Hasil untuk "Art"

Yoichi Ochiai, Takashi Shimizu

This paper critically re-examines "Digital Nature," a concept that has proliferated across various domains over the last ten years. By "Digital Nature," we refer to an evolving view of nature as a dynamic process of circulating computation and matter, one that extends into the realms of AI, XR, indigenous perspectives, and post-human theory. Despite its popularity, "Digital Nature" remains ambiguously defined. This paper provides a genealogical and philosophical survey of how the idea has emerged, diverged, and overlapped in media art, bio-art, and generative art, alongside relevant Eastern, Islamic, and indigenous worldviews. We then introduce a multi-axis framework (from real/virtual to anthropocentric/object-oriented, with sub-axes of enchantment and materialization), illustrating how digital technologies have reconceptualized the question "What is nature?" in unexpected ways. Finally, we discuss how the field might evolve, particularly through the lens of large language models, AGI, and "supernatural reality," while highlighting the ethical and political pitfalls of techno-occultism. Our ultimate goal is to re-situate "Digital Nature" as both an intellectual frontier and a collaborative platform that invites continuous dialogue between art, science, technology, and cultural philosophies.

Yingxuan Li, Jiafeng Mao, Qianru Qiu et al.

Understanding region-wise correspondences between manga line art images is fundamental for high-level manga processing, supporting downstream tasks such as line art colorization and in-between frame generation. Unlike natural images that contain rich visual cues, manga line art consists only of sparse black-and-white strokes, making it challenging to determine which regions correspond across images. In this work, we introduce a new task: predicting region-wise correspondence between raw manga line art images without any annotations. To address this problem, we propose a Transformer-based framework trained on large-scale, automatically generated region correspondences. The model learns to suppress noisy matches and strengthen consistent structural relationships, resulting in robust patch-level feature alignment within and across images. During inference, our method segments each line art and establishes coherent region-level correspondences through edge-aware clustering and region matching. We construct manually annotated benchmarks for evaluation, and experiments across multiple datasets demonstrate both high patch-level accuracy and strong region-level correspondence performance, achieving 78.4-84.4% region-level accuracy. These results highlight the potential of our method for real-world manga and animation applications.

Rune Lindahl-Jacobsen, Astrid Linnea Beck, Lærke Priskorn et al.

Abstract Recent decades have seen declining total fertility rates (TFR) globally, alongside increased use of assisted reproductive technology (ART). However, TFR includes ART births and excludes induced abortions, complicating assessments of population fecundity. Here, we examine trends in cohort total fertility rates (cTFR), induced abortions, and ART use through a nationwide cohort study of 1,648,971 pregnancies, including abortions, among Danish women aged 15–45 years born between 1958 and 1999. A new index, the Comprehensive Unassisted Pregnancy Rate (live births and induced abortions, excluding ART births), was developed. Our findings reveal a decline in unassisted pregnancy rates for women born after 1961, with an accelerated decline for those born after 1970. In contrast, cTFR increased for women born between 1958 and 1970 before decreasing, driven by trends in induced abortions and ART births. These differences highlight a disconnect between cTFR and fecundity measures. The declining unassisted pregnancy rates, reduced abortions, and increased ART demand raise concerns about population fecundity. Understanding these trends’ biological and socioeconomic drivers requires large-scale, transdisciplinary studies of representative populations. Our results emphasize the need for alternative measures, like the Comprehensive Unassisted Pregnancy Rate, to assess reproductive health and fertility trends accurately.

Reza Rastegar, Alex Roitershtein, Vadim Roytershteyn et al.

We introduce and study a mathematical model of an art collector. In our model, the collector is a rational agent whose actions in the art market are driven by two competing long-term objectives, namely sustainable financial health and maintaining the collection. Mathematically, our model is a two-dimensional random linear dynamical system with transformation matrix of a peculiar type. In some examples we are able to show that within the Kelly-type optimization paradigm, that is optimizing the system's Lyapunov exponent over a set of policy parameters, the dilemma ``art or money" can be successfully resolved, namely the optimal policy creates a coexistence equilibrium where the value of both is increasing over the time.

Tuo Zhang, Tiantian Feng, Yibin Ni et al.

Large vision-language models (VLMs) have demonstrated remarkable abilities in understanding everyday content. However, their performance in the domain of art, particularly culturally rich art forms, remains less explored. As a pearl of human wisdom and creativity, art encapsulates complex cultural narratives and symbolism. In this paper, we offer the Pun Rebus Art Dataset, a multimodal dataset for art understanding deeply rooted in traditional Chinese culture. We focus on three primary tasks: identifying salient visual elements, matching elements with their symbolic meanings, and explanations for the conveyed messages. Our evaluation reveals that state-of-the-art VLMs struggle with these tasks, often providing biased and hallucinated explanations and showing limited improvement through in-context learning. By releasing the Pun Rebus Art Dataset, we aim to facilitate the development of VLMs that can better understand and interpret culturally specific content, promoting greater inclusiveness beyond English-based corpora.

Галина Погребняк

The purpose of the research is to identify relevant narratives in the work of directors-authors of Middle Eastern cinema and its functioning in the space of international film festivals. Research methodology. The analytical method was used to develop the topic, which is necessary for studying the art history and cultural aspects of the problem. The researcher used the techniques of systematisation and generalisation, which came in handy for arguing the originality of the phenomenon of film directing in Middle Eastern countries, its place in modern culture-creating processes, as well as the determination of objective regularities that characterise directing practices in the contemporary intercultural space. A cross-cultural method was also applied, which contributed to identifying the peculiarities of the presentation of films by Middle Eastern directors at international film festivals. The cultural approach determined the generalised socio-cultural orientation of the research on the cinematography of Middle Eastern countries. The scientific novelty of the research lies in the fact that the problem of intercultural cooperation in the production and distribution of films in Middle Eastern countries in the context of the functioning of international support programs became the subject of a particular study for the first time; the work of filmmakers whose films were created as international projects and presented at international film festivals is singled out and characterised; the practicality of using the systematic method in the study of new narratives of films of the Middle Eastern region is proven. Conclusions. Familiarisation with the materials presented in the article expands the arsenal of knowledge about the specifics of the content and distribution of films by Middle Eastern directors within the framework of intercultural projects. It enables their use in educational courses on the theory and history of culture, cinema and directing.

Takako Yokota, Shuhei Kurosawa, Yukihiro Yoshimura et al.

A 44-year-old HIV-positive man diagnosed with diffuse large B-cell lymphoma in 2021 achieved complete remission with six cycles of R-CHOP therapy but had a relapse in November 2022. ESHAP therapy failed to induce remission, leading to complete remission with four cycles of Pola-BR therapy. Post-failure of autologous stem cell harvest, cord blood transplantation (CBT) was performed in June 2023. Notably, this case used recently approved intramuscular antiretroviral therapy (ART) with cabotegravir and rilpivirine, addressing gastrointestinal complications during CBT. This innovative use of intramuscular ART in the treatment of malignancy represents a first in the field, offering a pioneering approach to HIV-related lymphoma.

Satoko Yagi, Takuma Nakamura, Kazuki Hashimoto et al.

Non-destructive testing (NDT) is crucial for ensuring product quality and safety across various industries. Conventional methods, such as ultrasonic, terahertz, and x-ray imaging, have limitations in terms of probe-contact requirement, depth resolution, or radiation risks. Optical coherence tomography (OCT) is a promising alternative to solve these limitations, but it suffers from strong scattering, limiting its penetration depth. Recently, OCT in the mid-infrared (MIR) spectral region has attracted attention with a significantly lower scattering rate than in the near-infrared region. However, the highest reported A-scan rate of MIR-OCT has been 3 kHz, which requires long data acquisition time to take an image, unsatisfying industrial demands for real-time diagnosis. Here, we present a high-speed MIR-OCT system operating in the 3–4 µm region that employs the frequency-swept spectrum detection in OCT technique based on time-stretch infrared spectroscopy. By integrating a broadband femtosecond MIR pulsed laser operating at a repetition rate of 50 MHz, we achieved an A-scan rate of 1 MHz with an axial resolution of 11.6 µm, a 10 dB roll-off depth of about 700 µm, and a sensitivity of 55 dB. As a proof-of-concept demonstration, we imaged the surface of substrates covered by highly scattering paint coatings. The demonstrated A-scan rate surpasses previous state of the art by more than two orders of magnitude, paving the way for real-time NDT of industrial products, cultural assets, and structures.

Rima Damayanti, Khusnul Fatimah, Ahmad Bahrudin

The purpose of this research is to explore the values of local wisdom reflected in the stories, which include aspects of Pacitan community life such as religious system or religious ceremonies, community system, knowledge system, language, art, livelihood system, and tool system. The research method used is qualitative with a literary anthropology approach. The data were obtained through the data collection technique of listening and recording of quotations or sentences contained in the book “Kumpulan Cerita dari Kota 1001 Goa”. The analysis was carried out by dividing the data based on the identified local wisdom and then analyzing through description and interpretation of the research data. The results showed the existence of seven elements of local wisdom in the collection of stories, namely 9 data on religious systems and religious ceremonies, 3 data on community systems, 5 data on knowledge systems, 3 data on language, 4 data on art, 4 data on livelihood systems, and 8 data on tool systems. The existence of these seven elements is reflected in “Kumpulan Cerita dari Kota 1001 Goa” and can be revealed through an anthropology of literature approach. 

Zhiran Chen, Peng Han, Zijian He et al.

Evacuation lighting is a crucial component of cinema safety, significantly impacting operational safety and evacuation efficiency. It plays a key role in enhancing evacuation measures and ensuring the safety of cinema patrons. An experiment utilizing virtual reality technology was conducted at Beijing Forestry University with 62 subjects randomly assigned to either a control group or an experimental group. The experimental group was guided by a green flashing light as an evacuation indicator, while the control group relied on static lighting. Although some subjects overlooked the green flashing light, its presence still reduced the number of subjects choosing misleading exits. The flashing light notably improved pathfinding efficiency and evacuation performance, with the experimental group achieving an average evacuation time approximately 30% shorter than the control group. Additionally, subjects rated the sensory, cognitive, and functional aspects of the flashing light lighting from moderate to high. The findings indicate that dynamic and flashing evacuation lighting can effectively enhance fire escape efficiency in cinemas. The design of such systems should consider individual psychological responses and actual behavior patterns to optimize emergency evacuation instructions.

Zaenal Arifin, Hari Karyono, Wawan Gunawan

Penggunaan Aplikasi di sekolah SMAN 15 Surabaya sangat penting dalam proses pembelajaran. Aplikasi berguna bagi proses pemahaman siswa terhadap materi notasi musik. Penelitian ini bertujuan untuk mengenalkan dan mengembangkan penggunaan Sibelius 7 pada pembelajaran seni musik khususnya penelitian notasi. Penelitian pengembangan ini bersumber dari data kualitatif dan kuantitatif. Diperoleh skor hasil lembar pengisisan angket berupa form. Hasil penelitian menunjukkan validitas Aplikasi Sibelius 7 pada pembelajaran seni musik materi penulisan notasi musik kelas XI di SMAN 15 Surabaya tahun pelajaran 2020/2021 yaitu: (1) menurut ahli berada pada kualifikasi sangat baik yaitu 88,47%, dan (2) berdasarkan uji coba perorangan berada pada kualifikasi sangat baik yaitu 80,47%. Disimpulkan bahwa Aplikasi Sibelius 7 dalam pembelajaran seni musik penelitian notasi di SMAN 15 Surabaya terbukti bahwa produk layak dipakai dalam proses pembelajaran seni musik. Kejelasan materi yang disajikan, kemudahan akses, serta kemenarikan aplikasi Sibelius 7 membuat sehingga siswa dapat belajar secara mandiri. Abstract. The use of applications at SMAN 15 Surabaya is very important in the learning process. This application is useful for students' understanding of musical notation material. This study aims to introduce and develop the use of Sibelius 7 in learning the art of music, especially notation research. This development research is sourced from qualitative and quantitative data. The score obtained from the questionnaire filling sheet in the form of a form. The results of the study show the validity of the Sibelius 7 application in learning the art of music writing material for class XI music notation at SMAN 15 Surabaya for the 2020/2021 academic year, namely: (1) according to experts, it is in very good qualification, namely 88.47%, and (2) based on the test individual trials are in very good qualifications, namely 80.47%. It was concluded that the Sibelius 7 application in learning the art of music notation research at SMAN 15 Surabaya proved that the product was suitable for use in the process of learning the art of music. The clarity of the material presented, the ease of access, and the attractiveness of the Sibelius 7 application make it possible for students to study independently.

Fatima Rauf, Muhammad Attique Khan, Ali Kashif Bashir et al.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Leonhard Balduf, Martin Florian, Björn Scheuermann

We explore issues relating to the storage of digital art, based on an empirical investigation into the storage of audiovisual data referenced by non-fungible tokens (NFTs). We identify current trends in NFT data storage and highlight problems with implemented solutions. We particularly focus our investigation on the use of the Interplanetary Filesystem (IPFS), which emerges as a popular and versatile distributed storage solution for NFTs. Based on the analysis of discovered data storage techniques, we propose a set of best practices to ensure long-term storage survivability of NFT data. While helpful for forming the NFT art market into a legitimate long-term environment for digital art, our recommendations are also directly applicable for improving the availability and integrity of non-NFT digital art.

Giovanni Colavizza

Non-Fungible Tokens (NFTs) have recently surged to mainstream attention by allowing the exchange of digital assets via blockchains. NFTs have also been adopted by artists to sell digital art. One of the promises of NFTs is broadening participation to the arts market, a traditionally closed and opaque system, to sustain a wider and more diverse set of artists and collectors. A key sign of this effect would be the disappearance or at least reduction in importance of seller-buyer preferential ties, whereby the success of an artist is strongly dependent on the patronage of a single collector. We investigate NFT art seller-buyer networks considering several galleries and a large set of nearly 40,000 sales for over 230M USD in total volume. We find that NFT art is a highly concentrated market driven by few successful sellers and even fewer systematic buyers. High concentration is present in both the number of sales and, even more strongly, in their priced volume. Furthermore, we show that, while a broader-participation market was present in the early phase of NFT art adoption, preferential ties have dominated during market growth, peak and recent decline. We consistently find that the top buyer accounts on average for over 80% of buys for a given seller. Similar trends apply to buyers and their top seller. We conclude that NFT art constitutes, at the present, a highly concentrated market driven by preferential seller-buyer ties.

Divya Shanmugam, Katie Lewis, Jose Javier Gonzalez-Ortiz et al.

MIT wanted to commission a large scale artwork that would serve to 'illuminate a new campus gateway, inaugurate a space of exchange between MIT and Cambridge, and inspire our students, faculty, visitors, and the surrounding community to engage with art in new ways and to have art be part of their daily lives.' Among other things, the art was to reflect the fact that scientific discovery is often the result of many individual contributions, both acknowledged and unacknowledged. In this work, a group of computer scientists collaborated with a conceptual artist to produce a collective signature, or a signature learned from contributions of an entire community. After collecting signatures from two communities -- the university, and the surrounding city -- the computer scientists developed generative models and a human-in-the-loop feedback process to work with the artist create an original signature-like structure representative of each community. These signatures are now large-scale steel, LED and neon light sculptures that appear to sign two new buildings in Cambridge, MA.

Aleksander Kmak

Artykuł jest recenzją książki Sebastiana Jagielskiego Przerwane emancypacje. Polityka ekscesu w polskim kinie 1968-1982 (2022). Publikacja dotyczy wybranych filmów powstałych między Marcem '68 a wprowadzeniem stanu wojennego, charakteryzujących się formalną i tematyczną nadmiarowością, które autor umieszcza w precyzyjnie oddanym kontekście instytucjonalnym i społeczno-politycznym. Kategorię ekscesu, odczytywaną między innymi w kluczu teorii queer, autor traktuje jako symptom niewykorzystanego potencjału progresywnych ruchów kontrkulturowych zduszonych przez konserwatywną rewolucję początku lat 80. – symptom umożliwiający jednak skonstruowanie mniejszościowej narracji o polskiej kulturze, odmiennej od hegemonicznego modelu pamięci narodowo-katolickiej.

Julian Cendrero, Julio Gonzalo, Marcos Galletero et al.

A time-series of numerical data and a sequence of time-ordered documents are often correlated. This paper aims at modeling the impact that the underlying themes discussed in the text data have on the time series. To do so, we introduce an original topic model, Time Series Impact Through Topic Modeling (TSITM), that includes contextual data by coupling Latent Dirichlet Allocation (LDA) with linear regression, using an elastic net prior to set to zero the impact of uncorrelated topics. The resulting topics act as explanatory variables for the regression of the numerical time series, which allows us to understand the time series movements based on the events described on the text data. We have tested our model on two datasets: first, we used political news to explain the US president&#x2019;s disapproval ratings; then, we considered a corpus of economic news to explain the financial returns of 4 different multinational corporations. Our experiments show that an appropriate selection of hyperparameters (via repeated random subsampling validation and Bayesian optimization) leads to significant correlations: both an intrinsic baseline and state of the art methods were significantly outperformed by TSITM in MSE, MAE and out-of-sample <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>, according to our hypothesis tests. We believe that this framework can be useful in the context of reputational risk management.

Dhanalekshmi Prasad Yedurkar, Shilpa P. Metkar, Fadi Al-Turjman et al.

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject’s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain.

Ngan Yi Kitty Lam, Jeanne Tan, Anne Toomey et al.

Abstract For the integration of conductive yarns in e-textiles, knitting offers structural versatility and malleability for wider product applications in the contexts of wearables and interiors. To enable mass adoption of conductive materials, it is imperative for users to be able to launder these materials as part of product maintenance. Interactive textiles knitted from polymeric optical fibres (POFs) and silver-coated conductive yarns are able to illuminate and change colours via integrated touch sensor systems. Current research only focuses on the washability and abrasion resistance of conductive yarns solely and not both POF and conductive yarn within the same fabric structure. This study is novel as it investigates the washability and abrasion resistance of POF and silver-coated conductive yarn integrated knitted textiles with different loop structures and the impact to their illuminative function. POFs were knitted within the same fabric structure by the inlay method using a 7-gauge industrial hand-operated flatbed knitting machine. This study examined how washing and abrasion affect POFs and silver-coated conductive yarn in five different knit structures, and the illuminative function of the knitted textiles. Washing and abrasion affected the resistance of conductive yarns. Scratches and bent POFs were observed after 20 gentle washing cycles. However, washing had minimal impact on the illuminative function of the knitted e-textiles examined in this study. The experiments provide evidence that e-textiles knitted with POFs and conductive yarns in the same fabric structure withstand washing and abrasion and thus have the potential for mass market adoption in fashion and interior applications.

Sarah E. Marzen, James P. Crutchfield

Inferring models, predicting the future, and estimating the entropy rate of discrete-time, discrete-event processes is well-worn ground. However, a much broader class of discrete-event processes operates in continuous-time. Here, we provide new methods for inferring, predicting, and estimating them. The methods rely on an extension of Bayesian structural inference that takes advantage of neural network’s universal approximation power. Based on experiments with complex synthetic data, the methods are competitive with the state-of-the-art for prediction and entropy-rate estimation.