Multiple Styles Research Articles

Multi-Model Style-Aware Diffusion Learning for Semantic Image Synthesis

Semantic image synthesis aims to generate images from given semantic layouts, which is a challenging task that requires training models to capture the relationship between layouts and images. Previous works are usually based on Generative Adversarial Networks (GAN) or autoregressive (AR) models. However, the GAN model's training process is unstable, and the AR model’s performance is seriously affected by the independent image encoder and the unidirectional generation bias. Due to the above limitations, these methods tend to synthesize unrealistic, poorly aligned images and only consider single-style image generation. In this paper, we propose a Multi-model Style-aware Diffusion Learning (MSDL) framework for semantic image synthesis, including a training module and a sampling module. In the training module, a layout-to-image model is introduced to transfer the learned knowledge from a model pretrained with massive weak correlated text-image pairs data, making the training process more efficient. In the sampling module, we designed a map-guidance technique and creatively designed a multi-model style-guidance strategy for creating images in multiple styles, e.g., oil painting, Disney Cartoon, and pixel style. We evaluate our method on Cityscapes, ADE20K, and COCO-Stuff, making visual comparisons and computing with multiple metrics such as FID, LPIPS, etc. Experimental results demonstrate that our model is highly competitive, especially in terms of fidelity and diversity.

Life Histories of Taboo Knowledge Artifacts

Communicating about some vital topics---such as sexuality and health---is treated as taboo and subjected to censorship. How can we construct knowledge about these topics? Wikipedia is home to numerous high-quality knowledge artifacts about taboo topics like sexual organs and human reproduction. How did these artifacts come into being? How is their existence sustained? This mixed-methods comparative project builds on previous work on taboo topics in Wikipedia and draws from qualitative and quantitative approaches. We follow a sequential complementary design, developing a narrative articulation of the life of taboo articles, comparing them to nontaboo articles, and examining some of their quantifiable traits. We find that taboo knowledge artifacts develop through multiple successful collaboration styles and, unsurprisingly, that taboo subjects are the sites of conflict. We identify and describe six themes in the development of taboo knowledge artifacts. These artifacts need resilient leadership and engaged organizations to thrive under conditions of limited identifiability and disjointed sensemaking , while contributors simultaneously engage in emergent governance and imagining public audiences . Our observations have important implications for supporting public knowledge work on controversial subjects such as taboos and more generally.

Repertoire by Women Composers: A Guide for Beginning and Intermediate-Level Singers

Abstract: Although vocal music has one of the longest recorded histories of any art form, the songs of women composers remain largely excluded from the traditional canon. Recently, performers and educators have been working to change the way we decide which music and musicians are part of the classical repertoire. This article will address repertoire by women composers to suit early-stage singers in the first years of study. Repertoire presented will explore multiple languages, time periods, and styles within the Western-classical pedagogic tradition. The authors offer insight into each selection’s benefits. Online and traditional resources are suggested for further exploration.

The Sharing Energy Storage Mechanism for Demand Side Energy Communities

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side.

Enhancing Adult’s Oral Communication Through the Coaching Method

This research aimed at the implementation of the language coaching method in a group of eight adult learners at Centro de Estudios de Inglés Conversacional (CEIC) to improve their oral communication skills. Deficiencies in fluency, accuracy, vocabulary, pronunciation, and communication in the participants were identified. This method was applied to overcome weaknesses in this group of adult learners. It is centered on how the brain likes to learn (neurolanguage), and it is characterized by principles based on the multiple intelligences and learning styles. Among the several models it has, the ACHIEVE model was chosen for the purpose of this study.

A Novel Artificial General Intelligence Security Evaluation Scheme Based on an Analytic Hierarchy Process Model with a Generic Algorithm

The rapid development of Artificial General Intelligence (AGI) in recent years has provided many new opportunities and challenges for human social production. However, recent evaluation methods have some problems with regard to consistency, subjectivity and comprehensiveness. In order to solve the above problems, in this paper, we propose an Artificial General Intelligence Security Evaluation scheme (AGISE), which is based on analytic hierarchy process (AHP) technology with a genetic algorithm, to comprehensively evaluate the AGI security based on multiple security risk styles and complex indicators. Firstly, our AGISE combines AHP technology with a genetic algorithm to realize reliable, consistent and objective evaluation for AGI security. Secondly, in our AGISE, we propose implementing more effective AGI security evaluation classification and indicator settings. Finally, we demonstrate the effectiveness of our AGISE through experiments.

Influence of applying VARK learning styles on enhancing teaching skills: application of learning theories

BackgroundSocial media in our networks have been exploited as dynamic learning tools and free platforms.AimsThe main objective of this study is to determine the impact of VARK learning styles (visual (V), aural (A), read/write (R), and kinesthetic (K)) in enhancing parasitological laboratory skills using social media and various learning theories.MethodsA research sample of 100 chemists working in Mega Alfa labs underwent online learning of laboratory parasitology skills via Facebook posts and WhatsApp dictated messages for an average of 7 weeks. All posts served various VARK learning styles and were designed based on Zeigarnik’s effect (conducting information with tactical breaks), memory storage and retrieval strength theories (repetition of information). Trainees were classified according to their VARK learning style preferences and were evaluated through pre/post-tests. Data on VARK learning styles were summarized using frequency (count) and relative frequency (percentage). Data of pre-test and post-test scores were summarized using mean and standard deviation. T-test was used to compare pre-test and post-test scores. The difference between the pre-test results, the post-test results and the preferred learning style was analyzed using ANOVA with Tukey’s post-hoc testing. P-values less than 0.05 were considered statistically significant.ResultsIn a total of 100 trainees, tri-modal and multimodal learning styles were preferred by 40% and 30% of the trainees respectively; on the contrary, the unimodal and bimodal learning styles were the least preferred. In the trimodal and multimodal groups, the post-test results showed significant increase when compared with the pre-test results. Also, using the ANOVA test and a Tukey’s post-hoc comparison, the assemblage of multiple learning styles (tri-modal and multimodal) appeared to significantly improve the learning performance in the post-test results when compared with the unimodal and bimodal groups.ConclusionThe tri-modal and multimodal learning styles were found to influence the acquirement of the laboratory parasitology skills much better than the unimodal and bimodal learning styles. Kinesthetic learning should have a special emphasis in training.

Leadership Styles and Their Influence on Project Team Performance

Leadership styles play a pivotal role in shaping the dynamics and performance of project teams, directly influencing the success of projects across various industries. This study explores the impact of different leadership styles—transformational, transactional, laissez-faire, servant, situational, and distributed—on project team performance. Utilizing a mixed-methods approach, including quantitative surveys and qualitative interviews, the research provides a comprehensive analysis of how these leadership styles affect team outcomes. The findings reveal that transformational leadership significantly enhances team motivation, cohesion, and performance, particularly in environments requiring innovation and adaptability. Transactional leadership, while effective in structured settings, may limit creativity and flexibility. The study also highlights the challenges of laissez-faire leadership and underscores the value of servant and situational leadership in fostering team collaboration and addressing diverse needs. The implications for project managers and organizations include the importance of selecting and cultivating the appropriate leadership style to align with the specific demands of the project and team. Future research directions include examining the integration of multiple leadership styles and their long-term effects on team performance.

Assessing Helmet Fit for Varsity Ice Hockey Athletes: A Field Intervention Study

Background: Protective equipment is at the forefront of the discussion on reducing sports-related concussions, with the role of helmets being equivocal. Data suggest that modern helmets are not entirely protective against concussions, and ill-fitting helmets may contribute to concussions with longer-lasting symptoms. Helmet fit can be assessed via a checklist, but its application outside of youth sport is lacking. Objectives: The purpose of this study was to assess helmet fit and the effect of feedback for a men’s (n=22) and a women’s (n=20) university varsity ice hockey team across a season. Methods: This field intervention study used a 12-item helmet fit checklist which was divided into five categories, defining “proper fit” as meeting all criteria of the stability and size categories. Players’ concussion histories and helmet fit knowledge were assessed using a survey. Results: The number of properly fitting helmets for males increased from 23% to 77% over the season, while remaining consistent at 50% for females. For males, 73% had an increase in overall checklist scores and 27% saw no change; 45% of females had an increase, 5% saw no change, and 50% decreased. Reported female (85%) concussion rate was double that of males (41%), but perceived knowledge of helmet fit (male=36%, female=30%) and actual knowledge (male=77%, female=85%) were similar. Conclusions: Feedback only had a positive effect on helmet fit for males across the season, potentially due to helmet style differences for females. Improvements to checklist criteria and scoring would increase the effectiveness of assessing fit for multiple helmet styles.

FISTNet: FusIon of STyle-path generative Networks for facial style transfer

With the surge in emerging technologies such as Metaverse, spatial computing, and generative AI, the application of facial style transfer has gained much interest from researchers and startups enthusiasts alike. StyleGAN methods have paved the way for transfer-learning strategies that could reduce the dependency on the vast data available for the training process. However, StyleGAN methods tend to need to be more balanced, resulting in the introduction of artifacts in the facial images. Studies such as DualStyleGAN proposed multipath networks but required the networks to be trained for a specific style rather than simultaneously generating a fusion of facial styles. In this paper, we propose a Fusion of STyles (FIST) network for facial images that leverages pretrained multipath style transfer networks to eliminate the problem associated with the lack of enormous data volume in the training phase and the fusion of multiple styles at the output. We leverage pretrained styleGAN networks with an external style pass that uses a residual modulation block instead of a transform coding block. The method also preserves facial structure, identity, and details via the gated mapping unit introduced in this study. The aforementioned components enable us to train the network with minimal data while generating high-quality stylized images, opening up new possibilities for facial style transfer in emerging technologies. Our training process adapts curriculum learning strategy to perform efficient, flexible style, and model fusion in the generative space. We perform extensive experiments to show the superiority of the proposed FISTNet compared to existing state-of-the-art methods.

A Tapestry of Tongues: A Novel Provenance Approach forArabic Linguistic Styles and Lineage Tracing

This research introduces a novel framework for analyzing Arabic linguistic styles, focusing on the Quran as a case study. Unlike previous studies that often focus on a single style or regional variations, ours examines the unique features of multiple styles within the single text. Our approach addresses inconsistencies in Arabic character representation. This creates a standardized format for analyzing the text, ensuring consistent results. This research utilizes a Provenance Tracker to connect different linguistic styles and document their lineage through narrators By analyzing a single text that showcases multiple styles, this study paves the way for identifying the unique characteristics of each linguistic style. Additionally, the research develops algorithms to map individual letters, words, and diacritics between each style. Moreover, this allows for in-depth linguistic analysis, revealing the unique characteristics that define each style. This opens new avenues for research into the Arabic language’s rich stylistic diversity.

Auto-Diacritization and Stylistic Realization of Arabic Text using Deep Neural Networks

This paper presents QRDiaRec, an advanced diacritization system for Arabic Quranic texts. In Arabic, linguistic style refers to the variations in diacritic markings used to convey different pronunciations, dialects, meanings, and contextual understandings.QRDiaRec addresses the challenge of interpreting Arabic diacritics across multiple linguistic styles, which is crucial for accurate language processing. Unlike traditional systems that generate only one correct form of diacritics, QRDiaRec can recognize and produce multiple valid diacritic forms. This capability is due to its training on a dataset that encompasses seven Quranic linguistic styles.The Qur'an is an ideal case study because it is one text with multiple linguistic patterns, allowing us to recognize different forms of correct diacritization. QRDiaRec employs bidirectional LSTM, GRU, and transformer-based models to convert non-diacritic texts into annotated formats, achieving up to 94.2%accuracy. The system enhances Arabic language processing, impacting NLP, machine translation, and Arabic linguistics.

(019) EXPERIENCE WITH VARIOUS GENITAL VIBRATORS: A QUALITATIVE STUDY

Abstract Introduction Mainstream society and health professionals typically do not regard genital vibrators as medical devices. Vibrators have been shown to improve sexual satisfaction (Alexander et al., 2017). There are currently no studies that compare participants' preference, satisfaction of orgasm, and frequency of orgasms with multiple different styles of devices. Objective The primary outcome was to compare three different styles of genital vibrator devices via the level of satisfaction and reasons for satisfaction or dissatisfaction for the various devices. The secondary outcomes were to determine if there was a quantitative change in the Female Sexual Function Index (FSFI) scores or the Patient Global Impression of Improvement (PGI-I). Methods The study was a prospective observational design with mixed methods of qualitative and quantitative outcomes. Participants with female reproductive anatomy aged ≥18 that were sexually active with a partner or through self-stimulation and have achieved orgasm in the past were eligible. Participants completed the FSFI prior to undergoing the intervention. They then had six weeks to use the three devices (wand, dual pronged or clitoral suction device) at their own discretion. At the conclusion of the six weeks, they completed the FSFI and PGI-I. Participants then underwent an open and semi-structured interview via telephone. The information from these interviews was transcribed and analyzed using Dedoose software by two independent reviewers. Results Individuals with no self-reported sexual dysfunction aged 18-55 years old were recruited in January 2023. All completed both the pre- and post-intervention questionnaires. Nineteen participants completed the semi-structured interviews, one participant was lost to follow-up. Self-reported overall sexual satisfaction was improved in 90% of participants after introduction of the genital vibrators based on PGI-I (Figure 1). 55% of participants reported the suction clitoral stimulator device was their preferred vibrator, 35% preferred the wand device, and 10% preferred the dual pronged device. When comparing pre and post intervention FSFI there were no statistically significant differences in total FSFI scores nor any of the individual domains (27.15 vs. 28.25, p=0.104). Ease of use, enjoyment, comfort, intensity, partner use, change in orgasm, novelty, and battery/charge were commonly recurring themes during the interview. Table 1 demonstrates examples of quotes and frequency of mentions. There was no difference in number of times each vibrator was used based on self-report (p=0.875) (Figure 2). Conclusions The introduction of genital vibrators may lead to improved sexual satisfaction in individuals with no baseline sexual dysfunction. The majority (90%) of participants reported improvement in overall sexual satisfaction with the use of genital vibrators, with the suction clitoral stimulator device being the preferred style. Novelty, comfort, size, and intensity of vibrators were frequently addressed as important factors in improving the likelihood and quality of orgasm. Additionally, positive comments surrounding partner use were frequently addressed by participants, suggesting the importance of exploring vibrator use outside of masturbation alone. Use with a partner can lead to improved communication with the partner and a more satisfying sexual experience overall. Disclosure No.

Alice and Alisa

This article compares the aesthetics of Wonderland in two animated adaptations of Lewis Carroll’s classic tale, examining Disney’s 1951 rendition and Kievnauchfilm’s 1981 adaptation. Through an analysis of animation techniques, art styles, and cultural influences, it explores how each production visually depicts Alice’s adventures. Disney’s approach is characterized by a grounded, simplified style, while Kievnauchfilm fully embraces the surrealism of Carroll’s text. The essay discusses how Disney’s use of photographic references results in a more realistic portrayal, albeit somewhat lacking the unrestrained nonsense of Carroll’s world, and contrasts it with Kievnauchfilm’s use of multiple art styles to capture Wonderland’s absurdity. It also considers the influence of market demands and religious aesthetics on the American and the Soviet adaptation respectively, and it explores the social and ideological ramifications of Eisenstein’s concept of “plasmaticness” in the animation of both films. Ultimately, this research provides insights into how cultural context shapes the aesthetics of visual storytelling.

Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool.

The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the management, display, annotation and manipulation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 6 introduces a modernized and completely rewritten user interface, together with numerous new features. A new dataset type has been introduced (colored/labeled ranges), greatly upgrading the functionality of the previous simple colored range annotation function. Additional annotation options have been implemented for several existing dataset types. Dataset template files now support simple assignment of annotations to multiple tree nodes through substring matching, including full regular expression support. Node metadata handling has been greatly extended with novel display and exporting options, and it can now be edited interactively or bulk updated through annotation files. Tree labels can be displayed using multiple simultaneous font styles, with precise positioning, sizing and styling of each individual label part. Various bulk label editing functions have been implemented, simplifying large scale changes of all tree node labels. iTOL's automatic taxonomy assignment functions now support trees based on the Genome Taxonomy Database (GTDB), in addition to the NCBI taxonomy. The functionality of the optional user account pages has been expanded, simplifying the management, navigation and sharing of projects and trees. iTOL currently handles more than one and a half million trees from >130000 individual user accounts.

Efficient Supervised Pretraining of Swin-Transformer for Virtual Staining of Microscopy Images.

Fluorescence staining is an important technique in life science for labeling cellular constituents. However, it also suffers from being time-consuming, having difficulty in simultaneous labeling, etc. Thus, virtual staining, which does not rely on chemical labeling, has been introduced. Recently, deep learning models such as transformers have been applied to virtual staining tasks. However, their performance relies on large-scale pretraining, hindering their development in the field. To reduce the reliance on large amounts of computation and data, we construct a Swin-transformer model and propose an efficient supervised pretraining method based on the masked autoencoder (MAE). Specifically, we adopt downsampling and grid sampling to mask 75% of pixels and reduce the number of tokens. The pretraining time of our method is only 1/16 compared with the original MAE. We also design a supervised proxy task to predict stained images with multiple styles instead of masked pixels. Additionally, most virtual staining approaches are based on private datasets and evaluated by different metrics, making a fair comparison difficult. Therefore, we develop a standard benchmark based on three public datasets and build a baseline for the convenience of future researchers. We conduct extensive experiments on three benchmark datasets, and the experimental results show the proposed method achieves the best performance both quantitatively and qualitatively. In addition, ablation studies are conducted, and experimental results illustrate the effectiveness of the proposed pretraining method. The benchmark and code are available at https://github.com/birkhoffkiki/CAS-Transformer.

NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement

3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. Code at https://github.com/mv-lab/nilut

Unveiling Engagement in Asynchronous Writing: A Comprehensive Exploration of Approaches and Indicators – A Systematic Literature Review

In developing the learners’ language competence, many educational institutions altered teachers to integrate computer-mediated communication (CMC) synchronously and asynchronously. However, the issue of engagement becomes another issue to be handled. Accordingly, in this literature review of 20 qualitative, 7 quantitative, and 8 mixed-method studies published in the high-ranked database from 2000-2021, we aim to examine the approaches to evaluate and indicators to facilitate the learners’ engagement, specifically on writing asynchronously. Asynchronous CMC became the concern of this study as it supports the learners to follow the simultaneous discussion and supports multiple learning styles. The reviewed studies were identified using behavior, cognition, and emotion approaches to evaluate the learners’ engagement. Thus, the types of indicators to facilitate the learners’ engagement are input, process, and outcomes. Finally, this review suggests it is useful to design writing courses that effectively conceptualize evaluation approaches and indicators to facilitate the learners’ engagement.

A global perspective on school leadership: Evidence from a latent class analysis on OECD TALIS data

Previous research highlighted the existence of multiple educational leadership styles; however, evidence on their multidimensional application is limited. The current study defines an innovative cross-national classification for school leaders along the two dimensions of instructional and distributed leadership. We apply a three-step Latent Class Analysis to transnationally generalizable Organization for Economic Cooperation and Development's Teaching and Learning International Survey 2013 data, with N = 9384 school principals surveyed across 32 countries. From here, we identify four sub-groups of leaders: integrating leaders (64.9% of the sample), who adopt instructional and distributed leadership in an integrated way, participative leaders (22.2%) and supportive leaders (8.5%), who are respectively high only on distributed or instructional leadership, and contingent leaders (4.4%), without a specific a priori leadership approach. Features specific to the principals, the schools and the school context are significantly related to leadership sub-group membership and important moderators of the correlation between types of leadership and students’ test scores, as analyzed for a subset of countries. As implications for leadership policy and practice, the cross-country prevalence of integrating leaders might increase the level of organizational complexity, while the geographical distribution of leadership classes highlights the important role played by cultural aspects in influencing leadership practices.

Poetry and Psychotherapy: the Case for Fernando Pessoa as a Therapist. Poetry as a Tool in Psychotherapy

Nossrat Peseschkian incorporated the use of stories as a key element of Positive Psychotherapy (Peseschkian 2016). Poetry has also been proposed as a powerful tool in psychotherapy and defined as “the use of language, symbol and story in therapeutic, educational and community-building capacities.” (Mazza 2016). Among great poets Fernando Pessoa is relatively unknown due to his writing in Portuguese and to the complexity of his multiple styles, personas and perspectives. Yet, this very complexity makes his poetry, alongside his unique prose, powerful tools to be used in psychotherapy. Finally, his poems are easily accessible in both Portuguese and English today. This article starts with a brief biographical introduction to Pessoa, followed by examples of his work under different voices that can be used by mental health professionals in supporting persons in the process of healing through psychotherapy.