Speech-driven 3D facial animation has attracted increasing interest since its potential to generate expressive and temporally synchronized digital humans. While recent works have begun to explore emotion-aware animation, they still depend on explicit one-hot encodings to represent identity and emotion with given emotion and identity labels, which limits their ability to generalize to unseen speakers. Moreover, the emotional cues inherently present in speech are often neglected, limiting the naturalness and adaptability of generated animations. In this work, we propose LSF-Animation, a novel framework that eliminates the reliance on explicit emotion and identity feature representations. Specifically, LSF-Animation implicitly extracts emotion information from speech and captures the identity features from a neutral facial mesh, enabling improved generalization to unseen speakers and emotional states without requiring manual labels. Furthermore, we introduce a Hierarchical Interaction Fusion Block (HIFB), which employs a fusion token to integrate dual transformer features and more effectively integrate emotional, motion-related and identity-related cues. Extensive experiments conducted on the 3DMEAD dataset demonstrate that our method surpasses recent state-of-the-art approaches in terms of emotional expressiveness, identity generalization, and animation realism. The source code will be released at: https://github.com/Dogter521/LSF-Animation.
Jun Seong Kim, Kyaw Ye Thu, Javad Ismayilzada
et al.
In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should correctly identify kimchi (Korean food) in an image both when an Asian woman is eating it, as well as an African man is eating it. However, current MLLMs show an over-reliance on the visual features of the person, leading to misclassification of the entities. To examine the robustness of MLLMs to different ethnicity, we introduce MixCuBe, a cross-cultural bias benchmark, and study elements from five countries and four ethnicities. Our findings reveal that MLLMs achieve both higher accuracy and lower sensitivity to such perturbation for high-resource cultures, but not for low-resource cultures. GPT-4o, the best-performing model overall, shows up to 58% difference in accuracy between the original and perturbed cultural settings in low-resource cultures. Our dataset is publicly available at: https://huggingface.co/datasets/kyawyethu/MixCuBe.
Dorian Peters, Fernanda Espinoza, Marco da Re
et al.
There is justifiable interest in leveraging conversational AI (CAI) for health across the majority world, but to be effective, CAI must respond appropriately within culturally and linguistically diverse contexts. Therefore, we need ways to address the fact that current LLMs exclude many lived experiences globally. Various advances are underway which focus on top-down approaches and increasing training data. In this paper, we aim to complement these with a bottom-up locally-grounded approach based on qualitative data collected during participatory workshops in Latin America. Our goal is to construct a rich and human-centred understanding of: a) potential areas of cultural misalignment in digital health; b) regional perspectives on chatbots for health and c)strategies for creating culturally-appropriate CAI; with a focus on the understudied Latin American context. Our findings show that academic boundaries on notions of culture lose meaning at the ground level and technologies will need to engage with a broader framework; one that encapsulates the way economics, politics, geography and local logistics are entangled in cultural experience. To this end, we introduce a framework for 'Pluriversal Conversational AI for Health' which allows for the possibility that more relationality and tolerance, rather than just more data, may be called for.
The present study aimed to identify the skin features of the local dog. After anesthesia, five adult dogs took the skin samples, and the wounds were sutured that form after collecting the sample and the samples were fixated with 10% formalin and Bouin's solution.(special stain )in the present study, the skin consists of two layers epidermis and dermis, the epidermis is composed of epithelial cell layers comprised of four different types of cells of various studied regions. while the dermis was composed of two layers that embedded the hair follicles, sebaceous glands, and a few numbers of sweet and sebaceous glands associated with hair follicles. In conclusion, The skin is a large organ covering all body joints, underlying the muscles and bones. It is characterized by a few sebaceous and sweat glands.
This essay explores the interactions of violence, self-care, and nationalistic ideology in Rise of the Rōnin (2024), a historical action RPG set in Bakumatsu Japan (1853–1868). The study examines how elements of “cozy games” appear in gameplay through animal interactions and human relationships, emphasizing the contrast between brutal samurai combat and moments of peace. Findings build on model of “situational coziness” given by Waskiewicz and Bakun to examine the cozy elements distributed throughout the open-world game environment. Analyzing game mechanics, narrative design, and player agency, the research highlights how the game crafts an emotional engagement that both softens and legitimizes its ideological stance, positioning self-care and loyalty as virtues aligned with Japan's turbulent path to modernization. The inclusion of nationalist ideologue Yoshida Shōin (1830–1859) as a central figure in the narrative limits the game in terms of global distribution, most notably in South Korea.
Action segmentation of behavioral videos is the process of labeling each frame as belonging to one or more discrete classes, and is a crucial component of many studies that investigate animal behavior. A wide range of algorithms exist to automatically parse discrete animal behavior, encompassing supervised, unsupervised, and semi-supervised learning paradigms. These algorithms -- which include tree-based models, deep neural networks, and graphical models -- differ widely in their structure and assumptions on the data. Using four datasets spanning multiple species -- fly, mouse, and human -- we systematically study how the outputs of these various algorithms align with manually annotated behaviors of interest. Along the way, we introduce a semi-supervised action segmentation model that bridges the gap between supervised deep neural networks and unsupervised graphical models. We find that fully supervised temporal convolutional networks with the addition of temporal information in the observations perform the best on our supervised metrics across all datasets.
While affective expressions on social media have been extensively studied, most research has focused on the Western context. This paper explores cultural differences in affective expressions by comparing valence and arousal on Twitter/X (geolocated to the US) and Sina Weibo (in Mainland China). Using the NRC-VAD lexicon to measure valence and arousal, we identify distinct patterns of emotional expression across both platforms. Our analysis reveals a functional representation between valence and arousal, showing a negative offset in contrast to traditional lab-based findings which suggest a positive offset. Furthermore, we uncover significant cross-cultural differences in arousal, with US users displaying higher emotional intensity than Chinese users, regardless of the valence of the content. Finally, we conduct a comprehensive language analysis correlating n-grams and LDA topics with affective dimensions to deepen our understanding of how language and culture shape emotional expression. These findings contribute to a more nuanced understanding of affective communication across cultural and linguistic contexts on social media.
Since NFTs and large generative models (such as DALLE2 and Stable Diffusion) have been publicly available, artists have seen their jobs threatened and stolen. While artists depend on sharing their art on online platforms such as Deviantart, Pixiv, and Artstation, many slowed down sharing their work or downright removed their past work therein, especially if these platforms fail to provide certain guarantees regarding the copyright of their uploaded work. Text-to-image (T2I) generative models are trained using human-produced content to better guide the style and themes they can produce. Still, if the trend continues where data found online is generated by a machine instead of a human, this will have vast repercussions in culture. Inspired by recent work in generative models, we wish to tell a cautionary tale and ask what will happen to the visual arts if generative models continue on the path to be (eventually) trained solely on generated content.
Daniel Mota-Rojas, Alexandra L. Whittaker, Adriana Domínguez-Oliva
et al.
Several types of enrichment can be used to improve animal welfare. This review summarizes the literature on the use of mechanical brushes, tactile udder stimulation, music, and visual stimuli as enrichment methods for dairy cows. Mechanical brushes and tactile stimulation of the udder have been shown to have a positive effect on milk yield and overall behavioral repertoire, enhancing natural behavior. Classical music reduces stress levels and has similarly been associated with increased milk yield. A slow or moderate tempo (70 to 100 bpm) at frequencies below 70 dB is recommended to have this positive effect. Evidence on the impacts of other types of enrichment, such as visual stimulation through mirrors, pictures, and color lights, or the use of olfactory stimuli, is equivocal and requires further study.
Róger Jean Oliveira, Carolina Silveira Mascarenhas, Gertrud Müller
Abstract The aim of this study was to record Centrorhynchus sp. associated with the exotic species Aquarana catesbeiana (bullfrog) in southern Brazil and to present a checklist of vertebrate hosts in South America. Twenty-nine adults and juveniles of A. catesbeiana were collected in Capão do Leão, state of Rio Grande do Sul, Brazil, between October 2019 and December 2020. We found 275 specimens of Centrorhynchus sp. cystacanths in the stomach musculature and coelomic cavity of 55.1% of hosts (16). There was no significant differences in the prevalence and mean intensity of infection with cystacanths when compared males and females of A. catesbeiana. The prevalence was significantly higher in adults than in juveniles. The checklist presents 106 species of vertebrate hosts and 14 taxa of Centrorhynchus recorded in nine South American countries. Avian were the main definitive hosts of Centrorhynchus spp. and snakes Dipsadidae, anurans Hylidae and Leptodactylidae the main paratenic hosts in South America. This is the first record of Centrorhynchus cystacanths in A. catesbeiana in the South America. The study provides tools to help understand the parasitic relationships between species of Centrorhynchus and A. catesbeiana and other hosts in areas where bullfrog have been introduced.
Sikandar Ali, Abdullah Saghir Ahmad, Kamran Ashraf
et al.
Background and Aim: Anaplasma marginale is an obligate intraerythrocytic rickettsial parasite that infects cattle in tropical and subtropical regions. There is no evidence that A. marginale inoculation can be used to culture Rhipicephalus annulatus in rabbits. This study aimed to determine the molting of R. annulatus larvae, nymphs, and adults on rabbits as well as nymphs and adults of R. annulatus on calves with or without A. marginale. Transstadial, horizontal, and transovarial transmissions of A. marginale in R. annulatus reared on rabbits and calves were evaluated.
Materials and Methods: Engorged female ticks were collected from field samples of A. marginale-infected and non-infected cattle. We divided the eight rabbits into two groups: A and B. Group A rabbits were infected with A. marginale through parenteral inoculation, whereas Group B rabbits were kept as a control. The “clean rabbits” in Group B were observed for tick rearing without A. marginale. Polymerase chain reaction was used to screen A. marginale in rabbits and stages of tick. The complete life cycle of R. annulatus with or without A. marginale was observed on rabbits.
Results: A 6.5-day longer life cycle was observed in ticks harboring A. marginale than in ticks without A. marginale. To observe transstadial transmission, transstadial, horizontal, and transovarial transmissions of A. marginale in R. annulatus ticks were experimentally observed in one clean calf fed separately with infected nymphs and female adult ticks.
Conclusion: We experimentally observed transovarian, transstadial, and transovarial transmission of A. marginale in R. annulatus ticks as a biological vector reared on calves and rabbits. We used rabbits as a model animal for rearing R. annulatus ticks and culture of A. marginale.
Keywords: Anaplasma marginale, horizontal, rabbits, Rhipicephalus annulatus, transovarial, transstadial.
There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.
The study of cultural evolution benefits from detailed analysis of cultural transmission in specific human domains. Chess provides a platform for understanding the transmission of knowledge due to its active community of players, precise behaviors, and long-term records of high-quality data. In this paper, we perform an analysis of chess in the context of cultural evolution, describing multiple cultural factors that affect move choice. We then build a population-level statistical model of move choice in chess, based on the Dirichlet-multinomial likelihood, to analyze cultural transmission over decades of recorded games played by leading players. For moves made in specific positions, we evaluate the relative effects of frequency-dependent bias, success bias, and prestige bias on the dynamics of move frequencies. We observe that negative frequency-dependent bias plays a role in the dynamics of certain moves, and that other moves are compatible with transmission under prestige bias or success bias. These apparent biases may reflect recent changes, namely the introduction of computer chess engines and online tournament broadcasts. Our analysis of chess provides insights into broader questions concerning how social learning biases affect cultural evolution.
Erica Marchiori, Federica Obber, Roberto Celva
et al.
The red fox acts as reservoir for several helminthic infections which are of interest for both public and animal health. Huge efforts have been made for the assessment of the sensitivity of coprological tests for the detection of Echinococcus multilocularis, while less attention has been paid to other helminthic species. This study aimed at assessing the performance of two copromicroscopic techniques in the detection and prevalence estimation of gastrointestinal helminths in the red fox. Helminths were isolated from the small intestines of 150 red foxes from Bolzano province, Italy, with a scraping, filtration and counting technique (SFCT) and morphologically identified. Rectal contents were collected and submitted to simple flotation (FT) and, only for Taenids, a method based on the concentration of eggs and identification with multiplex PCR (CMPCR). Using SFCT as a reference standard, we estimated the sensitivity of the copromicroscopic tests. Three species of nematodes (namely, Toxocara canis, Uncinaria stenocephala and Pterygodermatites sp.) and five species of cestodes (E. multilocularis, Taenia crassiceps, T. polycantha, Hydatigera taeniaeformis, Mesocestoides sp.) were identified with SFCT, whereas eggs referable to the same taxa were detected with fecal diagnostics, except for Pterygodermatites sp. and Mesocestoides sp. The sensitivity of FT was low for all taxa, ranging from 9.8 to 36.3%, with lower values for Taeniidae. CMPCR was confirmed to perform better for the detection of Taeniidae eggs (23.5%) and the multiplex PCR on retrieved eggs was effective in the identification of the species. A meta-analysis of literature also suggested that our results are consistent with existing data, indicating that copromicroscopy tends to underestimate the prevalence of helminthic infections. The extent of such underestimation varies with taxon, being higher at high prevalence levels, in particular for cestodes. Irregular dynamics of egg shedding, and routine deep freezing of red fox feces may explain the frequency of false negatives with copromicroscopy. Low sensitivity of copromicroscopic tests should be accounted for when estimating prevalence and when defining the correct sample size for the detection of the parasites.
Modeling real-world sound is a fundamental problem in the creative use of machine learning and many other fields, including human speech processing and bioacoustics. Transformer-based generative models and some prior work (e.g., DDSP) are known to produce realistic sound, although they have limited control and are hard to interpret. As an alternative, we aim to use modular synthesizers, i.e., compositional, parametric electronic musical instruments, for modeling non-music sounds. However, inferring synthesizer parameters given a target sound, i.e., the parameter inference task, is not trivial for general sounds, and past research has typically focused on musical sound. In this work, we optimize a differentiable synthesizer from TorchSynth in order to model, emulate, and creatively generate animal vocalizations. We compare an array of optimization methods, from gradient-based search to genetic algorithms, for inferring its parameters, and then demonstrate how one can control and interpret the parameters for modeling non-music sounds.
The decisive role of Embryology in understanding the evolution of animal forms is founded and deeply rooted in the history of science. It is recognized that the emergence of multicellularity would not have been possible without the formation of the first embryo. We speculate that biophysical phenomena and the surrounding environment of the Ediacaran ocean were instrumental in co-opting a neoplastic functional module (NFM) within the nucleus of the first zygote. Thus, the neoplastic process, understood here as a biological phenomenon with profound embryologic implications, served as the evolutionary engine that favored the formation of the first embryo and cancerous diseases and allowed to coherently create and recreate body shapes in different animal groups during evolution. In this article, we provide a deep reflection on the Physics of the first embryogenesis and its contribution to the exaptation of additional NFM components, such as the extracellular matrix. Knowledge of NFM components, structure, dynamics, and origin advances our understanding of the numerous possibilities and different innovations that embryos have undergone to create animal forms via Neoplasia during evolutionary radiation. The developmental pathways of Neoplasia have their origins in ctenophores and were consolidated in mammals and other apical groups.
Matthew J. Atherton, Matthew J. Atherton, Antonia Rotolo
et al.
BackgroundChimeric antigen receptor-T (CAR-T) cells have transformed the treatment of human B cell malignancies. With the advent of CAR-T therapy, specific and in some cases severe toxicities have been documented with cytokine release syndrome (CRS) being the most frequently reported. As dogs develop tumors spontaneously and in an immunocompetent setting, they provide a unique translational opportunity to further investigate the activity and toxicities associated with CAR-T therapy. Although various adoptive cellular therapy (ACT) trials have been documented and several more are ongoing in canine oncology, CRS has not been comprehensively described in canine cancer patients.Case PresentationHere we present the clinical and serologic changes in a dog treated with autologous CAR-T for relapsed B cell lymphoma that presented with lethargy and fever 3 days following CAR-T. Multiplexed serum cytokine profiling revealed increases in key cytokines implicated in human CRS including IL-6, MCP-1, IFNγ and IL-10 at or shortly after peak CAR-T levels in vivo.ConclusionThe observations noted in this case report are consistent with CRS development following CAR-T therapy in a canine patient. The dog represents a compelling model to study the pathophysiology of CRS and pre-clinically screen novel therapeutics to prevent and treat this life-threatening condition in the setting of a complex and naturally evolved immune system.
Abstract Background Osteoarthritis (OA) is the most commonly diagnosed joint disease in companion animals, and proper tools are necessary to assess patients and response to treatment. We aimed to perform an initial psychometric evaluation of the Portuguese version of the Liverpool Osteoarthritis in Dogs (LOAD).Fifty Police working dogs with bilateral hip OA were assessed in a prospective, randomized, double-blinded study. Canine handlers, who were native Portuguese speakers, completed a copy of the translated version of the LOAD. Their results were compared with those of fifty sound dogs. Construct validity was evaluated by assessing differences between OA and sound animals with the Mann-Whitney test. Further evaluation was performed with the Kaiser-Meyer-Olkin measure of sampling adequacy, Eigenvalue, and scree-plot analysis. Internal consistency was tested with Cronbach’s α. Results A significant difference was observed between OA and sound dogs (p < 0.01), indicating construct validity. Two factors accounted for 81.5% of the total variance. Cronbach’s α was 0.96, and a high inter-item correlation was observed, raging from 0.76 to 0.95, showing strong internal consistency. We presented criterion and construct validity of the Portuguese version of the LOAD, which is valid for use in the Portuguese language. It is an additional stage in providing a broader number of clinicians with an accessible tool to evaluate dogs with osteoarthritis. Further studies are required.
Badal Soni, Debangan Thakuria, Nilutpal Nath
et al.
Anime is quite well-received today, especially among the younger generations. With many genres of available shows, more and more people are increasingly getting attracted to this niche section of the entertainment industry. As anime has recently garnered mainstream attention, we have insufficient information regarding users' penchant and watching habits. Therefore, it is an uphill task to build a recommendation engine for this relatively obscure entertainment medium. In this attempt, we have built a novel hybrid recommendation system that could act both as a recommendation system and as a means of exploring new anime genres and titles. We have analyzed the general trends in this field and the users' watching habits for coming up with our efficacious solution. Our solution employs deep autoencoders for the tasks of predicting ratings and generating embeddings. Following this, we formed clusters using the embeddings of the anime titles. These clusters form the search space for anime with similarities and are used to find anime similar to the ones liked and disliked by the user. This method, combined with the predicted ratings, forms the novel hybrid filter. In this article, we have demonstrated this idea and compared the performance of our implemented model with the existing state-of-the-art techniques.