Dance represents a true centre of gravity in Pier Paolo Pasolini’s poetics. His narrative works, selected poetic texts, and above all his filmography are deeply permeated by dancing situations that constitute a defining feature of the director-poet’s style. Pasolini himself was also a dancing body, particularly during his early years in Friuli. This aspect has inspired many choreographers from diverse backgrounds to dedicate ballets to him, drawing inspiration from his films or from his very persona.
Atefeh Shokrizadeh, Boniface Bahati Tadjuidje, Shivam Kumar
et al.
UI/UX designers often work under constraints like brand identity, design norms, and industry guidelines. How these constraints impact designers' ideation and exploration processes should be addressed in creativity-support tools for design. Through an exploratory interview study, we identified three designer personas with varying views on having constraints in the ideation process, which guided the creation of UIDEC, a GenAI-powered tool for supporting creativity under constraints. UIDEC allows designers to specify project details, such as purpose, target audience, industry, and design styles, based on which it generates diverse design examples that adhere to these constraints, with minimal need to write prompts. In a user evaluation involving designers representing the identified personas, participants found UIDEC compatible with their existing ideation process and useful for creative inspiration, especially when starting new projects. Our work provides design implications to AI-powered tools that integrate constraints during UI/UX design ideation to support creativity.
Effective explanations of video action recognition models should disentangle how movements unfold over time from the surrounding spatial context. However, existing methods based on saliency produce entangled explanations, making it unclear whether predictions rely on motion or spatial context. Language-based approaches offer structure but often fail to explain motions due to their tacit nature -- intuitively understood but difficult to verbalize. To address these challenges, we propose Disentangled Action aNd Context concept-based Explainable (DANCE) video action recognition, a framework that predicts actions through disentangled concept types: motion dynamics, objects, and scenes. We define motion dynamics concepts as human pose sequences. We employ a large language model to automatically extract object and scene concepts. Built on an ante-hoc concept bottleneck design, DANCE enforces prediction through these concepts. Experiments on four datasets -- KTH, Penn Action, HAA500, and UCF-101 -- demonstrate that DANCE significantly improves explanation clarity with competitive performance. We validate the superior interpretability of DANCE through a user study. Experimental results also show that DANCE is beneficial for model debugging, editing, and failure analysis.
This paper presents an evaluation of 18 children's in-the-wild experiences with the autonomous robot arm performer NED (Never-Ending Dancer) within the Thingamabobas installation, showcased across the UK. We detail NED's design, including costume, behaviour, and human interactions, all integral to the installation. Our observational analysis revealed three key challenges in child-robot interactions: 1) Initiating and maintaining engagement, 2) Lack of robot expressivity and reciprocity, and 3) Unmet expectations. Our findings show that children are naturally curious, and adept at interacting with a robotic art performer. However, our observations emphasise the critical need to optimise human-robot interaction (HRI) systems through careful consideration of audience's capabilities, perceptions, and expectations, within the performative arts context, to enable engaging and meaningful experiences, especially for young audiences.
Generating large-scale multi-character interactions is a challenging and important task in character animation. Multi-character interactions involve not only natural interactive motions but also characters coordinated with each other for transition. For example, a dance scenario involves characters dancing with partners and also characters coordinated to new partners based on spatial and temporal observations. We term such transitions as coordinated interactions and decompose them into interaction synthesis and transition planning. Previous methods of single-character animation do not consider interactions that are critical for multiple characters. Deep-learning-based interaction synthesis usually focuses on two characters and does not consider transition planning. Optimization-based interaction synthesis relies on manually designing objective functions that may not generalize well. While crowd simulation involves more characters, their interactions are sparse and passive. We identify two challenges to multi-character interaction synthesis, including the lack of data and the planning of transitions among close and dense interactions. Existing datasets either do not have multiple characters or do not have close and dense interactions. The planning of transitions for multi-character close and dense interactions needs both spatial and temporal considerations. We propose a conditional generative pipeline comprising a coordinatable multi-character interaction space for interaction synthesis and a transition planning network for coordinations. Our experiments demonstrate the effectiveness of our proposed pipeline for multicharacter interaction synthesis and the applications facilitated by our method show the scalability and transferability.
Pat Pataranutaporn, Chayapatr Archiwaranguprok, Phoomparin Mano
et al.
This paper introduces Text2Tradition, a system designed to bridge the epistemological gap between modern language processing and traditional dance knowledge by translating user-generated prompts into Thai classical dance sequences. Our approach focuses on six traditional choreographic elements from No. 60 in Mae Bot Yai, a revered Thai dance repertoire, which embodies culturally specific knowledge passed down through generations. In contrast, large language models (LLMs) represent a different form of knowledge--data-driven, statistically derived, and often Western-centric. This research explores the potential of AI-mediated systems to connect traditional and contemporary art forms, highlighting the epistemological tensions and opportunities in cross-cultural translation.
Badal Bhalla, Benjamin V. Lehmann, Kuver Sinha
et al.
The abundance of massive primordial black holes has historically been constrained by dynamical probes. Since these objects can participate in hard few-body scattering processes, they can readily transfer energy to stellar systems, and, in particular, can disrupt wide binaries. However, disruption is not the only possible outcome of such few-body processes. Primordial black holes could also participate in exchange processes, in which one component of a binary system is ejected and replaced by the black hole itself. In this case, the remaining object in the binary would dynamically appear to have an invisible companion. We study the rate of exchange processes for primordial black holes as a component of dark matter and evaluate possible mechanisms for detecting such binaries. We find that many such binaries plausibly exist in the Solar neighborhood, and show that this process can account for observed binary systems whose properties run counter to the predictions of isolated binary evolution.
Electron-phonon coupling in magic-angle twisted bilayer graphene is an important but difficult topic. We propose a scheme to simplify and understand this problem. Weighted by the coupling strength with the low-energy heavy electrons ($f$ orbitals), several moiré optical phonons are singled out which strongly couple to the flat bands. These modes have localized envelopes in the moiré scale, while in the atomic scale they inherit the monolayer oscillations like the Kekulé pattern. They flip the flavor of $f$ orbitals, helping stabilize some symmetry-breaking orders. Such electron-phonon couplings are incorporated into an effective extended Holstein model, where both phonons and electrons are written as moiré scale basis. We hope this model will inspire some insights guiding further studies about the superconductivity and other correlated effects in this system.
Perioperative treatment with conventional cytotoxic chemotherapy for resectable nonsmall
cell lung cancer (NSCLC) has proven clinical benefits in terms of achieving a higher
overall survival (OS) rate. With its success in the palliative treatment of NSCLC, immune
checkpoint blockade (ICB) has now become an essential component of treatment, even as
neoadjuvant or adjuvant therapy in patients with operable NSCLC. Both pre- and post-surgery
ICB applications have proven clinical efficacy in preventing disease recurrence. In addition,
neoadjuvant ICB combined with cytotoxic chemotherapy has shown a significantly
higher rate of pathologic regression of viable tumors compared with cytotoxic chemotherapy
alone. To confirm this, an early signal of OS benefit has been shown in a selected
population, with programmed death ligand 1 expression ≥50%. Furthermore, applying ICB
both pre- and post-surgery enhances its clinical benefits, as is currently under evaluation
in ongoing phase III trials. Simultaneously, as the number of available perioperative treatment
options increases, the variables to be considered for making treatment decisions become
more complex. Thus, the role of a multidisciplinary team-based treatment approach
has not been fully emphasized. This review presents up-to-date pivotal data that lead to
practical changes in managing resectable NSCLC. From the medical oncologist’s perspective,
it is time to dance with surgeons to decide on the sequence of systemic treatment,
particularly the ICB-based approach, accompanying surgery for operable NSCLC.
As a result of the Covid-19 virus, which has affected everyone, the educational community is in a tough situation right now. Technology's advancement in the teaching-learning instruction process is both a benefit and a drawback of globalization's educational approach. The study was primarily concerned with how the use of e-materials in the classroom might increase students' learning performance in physical education dancing. This study was a correlational investigation of 50 grade 9 students' dance performances and the utilization of e-materials in physical education in Sta. Catalina National High School, Candelaria, Quezon. The research was conducted using a descriptive-quantitative technique and a self-administered questionnaire through Google Form. The findings demonstrated that the use of e-materials in physical education dancing is always noted, and students' performance improves significantly when e-materials are used. Furthermore, there is no relationship between e-materials used to teach dance in physical education and student dance performance. It encouraged students to keep using and employing the effectiveness of e-materials in their dancing performances. This research will help to understand how effective the use of e-materials is in teaching dance in physical education and how it affects student performance and will provide valuable insights on the best practices for teaching dance in physical education and how to maximize the use of e-materials to enhance student performance.
Molecular clouds in the central molecular zone (CMZ) have been observed to feature turbulent line widths that are significantly higher, and scale with cloud size more steeply, than in the rest of the Milky Way. In the same Galactic region, the stellar density is also much higher than in the rest of the Milky Way, and the vertical stellar velocity dispersion is large, meaning that even young stars are likely to cross the entire vertical extent of the CMZ within their lifetimes. Here, we investigate whether interactions of CMZ molecular clouds with crossing stars can account for the extraordinary properties of observed turbulence in this part of the Galaxy. We calculated the rate of energy deposition by stars crossing CMZ clouds due to (a) stellar winds and (b) dynamical friction, and compared it to the rate of turbulence decay. We calculated the predicted scaling of turbulence line width with cloud size in each case. We find that energy deposition by stellar winds of crossing massive stars can account for both the level and the scaling of CMZ cloud turbulence with cloud size. We also find that the mechanism stops being effective at a Galactocentric distance comparable to the CMZ extent. On the other hand, we find that dynamical friction by crossing stars does not constitute a significant driver of turbulence for CMZ clouds.
Matteo Martinelli, Francesca Scarcella, Natalie B. Hogg
et al.
Primordial black holes (PBHs) are compact objects proposed to have formed in the early Universe from the collapse of small-scale over-densities. Their existence may be detected from the observation of gravitational waves (GWs) emitted by PBH mergers, if the signals can be distinguished from those produced by the merging of astrophysical black holes. In this work, we forecast the capability of the Einstein Telescope, a proposed third-generation GW observatory, to identify and measure the abundance of a subdominant population of distant PBHs, using the difference in the redshift evolution of the merger rate of the two populations as our discriminant. We carefully model the merger rates and generate realistic mock catalogues of the luminosity distances and errors that would be obtained from GW signals observed by the Einstein Telescope. We use two independent statistical methods to analyse the mock data, finding that, with our more powerful, likelihood-based method, PBH abundances as small as $f_\mathrm{PBH} \approx 7 \times 10^{-6}$ ($f_\mathrm{PBH} \approx 2\times10^{-6}$) would be distinguishable from $f_\mathrm{PBH} = 0$ at the level of $3σ$ with a one year (ten year) observing run of the Einstein Telescope. Our mock data generation code, darksirens, is fast, easily extendable and publicly available on GitLab.
ABSTRACTPoetry is a literary form where the poet expresses himself or herself through the representations they have carved. It defines a literary expression where the beliefs and perceptions get represented using mythical aspects to enhance cultural meanings. Margaret Atwood is well-known for using mythical female characters in her poems by placing them in the contemporary sphere. As Guerin once remarked, a tribe or a country might come together through mythology to engage in shared psychological and spiritual pursuits. The female characters which Atwood brings into her poems are metaphors representing the socio-political and cultural state of the modern era in which females are placed now in the present times. In her poem, “Helen of Troy Does Countertop Dancing,” Atwood brings in the historical figure of the famous Helen of Troy, who is well known in the literary domain for her beauty which formed the basis for the Trojan War. The paper argues how the aspect of revisiting myth in the poem trounces objectification by using the notion of female resistance.
The scholarly discipline of ethnochoreology or, as Hungarians would say, dance folkloristics, has had a profound and developed history in Hungary. From the very beginning of its establishing in early 1950s, various intense research activities have focused on the collection, documentation and systematization of traditional dances throughout the country, but also on developing particular theories about their historical and regional stratification. Along with dance notation (Labanotation), one of the main methods of collecting and documenting traditional dances has been their planned filming during pre-arranged sessions, financially and organizationally supported by the state institutions. Among early dance researchers, Labanotators and ethnochoreologists the most significant figure whose influence will far exceed the historical timing and national boundaries of his scholarly work certainly is György Martin (1932-1983). Thanks to the fertile environment of early traditional dance investigation in Hungary coupled with his academic education in folkloristics and ethnomusicology, but also, on the other hand, his huge passion for field research and cabinetwork equally, Martin has left a monumental academic foundation in all of his numerous writings, among which some were published after his early death. The book Selected papers of György Martin, edited by prominent dance researchers from Hungary (Fügedi, Szőnyi and Varga) and Ireland (Quigley), consists of the most significant Martin’s articles from various periods of his career. Besides Martin’s essays translated or re-edited in English, there are also dozens of papers by dance scholars mostly from Hungary. They critically reinterpret Martin’s achievements and reposition them not only within Hungarian but also within international scopes, pointing to their great importance for developing ethnochoreology as an academic scholarly discipline in general and to the continuation of their actuality in the second and third decades of the 21st century. Thanks to the fact that this capital publication is printed in English, Martin’s most significant papers and their scholarly interpretations will be available to a wider readership around the world, which will enable further theoretical, methodological and intellectual considerations about (traditional/folk) dance.
Dense retrieval conducts text retrieval in the embedding space and has shown many advantages compared to sparse retrieval. Existing dense retrievers optimize representations of queries and documents with contrastive training and map them to the embedding space. The embedding space is optimized by aligning the matched query-document pairs and pushing the negative documents away from the query. However, in such training paradigm, the queries are only optimized to align to the documents and are coarsely positioned, leading to an anisotropic query embedding space. In this paper, we analyze the embedding space distributions and propose an effective training paradigm, Contrastive Dual Learning for Approximate Nearest Neighbor (DANCE) to learn fine-grained query representations for dense retrieval. DANCE incorporates an additional dual training object of query retrieval, inspired by the classic information retrieval training axiom, query likelihood. With contrastive learning, the dual training object of DANCE learns more tailored representations for queries and documents to keep the embedding space smooth and uniform, thriving on the ranking performance of DANCE on the MS MARCO document retrieval task. Different from ANCE that only optimized with the document retrieval task, DANCE concentrates the query embeddings closer to document representations while making the document distribution more discriminative. Such concentrated query embedding distribution assigns more uniform negative sampling probabilities to queries and helps to sufficiently optimize query representations in the query retrieval task. Our codes are released at https://github.com/thunlp/DANCE.
One of the key criticisms of deep learning is that large amounts of expensive and difficult-to-acquire training data are required in order to train models with high performance and good generalization capabilities. Focusing on the task of monocular camera pose estimation via scene coordinate regression (SCR), we describe a novel method, Domain Adaptation of Networks for Camera pose Estimation (DANCE), which enables the training of models without access to any labels on the target task. DANCE requires unlabeled images (without known poses, ordering, or scene coordinate labels) and a 3D representation of the space (e.g., a scanned point cloud), both of which can be captured with minimal effort using off-the-shelf commodity hardware. DANCE renders labeled synthetic images from the 3D model, and bridges the inevitable domain gap between synthetic and real images by applying unsupervised image-level domain adaptation techniques (unpaired image-to-image translation). When tested on real images, the SCR model trained with DANCE achieved comparable performance to its fully supervised counterpart (in both cases using PnP-RANSAC for final pose estimation) at a fraction of the cost. Our code and dataset are available at https://github.com/JackLangerman/dance
Andreas Aristidou, Anastasios Yiannakidis, Kfir Aberman
et al.
Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre. In addition, our framework enables generation of diverse motions that are controlled by the content of the music, and not only by the beat. Our music-driven dance synthesis framework is a hierarchical system that consists of three levels: pose, motif, and choreography. The pose level consists of an LSTM component that generates temporally coherent sequences of poses. The motif level guides sets of consecutive poses to form a movement that belongs to a specific distribution using a novel motion perceptual-loss. And the choreography level selects the order of the performed movements and drives the system to follow the global structure of a dance genre. Our results demonstrate the effectiveness of our music-driven framework to generate natural and consistent movements on various dance types, having control over the content of the synthesized motions, and respecting the overall structure of the dance.