Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs

The human reasoning process is seldom a one-way process from an input leading to an output. Instead, it often involves a systematic deduction by ruling out other possible outcomes as a self-checking mechanism. In this paper, we describe the design of a hybrid neural network for logical learning that is similar to the human reasoning through the introduction of an auxiliary input, namely the indicators, that act as the hints to suggest logical outcomes. We generate these indicators by digging into the hidden information buried underneath the original training data for direct or indirect suggestions. We used the MNIST data to demonstrate the design and use of these indicators in a convolutional neural network. We trained a series of such hybrid neural networks with variations of the indicators. Our results show that these hybrid neural networks are very robust in generating logical outcomes with inherently higher prediction accuracy than the direct use of the original input and output in apparent models. Such improved predictability with reassured logical confidence is obtained through the exhaustion of all possible indicators to rule out all illogical outcomes, which is not available in the apparent models. Our logical learning process can effectively cope with the unknown unknowns using a full exploitation of all existing knowledge available for learning. The design and implementation of the hints, namely the indicators, become an essential part of artificial intelligence for logical learning. We also introduce an ongoing application setup for this hybrid neural network in an autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table

The Study on the Application of Learning-Plan Guidance in College English Reading Teaching

From the moment when Chinese students started to learn English, reading has always been the most important and difficult part. Even when students have entered university, reading still takes a large proportion. In College English reading classes, teachers cannot inspire students’ curiosity and consciousness towards reading and thus the whole teaching activity is in vain to improve their reading ability. In recent years, the learning-plan guidance theory which is popular in high schools makes the author see the changes in the class module so that in this thesis it will be applied to College English reading classes. To achieve the aim of this study, the approach of Quasi-experiment is adopted

Building a St\"uckelberg Portal

We construct explicit string theory models realizing the recently proposed "St\"uckelberg Portal" scenario, a framework for building Z' mediation models without the need to introduce unwanted exotic matter charged under the Standard Model. This scenario can be viewed purely field-theoretically, although it is particularly well motivated from string theory. By analyzing carefully the St\"uckelberg couplings between the Abelian gauge bosons and the RR axions, we construct the first global intersecting brane models which extend the Standard Model with a genuine hidden sector, to which it is nonetheless connected via U(1) mass mixings. Utilizing the explicit models we construct, we discuss some broad phenomenological properties and experimental implications of this scenario such as Z-Z' mixings, dark matter stability and relic density, and supersymmetry mediation. With an appropriate confining hidden sector, our setup also provides a minimal realization of the hidden valley scenario. We further explore the possibility of obtaining small Z' masses from a large ensemble of U(1) bosons. Related to the St\"uckelberg portal are two mechanisms that connect the visible and the hidden sectors, namely mediation by non-perturbative operators and the hidden photon scenario, on which we briefly comment.Comment: 39 pages, 11 figure

Function annotation of hepatic retinoid x receptor α based on genome-wide DNA binding and transcriptome profiling.

BackgroundRetinoid x receptor α (RXRα) is abundantly expressed in the liver and is essential for the function of other nuclear receptors. Using chromatin immunoprecipitation sequencing and mRNA profiling data generated from wild type and RXRα-null mouse livers, the current study identifies the bona-fide hepatic RXRα targets and biological pathways. In addition, based on binding and motif analysis, the molecular mechanism by which RXRα regulates hepatic genes is elucidated in a high-throughput manner.Principal findingsClose to 80% of hepatic expressed genes were bound by RXRα, while 16% were expressed in an RXRα-dependent manner. Motif analysis predicted direct repeat with a spacer of one nucleotide as the most prevalent RXRα binding site. Many of the 500 strongest binding motifs overlapped with the binding motif of specific protein 1. Biological functional analysis of RXRα-dependent genes revealed that hepatic RXRα deficiency mainly resulted in up-regulation of steroid and cholesterol biosynthesis-related genes and down-regulation of translation- as well as anti-apoptosis-related genes. Furthermore, RXRα bound to many genes that encode nuclear receptors and their cofactors suggesting the central role of RXRα in regulating nuclear receptor-mediated pathways.ConclusionsThis study establishes the relationship between RXRα DNA binding and hepatic gene expression. RXRα binds extensively to the mouse genome. However, DNA binding does not necessarily affect the basal mRNA level. In addition to metabolism, RXRα dictates the expression of genes that regulate RNA processing, translation, and protein folding illustrating the novel roles of hepatic RXRα in post-transcriptional regulation