Repetitive Patterns Research Articles

Experience with the cochlear implant enhances the neural tracking of spectrotemporal patterns in the Alberti bass

Cochlear implant (CI) users experience diminished music enjoyment due to the technical limitations of the CI. Nonetheless, behavioral studies have reported that rhythmic features are well-transmitted through the CI. Still, the gradual improvement of rhythm perception after the CI switch-on has not yet been determined using neurophysiological measures. To fill this gap, we here reanalyzed the electroencephalographic responses of participants from two previous mismatch negativity studies. These studies included eight recently implanted CI users measured twice, within the first six weeks after CI switch-on and approximately three months later; thirteen experienced CI users with a median experience of 7 years; and fourteen normally hearing (NH) controls. All participants listened to a repetitive four-tone pattern (known in music as Alberti bass) for 35 min. Applying frequency tagging, we aimed to estimate the neural activity synchronized to the periodicities of the Alberti bass. We hypothesized that longer experience with the CI would be reflected in stronger frequency-tagged neural responses approaching the responses of NH controls. We found an increase in the frequency-tagged amplitudes after only 3 months of CI use. This increase in neural synchronization may reflect an early adaptation to the CI stimulation. Moreover, the frequency-tagged amplitudes of experienced CI users were significantly greater than those of recently implanted CI users, but still smaller than those of NH controls. The frequency-tagged neural responses did not just reflect spectrotemporal changes in the stimuli (i.e., intensity or spectral content fluctuating over time), but also showed non-linear transformations that seemed to enhance relevant periodicities of the Alberti bass. Our findings provide neurophysiological evidence indicating a gradual adaptation to the CI, which is noticeable already after three months, resulting in close to NH brain processing of spectrotemporal features of musical rhythms after extended CI use.

Walking on Virtual Surface Patterns Leads to Changed Control Strategies.

Inclusive design does not stop at removing physical obstacles such as staircases. It also involves identifying architectural features that impose sensory burdens, such as repetitive visual patterns that are known to potentially cause dizziness or visual discomfort. In order to assess their influence on human gait and its stability, three repetitive patterns-random dots, repetitive stripes, and repetitive waves (Lisbon pattern)-were displayed in a coloured and greyscale variant in a virtual reality (VR) environment. The movements of eight participants were recorded using a motion capture system and electromyography (EMG). During all test conditions, a significant increase in the muscular activity of leg flexor muscles was identified just before touchdown. Further, an increase in the activity of laterally stabilising muscles during the swing phase was observed for all of the test conditions. The lateral and vertical centre of mass (CoM) deviation was statistically evaluated using a linear mixed model (LMM). The patterns did cause a significant increase in the CoM excursion in the vertical direction but not in the lateral direction. These findings are indicative of an inhibited and more cautious gait style and a change in control strategy. Furthermore, we quantified the induced discomfort by using both algorithmic estimates and self-reports. The Fourier-based methods favoured the greyscaled random dots over repetitive stripes. The colour metric favoured the striped pattern over the random dots. The participants reported that the wavey Lisbon pattern was the most disruptive. For architectural and structural design, this study indicates (1) that highly repetitive patterns should be used with care in consideration of their impact on the human visuomotor system and its behavioural effects and (2) that coloured patterns should be used with greater caution than greyscale patterns.

Linear diffusion noise boosted deep image prior for unsupervised sparse-view CT reconstruction

Objective. Deep learning has markedly enhanced the performance of sparse-view computed tomography reconstruction. However, the dependence of these methods on supervised training using high-quality paired datasets, and the necessity for retraining under varied physical acquisition conditions, constrain their generalizability across new imaging contexts and settings. Approach. To overcome these limitations, we propose an unsupervised approach grounded in the deep image prior framework. Our approach advances beyond the conventional single noise level input by incorporating multi-level linear diffusion noise, significantly mitigating the risk of overfitting. Furthermore, we embed non-local self-similarity as a deep implicit prior within a self-attention network structure, improving the model’s capability to identify and utilize repetitive patterns throughout the image. Additionally, leveraging imaging physics, gradient backpropagation is performed between the image domain and projection data space to optimize network weights. Main Results. Evaluations with both simulated and clinical cases demonstrate our method’s effective zero-shot adaptability across various projection views, highlighting its robustness and flexibility. Additionally, our approach effectively eliminates noise and streak artifacts while significantly restoring intricate image details. Significance. Our method aims to overcome the limitations in current supervised deep learning-based sparse-view CT reconstruction, offering improved generalizability and adaptability without the need for extensive paired training data.

Commuting-driven competition between transmission chains shapes seasonal influenza virus epidemics in the United States.

Despite intensive study, much remains unknown about the dynamics of seasonal influenza virus epidemic establishment and spread in the United States (US) each season. By reconstructing transmission lineages from seasonal influenza virus genomes collected in the US from 2014 to 2023, we show that most epidemics consisted of multiple distinct transmission lineages. Spread of these lineages exhibited strong spatiotemporal hierarchies and lineage size was correlated with timing of lineage establishment in the US. Mechanistic epidemic simulations suggest that mobility-driven competition between lineages determined the extent of individual lineages' geographical spread. Based on phylogeographic analyses and epidemic simulations, lineage-specific movement patterns were dominated by human commuting behavior. These results suggest that given the locations of early-season epidemic sparks, the topology of inter-state human mobility yields repeatable patterns of which influenza viruses will circulate where, but the importance of short-term processes limits predictability of regional and national epidemics.

The Changsha historic urban area: a study on the evolution characteristics and influencing factors of the connectivity of construction land

This study aims to understand the connectivity characteristics of construction land during the spatial evolution of historic urban areas. The complex network analysis method is employed to examine the evolution of the spatial structure of construction land in the four periods of the Changsha historic urban area. The results indicate that the spatial network morphology of construction land follows a repeated development pattern resembling a cluster–mother–child relationship. Additionally, the vulnerability in the evolution of land use spatial relationships is very low, while the overall stability of the land use network structure decreases. The spatial equilibrium of land use shows a downwards trend, the accessibility of land where existing cultural relics and historic sites are located is generally moderate to low, and some plots require improvement. In the evolution of connectivity in the construction land of historic urban areas, the main development challenges are the uneven growth caused by imbalanced land value influenced by capital and the need to balance the preservation of land use patterns with improved accessibility to cultural relics and historic sites. Therefore, it is highly important to dynamically detect and optimize the network structure of construction land to protect and develop historic urban areas.

Structures and genetic information of control region in mitogenomes of Odonata

Mitogenome data of Odonata is accumulating and widely used in phylogenetic analysis. However, noncoding regions, especially control region, were usually omitted from the phylogenetic reconstruction. In an effort to uncover the phylogenetic insights offered by the control region, we have amassed 65 Odonata mitogenomes and conducted an examination of their control regions. Our analysis discovered that species belonging to Anisoptera and Anisozygoptera exhibited a stem-loop structure, which was formed by a conserved polyC-polyG stretch located near the rrns gene (encoding 12S rRNA). Conversely, the polyC-polyG region was not a conserved fragment in Zygoptera. The length and number of repetitions within the control region were identified as the primary determinants of its overall length. Further, sibling species within Odonata, particularly those in the genus Euphaea, displayed similar patterns of repetition in their control region. Collectively, our research delineates the structural variations within the control region of Odonata and suggests the potential utility of this region in elucidating phylogenetic relationships among closely related species.

Gut Microbiota and Autism Spectrum Disorder: A Neuroinflammatory Mediated Mechanism of Pathogenesis?

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social communication and behavior, frequently accompanied by restricted and repetitive patterns of interests or activities. The gut microbiota has been implicated in the etiology of ASD due to its impact on the bidirectional communication pathway known as the gut-brain axis. However, the precise involvement of the gut microbiota in the causation of ASD is unclear. This study critically examines recent evidence to rationalize a probable mechanism in which gut microbiota symbiosis can induce neuroinflammation through intermediator cytokines and metabolites. To develop ASD, loss of the integrity of the intestinal barrier, activation of microglia, and dysregulation of neurotransmitters are caused by neural inflammatory factors. It has emphasized the potential role of neuroinflammatory intermediates linked to gut microbiota alterations in individuals with ASD. Specifically, cytokines like brain-derived neurotrophic factor, calprotectin, eotaxin, and some metabolites and microRNAs have been considered etiological biomarkers. We have also overviewed how probiotic trials may be used as a therapeutic strategy in ASD to reestablish a healthy balance in the gut microbiota. Evidence indicates neuroinflammation induced by dysregulated gut microbiota in ASD, yet there is little clarity based on analysis of the circulating immune profile. It deems the repair of microbiota load would lower inflammatory chaos in the GI tract, correct neuroinflammatory mediators, and modulate the neurotransmitters to attenuate autism. The interaction between the gut and the brain, along with alterations in microbiota and neuroinflammatory biomarkers, serves as a foundational background for understanding the etiology, diagnosis, prognosis, and treatment of autism spectrum disorder.

Therapeutic Garden Design at Marzoeki Mahdi Hospital

Abstract Mental disorders is characterized by clinically significant disturbance in a person’s cognition, emotional regulation, or behavior. Dr. H. Marzoeki Mahdi Hospital (RSMM) is one of the mental hospitals that provides psychosocial rehabilitation services. Psychosocial rehabilitation consists of various activities to hone the patient’s ability so that they live independently. One of the activities is vocational and occupational therapy. The hospital garden is a necessity in the patient rehabilitation process so it needs to be designed to make it can be utilized optimally. The research objectives are to identify and analyze activities, user needs, potentials and constraints on the site; determine the design concept and create a therapeutic garden design for psychosocial rehabilitation at Marzoeki Mahdi Hospital. This research uses a descriptive method with stages such as preparation, research and analysis, concept and design. Data was collected through surveys, interviews, questionnaires and literature studies. The proposed design is a therapeutic garden to support psychosocial rehabilitation. The design concept applies one of patients rehabilitaion activities, namely socializing, which is depicted in a circular pattern and rehabilitation activities carried out repeatedly are depicted in a repetitive pattern. In addition, a symmetrical pattern is also applied which illustrates organized life and firmness. Based on this pattern, spaces are designed to support rehabilitation activities, namely the welcome area, active therapy area, passive therapy area and parking area.

Examination of a case of "treatment failure" in long-term psychoanalytic psychotherapy for treatment-resistant depression.

Randomized controlled trials have reported psychoanalytic psychotherapy to improve longer-term post-treatment outcomes in patients with treatment-resistant depression. In this case study, we examine the therapy process of a female trial participant diagnosed with treatment-resistant depression. Structured clinical assessments indicated that the patient's level of depression remained unchanged during and after treatment. Over the course of the therapy, she repeatedly broke away from important others and finally also from the therapy itself, which we linked to the impact of earlier experiences of abandonment on her internal world. In the discussion, we present a variety of reflections that were put forward by the authors during a series of case discussion meetings. Some of these reflections relate to how the inner world of this patient might have triggered a negative therapeutic reaction and a destructive pattern of repetition. The interpretative stance, in which the therapist interpreted this reaction as indicative of a psychic conflict and linked this conflict to the therapeutic relationship, seemed to be experienced by the patient as unhelpful and persecutory. Other elements that were brought up include basic distrust, lack of symbolization and trauma in the patient, as well as the constraints of the research context.

The De Bruijn graph of non-sequential pattern repetitions in DNA strings

In molecular biology, constructing a genome based on substantially many reads from multitudes of deoxyribonucleic acid (DNA) strings has become an insurmountable task; one which has been continuously addressed by the introduction of various assembly algorithms based on three steps called the overlap-layout-consensus strategy. In the overlap step, the De Bruijn graph is one of many graphs that illustrate the data of all the assembly algorithms. In this article, by using definitions and methods of mathematical induction, some properties of the De Bruijn graph of one time and two times non-sequential repetition of patterns in a DNA string are presented. Examples of these De Bruijn graphs are also given. From there, a generalisation of said properties for m times non-sequential pattern repetition in a DNA string is acquired by means of mathematical induction, as well. The theoretical work in this research is invaluable to develop algorithms that increase the computational efficiency of assembly algorithms.

Binary segmentation of relief patterns on point clouds

Analysis of 3D textures, also known as relief patterns is a challenging task that requires separating repetitive surface patterns from the underlying global geometry. Existing works classify entire surfaces based on one or a few patterns by extracting ad-hoc statistical properties. Unfortunately, these methods are not suitable for objects with multiple geometric textures and perform poorly on more complex shapes. In this paper, we propose a neural network for binary segmentation to infer per-point labels based on the presence of surface relief patterns. We evaluated the proposed architecture on a high resolution point cloud dataset, surpassing the state-of-the-art, while maintaining memory and computation efficiency.

THE ARTISTRY OF MIRROR EMBELLISHMENTS IN FASHION

Mirror work is a traditional textile embellishment technique that involves stitching small pieces of reflective material, often mirrors, onto fabric. This intricate craft has been used for centuries to adorn garments, particularly in Indian culture, where it is believed to ward off evil and bring good luck. This style combines traditional craftsmanship with modern manufacturing methods, producing high-quality, market-ready products. Digital design techniques have streamlined the process, enabling precise and repeated patterns that enhance the visual appeal. This fusion of traditional craftsmanship with contemporary technology exemplifies the potential for innovation in the textile industry. This study explores the historical significance, Global Designers and Innovation in mirror work, various types of mirror work, techniques, variations used in the fashion fields, and different methods of attaching mirrors to fabric, including frames for the mirror. This study also discusses the applications of mirror work in fashion, highlighting its use in traditional and trendy garments. The fusion of mirror works into textile designs, informed by extensive secondary data, has led to the development of innovative and aesthetically pleasing products.

A Linguistic-Discourse Analysis of the Language of Psalm 23

This paper examines the linguistic-discourse Analysis of Psalm 23, from the holy Bible, the book of the Christian religion. Linguistic-discourse analysis is one of the approaches used in disbanding the entanglements and ambiguities in language study. The linguistics and discourse features discussed include the graphological features, phonological features, syntactic structure,lexico-semantic representation, and cohesive relations. The graphological features are the punctuation marks, indentation, paragraphing and capitalization of letters in the text. The phonological features in the text are alliteration and assonance. Moreover, the syntactic structure includes short phrases, clauses, sentences, and pattern repetition, and the use of archaic pronouns which form part of the clauses and sentences. The lexico-semantic representation explains the linguistic explanation of the structure and meaning of the text. Consequently, the cohesive relations discussed in the text are anaphora, cataphora, substitution, conjunction, synonym,repetition and collocational converse. The theoretical framework adopted in this study is the Neo-Firthian Hallidayan Systemic Functional Grammar . The word 'systemic' means that language is viewed as a set of options for making meaning (Halliday, 1990; Obodeh, 2018). Brown and Yule (1983) Theory on Discourse analysis is also used as a backup to Halliday systemic grammar. The theory contains four approaches which include reference, presupposition, implicative and influence. Brown and Yule stated that the theory would help in the analysis of written textual, spoken and interaction for the research and also for the reader of the analysis, and this paper is an analysis of a written discourse. The library is also one of the research methods for this study. This is where books, journals and other useful research information are kept. The analysis in the study portrays the fact that a religious text could be analysed using the tenets of linguistics discourse. Since language is a tool of all activities in life, this paper therefore has tried to solve some language problems by analyzing the linguistics and discourse features of the language of the text, Psalm 23, for better understanding of the text by readers, clergy, congregations, and scholar of language and literature in particular and the academia in general.

Long-term prediction of Iranian blood product supply using LSTM: a 5-year forecast

BackgroundThis study aims to predict the trend of procurement and storage of various blood products, as well as planning and monitoring the consumption of blood products in different centers across Iran based on artificial intelligence until the year 2027.MethodsThis research constitutes a time-series investigation within the realm of longitudinal studies. In this study, information on the number of packed red blood cells (RBC), leukoreduced red blood cells (LR-RBC), and platelets (PLT), PLT-Apheresis, and fresh frozen plasma (FFP) was requested from all blood transfusion centers in the country and extracted using a unified protocol. After the initial examination of the information and addressing data issues and inconsistencies, the corrected data were analyzed. Both conventional and artificial intelligence approaches were used to predict each product in this study. The best model was selected based on goodness-of-fit indicators RMSE and MAPE.ResultsBased on the obtained results, the FFP product will follow a relatively consistent process similar to previous years in the next five years. The PLT product is predicted to have a growing trend over the next 5 years, which applies to both the demand and supply of the product. The PLT-Apheresis product also shows a similar upward trend, albeit with a lower growth rate. The RBC product will have a constant trend over a 5-year period (long-term) according to both models, taking into account short-term changes. Similarly, there is a similar trend in LR-RBC, with the expectation that short-term pattern repetition will continue over a 5-year period (long-term). Comparing the goodness-of-fit results, the LSTM model proved to be better for predicting the dominant blood products.ConclusionsThe growth of the elderly population and diseases related to old age, and on the other hand, the trend of increasing the consumption of the product with a short lifespan (PLT) requires the activation of the management of the patient’s blood, especially in relation to this product in medical centers. The trend for other products in the next five years is similar to previous years, and no growth in demand is observed. The LSTM method, considering periodic and cyclical events, has performed the prediction.

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

Variation of reproductive behavior lines and reproductive cycle types

The article analyzes the reproductive behavior lines, traced on the basis of pregnancy history, and proposes a classification of the reproductive cycle types. The article is based on both theoretical constructs of typologization and an assessment of the prevalence of various reproductive behavior lines obtained based on the data from a representative sociological study SeDOZH-2019. A pregnancy history study of 1005 participants shows a fairly high homogeneity of reproductive behavior lines; patterns of repetition of the same reproductive cycles types (more often corresponding to the natural course without the use of contraception and induced abortions) can be traced. An analysis of behavior after the birth of the last child was carried out and after the second child birth a statistically significant predominance of the strategy consisting of avoiding new pregnancies was shown. There is also a correlation between the preference for this strategy and the degree of satisfying of the need for children: if the need is fully satisfied, in most cases (more than 80%), a new pregnancy does not occur, which indicates the leading role of the value component in the regulation of reproductive behavior. A comparison of the behavior of various socio-demographic groups of respondents showed only slight differences: younger and more educated women more often choose strategies that include the use of contraception and avoidance of abortion. The results obtained were analyzed from the point of view of recommendations for designing demographic policy measures.

Bentuk Lagu Siontong Tabang Pada Ensambel Gandang Tasa Di Nagari Sunua Kurai Taji Pariaman

This research aims to describe the form of the Siontong Tabang song in the Gandang Tasa ensemble in Nagari Sunua Kurai Taji Pariaman. This study uses a descriptive qualitative method with the researcher as the main instrument, assisted by writing tools and a camera. Data were collected through literature study, observation, interviews, and documentation, with data analysis carried out through the steps of data collection, description, and conclusion drawing. The results show that Gandang Tasa is a traditional art from Pariaman, performed by an ensemble consisting of 6 to 7 players, including 6 Gandang players and 1 Tasa player. Gandang Tasa is usually performed at certain events, especially in the Tabuik ritual, to boost the spirit and solemnity of the community. The songs commonly performed in the Tabuik ritual include Oyak Tabuik, Sosoh, and Katidiang Sompong. The Siontong Tabang song plays an important role as pangka maatam in the Tabuik procession, with a short repetitive rhythmic pattern starting with Tasa playing. In the modern era, Gandang Tasa is no longer limited to traditional or ritual processes but also serves as entertainment and a means of cultural preservation for the people of Pariaman and its surroundings.

Evaluating the Benefits of Occupational Therapy in Children With Autism Spectrum Disorder Using the Autism Behavior Checklist.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder typically manifesting before the age of three years, characterized by significant impairments in social interaction and communication, as well as restricted and repetitive patterns of interests and activities. In our study, we assessed the benefits of sensory integration therapy in children with ASDusing the Autism Behavior Checklist (ABC), focusing on improvements in sensory processing, relationship-building, language skills, and social and self-care abilities. The study was conducted with 40 children aged three to nine years, diagnosed with ASD, and whose parents provided consent for their participation in therapy between December 2022 and March 2023 at the Private Adana Metro Hospital Child Psychiatry Clinic. The ages and genders of the patients were recorded. Before occupational therapy, after five sessions of occupational therapy, and after 10 sessions of occupational therapy, the ABC test was administered under the supervision of a child psychiatrist and an occupational therapist. The results were recorded, and statistical analyses were performed. In the ABC test conducted after 10 sessions, a decreasing trend was observed in sensory, relationship-building, body and object usage, language skills, social and self-care, and total scale scores compared to pre-occupational therapy test scores (p < 0.000). Occupational therapy improves sensory skills, relationship-building skills, body and object usage abilities, language skills, and social and self-care skills.

Indigenous Gond Art in Art Therapy: A Nature-Based Creative Process

There is a growing interest in understanding the connection between Indigenous art, nature, and art therapy, however information on the health implications of Indian Indigenous art practices is scarce. This case study highlights the application of nature-based Indian Indigenous art form Gond and explores its therapeutic potential in art therapy. A workshop was organized, combining nature-based guided imagery visualization with Gond folktale narration, followed by Gond-inspired artmaking using natural pigments and repetitive patterns. Workshop participants’ responses indicated that this approach fostered engagement, intense concentration, present moment awareness, a relaxed meditative state, and personal expression. Sensitively adapting Indigenous knowledge, like Gond practices, can promote awareness about interconnectedness with nature, artmaking, and storytelling, offering therapeutic benefits for self-care in art therapy.

The first step is the hardest: pitfalls of representing and tokenizing temporal data for large language models.

Large language models (LLMs) have demonstrated remarkable generalization and across diverse tasks, leading individuals to increasingly use them as personal assistants due to their emerging reasoning capabilities. Nevertheless, a notable obstacle emerges when including numerical/temporal data into these prompts, such as data sourced from wearables or electronic health records. LLMs employ tokenizers in their input that break down text into smaller units. However, tokenizers are not designed to represent numerical values and might struggle to understand repetitive patterns and context, treating consecutive values as separate tokens and disregarding their temporal relationships. This article discusses the challenges of representing and tokenizing temporal data. It argues that naively passing timeseries to LLMs can be ineffective due to the modality gap between numbers and text. We conduct a case study by tokenizing a sample mobile sensing dataset using the OpenAI tokenizer. We also review recent works that feed timeseries data into LLMs for human-centric tasks, outlining common experimental setups like zero-shot prompting and few-shot learning. The case study shows that popular LLMs split timestamps and sensor values into multiple nonmeaningful tokens, indicating they struggle with temporal data. We find that preliminary works rely heavily on prompt engineering and timeseries aggregation to "ground" LLMs, hinting that the "modality gap" hampers progress. The literature was critically analyzed through the lens of models optimizing for expressiveness versus parameter efficiency. On one end of the spectrum, training large domain-specific models from scratch is expressive but not parameter-efficient. On the other end, zero-shot prompting of LLMs is parameter-efficient but lacks expressiveness for temporal data. We argue tokenizers are not optimized for numerical data, while the scarcity of timeseries examples in training corpora exacerbates difficulties. We advocate balancing model expressiveness and computational efficiency when integrating temporal data. Prompt tuning, model grafting, and improved tokenizers are highlighted as promising directions. We underscore that despite promising capabilities, LLMs cannot meaningfully process temporal data unless the input representation is addressed. We argue that this paradigm shift in how we leverage pretrained models will particularly affect the area of biomedical signals, given the lack of modality-specific foundation models.