Understanding user identity and behavior is central to applications such as personalization, recommendation, and decision support. Most existing approaches rely on deterministic embeddings or black-box predictive models, offering limited uncertainty quantification and little insight into what latent representations encode. We propose a probabilistic digital twin framework in which each user is modeled as a latent stochastic state that generates observed behavioral data. The digital twin is learned via amortized variational inference, enabling scalable posterior estimation while retaining a fully probabilistic interpretation. We instantiate this framework using a variational autoencoder (VAE) applied to a user-response dataset designed to capture stable aspects of user identity. Beyond standard reconstruction-based evaluation, we introduce a statistically grounded interpretation pipeline that links latent dimensions to observable behavioral patterns. By analyzing users at the extremes of each latent dimension and validating differences using nonparametric hypothesis tests and effect sizes, we demonstrate that specific dimensions correspond to interpretable traits such as opinion strength and decisiveness. Empirically, we find that user structure is predominantly continuous rather than discretely clustered, with weak but meaningful structure emerging along a small number of dominant latent axes. These results suggest that probabilistic digital twins can provide interpretable, uncertainty-aware representations that go beyond deterministic user embeddings.
L’obiettivo di questa ricerca è indagare il ruolo della pratica performativa del dj set per soggettività e comunità queer. Indaghiamo la formazione di culture e scene musicali queer come sinergia tra dimensione musicale e sessuale (Taylor 2008, 2012) basate sull’inclusione e l’accoglienza di gusti musicali e soggettività di vario genere. Ci soffermiamo in particolare sull’elettronica come luogo sperimentazioni musicali, tecnologiche, artistiche transfemministe e queer (Attimonelli 2018; Attimonelli, Tomeo 2022) e sul ruolo sociale del dj set, esplorando il suono come artefatto e come spazio. Proponiamo una metodologia basata su momenti etnografici ed interviste ad esponenti di una scena cittadina, con percorsi, esperienze e forme espressive differenti. L’analisi attraverso la lente del genere fa emergere forme diverse di attivismo al di fuori dalle logiche sessiste e pratiche di queerizzazione e rinegoziazione di spazi nell’ottica di creazioni di safer spaces.
José Gregorio Rodríguez-Nieto, Olga Patricia Salazar-Díaz, Ricardo Esteban Vallejos-Cifuentes
et al.
In this paper we study some algebraic properties of the rack structure as well as the representation theory of it, following the ideas given by M. Elhamdadi and E. M. Moutuou in \cite{Elhamdadi}. We establish a correspondence between the irreducible strong representations of a finite and connected rack with the irreducible representation of its finite enveloping group which allows to use techniques of the latter topic in the other setting.
Reasoning is an essential component of human intelligence as it plays a fundamental role in our ability to think critically, support responsible decisions, and solve challenging problems. Traditionally, AI has addressed reasoning in the context of logic-based representations of knowledge. However, the recent leap forward in natural language processing, with the emergence of language models based on transformers, is hinting at the possibility that these models exhibit reasoning abilities, particularly as they grow in size and are trained on more data. Despite ongoing discussions about what reasoning is in language models, it is still not easy to pin down to what extent these models are actually capable of reasoning. The goal of this workshop is to create a platform for researchers from different disciplines and/or AI perspectives, to explore approaches and techniques with the aim to reconcile reasoning between language models using transformers and using logic-based representations. The specific objectives include analyzing the reasoning abilities of language models measured alongside KR methods, injecting KR-style reasoning abilities into language models (including by neuro-symbolic means), and formalizing the kind of reasoning language models carry out. This exploration aims to uncover how language models can effectively integrate and leverage knowledge and reasoning with it, thus improving their application and utility in areas where precision and reliability are a key requirement.
Purpose: The general purpose objective of the study was to explore cultural representation in modern theater. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cultural representation in modern theater. Preliminary empirical review revealed that while there had been significant progress in including diverse narratives, challenges remained, particularly in leadership roles dominated by majority cultural groups, limiting minority influence. Despite regional variations and structural imbalances, theater increasingly provided visibility to marginalized voices, enhancing empathy and understanding. The study emphasized the need for comprehensive diversity and inclusion strategies and collaborations to sustain this evolution, highlighting that continued efforts were essential for theater to reflect societal diversity and serve as a catalyst for cultural dialogue and social change. Unique Contribution to Theory, Practice and Policy: The Critical Race Theory, Cultural Hegemony Theory and Intersectionality Theory may be used to anchor future studies on cultural representation in modern theater. The study recommended that theater practitioners adopt inclusive casting and storytelling practices, collaborate with community groups, and prioritize diversity in creative teams. Policy recommendations included implementing diversity-focused funding, establishing benchmarks, and creating incentives for inclusive practices. The study emphasized conducting audits, providing diversity training, and implementing mentorship programs to support underrepresented talent. It also advocated for increased funding, national awards, and advisory boards to ensure long-term commitment to cultural representation in theater.
Recently, a new line of works has emerged to understand and improve self-attention in Transformers by treating it as a kernel machine. However, existing works apply the methods for symmetric kernels to the asymmetric self-attention, resulting in a nontrivial gap between the analytical understanding and numerical implementation. In this paper, we provide a new perspective to represent and optimize self-attention through asymmetric Kernel Singular Value Decomposition (KSVD), which is also motivated by the low-rank property of self-attention normally observed in deep layers. Through asymmetric KSVD, $i$) a primal-dual representation of self-attention is formulated, where the optimization objective is cast to maximize the projection variances in the attention outputs; $ii$) a novel attention mechanism, i.e., Primal-Attention, is proposed via the primal representation of KSVD, avoiding explicit computation of the kernel matrix in the dual; $iii$) with KKT conditions, we prove that the stationary solution to the KSVD optimization in Primal-Attention yields a zero-value objective. In this manner, KSVD optimization can be implemented by simply minimizing a regularization loss, so that low-rank property is promoted without extra decomposition. Numerical experiments show state-of-the-art performance of our Primal-Attention with improved efficiency. Moreover, we demonstrate that the deployed KSVD optimization regularizes Primal-Attention with a sharper singular value decay than that of the canonical self-attention, further verifying the great potential of our method. To the best of our knowledge, this is the first work that provides a primal-dual representation for the asymmetric kernel in self-attention and successfully applies it to modeling and optimization.
Representation learning has proven to be a powerful methodology in a wide variety of machine learning applications. For atmospheric dynamics, however, it has so far not been considered, arguably due to the lack of large-scale, labeled datasets that could be used for training. In this work, we show that the difficulty is benign and introduce a self-supervised learning task that defines a categorical loss for a wide variety of unlabeled atmospheric datasets. Specifically, we train a neural network on the simple yet intricate task of predicting the temporal distance between atmospheric fields from distinct but nearby times. We demonstrate that training with this task on ERA5 reanalysis leads to internal representations capturing intrinsic aspects of atmospheric dynamics. We do so by introducing a data-driven distance metric for atmospheric states. When employed as a loss function in other machine learning applications, this Atmodist distance leads to improved results compared to the classical $\ell_2$-loss. For example, for downscaling one obtains higher resolution fields that match the true statistics more closely than previous approaches and for the interpolation of missing or occluded data the AtmoDist distance leads to results that contain more realistic fine scale features. Since it is derived from observational data, AtmoDist also provides a novel perspective on atmospheric predictability.
The goal in offline data-driven decision-making is synthesize decisions that optimize a black-box utility function, using a previously-collected static dataset, with no active interaction. These problems appear in many forms: offline reinforcement learning (RL), where we must produce actions that optimize the long-term reward, bandits from logged data, where the goal is to determine the correct arm, and offline model-based optimization (MBO) problems, where we must find the optimal design provided access to only a static dataset. A key challenge in all these settings is distributional shift: when we optimize with respect to the input into a model trained from offline data, it is easy to produce an out-of-distribution (OOD) input that appears erroneously good. In contrast to prior approaches that utilize pessimism or conservatism to tackle this problem, in this paper, we formulate offline data-driven decision-making as domain adaptation, where the goal is to make accurate predictions for the value of optimized decisions ("target domain"), when training only on the dataset ("source domain"). This perspective leads to invariant objective models (IOM), our approach for addressing distributional shift by enforcing invariance between the learned representations of the training dataset and optimized decisions. In IOM, if the optimized decisions are too different from the training dataset, the representation will be forced to lose much of the information that distinguishes good designs from bad ones, making all choices seem mediocre. Critically, when the optimizer is aware of this representational tradeoff, it should choose not to stray too far from the training distribution, leading to a natural trade-off between distributional shift and learning performance.
The $α'$-expansion of string theory provides a rich set of higher-dimension operators, indexed by $ζ$ values, which can be used to study color-kinematics duality and the double copy. These two powerful properties, actually first noticed in tree-level string amplitudes, simplify the construction of both gauge and gravity amplitudes. However, their applicability and limitations are not fully understood. We attempt to construct a set of color-kinematics dual numerators at one loop and four points for insertions of operator combinations corresponding to the lowest four $ζ_2$-free operator insertions from the open superstring: $ζ_3$, $ζ_5$, $ζ_3^2$, and $ζ_7$. We succeed in finding a representation for the first three in terms of box, triangle, and bubble numerators. In the case of $ζ_7$ we find an obstruction to a fully color-dual representation related to the bubble-on-external-leg type diagrams. We discuss two paths around this obstruction, both of which signal an incompatability between color-kinematics duality and manifesting certain desired properties. Using the constructed color-dual numerators, we find two different Bern-Carrasco-Johansson double copies that produce candidate closed-string-insertion numerators. Both approaches to the double copy match the kinematics of the cuts, with relative normalization set by either summing over both double copies including degeneracy or by including an explicit prefactor on the double-copy numerator definitions.
Water Stains on the Wall is a pivotal example that demonstrates the world-renowned Taiwanese choreographer Lin Hwai-min’s cosmopolitical perspective. This article examines the performance in the context of Lin’s other works and demonstrates how he contests our presumption and consumption of Otherness in the dancescape. Lin highlights the transformative power of calligraphic kinesthesia by engaging with and interrogating a hybrid synthesis of Eastern and Western embodied knowledge, and geo- and body-politics. Water Stains provides a cosmopolitical intervention beyond an Orientalist or globalist framework, as Lin questions the social, cultural, technological, and ideological resonances in today’s global circulation of projected images and desires.
O artigo identifica a emergência de diagnósticos e soluções em torno dos fins e dos meios do ensino da dança em Portugal, e que derivaram na defesa continuada de um Conservatório por vir. Concentra-se em dois momentos - o século XIX e a Primeira República - durante os quais se impôs um discurso em torno da antinomia aptidão natural vs. aprendizagem universal. Com os programas de dança teatral estatuídos em 1911, procura-se suspender esta dicotomia. Porém, estas formações discursivas continuaram a reconduzir os princípios da graça estética e do individualismo do génio, exponenciados pela perceção que então se cultivava da vanguarda estrangeira (Isadora Duncan e Ballets Russes).
This article presents a case study considering key scenes from Bertolt Brecht’s play The Threepenny Opera that were performed in a Moscow school theatre of the late USSR, in 1982. Collective working under the conditions of a theatre laboratory engendered a new methodological approach referring to another Brechtian format, namely, the learning play. The article problematizes the self-reflective learning of the students and their teacher whilst working under theatre laboratory conditions on a Brechtian text. The self-reflective practices were discovered by the amateur actors through the sudden but inevitable effect of Brecht’s alienation technique within the emerging learning play format.
Konstantinos Bountrogiannis, George Tzagkarakis, Panagiotis Tsakalides
Due to the importance of the lower bounding distances and the attractiveness of symbolic representations, the family of symbolic aggregate approximations (SAX) has been used extensively for encoding time series data. However, typical SAX-based methods rely on two restrictive assumptions; the Gaussian distribution and equiprobable symbols. This paper proposes two novel data-driven SAX-based symbolic representations, distinguished by their discretization steps. The first representation, oriented for general data compaction and indexing scenarios, is based on the combination of kernel density estimation and Lloyd-Max quantization to minimize the information loss and mean squared error in the discretization step. The second method, oriented for high-level mining tasks, employs the Mean-Shift clustering method and is shown to enhance anomaly detection in the lower-dimensional space. Besides, we verify on a theoretical basis a previously observed phenomenon of the intrinsic process that results in a lower than the expected variance of the intermediate piecewise aggregate approximation. This phenomenon causes an additional information loss but can be avoided with a simple modification. The proposed representations possess all the attractive properties of the conventional SAX method. Furthermore, experimental evaluation on real-world datasets demonstrates their superiority compared to the traditional SAX and an alternative data-driven SAX variant.
Humans have a strong intuitive understanding of the 3D environment around us. The mental model of the physics in our brain applies to objects of different materials and enables us to perform a wide range of manipulation tasks that are far beyond the reach of current robots. In this work, we desire to learn models for dynamic 3D scenes purely from 2D visual observations. Our model combines Neural Radiance Fields (NeRF) and time contrastive learning with an autoencoding framework, which learns viewpoint-invariant 3D-aware scene representations. We show that a dynamics model, constructed over the learned representation space, enables visuomotor control for challenging manipulation tasks involving both rigid bodies and fluids, where the target is specified in a viewpoint different from what the robot operates on. When coupled with an auto-decoding framework, it can even support goal specification from camera viewpoints that are outside the training distribution. We further demonstrate the richness of the learned 3D dynamics model by performing future prediction and novel view synthesis. Finally, we provide detailed ablation studies regarding different system designs and qualitative analysis of the learned representations.
This essay takes Diderot’s claim that the actor is everything and nothing at the same time as the starting point to rethink the formation of the self. Going beyond Diderot’s paradox as a theory of acting, this article argues in favor of a deconstructive analysis of the actor’s mimetic practice, put forward by Lacoue-Labarthe, which allows us to address the ontological conditions of the interplay between possession and dispossession, nothingness and possibility, distinctiveness and malleability. This essay shows that Diderot’s indirect subversion of the distinction between passive and active mimesis underlying the performing body, problematizes the question of the subject as such.
The quality of learned features by representation learning determines the performance of learning algorithms and the related application tasks (such as high-dimensional data clustering). As a relatively new paradigm for representation learning, Concept Factorization (CF) has attracted a great deal of interests in the areas of machine learning and data mining for over a decade. Lots of effective CF based methods have been proposed based on different perspectives and properties, but note that it still remains not easy to grasp the essential connections and figure out the underlying explanatory factors from exiting studies. In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods. Specifically, we first re-view the root CF method, and then explore the advancement of CF-based representation learning ranging from shallow to deep/multilayer cases. We also introduce the potential application areas of CF-based methods. Finally, we point out some future directions for studying the CF-based representation learning. Overall, this survey provides an insightful overview of both theoretical basis and current developments in the field of CF, which can also help the interested researchers to understand the current trends of CF and find the most appropriate CF techniques to deal with particular applications.
This paper proposes a new approach to deriving a finite particle content, suitable for the construction of a gauge theory. Specifically, the outlined construction generates a finite set of irreducible gauge representations, which are interpreted as describing a full set of elementary particles. These representations are constructed from endofunctions between restricted representations of some symmetry group $G$ acting on some space $V$. As a proof of concept, we show how a set of irreducible representations arise as endofunctions on the vector space $V=\mathbb{C}^8$ equipped with the exceptional Lie group $G=G_2$ as its symmetry group. We discuss how the irreducible representations of our simple example compare to the various particle types of the Standard Model. The process through which the particle content is constructed yields adjoint, fundamental, and Higgs-like representations, thereby reproducing the essential types of particle transformations seen in the Standard Model. In particular we focus on the discrimination of gauge structures and the natural appearance of Higgs-like representations. Avenues to generalizing the construction are considered, and some inevitable consequences are discussed. We conclude by comparing our results to those of non-commutative geometry, commenting on key similarities and differences between the two approaches.