Γ0(2)\Gamma^{0}(2) modular forms and anomaly cancellation formulas

In [5], [6] and [8], the authors gave some modular forms over $\Gamma^0(2)$. In this note, we proceed with the study of cancellation formulas relating to the modular forms.Comment: 16pages. arXiv admin note: substantial text overlap with arXiv:2308.1118

The J-twist D_J of the Dirac operator and the Kastler-Kalau-Walze type theorem for six-dimensional manifolds with boundary

In [22], the authors proved a Kastler-Kalau-Walze type theorem for the J-twist D_J of the Dirac operator on 3-dimensional and 4-dimensional almost product Riemannian spin manifold with boundary. In this paper, we develop the Kastler-Kalau-Walze type theorem for the J-twist D_J of the Dirac operator on a 6-dimensional almost product Riemannian spin manifold with boundary.Comment: 36 pages. arXiv admin note: substantial text overlap with arXiv:2203.1046

A note on modular forms and generalized anomaly cancellation formulas 2

In [7], Liu and Wang generalized the Han-Liu-Zhang cancellation formulas to the (a, b) type cancellation formulas. In this note, we prove some another (a, b) type cancellation formulas for even-dimensional Riemannian manifolds. And by transgression, we obtain some characteristic forms with modularity properties on odd-dimensional manifolds.Comment: 16 page

GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

The Space Experiment of the Exo-ecosystem

The experiment of exo-ecosystem and the exploration of extraterrestrial habitability aims to explore the adaptation of terrestrial life in space conditions for the manned space program and the future interstellar migration, which shows great scientific significance and public interests. By our knowledge the early life on Earth, archaea and extremophile have the ability to adapt to extreme environmental conditions and can potentially habitat in extraterrestrial environments. Here we proposed a design and framework for the experiment on exo-ecosystem and extraterrestrial habitability. The conceptual approach involves building an ecosystem based on archaea and extremophiles in a simulated extraterrestrial environment, with a focus on assessing the exobiological potential and adaptability of terrestrial life forms in such conditions through controlled experiments. Specifically, we introduce the Chinese Exo-Ecosystem Space Experiment (CHEESE), which investigates the survivability and potential for sustained growth, reproduction, and ecological interactions of methanogens under simulated Mars and Moon environments using the China Space Station (CSS) as a platform. We highlight that the space station provides unique yet relatively comprehensive conditions for simulating extraterrestrial environments. In conclusion, space experiments involving exo-ecosystems could pave the way for long-term human habitation in space, ensuring our ability to sustain colonies and settlements beyond Earth while minimizing our ecological impact on celestial bodies

Chinese Discourse Annotation Reference Manual

This document provides extensive guidelines and examples for Rhetorical Structure Theory (RST) annotation in Mandarin Chinese. The guideline is divided into three sections. We first introduce preprocessing steps to prepare data for RST annotation. Secondly, we discuss syntactic criteria to segment texts into Elementary Discourse Units (EDUs). Lastly, we provide examples to define and distinguish discourse relations in different genres. We hope that this reference manual can facilitate RST annotations in Chinese and accelerate the development of the RST framework across languages

High density genetic map of Miscanthus sinensis reveals inheritance of zebra stripe

Miscanthus is a bioenergy feedstock crop that has only recently become the subject of modern breeding efforts. It also has more than a 100 year history as an ornamental crop in the U.S., with many cultivars currently sold by the horticulture trade. Miscanthus is a perennial, self-incompatible, C4 grass, with some genotypes capable of maintaining high rates of photosynthesis under cold temperatures, which makes it a good choice for biomass production in the Midwestern U.S. The efficiency of breeding improved Miscanthus biomass cultivars would be greatly increased by using marker-assisted selection, because phenotypic selection for yield traits must typically be done in the second and third years of field trials. Thus, high-density genetic maps will be useful for identifying marker-trait associations that could facilitate mapping and breeding efforts in Miscanthus. Recently, a framework genetic map for M. sinensis was developed at the University of Illinois based on 658 single nucleotide polymorphism (SNP) markers using a GoldenGate genotyping array. However, sequencing of restriction site associated DNA tags (RAD-seq) is a promising approach for obtaining thousands of SNPs at lower cost than with GoldenGate. A major goal of the current research was to develop high density genetic maps of M. sinensis that integrate thousands of new RAD-seq SNPs with previously mapped but less numerous GoldenGate SNPs. In the present work, a mapping population consisted of 261 F1 progeny was developed from a cross between two diploid M. sinensis cultivars, ‘Strictus’ and ‘Kaskade’. SNP genotyping included 138 previously mapped GoldenGate SNPs and 3,044 single copy RAD tags assayed by high-throughput sequencing and called via the UNEAK pipeline in Tassel 3.0. Separate high-density genetic maps were produced for both the female parent (‘Strictus’) and the male parent (‘Kaskade’) using the regression mapping algorithm in JoinMap4.1. A composite genetic map was constructed for M. sinensis using the maximum likelihood mapping algorithm in JoinMap4.1. Zebra stripe mutants, characterized by horizontal yellow-green/white crossbands on the leaves (perpendicular to the leaf axis), have been found in many grasses, and a number of zebra stripe genes have been mapped in maize and rice. However, only one study on zebra stripe in Miscanthus has been published; a single locus model was suggested for this striping trait but it was not mapped. In the present study, segregation of zebra striping was observed in the F1 mapping population and mapped as an example to confirm the utility of the new map. Quantitative trait loci (QTL) analysis identified three QTL for zebra stripe presence/absence and three for zebra stripe intensity. Two of the zebra stripe intensity QTL may be the same as two of the zebra stripe presence/absence QTL, or tightly linked. We determined that the inheritance of the trait was recessive but incomplete penetrance was observed for each zebra stripe presence/absence QTL. Epistatic interactions were important to the expression of the trait. Three-loci models explained up to 63% of the total variation for zebra stripe presence/absence and 68% for zebra stripe intensity. Comparative mapping indicated putative correspondence between QTL detected in Miscanthus and previously cloned genes conferring zebra stripe in maize and rice

CCFL: Computationally Customized Federated Learning

Federated learning (FL) is a method to train model with distributed data from numerous participants such as IoT devices. It inherently assumes a uniform capacity among participants. However, participants have diverse computational resources in practice due to different conditions such as different energy budgets or executing parallel unrelated tasks. It is necessary to reduce the computation overhead for participants with inefficient computational resources, otherwise they would be unable to finish the full training process. To address the computation heterogeneity, in this paper we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Learning (CCFL), which allows each participant to determine whether to perform conventional local training or model estimation in each round based on its current computational resources. Both theoretical analysis and exhaustive experiments indicate that CCFL has the same convergence rate as FedAvg without resource constraints. Furthermore, CCFL can be viewed of a computation-efficient extension of FedAvg that retains model performance while considerably reducing computation overhead