Machine Learning and Deep Learning powered satellite communications: Enabling technologies,applications, open challenges,and future research directions

The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.     

环结构神经网络及其互联想性能

本文提出了一种称为环结构神经网络(LANN)模型及其学习算法。它能像Hopfield网络,双向联想记忆(BAM)网络和其它类似网络一样工作,特别是它能执行多类样本之间的互联想记忆。理论分析和计算机模拟都证明LANN具有很好的收敛性,是一种有效的网络结构。最后本文给出了计算机模拟结果。     

Evidence based rules and learning in symmetric normal-form games

We put forth a general theory of boundedly rational behavior and learning for symmetric normal-form games with unique symmetric Nash equilibria. A class of evidence-based behavioral rules is specified, which includes best-responding to a prior and Nash play. A player begins with initial propsenities towards the rules, and given experience over time adjusts his/her propensities in proportion to the past performance of the rules. We focus on scenarios in which the past distribution of play is revealed to all players. Confronting this theory with experimental data, we find significant support for rule learning and heterogeneity among participants. Received June 1996/Final version April 1997     

纳络酮对氯化铝导致的小鼠学习记忆障碍及血中锌铜锰含量变？…

通过口服氯化建立小鼠学习记忆障碍模型,在此模型的基础上用跳台法观察了纳络酮对铝中毒小鼠学习记忆障碍的改善作用,并采用火焰原子吸收分光光度法测定了小鼠血中锌,铜,锰的含量。结果显示,氯化铝可导致小习学生记忆障碍,且降低血中Ｚｎ、Ｃｕ含量（Ｐ〈０．０５）,而纳络酮有改善铝中毒小鼠学习记忆障碍的作用,同时也增加了血中Ｚｎ、Ｃｕ含量。     

有机锗对铅所致小鼠学习记忆障碍的拮抗作用

用避暗法和水迷宫法研究了对铅所致的小鼠记忆障碍的影响。ＩＣＲ系小鼠隔日腹腔注射羧乙基锗 半氧化物（Ｇｅ－１３２,２５ｍｇ／ｋｇ）,和／或醋酸铅（１５ｍｇ／ｋｇ）２８ｄ。结果表明暴露于铅２０ｄ后小鼠学习记忆能力受障碍,同时给予Ｇｅ－１３２能明显拮抗铅的毒性作用。试验提示Ｇｅ－１３２能拮抗所致的神经行为为功能的破坏作用。     

对称神经元网络：渐近稳定性和学习

本文在分析对称神经元网络的渐近稳定性基础上,提出了一种非约束优化学习算法,保证训练样本成为稳定吸引子,具有一定大小的吸引域.理论上,我们证明了算法的收敛性以及形成的吸引域下界.计算机实验结果充分说明了学习算法的优越性.     

Accurately learning from few examples with a polyhedral classifier

In the context of learning theory many efforts have been devoted to developing classification algorithms able to scale up with massive data problems. In this paper the complementary issue is addressed, aimed at deriving powerful classification rules by accurately learning from few data. This task is accomplished by solving a new mixed integer programming model that extends the notion of discrete support vector machines, in order to derive an optimal set of separating hyperplanes for binary classification problems. According to the cardinality of the set of hyperplanes, the classification region may take the form of a convex polyhedron or a polytope in the original space where the examples are defined. Computational tests on benchmark datasets highlight the effectiveness of the proposed model, that yields the greatest accuracy when compared to other classification approaches. This research was partially supported by PRIN grant 2004132117.     

Small-group searches for mathematical proofs and individual reconstructions of mathematical concepts

This is a study of mathematics students working in small groups. Our research methodology allows us to examine how individual ideas develop in a social context. The research perspective used in this study is based on a co-constructive view of learning. Groups of three or four undergraduate mathematics majors, with prior experience writing mathematical proofs together, were asked to prove three statements. Computer software, such as Geometers Sketchpad, was available. Group work sessions were videotaped. Later, individuals viewed segments of the group video and were asked to reflect on group activities. Students in some groups did not share a common conception of proof, which seemed to hamper their collaboration. We observed interactions that fit with the co-constructive theory, with bidirectional interactions that shaped both group and individual conceptions of the tasks. These changes in understanding may result from parallel and successive internalization and externalization of ideas by individuals in a social context.     

Neural networks and bounded rationality

Traditionally the emphasis in neural network research has been on improving their performance as a means of pattern recognition. Here we take an alternative approach and explore the remarkable similarity between the under-performance of neural networks trained to behave optimally in economic situations and observed human performance in the laboratory under similar circumstances. In particular, we show that neural networks are consistent with observed laboratory play in two very important senses. Firstly, they select a rule for behavior which appears very similar to that used by laboratory subjects. Secondly, using this rule they perform optimally only approximately 60% of the time.     

电子邮件智能分类系统的设计

互联网时代,人类的几种主要通讯方式中,电子邮件是既快捷又经济的方式.对当前人们往来的大量电子邮件,一般在电脑网络中,使用基于关键字比较之分类系统的过滤器对邮件进行分类.这种传统方式存在缺乏灵活性,不能有效地处理概括性描述复杂性问题等缺点.使用效果不甚理想.本文在传统规则分类方法的基础上引入机器学习的知识,设计了一个新的电子邮件智能分类系统,较好地解决了概括性的分类问题,并且通过实验说明了该系统的可行性.