TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time

Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.

Spatial Coevolution for Generative Adversarial Network Training

Generative Adversarial Networks (GANs) are difficult to train because of pathologies such as mode and discriminator collapse. Similar pathologies have been studied and addressed in competitive evolutionary computation by increased diversity. We study a system, Lipizzaner, that combines spatial coevolution with gradient-based learning to improve the robustness and scalability of GAN training. We study different features of Lipizzaner’s evolutionary computation methodology. Our ablation experiments determine that communication, selection, parameter optimization, and ensemble optimization each, as well as in combination, play critical roles. Lipizzaner succumbs less frequently to critical collapses and, as a side benefit, demonstrates improved performance. In addition, we show a GAN-training feature of Lipizzaner: the ability to train simultaneously with different loss functions in the gradient descent parameter learning framework of each GAN at each cell. We use an image generation problem to show that different loss function combinations result in models with better accuracy and more diversity in comparison to other existing evolutionary GAN models. Finally, Lipizzaner with multiple loss function options promotes the best model diversity while requiring a large grid size for adequate accuracy.

Categorizing Sexism and Misogyny through Neural Approaches

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policymakers in studying and thereby countering sexism. The existing work on sexism classification has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s). 1 We also consider the related task of misogyny classification. While sexism classification is performed on textual accounts describing sexism suffered or observed, misogyny classification is carried out on tweets perpetrating misogyny. We devise a novel neural framework for classifying sexism and misogyny that can combine text representations obtained using models such as Bidirectional Encoder Representations from Transformers with distributional and linguistic word embeddings using a flexible architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. To evaluate the versatility of our neural approach for tasks pertaining to sexism and misogyny, we experiment with adapting it for misogyny identification. For categorizing sexism, we investigate multiple loss functions and problem transformation techniques to address the multi-label problem formulation. We develop an ensemble approach using a proposed multi-label classification model with potentially overlapping subsets of the category set. Proposed methods outperform several deep-learning as well as traditional machine learning baselines for all three tasks.

Amino acid synthesis loss in parasitoid wasps and other hymenopterans

Insects utilize diverse food resources which can affect the evolution of their genomic repertoire, including leading to gene losses in different nutrient pathways. Here, we investigate gene loss in amino acid synthesis pathways, with special attention to hymenopterans and parasitoid wasps. Using comparative genomics, we find that synthesis capability for tryptophan, phenylalanine, tyrosine, and histidine was lost in holometabolous insects prior to hymenopteran divergence, while valine, leucine, and isoleucine were lost in the common ancestor of Hymenoptera. Subsequently, multiple loss events of lysine synthesis occurred independently in the Parasitoida and Aculeata. Experiments in the parasitoid Cotesia chilonis confirm that it has lost the ability to synthesize eight amino acids. Our findings provide insights into amino acid synthesis evolution, and specifically can be used to inform the design of parasitoid artificial diets for pest control.