework for text style transfer (TST). Our method autoregressively replaces each token of the source sentence with a text span that has a similar meaning but in the target style. The new span is generated via a non-autoregressive masked language model, which can better preserve the local-contextual meaning of the replaced token. This RLM generation scheme gathers the flexibility of autoregressive models and the accuracy of non-autoregressive models, which bridges the gap between sentence-level and word-level style transfer methods. To control the generation style more precisely, we conduct a token-level style-content disentanglement on the hidden representations of RLM. Empirical results on real-world text datasets demonstrate the effectiveness of RLM compared with other TST baselines. The code is at https://github.com/Linear95/RLM.

Xu Yao, Alasdair Newson, Yann Gousseau et al.

We propose a novel architecture for GAN inversion, which we call Feature-Style encoder. The style encoder is key for the manipulation of the obtained latent codes, while the feature encoder is crucial for optimal image reconstruction. Our model achieves accurate inversion of real images from the latent space of a pre-trained style-based GAN model, obtaining better perceptual quality and lower reconstruction error than existing methods. Thanks to its encoder structure, the model allows fast and accurate image editing. Additionally, we demonstrate that the proposed encoder is especially well-suited for inversion and editing on videos. We conduct extensive experiments for several style-based generators pre-trained on different data domains. Our proposed method yields state-of-the-art results for style-based GAN inversion, significantly outperforming competing approaches. Source codes are available at https://github.com/InterDigitalInc/FeatureStyleEncoder .