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1. Hadron-quark phase transition at finite density in the presence of a magnetic eld: Anisotropic approach

   https://doi.org/doi.org/10.3390/universe7120458  

   E. J. Ferrer and A. Hackebill (May 2022, International journal of modern physics A)

   World Scientific (Ed.)

   We investigate the hadron-quark phase transition at nite density in the presence of a magnetic eld taking into account the anisotropy created by a uniform magnetic eld in the system's equations of state.We fi nd a new anisotropic equilibrium condition that will drive the fi rst-order phase transition along the boundary between the two phases. Fixing the magnetic eld in the hadronic phase, the phase transition is realized by increasing the baryonic chemical potential at zero-temperature. It is shown that the magnetic eld is mildly boosted after the system transitions from the hadronic to the quark phase. The magnetic- eld discontinuity between the two phases is supported by a surface density of magnetic monopoles, which accumulate at the boundary separating the two phases. The mechanism responsible for the monopole charge density generation is discussed. Each phase is found to be paramagnetic with higher magnetic susceptibility in the quark phase. The connection with the physics of neutron stars is highlighted throughout the paper. 

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2. A Method for Estimating Driving Factors of Illicit Trade Using Node Embeddings and Clustering

   https://doi.org/10.1007/978-3-030-49076-8_22  

   González Ordiano, Jorge Ángel; Finn, Lisa; Winterlich, Anthony; Moloney, Gary; Simske, Steven (April 2020, Mexican Conference on Pattern Recognition)

   The trade on illegal goods and services, also known as illicit trade, is expected to drain 4.2 trillion dollars from the world economy and put 5.4 million jobs at risk by 2022. These estimates reflect the importance of combating illicit trade, as it poses a danger to individuals and undermines governments. To do so, however, we have to fi rst understand the factors that influence this type of trade. Therefore, we present in this article a method that uses node embeddings and clustering to compare a country based illicit supply network to other networks that represent other types of country relationships (e.g., free trade agreements, language). The results offer initial clues on the factors that might be driving the illicit trade between countries. 

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3. A new species of Polyamia DeLong (Hemiptera: Cicadellidae: Deltocephalinae: Deltocephalini) representing the first record of the genus from South America

   https://doi.org/10.11646/zootaxa.4457.4.5  

   DUAN, YANI; DIETRICH, CHRISTOPHER H. (August 2018, Zootaxa)

   Polyamia (Polyamia) choromorica sp. n., representing the first record of the genus Polyamia DeLong from South America, is described and illustrated. Previously described species of Polyamia DeLong appear to be restricted to North America. Color illustrations of Polyamia (Copolyamia) caperata (Ball), Polyamia (Copolyamia) similaris DeLong & Davidson and Polyamia (Polyamia) weedi Van Duzee are also provided for comparison. A species checklist and distribution summary for the genus is provided. Notes on other South American species of Deltocephalini with supernumerary forewing crossveins are also provided. 

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4. Deformation Robust Roto-Scale-Translation Equivariant CNNs

   Mars Gao, L; Lin, Guang; Zhu, Wei (July 2022, Transactions on machine learning research)

   Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design. By producing features that are guaranteed to transform covariantly to the group actions on the inputs, group-equivariant convolutional neural networks (G-CNNs) achieve significantly improved generalization performance in learning tasks with intrinsic symmetry. General theory and practical implementation of G-CNNs have been studied for planar images under either rotation or scaling transformation, but only individually. We present, in this paper, a roto-scale-translation equivariant CNN (RST -CNN), that is guaranteed to achieve equivariance jointly over these three groups via coupled group convolutions. Moreover, as symmetry transformations in reality are rarely perfect and typically subject to input deformation, we provide a stability analysis of the equivariance of representation to input distortion, which motivates the truncated expansion of the convolutional filters under (pre-fixed) low-frequency spatial modes. The resulting model provably achieves deformation-robust RST equivariance, i.e., the RST symmetry is still “approximately” preserved when the transformation is “contaminated” by a nuisance data deformation, a property that is especially important for out-of-distribution generalization. Numerical experiments on MNIST, Fashion-MNIST, and STL-10 demonstrate that the proposed model yields remarkable gains over prior arts, especially in the small data regime where both rotation and scaling variations are present within the data. 

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5. Curriculum Design for Machine Learners in Sequential Decision Tasks

   Peng, Bei; MacGlashan, James; Loftin, Robert; Littman, Michael L.; Roberts, David L.; Taylor, Matthew E. (April 2017, AAMAS)

   Existing machine-learning work has shown that algorithms can bene t from curricula---learning fi rst on simple examples before moving to more difficult examples. While most existing work on curriculum learning focuses on developing automatic methods to iteratively select training examples with increasing difficulty tailored to the current ability of the learner, relatively little attention has been paid to the ways in which humans design curricula. We argue that a better understanding of the human-designed curricula could give us insights into the development of new machine-learning algorithms and interfaces that can better accommodate machine- or human-created curricula. Our work addresses this emerging and vital area empirically, taking an important step to characterize the nature of human-designed curricula relative to the space of possible curricula and the performance benefits that may (or may not) occur. 

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