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73
Date Added: Jun 11, 2021
Date Added: Jun 11, 2021
To consider the catastrophic situation of our environment, this environment sends lot of alarming events for us, not limited to the following: global warming, climate change, and pollution. Green purchasing behavior is one of the behaviors recommended to help sustain the environment. Three factors (social influence, environmental attitude, and environmental concern) are tested to see how they affect green purchasing behavior. A significant result was indicated between Social influence, Environmental concern and green purchasing behavior. The results provided empirical support to previous studies. Future research and limitation were discussed as well.
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98
Date Added: Jun 8, 2021
Authors: Amitai Armon, Ravid Shwartz-Ziv, Amitai Armon
Journal: Arxiv
Date Added: Jun 8, 2021
Authors: Amitai Armon, Ravid Shwartz-Ziv, Amitai Armon
Journal: Arxiv
A key element of AutoML systems is setting the types of models that will be used for each type of task. For classification and regression problems with tabular data, the use of tree ensemble models (like XGBoost) is usually recommended. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use-cases. In this paper, we explore whether these deep models should be a recommended option for tabular data, by rigorously comparing the new deep models to XGBoost on a variety of datasets. In addition to systematically comparing their accuracy, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of the deep models and XGBoost performs better on these datasets than XGBoost alone.
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64
Date Added: Jun 4, 2021
Authors: Boqing Gong, Xiangning Chen, Cho-Jui Hsieh, Boqing Gong
Journal: Arxiv
Date Added: Jun 4, 2021
Authors: Boqing Gong, Xiangning Chen, Cho-Jui Hsieh, Boqing Gong
Journal: Arxiv
Vision Transformers (ViTs) and MLPs signal further efforts on replacing hand-wired features or inductive biases with general-purpose neural architectures. Existing works empower the models by massive data, such as large-scale pretraining and/or repeated strong data augmentations, and still report optimization-related problems (e.g., sensitivity to initialization and learning rate). Hence, this paper investigates ViTs and MLP-Mixers from the lens of loss geometry, intending to improve the models' data efficiency at training and generalization at inference. Visualization and Hessian reveal extremely sharp local minima of converged models. By promoting smoothness with a recently proposed sharpness-aware optimizer, we substantially improve the accuracy and robustness of ViTs and MLP-Mixers on various tasks spanning supervised, adversarial, contrastive, and transfer learning (e.g., +5.3\% and +11.0\% top-1 accuracy on ImageNet for ViT-B/16 and Mixer-B/16, respectively, with the simple Inception-style preprocessing). We show that the improved smoothness attributes to sparser active neurons in the first few layers. The resultant ViTs outperform ResNets of similar size and throughput when trained from scratch on ImageNet without large-scale pretraining or strong data augmentations. They also possess more perceptive attention maps.
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62
Date Added: Jun 10, 2021
Authors: Tarik Abou-Chadi, Thomas Kurer
Journal: World Politics
Date Added: Jun 10, 2021
Authors: Tarik Abou-Chadi, Thomas Kurer
Journal: World Politics
ABSTRACT This article investigates how unemployment risk within households affects voting for the radical right. The authors contribute to recent advances in the literature that have highlighted the role of economic threat for understanding the support of radical-right parties. In contrast to existing work, the authors do not treat voters as atomistic individuals; they instead investigate households as a crucial site of preference formation. Combining largescale labor market data with comparative survey data, they confirm the expectations of their theoretical framework by demonstrating that the effect of occupational unemployment risk on radical-right support is strongly conditioned by household-risk constellations. Voting for the radical right is a function not only of a voter’s own risk, but also of his or her partner’s risk. The article provides additional evidence on the extent to which these effects are gendered and on the mechanisms that link household risk and party choice. The results imply that much of the existing literature on individual risk exposure potentially underestimates its effect on political behavior due to the neglect of multiplier effects within households.
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29
Date Added: Jun 9, 2021
Authors: Van Battum, Eljo, et al
Journal: eLife
Date Added: Jun 9, 2021
Authors: Van Battum, Eljo, et al
Journal: eLife
Plexin-B2 deletion leads to aberrant lamination of cerebellar granule neurons (CGNs) and Purkinje cells. Although in the cerebellum Plexin-B2 is only expressed by proliferating CGN precursors in the outer external granule layer (oEGL), its function in CGN development is still elusive. Here, we used 3D imaging, in vivo electroporation and live-imaging techniques to study CGN development in novel cerebellum-specific Plxnb2 conditional knockout mice. We show that proliferating CGNs in Plxnb2 mutants not only escape the oEGL and mix with newborn postmitotic CGNs. Furthermore, motility of mitotic precursors and early postmitotic CGNs is altered. Together, this leads to the formation of ectopic patches of CGNs at the cerebellar surface and an intermingling of normally time-stamped parallel fibers in the molecular layer (ML), and aberrant arborization of Purkinje cell dendrites. There results suggest that Plexin-B2 restricts CGN motility and might have a function in cytokinesis.
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12
Date Added: Jun 4, 2021
Authors: Daniel Medina García, Miguel Sebastián Ortiz Rivero
Journal: Derecho Penal y Criminología
Date Added: Jun 4, 2021
Authors: Daniel Medina García, Miguel Sebastián Ortiz Rivero
Journal: Derecho Penal y Criminología
La delincuencia organizada es un fenómeno que afecta de manera ostensible la seguridad ciudadana y la institucionalidad de un Estado. Por lo anterior resulta útil explicar cómo se puede reprimir esta forma de crimina­lidad por parte del derecho penal, a pesar de que los delitos cometidos por estos grupos en ocasiones son atribuibles a sujetos que no intervienen en la ejecución de ellos.
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32
Date Added: Jun 8, 2021
Authors: Geiger, Andreas, et al
Journal: Arxiv
Date Added: Jun 8, 2021
Authors: Geiger, Andreas, et al
Journal: Arxiv
In recent years, neural implicit representations gained popularity in 3D reconstruction due to their expressiveness and flexibility. However, the implicit nature of neural implicit representations results in slow inference time and requires careful initialization. In this paper, we revisit the classic yet ubiquitous point cloud representation and introduce a differentiable point-to-mesh layer using a differentiable formulation of Poisson Surface Reconstruction (PSR) that allows for a GPU-accelerated fast solution of the indicator function given an oriented point cloud. The differentiable PSR layer allows us to efficiently and differentiably bridge the explicit 3D point representation with the 3D mesh via the implicit indicator field, enabling end-to-end optimization of surface reconstruction metrics such as Chamfer distance. This duality between points and meshes hence allows us to represent shapes as oriented point clouds, which are explicit, lightweight and expressive. Compared to neural implicit representations, our Shape-As-Points (SAP) model is more interpretable, lightweight, and accelerates inference time by one order of magnitude. Compared to other explicit representations such as points, patches, and meshes, SAP produces topology-agnostic, watertight manifold surfaces. We demonstrate the effectiveness of SAP on the task of surface reconstruction from unoriented point clouds and learning-based reconstruction.
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30
Date Added: Jun 4, 2021
Authors: Meinel, Christoph, et al
Journal: Arxiv
Date Added: Jun 4, 2021
Authors: Meinel, Christoph, et al
Journal: Arxiv
Mutual knowledge distillation (MKD) improves a model by distilling knowledge from another model. However, not all knowledge is certain and correct, especially under adverse conditions. For example, label noise usually leads to less reliable models due to the undesired memorisation [1, 2]. Wrong knowledge misleads the learning rather than helps. This problem can be handled by two aspects: (i) improving the reliability of a model where the knowledge is from (i.e., knowledge source's reliability); (ii) selecting reliable knowledge for distillation. In the literature, making a model more reliable is widely studied while selective MKD receives little attention. Therefore, we focus on studying selective MKD and highlight its importance in this work. Concretely, a generic MKD framework, Confident knowledge selection followed by Mutual Distillation (CMD), is designed. The key component of CMD is a generic knowledge selection formulation, making the selection threshold either static (CMD-S) or progressive (CMD-P). Additionally, CMD covers two special cases: zero knowledge and all knowledge, leading to a unified MKD framework. We empirically find CMD-P performs better than CMD-S. The main reason is that a model's knowledge upgrades and becomes confident as the training progresses. Extensive experiments are present to demonstrate the effectiveness of CMD and thoroughly justify the design of CMD. For example, CMD-P obtains new state-of-the-art results in robustness against label noise.
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19
Date Added: May 14, 2021
Authors: Baroiu, Liliana, et al
Journal: World Journal of Clinical Cases
Date Added: May 14, 2021
Authors: Baroiu, Liliana, et al
Journal: World Journal of Clinical Cases
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