Hasil untuk "Style. Composition. Rhetoric"

Shiwen Zhang, Haibin Huang, Chi Zhang et al.

Content-Preserving Style transfer, given content and style references, remains challenging for Diffusion Transformers (DiTs) due to its internal entangled content and style features. In this technical report, we propose the first content-preserving style transfer model trained on Qwen-Image-Edit, which activates Qwen-Image-Edit's strong content preservation and style customization capability. We collected and filtered high quality data of limited specific styles and synthesized triplets with thousands categories of style images in-the-wild. We introduce the Curriculum Continual Learning framework to train QwenStyle with such mixture of clean and noisy triplets, which enables QwenStyle to generalize to unseen styles without degradation of the precise content preservation capability. Our QwenStyle V1 achieves state-of-the-art performance in three core metrics: style similarity, content consistency, and aesthetic quality.

Anuj Gupta, Ann Shivers-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.

Jingyi Qiu, Hong Chen, Zongyi Li

The rise of AI has fueled growing concerns about ``hype'' in machine learning papers, yet a reliable way to quantify rhetorical style independently of substantive content has remained elusive. Because bold language can stem from either strong empirical results or mere rhetorical style, it is often difficult to distinguish between the two. To disentangle rhetorical style from substantive content, we introduce a counterfactual, LLM-based framework: multiple LLM rhetorical personas generate counterfactual writings from the same substantive content, an LLM judge compares them through pairwise evaluations, and the outcomes are aggregated using a Bradley--Terry model. Applying this method to 8,485 ICLR submissions sampled from 2017 to 2025, we generate more than 250,000 counterfactual writings and provide a large-scale quantification of rhetorical style in ML papers. We find that visionary framing significantly predicts downstream attention, including citations and media attention, even after controlling for peer-review evaluations. We also observe a sharp rise in rhetorical strength after 2023, and provide empirical evidence showing that this increase is largely driven by the adoption of LLM-based writing assistance. The reliability of our framework is validated by its robustness to the choice of personas and the high correlation between LLM judgments and human annotations. Our work demonstrates that LLMs can serve as instruments to measure and improve scientific evaluation.

Francesco Bilotta, Alberto Binetti, Giacomo Manferdini

This paper studies the supply and effects of causal rhetoric in U.S. politics. We define causal rhetoric as assigning responsibility for political outcomes, via claims of blame and merit. Training a supervised classifier, we detect causal rhetoric in over a decade of congressional tweets, finding that its supply has risen rapidly and pervasively, displacing affective messaging. We show that the production of causal rhetoric involves a trade-off between revenues and costs. First, quasi-random variation in Twitter adoption shows that blame increases small-donor revenues by expanding donor count, while merit raises average donation size. Second, fine-grained legislative data suggest that policy ownership determines relative costs: blame is cheaper for opponents, merit for proposers. Finally, causal rhetoric has downstream effects on societal outcomes, fostering protest activity and shaping polarization and institutional trust.

Chanseok Lee, Fakhriyya Mammadova, Jiseong Barg et al.

Inline holographic imaging presents an ill-posed inverse problem of reconstructing objects' complex amplitude from recorded diffraction patterns. Although recent deep learning approaches have shown promise over classical phase retrieval algorithms, they often require high-quality ground truth datasets of complex amplitude maps to achieve a statistical inverse mapping operation between the two domains. Here, we present a physics-aware style transfer approach that interprets the object-to-sensor distance as an implicit style within diffraction patterns. Using the style domain as the intermediate domain to construct cyclic image translation, we show that the inverse mapping operation can be learned in an adaptive manner only with datasets composed of intensity measurements. We further demonstrate its biomedical applicability by reconstructing the morphology of dynamically flowing red blood cells, highlighting its potential for real-time, label-free imaging. As a framework that leverages physical cues inherently embedded in measurements, the presented method offers a practical learning strategy for imaging applications where ground truth is difficult or impossible to obtain.

Mariano Vilar

Este artículo analiza el De falso credita et ementita Constantini donatione de Lorenzo Valla a partir de sus vínculos con el género dialógico que cultivó en obras como De vero bono, De libero arbitrio y De professione religiosorum. Aunque estructurado como una oratio forense, el texto incorpora recursos propios de la disputatio humanística —prosopopeya, apóstrofe, enargeia, concessio— que lo convierten en un “diálogo in absentia” con adversarios históricos e imaginarios. El estudio muestra cómo Valla construye escenas verosímiles para resaltar la inverosimilitud del Constitutum Constantini, personifica figuras como el falsificador “Palea” para exhibir su ignorancia o hipocresía, y emplea la concessio como trampa dialéctica que refuerza la refutación. Estas estrategias no cumplen solo una función estilística, sino que configuran una “crítica dialógica” en la que la filología se vuelve performativa, revive el pasado y despoja de autoridad a textos e instituciones. Se concluye que esta obra combina retórica forense y dramatización dialógica para transformar la crítica filológica en un acto de emancipación intelectual, afirmando la primacía de la razón y de la evidencia frente a toda jerarquía.

Abhishek Dangeti, Pavan Gajula, Vivek Srivastava et al.

Clustering artworks based on style can have many potential real-world applications like art recommendations, style-based search and retrieval, and the study of artistic style evolution of an artist or in an artwork corpus. We introduce and deliberate over the notion of 'Style-based clustering of visual artworks'. We argue that clustering artworks based on style is largely an unaddressed problem. We explore and devise different neural feature representations - from the style-classification, style-transfer to large language vision models - that can be then used for style-based clustering. Our objective is to assess the relative effectiveness of these devised style-based clustering approaches through qualitative and quantitative analysis by applying them to multiple artwork corpora and curated synthetically styled datasets. Besides providing a broad framework for style-based clustering and evaluation, our analysis provides some key novel insights on feature representations, architectures and implications for style-based clustering.

Nuowei Liu, Xinhao Chen, Hongyi Wu et al.

Existing rhetorical understanding and generation datasets or corpora primarily focus on single coarse-grained categories or fine-grained categories, neglecting the common interrelations between different rhetorical devices by treating them as independent sub-tasks. In this paper, we propose the Chinese Essay Rhetoric Dataset (CERD), consisting of 4 commonly used coarse-grained categories including metaphor, personification, hyperbole and parallelism and 23 fine-grained categories across both form and content levels. CERD is a manually annotated and comprehensive Chinese rhetoric dataset with five interrelated sub-tasks. Unlike previous work, our dataset aids in understanding various rhetorical devices, recognizing corresponding rhetorical components, and generating rhetorical sentences under given conditions, thereby improving the author's writing proficiency and language usage skills. Extensive experiments are conducted to demonstrate the interrelations between multiple tasks in CERD, as well as to establish a benchmark for future research on rhetoric. The experimental results indicate that Large Language Models achieve the best performance across most tasks, and jointly fine-tuning with multiple tasks further enhances performance.

Preetika Verma, Kokil Jaidka, Svetlana Churina

Large language models (LLMs) play a key role in generating evidence-based and stylistic counter-arguments, yet their effectiveness in real-world applications has been underexplored. Previous research often neglects the balance between evidentiality and style, which are crucial for persuasive arguments. To address this, we evaluated the effectiveness of stylized evidence-based counter-argument generation in Counterfire, a new dataset of 38,000 counter-arguments generated by revising counter-arguments to Reddit's ChangeMyView community to follow different discursive styles. We evaluated generic and stylized counter-arguments from basic and fine-tuned models such as GPT-3.5, PaLM-2, and Koala-13B, as well as newer models (GPT-4o, Claude Haiku, LLaMA-3.1) focusing on rhetorical quality and persuasiveness. Our findings reveals that humans prefer stylized counter-arguments over the original outputs, with GPT-3.5 Turbo performing well, though still not reaching human standards of rhetorical quality nor persuasiveness indicating a persisting style-evidence tradeoff in counter-argument generation by LLMs. We conclude with an examination of ethical considerations in LLM persuasion research, addressing potential risks of deceptive practices and the need for transparent deployment methodologies to safeguard against misuse in public discourse. The code and dataset are available at https://github.com/Preetika764/Style_control/.

Marc Angenot, Marianne Doury, Théophile Robineau

Dans l’entretien qu’il a accordé à Marianne Doury et Théophile Robineau, Marc Angenot revient sur la place de la rhétorique dans ses travaux. Rappelant que l’ancienne rhétorique se fonde essentiellement sur le modèle judiciaire, il en souligne l’intérêt, mais aussi les limites, pour explorer le discours social dont il cherche à en rendre compte. Il en reprend la perspective globalisante, mobilisant ethos, pathos et logos, dont la prise en compte conjuguée est nécessaire à la compréhension des idées et de la façon dont elles sont portées et discutées dans la société. Mais il insiste sur la nécessaire prise en compte de l’inscription du discours social dans une histoire de plus ou moins long terme, condition à son intelligibilité. Le fait de se donner le discours social comme objet de recherche oblige également à reconsidérer la notion de situation telle que l’envisage traditionnellement la rhétorique, et à redéfinir le regard porté sur la question de la persuasion.

S. Werner

Horace's fifth Roman Ode has often been taken as a push for war with Parthia and thus interpreted as an uncomfortable insertion of Horace's voice into politics. This article argues for a profoundly different take on the ode's rhetoric and thus on its political relevance. The analysis brings to bear a theoretical perspective recoverable from ancient rhetorical handbooks, which saw analogy, exemplarity, and some form of visual comparison as three aspects of a single argumentative or stylistic figure: comparison. The ode—structured as a movement through these forms of comparison—aims at nothing less than the articulation of a vision of Rome's, and Augustus’, place in the establishment of a new cosmic order along with a return to the old Roman virtues. Within the ode, oratorical figures of style and thought work together with a poetic strategy—Horace's deployment of an intricate and previously undetected ring composition—to produce meaning on many levels, embracing aesthetic and literary practices, rhetorical and ancient theoretical approaches, and political, religious and cosmological, and philosophical concerns.

Sergey A. Dekhanov

The article provides a brief summary of the rhetoric of Herennius. The rhetoric of Herennius, along with the works of Aristotle, Cicero, and Quintilian, is the pearl of ancient rhetoric and is the first judicial rhetoric that was used as a textbook in the Middle Ages. In the rhetoric of Herennius, an analysis of the types of speeches is given with exhaustive completeness (a demonstrative speech is devoted to the praise or censure of a famous person, a deliberative speech consists of discussing political issues and is aimed at choosing a particular decision, a judicial speech is based on a legal dispute); The skills of invention (inventio), composition (dispositio), style (elocutio), memorization (memoria) and performance (pronuntiactio and methods of their application) are analyzed in detail. It is concluded that creative achievements of this kind (like the rhetoric of Herennius) appear once or twice every millennium, but still they exist and it is they that motivate modern researchers in the field of judicial rhetoric to deeply comprehend their depths and move on.

A. Voloshina

The article examines comparisons with music found in the rhetorical works of Dionysius of Halicarnassus. The aim of the study is to analyze the peculiarities of these comparisons, to explain their significance for the ancient critic. In his comparisons, Dionysius introduces musical concepts and terms, both common and more specific. The comparisons made by Dionysius reveal the influence not only of musical theory, but also of philosophical tradition, particularly Aristotle and the Peripatetics. Dionysius emphasizes the similarity of rhetoric and music in the ability of the orator to weaken and strengthen el ements of style in the same way as a musician changes the tension of strings, as well as in acquiring the skills to master both arts. Dionysius’ musical comparisons are important explaining tools: comparisons of the style of Lysias and Thucydides with νήτη and ὑ πάτη, the average type of word composition with μέση are essential for understanding the key concepts of three styles of eloquence and three types of composition.

Amir Hertz, Andrey Voynov, Shlomi Fruchter et al.

Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling these models to ensure consistent style remains challenging, with existing methods necessitating fine-tuning and manual intervention to disentangle content and style. In this paper, we introduce StyleAligned, a novel technique designed to establish style alignment among a series of generated images. By employing minimal `attention sharing' during the diffusion process, our method maintains style consistency across images within T2I models. This approach allows for the creation of style-consistent images using a reference style through a straightforward inversion operation. Our method's evaluation across diverse styles and text prompts demonstrates high-quality synthesis and fidelity, underscoring its efficacy in achieving consistent style across various inputs.

P. N. Deelaka

A neural artistic style transformation (NST) model can modify the appearance of a simple image by adding the style of a famous image. Even though the transformed images do not look precisely like artworks by the same artist of the respective style images, the generated images are appealing. Generally, a trained NST model specialises in a style, and a single image represents that style. However, generating an image under a new style is a tedious process, which includes full model training. In this paper, we present two methods that step toward the style image independent neural style transfer model. In other words, the trained model could generate semantically accurate generated image under any content, style image input pair. Our novel contribution is a unidirectional-GAN model that ensures the Cyclic consistency by the model architecture.Furthermore, this leads to much smaller model size and an efficient training and validation phase.

Eleonora Lima

This article examines Levi’s treatment of eugenics in “I sintetici” and “Procacciatori d’affari” from Vizio di forma. The study builds upon Francesco Cassata’s analysis, which established that Levi held complex and conflicting views on the topic. These views mirrored his strong belief in avoiding limitations on scientific research while also revealing his ethical concerns. To further understand this predicament, the study reads Levi’s stories against the debate on eugenics that took place in England in the 1920s-1930. This debate engaged scientists and writers who significantly influenced Levi beyond this subject, including the Huxley brothers and Bertrand Russell. In this intellectual milieu, science fiction emerged as a favoured genre for exploring the intricate facets of eugenics and its ethical ramifications. By undertaking a comparative analysis between these antecedents and Vizio di forma, this study investigates how and why Levi turned to science fiction to articulate his conflicting thoughts on eugenics.

I. Korotkina

Written to commemorate the 10th anniversary of the rubric “Academic Writing and Research Competences” established by the journal’s late editor-in-chief Mikhail Sapunov, the paper focuses on the origins of academic writing and traces its development in terms of rhetoric. The five stages of classical rhetoric are interpreted as five key components of academic writing: research, logic, culture, knowledge, and language. This approach helps visualize academic writing as a wholesome model composed of cognitive and linguistic elements, describe the impact of this model on the rhetorical and publishing conventions of the global academic discourse, and define the problems in knowledge construction as deviations from the model’s unity in various sociocultural contexts. The study concludes that the low quality of an academic text may result from either losing the predominance of the first two stages of rhetoric (invention and arrangement) or of the other three (style, memory, and delivery). The former signifies an ideological pressure on researchers to substitute their own rhetoric with quotes from canonized sources, whereas the latter provokes them to disregard language and style as inferior to research, because of which texts diminish in clarity. In either case, communication lacks in efficiency. The study of academic writing in the historical perspective contributes to better understanding of the latest trends in its development and elicits the problems which impede the quality of Russian scholarly and academic texts.

Daniel Healy

abstract:Structuralism as a working method has not come into contact with the body of compositionist scholarship for quite some time, leading writing studies scholars to conclude that its former place of prominence in the discipline was an empiricist reaction to language's inescapable ambiguity (Crowley), or even a radical mistake counter to the very spirit of hermeneutics (Berthoff). This article takes an archival approach toward excavating composition studies' institutional forums to better map American structuralism's once-central role within a discipline that has long since rejected it. Furthermore, it aims to raise the specter that seemingly dead-end structuralist methodologies—A. J. Greimas' structural semantics (1966/1983, Structural Semantics), Frank DeAngelo's theoretical bridging of structural linguistics, rhetoric, and case grammar ("Generative Stylistics: Between Grammar and Rhetoric," "Notes toward a Semantic Theory of Rhetoric within a Case Grammar Framework")—hold possibilities for linking sentence-level style to whole-text rhetorical meaning in theorizing and teaching writing.

Tianxin Tao, Xiaohang Zhan, Zhongquan Chen et al.

Motion style transfer is a common method for enriching character animation. Motion style transfer algorithms are often designed for offline settings where motions are processed in segments. However, for online animation applications, such as realtime avatar animation from motion capture, motions need to be processed as a stream with minimal latency. In this work, we realize a flexible, high-quality motion style transfer method for this setting. We propose a novel style transfer model, Style-ERD, to stylize motions in an online manner with an Encoder-Recurrent-Decoder structure, along with a novel discriminator that combines feature attention and temporal attention. Our method stylizes motions into multiple target styles with a unified model. Although our method targets online settings, it outperforms previous offline methods in motion realism and style expressiveness and provides significant gains in runtime efficiency

Juyong Lee, Seokjun Ahn, Jaesik Park

We present a novel method of learning style-agnostic representation using both style transfer and adversarial learning in the reinforcement learning framework. The style, here, refers to task-irrelevant details such as the color of the background in the images, where generalizing the learned policy across environments with different styles is still a challenge. Focusing on learning style-agnostic representations, our method trains the actor with diverse image styles generated from an inherent adversarial style perturbation generator, which plays a min-max game between the actor and the generator, without demanding expert knowledge for data augmentation or additional class labels for adversarial training. We verify that our method achieves competitive or better performances than the state-of-the-art approaches on Procgen and Distracting Control Suite benchmarks, and further investigate the features extracted from our model, showing that the model better captures the invariants and is less distracted by the shifted style. The code is available at https://github.com/POSTECH-CVLab/style-agnostic-RL.