Task Representation Research Articles

HeGAE-AC: Heterogeneous graph auto-encoder for attribute completion

Heterogeneous Graphs (HGs) are widely used to model complex real-world networks that involve multiple types of nodes and relations. Many heterogeneous graph neural networks (HGNNs) based methods have been developed to learn low-dimensional representations for nodes in HGs with the assumption of complete attributes for all nodes. However, in many real applications, the attributes of certain nodes may be completely missing, leading to attribute-missing issues. Existing methods for attribute completion in HGs separate topological embedding learning from attribute completion, which can result in sub-optimal solutions. Moreover, the information of higher-order connected nodes is not fully exploited in the process of attribute completion. To address the issues mentioned above, we integrate topological embedding learning, attribute completion, and heterogeneous graph representation learning into an end-to-end framework called HeGAE-AC. Firstly, we utilize a heterogeneous graph auto-encoder (HeGAE) to incorporate the graph topology structure and attribute information of higher-order connected nodes into low-dimensional embeddings. Then, HeGAE decodes embeddings to generate attributes of all nodes. Finally, an HGNN model is employed to obtain node representations for downstream tasks using the completed HGs. We conduct extensive experiments on four real-world networks, which demonstrate that our approach achieves significant improvements over state-of-the-art methods.

Supporting AI Planning and Collaboration for Robotic Applications Using TAEMS

This paper describes a general approach to integrating higher-level reasoning mechanisms including planning and scheduling methods with lower-level robotic control processes. We adopt a domain-independent task representation language TAEMS to describe the knowledge of tasks, resources, and their interrelationships. This TAEMS representation language serves as the input of the reasoning functions, which generate a schedule of executable methods to be executed by the robot in the physical world. In the execution process of this goal-directed plan, the robot also needs to attend to basic functions. The potential interactions between the plan and these basic functions would lead to interesting challenges that will be discussed. An integrated development platform with a simulator that supports real-world physics is also presented.

Artificial intelligence as enabler of future library services: how prepared are librarians in African university libraries

PurposeThe purpose of this study is to investigate artificial intelligence (AI) as enabler of future library services, with consideration to how prepared are librarians in African university libraries.Design/methodology/approachThis study applied the interpretive content/document analysis of literature harvested from different databases of Scopus and Web of Science. AI could be used to perform daily routines in circulation, serial, reference and selective dissemination of information among others. It could also be applied to the provision of innovative services of recognition of library activities such as answering research quarries, cataloguing and classification of library materials and management of library system software of different databases within the library systems.FindingsIt could be deduced from the study that AI would continue to serve as a panacea to future library services irrespective of its geographical context. Due to the evolving nature of knowledge growth, AI having its roots in the field of engineering has been found useful to support future library services. The support accrued from library service delivery in the library profession has made librarians continue to interact with other intelligent machines that can demonstrate human behaviour even though they are not real human beings. The behaviour of machines and AI where human beings play a significant role has brought many renovations in the management of complex tasks of processing, communication, knowledge representation, decision making and suggestions, on potentials of diverse work operations.Practical implicationsThe understanding that the present paper portrays in the context of future library services is that there is no way the AI could function without a human interaction perspective when drawing an analogy from computer science, information science and information systems fields of study.Social implicationsThe interest of users across their background would be strengthen if AI advances transformed the handling complex tasks of processing, communication, knowledge representation, decision-making and giving suggestions, among other things. The possibilities of diverse work operations from empirical evidence of studies consulted in recent times gave the authors the impetus to consider AI as the enabler of future library services.Originality/valueThe increasing demands from library patrons have prompted librarians to adapt their methods of delivering services. These emerging technologies have also brought about shifts in approaches to teaching and learning. Consequently, the recent surge in digital technology-driven service innovations has ushered in a fresh paradigm for education and research. In response to these changes, librarians are actively seeking novel and innovative technologies to enhance user experiences within their libraries. They serve as catalysts for introducing modern and advanced technologies, consistently adapting to contemporary tools that enhance their offerings.

Improving cell type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning.

Cell type identification is an important task for single-cell RNA-sequencing (scRNA-seq) data analysis. Many prediction methods have recently been proposed, but the predictive accuracy of difficult cell type identification tasks is still low. In this work, we proposed a novel Gaussian noise augmentation-based scRNA-seq contrastive learning method (GsRCL) to learn a type of discriminative feature representations for cell type identification tasks. A large-scale computational evaluation suggests that GsRCL successfully outperformed other state-of-the-art predictive methods on difficult cell type identification tasks, while the conventional random genes masking augmentation-based contrastive learning method also improved the accuracy of easy cell type identification tasks in general.

Comparative Assessment of Neural Radiance Fields and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction

This paper conducts a comparative evaluation between Neural Radiance Fields (NeRF) and photogrammetry for 3D reconstruction in the cultural heritage domain. Focusing on three case studies, of which the Terpsichore statue serves as a pilot case, the research assesses the quality, consistency, and efficiency of both methods. The results indicate that, under conditions of reduced input data or lower resolution, NeRF outperforms photogrammetry in preserving completeness and material description for the same set of input images (with known camera poses). The study recommends NeRF for scenarios requiring extensive area mapping with limited images, particularly in emergency situations. Despite NeRF’s developmental stage compared to photogrammetry, the findings demonstrate higher potential for describing material characteristics and rendering homogeneous textures with enhanced visual fidelity and accuracy; however, NeRF seems more prone to noise effects. The paper advocates for the future integration of NeRF with photogrammetry to address respective limitations, offering more comprehensive representation for cultural heritage preservation tasks. Future developments include extending applications to planar surfaces and exploring NeRF in virtual and augmented reality, as well as studying NeRF evolution in line with emerging trends in semantic segmentation and in-the-wild scene reconstruction.

Deep Modular Co-Attention Shifting Network for Multimodal Sentiment Analysis

Human Multimodal Sentiment Analysis (MSA) is an attractive research that studies sentiment expressed from multiple heterogeneous modalities. While transformer-based methods have achieved great success, designing an effective “co-attention” model to associate text modality with nonverbal modalities remains challenging. There are two main problems: 1) the dominant role of the text in modalities is underutilization, and 2) the interaction between modalities is not sufficiently explored. This paper proposes a deep modular Co-Attention Shifting Network (CoASN) for MSA. A Cross-modal Modulation Module based on Co-attention (CMMC) and an Advanced Modality-mixing Adaptation Gate (AMAG) are constructed. The CMMC consists of the Text-guided Co-Attention (TCA) and Interior Transformer Encoder (ITE) units to capture inter-modal features and intra-modal features. With text modality as the core, the CMMC module aims to guide and promote the expression of emotion in nonverbal modalities, and the nonverbal modalities increase the richness of the text-based multimodal sentiment information. In addition, the AMAG module is introduced to explore the dynamical correlations among all modalities. Particularly, this efficient module first captures the nonverbal shifted representations and then combines them to calculate the shifted word embedding representations for the final MSA tasks. Extensive experiments on two commonly used datasets, CMU-MOSI and CMU-MOSEI, demonstrate that our proposed method is superior to the state-of-the-art performance.

The beneficial effects of concept definition and interactive imagery tasks on associative memory: Evidence from event-related potentials

It is widely accepted that familiarity can support associative memory when the to-be-remember items are unitized into a new representation. However, there has been relatively little attention devoted to investigating the effects of different unitization manipulations on associative memory. The present study aimed to address this gap by examining the effects of varying levels of unitization through three tasks: Concept definition, interactive imagery, and sentence frame tasks. The behavioral results revealed that associative memory was significantly enhanced in the interactive imagery task compared to the sentence frame task. However, no significant differences were found between the sentence frame and concept definition tasks, or between the concept definition and interactive imagery tasks. In terms of the neural correlates, the event-related potential (ERP) results revealed that the sentence frame task only elicited a significant recollection-related LPC old/new effect, while the concept definition task only elicited a significant familiarity-related FN400 old/new effect. However, the interactive representation task elicited both of these distinct effects. These findings suggest that both the concept definition and interactive imagery tasks can enhance familiarity for supporting associative memory, but their beneficial effects on associative memory or LPC old/new effects may be different.

Multi-Task Multi-Agent Reinforcement Learning With Interaction and Task Representations.

Multi-task multi-agent reinforcement learning (MT-MARL) is capable of leveraging useful knowledge across multiple related tasks to improve performance on any single task. While recent studies have tentatively achieved this by learning independent policies on a shared representation space, we pinpoint that further advancements can be realized by explicitly characterizing agent interactions within these multi-agent tasks and identifying task relations for selective reuse. To this end, this article proposes Representing Interactions and Tasks (RIT), a novel MT-MARL algorithm that characterizes both intra-task agent interactions and inter-task task relations. Specifically, for characterizing agent interactions, RIT presents the interactive value decomposition to explicitly take the dependency among agents into policy learning. Theoretical analysis demonstrates that the learned utility value of each agent approximates its Shapley value, thus representing agent interactions. Moreover, we learn task representations based on per-agent local trajectories, which assess task similarities and accordingly identify task relations. As a result, RIT facilitates the effective transfer of interaction knowledge across similar multi-agent tasks. Structurally, RIT develops universal policy structure for scalable multi-task policy learning. We evaluate RIT against multiple state-of-the-art baselines in various cooperative tasks, and its significant performance under both multi-task and zero-shot settings demonstrates its effectiveness.

Self-Supervised Temporal Graph Learning With Temporal and Structural Intensity Alignment.

Temporal graph learning aims to generate high-quality representations for graph-based tasks with dynamic information, which has recently garnered increasing attention. In contrast to static graphs, temporal graphs are typically organized as node interaction sequences over continuous time rather than an adjacency matrix. Most temporal graph learning methods model current interactions by incorporating historical neighborhood. However, such methods only consider first-order temporal information while disregarding crucial high-order structural information, resulting in suboptimal performance. To address this issue, we propose a self-supervised method called S2T for temporal graph learning, which extracts both temporal and structural information to learn more informative node representations. Notably, the initial node representations combine first-order temporal and high-order structural information differently to calculate two conditional intensities. An alignment loss is then introduced to optimize the node representations, narrowing the gap between the two intensities and making them more informative. Concretely, in addition to modeling temporal information using historical neighbor sequences, we further consider structural knowledge at both local and global levels. At the local level, we generate structural intensity by aggregating features from high-order neighbor sequences. At the global level, a global representation is generated based on all nodes to adjust the structural intensity according to the active statuses on different nodes. Extensive experiments demonstrate that the proposed model S2T achieves at most 10.13% performance improvement compared with the state-of-the-art competitors on several datasets.

Transformer-Based Self-Supervised Multimodal Representation Learning for Wearable Emotion Recognition

Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios. However, how to effectively fuse multimodal data remains a challenging problem. Moreover, traditional fully-supervised based approaches suffer from overfitting given limited labeled data. To address the above issues, we propose a novel self-supervised learning (SSL) framework for wearable emotion recognition, where efficient multimodal fusion is realized with temporal convolution-based modality-specific encoders and a transformer-based shared encoder, capturing both intra-modal and inter-modal correlations. Extensive unlabeled data is automatically assigned labels by five signal transforms, and the proposed SSL model is pre-trained with signal transformation recognition as a pretext task, allowing the extraction of generalized multimodal representations for emotion-related downstream tasks. For evaluation, the proposed SSL model was first pre-trained on a large-scale self-collected physiological dataset and the resulting encoder was subsequently frozen or fine-tuned on three public supervised emotion recognition datasets. Ultimately, our SSL-based method achieved state-of-the-art results in various emotion classification tasks. Meanwhile, the proposed model was proved to be more accurate and robust compared to fully-supervised methods on low data regimes.

Exploring elliptic geometry in neural networks : The concept of elliptic neural networks (ENN)

The exploration of non-Euclidean geometries in the context of neural network architectures presents a novel avenue for enhancing the processing of complex data structures. This paper introduces the concept of Elliptic Neural Networks (ENN), a framework that integrates the intrinsic properties of elliptic geometry into the fabric of neural computation. Characterized by constant positive curvature and closed geodesic paths, elliptic geometry provides a unique perspective for representing data, especially advantageous for configurations exhibiting cyclic and dense hierarchical interconnections. We begin by delineating the fundamental aspects of elliptic geometry, focusing on its impact on conventional notions of distance and global structural interpretation within data sets. This foundational understanding paves the way for our proposed mathematical schema for ENNs. Within this framework, we articulate the methodologies for embedding data in elliptic spaces and adapt neural network operations by encompassing neuron activation, signal propagation, and learning paradigms to conform to the topological idiosyncrasies of elliptic geometry. In this paper addressing the implementation of ENNs, we discuss the computational challenges and the development of novel algorithms requisite for navigating the elliptic geometric landscape. The prospective applications of ENNs are extensive and diverse, ranging from the enhancement of cyclic pattern recognition in time-series analysis to more sophisticated representations in graph-based data and natural language processing tasks. This theoretical exposition aims to set the groundwork for subsequent empirical research to validate the proposed model and assess its practical performance relative to conventional neural network architectures. By harnessing the distinctive characteristics of elliptic geometry, ENNs mark a significant stride towards enriching the arsenal of machine learning methodologies, potentially leading to more versatile and efficacious computational tools in data analysis and artificial intelligence.

Unsupervised Part Discovery via Dual Representation Alignment.

Object parts serve as crucial intermediate representations in various downstream tasks, but part-level representation learning still has not received as much attention as other vision tasks. Previous research has established that Vision Transformer can learn instance-level attention without labels, extracting high-quality instance-level representations for boosting downstream tasks. In this paper, we achieve unsupervised part-specific attention learning using a novel paradigm and further employ the part representations to improve part discovery performance. Specifically, paired images are generated from the same image with different geometric transformations, and multiple part representations are extracted from these paired images using a novel module, named PartFormer. These part representations from the paired images are then exchanged to improve geometric transformation invariance. Subsequently, the part representations are aligned with the feature map extracted by a feature map encoder, achieving high similarity with the pixel representations of the corresponding part regions and low similarity in irrelevant regions. Finally, the geometric and semantic constraints are applied to the part representations through the intermediate results in alignment for part-specific attention learning, encouraging the PartFormer to focus locally and the part representations to explicitly include the information of the corresponding parts. Moreover, the aligned part representations can further serve as a series of reliable detectors in the testing phase, predicting pixel masks for part discovery. Extensive experiments are carried out on four widely used datasets, and our results demonstrate that the proposed method achieves competitive performance and robustness due to its part-specific attention. The code will be released upon paper acceptance.

Dual Balanced Class-Incremental Learning With im-Softmax and Angular Rectification.

Owing to the superior performances, exemplar-based methods with knowledge distillation (KD) are widely applied in class incremental learning (CIL). However, it suffers from two drawbacks: 1) data imbalance between the old/learned and new classes causes the bias of the new classifier toward the head/new classes and 2) deep neural networks (DNNs) suffer from distribution drift when learning sequence tasks, which results in narrowed feature space and deficient representation of old tasks. For the first problem, we analyze the insufficiency of softmax loss when dealing with the problem of data imbalance in theory and then propose the imbalance softmax (im-softmax) loss to relieve the imbalanced data learning, where we re-scale the output logits to underfit the head/new classes. For another problem, we calibrate the feature space by incremental-adaptive angular margin (IAAM) loss. The new classes form a complete distribution in feature space yet the old are squeezed. To recover the old feature space, we first compute the included angle of normalized features and normalized anchor prototypes, and use the angle distribution to represent the class distribution, then we replenish the old distribution with the deviation from the new. Each anchor prototype is predefined as a learnable vector for a designated class. The proposed im-softmax reduces the bias in the linear classification layer. IAAM rectifies the representation learning, reduces the intra-class distance, and enlarges the inter-class margin. Finally, we seamlessly combine the im-softmax and IAAM in an end-to-end training framework, called the dual balanced class incremental learning (DBL), for further improvements. Experiments demonstrate the proposed method achieves state-of-the-art (SOTA) performances on several benchmarks, such as CIFAR10, CIFAR100, Tiny-ImageNet, and ImageNet-100.

M 2 D-VAE: Self-Supervised Probabilistic Temporal- Spatial Latent Representation Learning for Unsupervised Industrial Operational Applications Under Missing Value Interference.

Due to sensor malfunctions and data transmission corruptions, the industrial process data collected commonly contain missing values. It poses a significant challenge for data-driven approaches in aggregating temporal-spatial correlations that reflect dependencies across both variables and times, which makes it difficult to directly carry out downstream industrial operational applications. In this study, a self-supervised representation learning model is proposed to extract probabilistic temporal-spatial latent variables (LVs) from sequential data under missing value interference. The extracted LVs can be utilized for typical industrial operational applications through a unified framework. First, a novel deep dynamic probabilistic latent variable model, named Markov dynamic variational autoencoder (MD-VAE), is proposed to explicitly model the temporal-spatial dependencies between LVs. The latent posteriors are Bayesian smoothed by global sequence information for effective variational inference (VI). Second, a self-supervised learning approach, termed masked MD-VAE (), is proposed to address the challenge of directly extracting temporal-spatial LVs under missing value interference. Controllable constraints with practical interpretations are introduced to balance the latent bottleneck capacity with reconstruction accuracy during model optimization. A unified framework is proposed to utilize the latent representations for typical industrial downstream tasks. Case studies conducted on a real-world multiphase flow process demonstrate the superiority of in unsupervised industrial operational applications including missing value imputation and dynamic process monitoring under missing value interference.

Analyzing the effect of depopulation on the spatial structure of the city of Łódź, Poland: Development and application of an integrated land use and transportation model

This article evaluates the effect of depopulation, as a common trend in European countries and post-industrial cities, on the selected components of urban spatial structure, as an example of Łódź (Poland). An integrated land use and transportation model was adapted and calibrated based on the available data in the study area and modeled the evolution of the city structure from 2020 to 2040. For the land use part, the spatial input-output concept and economic base theory are combined to compute the number of activities in the study area. First, the economic sectors (i.e., land, population, base, and service classes) are defined. Second, the production and consumption of each sector in Łódź is calculated. Third, productions and consumptions are allocated to spatial units for determining the level of activities and flow between them. This process, which was programmed in Python, iterates until it reaches equilibrium in the activity system. For the transportation part, the PTV Visum simulator does the tasks of network representation and traffic assignment. Following the existing literature, changes in the distribution of employment, population, and land consumption within the activity system, along with trip length, trip production, and traffic loading in the mobility system, are regarded as urban structure factors and used for assessing the integrated model outputs. Getis-Ord and Theil's indices are also calculated to obtain the auxiliary results. Decentralization and dispersion in the spatial distribution of activities were identified as the chief characteristics of the city form. As the population reduces in Łódź, the average land consumption and service activities per spatial analysis units drop in the simulation period, which lies in the correlation between population decline and the economic fall. In addition, depopulation leads to flattened spatial structure and lesser development intensity due to the scattered and de-intensified pattern of land consumption. Besides, the population shrinkage causes a decrease in inequality level by 60% and 25% from 2020 to 2030 and 2030 to 2040, respectively, which might root in the reduced and lower difference between population and service simultaneously. Moreover, computations showed that while the number of trips decreases, the trip length increases, which means after depopulation, people travel less between spatial units to fulfill their needs because the city shifts to the borderlines. Findings also assist decision- and policy-makers in recognizing the effect of population loss on Łódź’s spatial structure and offer implications of integrated models for addressing such phenomena.

On the interpretability of part-prototype based classifiers: a human centric analysis

Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In addition, previous works have had major issues with following proper experiment design and task representation that limit their reliability and validity. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective that solves these issues. The proposed framework consists of three actionable metrics and experiments. The results of these experiments will reveal important and reliable interpretability related properties of such models. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework.

On the impact of multiple source code representations on software engineering tasks — An empirical study

Efficiently representing source code is crucial for various software engineering tasks such as code classification and clone detection. Existing approaches primarily use Abstract Syntax Tree (AST), and only a few focus on semantic graphs such as Control Flow Graph (CFG) and Program Dependency Graph (PDG), which contain information about source code that AST does not. Even though some works tried to utilize multiple representations, they do not provide any insights about the costs and benefits of using multiple representations. The primary goal of this paper is to discuss the implications of utilizing multiple source code representations, specifically AST, CFG, and PDG. We modify an AST path-based approach to accept multiple representations as input to an attention-based model. We do this to measure the impact of additional representations (such as CFG and PDG) over AST. We evaluate our approach on three tasks: Method Naming, Program Classification, and Clone Detection. Our approach increases the performance on these tasks by 11% (F1), 15.7% (Accuracy), and 9.3% (F1), respectively, over the baseline. In addition to the effect on performance, we discuss timing overheads incurred with multiple representations. We envision that this work can provide a base for researchers to explore and experiment with a variety of source code representations for software engineering tasks.

SeMalBERT: Semantic-based malware detection with bidirectional encoder representations from transformers

Machine learning models are widely used for identifying malicious software. However, existing models suffer from issues such as imprecise polysemous representations and a lack of contextual semantic representations, leading to the failure to recognize certain types of malicious software. In this paper, we propose a semantic-based intelligent malware detection model called SeMalBERT for identifying malicious software in Windows systems. Specifically, the model utilizes the API function sequences of malicious software as the learned features. Firstly, BERT is applied to accomplish word representation tasks and extract semantic information from the sequences. Secondly, a hybrid discriminator based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is used to explore the chaining relationships between functions. Lastly, an external attention mechanism is added after the LSTM to enable the model to better focus on key information within the text. Experimental results demonstrate that SeMalBERT outperforms existing malware detection techniques in terms of accuracy, F1 score, and loss function value on a general dataset.

Surveying the landscape of text summarization with deep learning: A comprehensive review

In recent years, deep learning has revolutionized natural language processing (NLP) by enabling the development of models that can learn complex representations of language data, leading to significant improvements in performance across a wide range of NLP tasks. Deep learning models for NLP typically use large amounts of data to train deep neural networks, allowing them to learn the patterns and relationships in language data. This is in contrast to traditional NLP approaches, which rely on hand-engineered features and rules to perform NLP tasks. The ability of deep neural networks to learn hierarchical representations of language data, handle variable-length input sequences, and perform well on large datasets makes them well-suited for NLP applications. Driven by the exponential growth of textual data and the increasing demand for condensed, coherent, and informative summaries, text summarization has been a critical research area in the field of NLP. Applying deep learning to text summarization refers to the use of deep neural networks to perform text summarization tasks. In this survey, we begin with a review of fashionable text summarization tasks in recent years, including extractive, abstractive, multi-document, and so on. Next, we discuss most deep learning-based models and their experimental results on these tasks. The paper also covers datasets and data representation for summarization tasks. Finally, we delve into the opportunities and challenges associated with summarization tasks and their corresponding methodologies, aiming to inspire future research efforts to advance the field further. A goal of our survey is to explain how these methods differ in their requirements as understanding them is essential for choosing a technique suited for a specific setting. This survey aims to provide a comprehensive review of existing techniques, evaluation methodologies, and practical applications of automatic text summarization.

“The Authors” Make Me Think Equally of Women and Men: Exploring Mixed-Gender Representations in a Visual Categorisation Task

Abstract A common goal for gender-fair language policies is to promote terms that elicit balanced activation of gender categories. Expanding previous research on the activation of feminine versus masculine categories through person nouns, we used a word-picture response priming design with gendered human faces as target stimuli, to explore whether a simultaneous activation of more than one gender category can be captured empirically. Focusing on Norwegian (Bokmål), we tested whether reading stereotypical (i.e. role nouns, e.g. “care givers”) and categorical gendered person nouns (i.e. name pairs, e.g. “Elin and Sandra”) facilitates the categorisation of face pairs that match the gender of the designated people. In Experiment 1 (N = 32), gender-specific (feminine or masculine) word primes were tested, before gender-balanced word primes (non-stereotyped role nouns; pairs of a female and a male name) were added in Experiment 2 (N = 39). In both experiments, the visual targets were pairs of faces (two female faces, two male faces, or one male and one female face). Consistent with previous results for English, we found gender-specific priming effects, supporting the notion that gender categories activated by linguistic stimuli may also exert influence outside of language processing. Most importantly, mixed-gender faces were successfully primed by non-stereotypical role nouns providing initial support for the idea of a balanced activation of gender categories.