Conventional Representation Research Articles

A rheological model for concrete additive manufacturing

The advent of 3D construction printing has ushered in a revolutionary era, demanding deeper insights into the understanding of cementitious fluid materials. Despite notable progress in additive manufacturing technologies, traditional rheological models (i.e., Bingham Plastic and Herschel-Bulkley) due to asymptotic behaviors of its plastic viscosity term fall short in describing the fluid mechanical properties of 3D printable concrete. This poses substantial challenges to achieving precise control and consistency in its boundless possibilities of mix design formulation. In this paper, we introduce an original rheological model that broadly characterizes the physiomechanical behaviours of non-Newtonian fluids, with a primary emphasis on thixotropic 3D printable cementitious pastes. Contrary to conventional representations, it is successfully demonstrated and validated herein that flow characteristics of 3D printable concrete may be described by two distinct viscosity terms, both of which collectively defines an upper and lower bound apparent viscosity with respect to a proposed activation function.

The Collapse of the Male Gaze in the Cinema of Bahram Beyzaie: The Crow and Maybe Some Other Time

Abstract This article explores the correlation between two of Bahram Beyzaie's films and Laura Mulvey's theories on the male gaze. The films in question are The Crow (Kalāgh, 1977) and Maybe Some Other Time (Shāyad Vaqti Digar, 1987). This article delves into the films’ narrative structures, revealing a subtle yet significant shift in the dynamics of power and gaze. Initially, both films appear to conform to conventional representations of male dominance, establishing a seemingly patriarchal landscape. However, as the narratives unfold, a gradual transformation occurs, subverting conventions of the male gaze. By employing Mulvey's framework, this article deciphers the interplay between Beyzaie's cinematic language and Mulvey's theoretical underpinnings, highlighting Beyzaie's role in deconstructing patriarchal structures and crafting a more nuanced portrayal of gender relations.

Advancing human mobility modeling: a novel path flow approach to mining traffic congestion dynamics

Mining traffic congestion dynamics presents difficulties in data structure and spatiotemporal analysis. Existing studies mainly provide insights from a supply perspective, with a restricted examination of why congestion occurs and how travel demands affect congestion. This study introduces an innovative framework to mine congestion dynamics from the perspective of human mobility. In human mobility modeling, we refine the conventional origin-destination representation of activity flow by introducing "path flow" (PF) which considers space-time paths and movement patterns. In congestion scenarios, congestion-related path flow (CPF) and congested path sub-flow (CPSF) are extended to track individuals’ congestion exposure and explore the correlations between congestion and human mobility. To finely classify congestion-related travel demands, a Bayesian inference approach, incorporating destination and spatiotemporal heterogeneities, is developed to deduce trip purposes. The experiments conducted in Wuhan demonstrate the availability and importance of PF in spatiotemporal dynamics analysis of human mobility. Interestingly, we find that 1) the job-housing relationship is imbalanced, with massive residents opting for cross-district living and working; 2) individuals tend to visit tertiary hospitals on weekends and secondary medical facilities with less congestion on weekdays. Notably, path flow can promote the fine-grained modeling of human mobility and provide theoretical support for many urban issues.

Low-Cost Constant Time Signed Digit Selection for Most Significant Bit First Multiplication

Serial binary multiplication is frequently used in many digital applications. In particular, left-to-right (aka online) manipulation of operands promotes the real-time generation of product digits for immediate utilization in subsequent online computations (e.g., successive layers of a neural network). In the left-to-right arithmetic operations, where a residual is maintained for digit selection, utilization of a redundant number system for the representation of outputs is mandatory, while the input operands and the residual may be redundant or non-redundant. However, when the input data paths are narrow (e.g., eight bits as in BFloat16), conventional non-redundant representations of inputs and residual provide some advantages. For example, the immediate and costless sign detection of the residual that is necessary for the next digit selection; a property not shared by redundant numbers. Nevertheless, digit selection, as practiced in the previous realizations, with both redundant and non-redundant inputs and/or residual, is slow and rather complex. Therefore, in this paper, we offer an imprecise, but faster digit selection scheme, with the required correction in the next cycle. Analytical evaluations and synthesis of the proposed circuits on FPGA platform, shows 30% speedup and less cost with respect to both cases with redundant and non-redundant inputs and residual.

ChronoVectors: Mapping Moments through Enhanced Temporal Representation

Time-series data are prevalent across various fields and present unique challenges for deep learning models due to irregular time intervals and missing records, which hinder the ability to capture temporal information effectively. This study proposes ChronoVectors, a novel temporal representation method that addresses these challenges by enabling a more specialized encoding of temporal relationships through the use of learnable parameters tailored to the dataset’s dynamics while maintaining consistent time intervals post-scaling. The theoretical demonstration shows that ChronoVectors allow the transformed encoding tensors to map moments in time to continuous spaces, accommodating potentially infinite extensions of the sequence and preserving temporal consistency. Experimental validation using the Parking Birmingham and Metro Interstate Traffic Volume datasets reveals that ChronoVectors enhanced the predictive capabilities of deep learning models by reducing prediction error for regression tasks compared to conventional time representations, such as vanilla timestamp encoding and Time2Vec. These findings underscore the potential of ChronoVectors in handling irregular time-series data and showcase its ability to improve deep learning model performance in understanding temporal dynamics.

Building peace narratives between young conflict victims and university students: an analysis of participatory animation strategies in My story: The children who fought war in Colombia

ABSTRACT This article presents an analysis of the production of the animated documentary series My Story. The Children Who Fought War in Colombia. The series was developed through a participatory creation process between young victims of the armed conflict and student animators from the Visual Arts programme at Universidad Javeriana, Colombia. The objective was to construct narratives that promote peace and generate new imaginaries. Interviews with the series director, Mathew Charles, and an animator, along with a review of the creative process, revealed the distinctive elements of this production. The study concentrated on the manner in which participatory animation encourages the formulation of innovative paradigms that challenge the conventional representations of the conflict. The objective was to cultivate empathy and mutual understanding among the participants. The findings emphasise the importance of participatory animation in the reconstruction of peace narratives, offering innovative perspectives in post-conflict contexts. This research demonstrates the significance of artistic intervention, particularly through participatory animation, as a valuable tool for constructing narratives that encourage critical reflection and promote a deeper, empathetic understanding of the experiences of those who have endured war.

The Politics of Transgression in De Humani Corporis Fabrica

The film De Humani Corporis Fabrica has repeatedly been dubbed in the English-speaking film criticism media as a “non-fiction shocker” (Chang, 2023), a sample of “tactile cinema” (Mullen, 2023) with “ample gore on display” (Mintzer, 2022). This paper, firstly, aims to contradict these claims, as the film’s goal is not to elicit an immersive representation of the human body but a reflection on how it is instead objectified, abstracted, and interpreted by means of deconstructing the medical gaze and via a complex interplay between the viewer’s quasi-identification and alienation. To this end, it closely analyses the patterns in the camerawork, the various degrees of remediation of the medical imagery, and the relationships between denotation and connotation, highlighting the prevalence of de-dramatization strategies, which run counter to societal taboos and conventional representations of the hospital in popular culture as a sensationalized site of emergencies. As such, we argue that De Humani Corporis Fabrica is an essay film that seeks neither to “shock” viewers nor to mount a narrow institutional critique of the French sanitary system. By delving into the film’s intricate biopolitics, we outline how the various levels of transgression, the emphasis on “malfunction,” and the pervasiveness of technology are framed as symptoms of broader tensions between paternalistic modes of interaction and patient-centred approaches within the Western sanitary system at large. Finally, by interpreting the ending as a counterpoint to the objectifying gaze that the film lures us into adopting throughout, we consequently stress the film’s defiant sense of “anarchy,” as it urges us to reclaim “our” bodies, be them individual or collective.

Female Animal Characters in Roald Dahl’s Children’s Books: A Misogynistic Portrayal

Children’s literature introduces children to the world, stimulates their imagination, and mirrors the society they live in by reproducing its social rules and accepted norms. This is especially true with gender stereotypes, which display and reinforce the masculine and feminine roles constructed by a given society. This binary, onedimensional, and conventional representation is harmful as it negatively impacts young readers’ apprehension of gender roles as well as their personality, behaviour, and aspirations for the future. World-renowned children’s author Roald Dahl has recently been criticised as a controversial author and a racist, misogynistic person. By adopting a feminist literary critical approach, this study analyses Dahl and his illustrator Quentin Blake’s portrayal of female anthropomorphic characters, generally neglected by previous researchers in favour of human characters, in four books: James and the Giant Peach (1961), The Magic Finger (1966), Fantastic Mr Fox (1970), and The Giraffe and the Pelly and Me (1985). Female characters are weak, inactive, confined indoors, and constantly belittled by their male counterparts who are portrayed as adventurous and as decision makers. Therefore, this study aims to encourage parents and educators to teach young learners to read children’s books with a critical eye to identify and interpret different stereotypical representations.

Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction in graph structures. In the pursuit of predicting gene expression, an endeavor shared by predecessors like the L1000 and Enformer methods, we introduce a novel spatial graph-neural network (GNN) approach. This innovative strategy incorporates graph features, encompassing both regulatory and structural elements. The regulatory elements include pair-wise gene correlation, biological pathways, protein-protein interaction networks, and transcription factor regulation. The spatial structural elements include chromosomal distance, histone modification and Hi-C inferred 3D genomic features. Principal Node Aggregation models, validated independently, emerge as frontrunners, demonstrating superior performance compared to traditional regression and other deep learning models. By embracing the spatial GNN paradigm, our method significantly advances the description of the intricate network of gene interactions, surpassing the performance, predictable scope, and initial requirements set by previous methods.

Vector representations and unit vector representations of fields: Problems of understanding and possible teaching strategies

Vector fields are a highly abstract physical concept that is often taught using visualizations. Although vector representations are particularly suitable for visualizing quantitative data, they are often confusing, especially when describing real fields such as magnetic and electric fields, as the vector arrows can overlap. The present study investigates vector understanding at the end of secondary education. In particular, the extent to which the geometry of the field can be derived from conventional unit vector representations and representations with centered unit vectors was examined. To support this understanding, two exercises were compared. The unirepresentational exercise argued within the conventional unit vector representation, while the multirepresentational exercise attempted to support the link between centered and conventional unit vectors. The results show that almost all test subjects solved the items for generating vector representations correctly, but significant difficulties were encountered in interpreting vector representations. Drawing and interpreting vector representations therefore appear to be different skills that should be practiced intensively and in an integrated way. Various problems could be identified when interpreting vector representations. For example, the number of vectors is often erroneously used to estimate the strength of the field, although more vectors per surface element actually only increase the resolution of the representation. Here, however, the results suggest that the longitudinal density and the transverse density of the drawn vectors are perceived differently by the learners. Furthermore, the learners recognized the field’s geometry much more readily from centered unit vectors than from conventional unit vectors. Errors occur especially when interpreting the geometry of conventional unit vector representations of rotational fields and fields containing both sources and sinks while the geometries of fields containing only sinks were interpreted quite well. The comparison between the two training exercises showed that a promising approach to deepen students’ understanding would be to use an exercise that contrasts conventional and centered unit vector representations and explains how to translate from one representation to the other, rather than describing the main elements of only a single representation. Finally, based on the results of the study, we propose a strategy for teaching vector representations in schools. Given the significantly improved readability of the representation with centered unit vectors, the results even raise the question of whether this type of representation could possibly replace the conventional representation in textbooks and learning materials in the future. Published by the American Physical Society 2024

New Materialist Freedom in Chloé Zhao's Nomadland

In Chloé Zhao's 2020 film Nomadland, Fern's commitment to eschewing a geolocalizable “home” uproots her from conventional patterns of domesticity and transforms her into an inhabitant of planet earth as a whole, enabling a new way of thinking about identity, environment, and the nature of human freedom. The kind of freedom that Fern's narrative evokes stands in deliberate contrast to the masculinized, heroic style of freedom that American films have done so much to promulgate. In contrast to this conventional representation of freedom, new materialist criticism has examined the many ways that human agency emerges from within a dense web of interconnections that bind together human beings with one another, with other life forms, with inanimate matter, and with the cosmic expanses of time and space. Nomadland can be understood as a film that picks up the iconography of conventional American cinematic freedom in order to redirect this iconography into a new materialist worldview.

The importance of definitions in crystallography.

This paper was motivated by the articles `Same or different - that is the question' in CrystEngComm (July 2020) and `Change to the definition of a crystal' in the IUCr Newsletter (June 2021). Experimental approaches to crystal comparisons require rigorously defined classifications in crystallography and beyond. Since crystal structures are determined in a rigid form, their strongest equivalence in practice is rigid motion, which is a composition of translations and rotations in 3D space. Conventional representations based on reduced cells and standardizations theoretically distinguish all periodic crystals. However, all cell-based representations are inherently discontinuous under almost any atomic displacement that can arbitrarily scale up a reduced cell. Hence, comparison of millions of known structures in materials databases requires continuous distance metrics.

Not your private tête-à-tête: leveraging the power of higher-order networks to study animal communication.

Animal communication is frequently studied with conventional network representations that link pairs of individuals who interact, for example, through vocalization. However, acoustic signals often have multiple simultaneous receivers, or receivers integrate information from multiple signallers, meaning these interactions are not dyadic. Additionally, non-dyadic social structures often shape an individual's behavioural response to vocal communication. Recently, major advances have been made in the study of these non-dyadic, higher-order networks (e.g. hypergraphs and simplicial complexes). Here, we show how these approaches can provide new insights into vocal communication through three case studies that illustrate how higher-order network models can: (i) alter predictions made about the outcome of vocally coordinated group departures; (ii) generate different patterns of song synchronization from models that only include dyadic interactions; and (iii) inform models of cultural evolution of vocal communication. Together, our examples highlight the potential power of higher-order networks to study animal vocal communication. We then build on our case studies to identify key challenges in applying higher-order network approaches in this context and outline important research questions that these techniques could help answer. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

Brain2GAN: Feature-disentangled neural encoding and decoding of visual perception in the primate brain.

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

THE THE SKULL EMOJI IN GEN-Z INTERNET SLANG: A STUDY OF ITS USE AS TONE TAG AND PUNCTUATION

Emojis are employed to enhance communication in the digital age on the daily. And the digital-natives, the Gen-Zers, seem to be particularly creative when it comes to their use. Recognizing this, this qualitative study examines the evolving use of one particular emoji – the skull emoji, as a tone tag within Gen-Z internet slang. By analyzing various examples from different social media platforms, the paper highlights how this emoji, beyond its conventional representation of death, has been repurposed by this generation of speakers to denote laughter or embarrassment. The study further argues that the modern use of the skull emoji serves a dual function: it not only signifies humor/awkwardness but also acts as a tool to separate text, thereby being used as a makeshift punctuation mark.

에이드리언 리치의 시

Adrienne Rich, an American feminist poet and essayist of the late twentieth century, has influenced contemporary women’s movement. Her early poems started with traditional and restrained voices as male poets. But through marriage, she has experienced patriarchy in person and afterwards her poem has gone through continual changes. She offers women the knowledge they need to overcome the unreasonableness of their lives, thus allowing them to change the future of their own lives as well as the world. Rich as a feminist delineates the ideal community for women as a means for survival in a patriarchal world. In this paper, we will consider the development of re-reading and re-writing, namely “re-vision”, which Rich worked on as a feminist. She writes that “re-vision is the act of looking back, of seeing with fresh eyes, of entering an old text from a new critical direction. For women, re-vision is more than a chapter in cultural history; it is ‘an act of survival’.” The re-visioning woman works to reconstruct conventional symbolic representations in a new and meaningful way. The transformative and flexible power of re-vision can change every reality, every myth, and every structure. Through this attempt, we also consider the various effects that Rich has given to all women and her readers will participating in building the communal society.

Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections

This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.

Material Property Prediction Using Graphs Based on Generically Complete Isometry Invariants

The structure–property hypothesis says that the properties of all materials are determined by an underlying crystal structure. The main obstacle was the ambiguity of conventional crystal representations based on incomplete or discontinuous descriptors that allow false negatives or false positives. This ambiguity was resolved by the ultra-fast pointwise distance distribution, which distinguished all periodic structures in the world’s largest collection of real materials (Cambridge structural database). State-of-the-art results in property prediction were previously achieved by graph neural networks based on various graph representations of periodic crystals, including the Crystal Graph with vertices at all atoms in a crystal unit cell. This work adapts the pointwise distance distribution for a simpler graph whose vertex set is not larger than the asymmetric unit of a crystal structure. The new Distribution Graph reduces mean absolute error by 0.6–12% while having 44–88% of the number of vertices when compared to the Crystal Graph when applied on the Materials Project and Jarvis-DFT datasets using CGCNN and ALIGNN. Methods for hyper-parameters selection for the graph are backed by the theoretical results of the pointwise distance distribution and are then experimentally justified.

Knowledge Enhanced Representation Learning for Drug Discovery

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences. While these representations have significantly enhanced the predictions, they are usually based on a limited set of modalities, and they do not exploit available knowledge about existing relations among molecules and proteins. Our study reveals that enhanced representations, derived from multimodal knowledge graphs describing relations among molecules and proteins, lead to state-of-the-art results in well-established benchmarks (first place in the leaderboard for Therapeutics Data Commons benchmark ``Drug-Target Interaction Domain Generalization Benchmark", with an improvement of 8 points with respect to previous best result). Moreover, our results significantly surpass those achieved in standard benchmarks by using conventional pre-trained representations that rely only on sequence or SMILES data. We release our multimodal knowledge graphs, integrating data from seven public data sources, and which contain over 30 million triples. Pretrained models from our proposed graphs and benchmark task source code are also released.

Advancing the democratization of work: A new intellectual history of transformational leadership theory

Amidst growing demands for more democratic forms of organizing, we argue that better understanding the origins of transformational leadership theory offers a way forward. Transformational leadership theory, originally developed by American political scientist James MacGregor Burns in the late 1970s, is the best-known and most influential leadership theory in management studies. Transformational leaders are visionaries who engage with followers’ higher-level needs and inspire them to deliver extraordinary outcomes for their organizations. Democracy was at the core of Burns’ conception of transformational leadership: voters selected their leaders and voted them out if they failed to deliver on their visions. However, this was overlooked by those who introduced the theory to management studies. Using intellectual history, we contrast the conventional representation of transformational leadership theory in business with Burns’ original conception. We explore how and why the democratic foundation of the theory was lost, why this matters, and what can be done to recover it.