Universum-inspired Supervised Contrastive Learning

As an effective data augmentation method, Mixup synthesizes an extra amount of samples through linear interpolations. Despite its theoretical dependency on data properties, Mixup reportedly performs well as a regularizer and calibrator contributing reliable robustness and generalization to deep model training. In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum. We find that in the framework of supervised contrastive learning, Mixup-induced universum can serve as surprisingly high-quality hard negatives, greatly relieving the need for large batch sizes in contrastive learning. With these findings, we propose Universum-inspired supervised Contrastive learning (UniCon), which incorporates Mixup strategy to generate Mixup-induced universum as universum negatives and pushes them apart from anchor samples of the target classes. We extend our method to the unsupervised setting, proposing Unsupervised Universum-inspired contrastive model (Un-Uni). Our approach not only improves Mixup with hard labels, but also innovates a novel measure to generate universum data. With a linear classifier on the learned representations, UniCon shows state-of-the-art performance on various datasets. Specially, UniCon achieves 81.7% top-1 accuracy on CIFAR-100, surpassing the state of art by a significant margin of 5.2% with a much smaller batch size, typically, 256 in UniCon vs. 1024 in SupCon using ResNet-50. Un-Uni also outperforms SOTA methods on CIFAR-100. The code of this paper is released on https://github.com/hannaiiyanggit/UniCon.Comment: Accepted by IEEE Transactions on Image Processin

Unsupervised Adversarial Domain Adaptation for Cross-Lingual Speech Emotion Recognition

Cross-lingual speech emotion recognition (SER) is a crucial task for many real-world applications. The performance of SER systems is often degraded by the differences in the distributions of training and test data. These differences become more apparent when training and test data belong to different languages, which cause a significant performance gap between the validation and test scores. It is imperative to build more robust models that can fit in practical applications of SER systems. Therefore, in this paper, we propose a Generative Adversarial Network (GAN)-based model for multilingual SER. Our choice of using GAN is motivated by their great success in learning the underlying data distribution. The proposed model is designed in such a way that can learn language invariant representations without requiring target-language data labels. We evaluate our proposed model on four different language emotional datasets, including an Urdu-language dataset to also incorporate alternative languages for which labelled data is difficult to find and which have not been studied much by the mainstream community. Our results show that our proposed model can significantly improve the baseline cross-lingual SER performance for all the considered datasets including the non-mainstream Urdu language data without requiring any labels.Comment: Accepted in Affective Computing & Intelligent Interaction (ACII 2019

Survey of deep representation learning for speech emotion recognition

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of handcrafted features for complex SER tasks requires significant manual eort, which impedes generalisability and slows the pace of innovation. This has motivated the adoption of representation learning techniques that can automatically learn an intermediate representation of the input signal without any manual feature engineering. Representation learning has led to improved SER performance and enabled rapid innovation. Its effectiveness has further increased with advances in deep learning (DL), which has facilitated \textit{deep representation learning} where hierarchical representations are automatically learned in a data-driven manner. This paper presents the first comprehensive survey on the important topic of deep representation learning for SER. We highlight various techniques, related challenges and identify important future areas of research. Our survey bridges the gap in the literature since existing surveys either focus on SER with hand-engineered features or representation learning in the general setting without focusing on SER

Programas de desarrollo dirigidos a agricultoras : identificando clústeres para el caso del programa "Formación y capacitación para mujeres campesinas" de Chile

This article aims to contribute to the evaluation of development policies for female farmers based on their beneficiaries attitudes. For this, it was conducted a survey in the Metropolitan Region of Santiago, Chile, to a representative sample of participants in the "Education and training program for rural women" of the Chilean National Institute for Agricultural Development. The questionnaire applied was divided into the following sections: i) personal characteristics of the farmers and their family unit; ii) technical, productive and commercial features; iii) farm and household income; and iv) vision of themselves and program-related attitudes. The data collected was processed by descriptive and multivariate techniques such as principal components and cluster analysis. The results show a positive assessment of the program on an aggregate level, although there are significant dissimilarities within the sample, allowing three clusters to be identified: "reticent participants" (42.3%), "associative participants" (20.5%) and "empowered participants" (37.2%). The farmers on those clusters present differences not only in their attitudes towards the program but also in their education level, income, farm profitability and balance between productive and domestic roles. Its concluded that individual characteristics and circumstances impact beneficiaries perception of the programs, which should be considered in their design and implementation.Este artículo tiene como propósito contribuir a la evaluación de las políticas de desarrollo dirigidas a mujeres campesinas, considerando en este sentido las actitudes de sus propias beneficiarias. Para ello, se aplicó una encuesta en la Región Metropolitana de Santiago, Chile, a una muestra representativa de participantes del programa "Formación y Capacitación para mujeres campesinas" del Instituto Nacional de Desarrollo Agropecuario. El cuestionario utilizado se dividió en las siguientes secciones: i) características personales de las productoras y de sus unidades familiares; ii) rasgos técnicos, productivos y comerciales; iii) ingreso predial y familiar; y iv) visión respecto de ellas mismas; así como actitudes frente al programa. La información levantada se procesó mediante técnicas descriptivas y multivariantes, como componentes principales y análisis de conglomerados. Los resultados obtenidos muestran una valoración positiva del programa a nivel agregado, sin embargo con diferencias significativas entre las encuestadas, permitiendo identificar tres grupos: "participantes reticentes" (42,3%), "participantes asociativas" (20,5%) y "participantes empoderadas" (37,2%). Las agricultoras en cada grupo divergen no solo en sus actitudes respecto del programa, sino también en su nivel educacional, ingreso, rentabilidad de sus negocios y relación entre los roles productivo y doméstico. Se concluye que las características y circunstancias individuales impactan en la percepción que las beneficiarias tienen sobre los programas, lo que debe ser considerado en su diseño y ejecución.Fil: Boza, Sofía. Universidad de Chile. Facultad de Ciencias Agronómicas.Fil: Muñoz, Tomás. Universidad de Chile. Facultad de Ciencias Agronómicas.Fil: Rico, Margarita. Universidad de Valladolid (España)Fil: Muñoz, Jazmín. Universidad de Chile. Facultad de Ciencias Agronómicas

Low dimensional dualities:Matrix models, two-dimensional quantum gravity & black holes

This thesis focuses on low dimensional dualities as tractable models to explore two-dimensional de Sitter space and black holes. The first two chapters review, discuss and explore the framework for a novel attempt to create a connection between de Sitter space and the conjectured duality between matrix models and two-dimensional quantum gravity. The hope is that this could pave a path toward understanding the so far unknown microscopic picture of our Universe. In the last chapter we address fundamental problems about the microscopic picture of black holes through a low dimensional duality dubbed the near-AdS2/near-CFT1 correspondence

The development of best practice guidelines for the contingency management of health-related absenteeism in the motor manufacturing industry

The research problem in this study was to identify best practices for the contingency management of health-related absenteeism. To achieve this goal, the following actions were taken: A literature study was conducted to identify the scope and impact of health-related absenteeism on organisations and the legal parameters within which health-related absenteeism should be managed. A literature study was also conducted to identify strategies to prevent and reduce health-related absenteeism and strategies to ensure the continuous provision of products and services in periods of high absenteeism. The theoretical study focused on the management of absenteeism, wellness, ill-health/mental problems and HIV/AIDS, as well as contingency strategies aimed at maintaining production and service provision. iii The findings from the literature study were integrated into a model of best practices for the contingency management of health-related absenteeism. This model was used as a basis for the development of a survey questionnaire to determine whether senior human resources practitioners, occupational health practitioners or line managers, who were responsible for the management of health-related absenteeism in organisations, agreed with the best practice guidelines developed in the study. The survey was conducted in the motor and motor component industry in the Nelson Mandela Metropolitan Municipality and Buffalo City Metropole. The empirical results from the study showed a strong concurrence with the best practices guidelines developed in the study, with the exception of the strategies aimed at maintaining undisrupted production and service provision during periods of high absenteeism. In particular, disagreement was shown with regard to alternative work arrangements such as flexible work-hours, a compressed workweek, telecommuting and job-sharing. Absenteeism, in general, is an issue that organisations are challenged with on a daily basis. The proliferation of various diseases, specifically HIV/AIDS, is contributing to this problem. An integrated and strategic approach is required to deal effectively and constructively with the immediate and expected future impact of health-related issues on absenteeism. Organisations could use the best practices guidelines, identified in this study, as a mechanism to benchmark how well they manage health-related absenteeis

Towards Comprehensive Foundations of Computational Intelligence

Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

Topological Strings and Quantum Curves

This thesis presents several new insights on the interface between mathematics and theoretical physics, with a central role for fermions on Riemann surfaces. First of all, the duality between Vafa-Witten theory and WZW models is embedded into string theory. Secondly, this model is generalized to a web of dualities connecting topological string theory and N=2 supersymmetric gauge theories to a configuration of D-branes that intersect over a Riemann surface. This description yields a new perspective on topological string theory in terms of a KP integrable system based on a quantum curve. Thirdly, this thesis describes a geometric analysis of wall-crossing in N=4 string theory. And lastly, it offers a novel approach to construct metastable vacua in type IIB string theory.Comment: PhD thesis, July 2009, 308 pages, 65 figure