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

Secretory RING finger proteins function as effectors in a grapevine galling insect.

BackgroundAll eukaryotes share a conserved network of processes regulated by the proteasome and fundamental to growth, development, or perception of the environment, leading to complex but often predictable responses to stress. As a specialized component of the ubiquitin-proteasome system (UPS), the RING finger domain mediates protein-protein interactions and displays considerable versatility in regulating many physiological processes in plants. Many pathogenic organisms co-opt the UPS through RING-type E3 ligases, but little is known about how insects modify these integral networks to generate novel plant phenotypes.ResultsUsing a combination of transcriptome sequencing and genome annotation of a grapevine galling species, Daktulosphaira vitifoliae, we identified 138 putatively secretory protein RING-type (SPRINGs) E3 ligases that showed structure and evolutionary signatures of genes under rapid evolution. Moreover, the majority of the SPRINGs were more expressed in the feeding stage than the non-feeding egg stage, in contrast to the non-secretory RING genes. Phylogenetic analyses indicated that the SPRINGs formed clusters, likely resulting from species-specific gene duplication and conforming to features of arthropod host-manipulating (effector) genes. To test the hypothesis that these SPRINGs evolved to manipulate cellular processes within the plant host, we examined SPRING interactions with grapevine proteins using the yeast two-hybrid assay. An insect SPRING interacted with two plant proteins, a cellulose synthase, CSLD5, and a ribosomal protein, RPS4B suggesting secretion reprograms host immune signaling, cell division, and stress response in favor of the insect. Plant UPS gene expression during gall development linked numerous processes to novel organogenesis.ConclusionsTaken together, D. vitifoliae SPRINGs represent a novel gene expansion that evolved to interact with Vitis hosts. Thus, a pattern is emerging for gall forming insects to manipulate plant development through UPS targeting

An adsorbed gas estimation model for shale gas reservoirs via statistical learning

Shale gas plays an important role in reducing pollution and adjusting the structure of world energy. Gas content estimation is particularly significant in shale gas resource evaluation. There exist various estimation methods, such as first principle methods and empirical models. However, resource evaluation presents many challenges, especially the insufficient accuracy of existing models and the high cost resulting from time-consuming adsorption experiments. In this research, a low-cost and high-accuracy model based on geological parameters is constructed through statistical learning methods to estimate adsorbed shale gas conten

Learning Depth from Monocular Videos using Direct Methods

The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo. In previous works, separate pose and depth CNN predictors had to be determined such that their joint outputs minimized the photometric error. Inspired by recent advances in direct visual odometry (DVO), we argue that the depth CNN predictor can be learned without a pose CNN predictor. Further, we demonstrate empirically that incorporation of a differentiable implementation of DVO, along with a novel depth normalization strategy - substantially improves performance over state of the art that use monocular videos for training

Growth and Development of Two Broiler Strains with Low Protein and Crystalline Amino Acid Supplemented Diets

The objective of this research was to compare the growth performance of broilers from two commercial breeds with control, low protein and low protein supplemented with crystalline amino acids diets. This was a randomized block design, and identical experiments were conducted on successively in two years. In each experiment, day-old chicks, Ross 708 broilers and Cobb 405 broilers, were randomly assigned into three dietary treatments: 1) positive control, 2) low crude protein (LP), and 3) LP + crystalline amino acids (CAA). A three phase feeding program was used. Feed and water were provided ad-libitum. On d 12, 19, 26, 33, 40, 47, and 54, two birds per pen were randomly selected, weighed, and euthanized by carbon dioxide asphyxiation for further dissection. Three muscles (M. peronaeus longus, M. iliotibialis, and M. pectoralis thoracica), and three bones (tibia, femur, and radius), and organs were collected. Abdominal fat was only collected at the end of the experiment in the first year. The results showed that dietary protein restriction by 6% units had a retarding influence on the growth and development of visceral organs, muscle tissues and bone mass. The supporting effect of CAA helped compensate the negative effect of low protein diet on the bodyweight, organs, muscles and bones growth, but only during the early growing stages. Cobb broilers had a significantly heavier bodyweight with both low protein and low protein with CAA diets. However, Ross broilers produced significant heavier pectoralis, and had more pectoralis yield than Cobb broilers by feeding the control and low protein with CAA diets. The relative growth of pectoralis in both breeds was significantly inhibited by feeding low protein diet, and the decrease of pectoralis proportion even showed a week earlier, compared to the absolute pectoralis growth. The CAA supplementation enabled both breeds to produce of close pectoralis proportion compared to those on control diet, and this supportive effect of CAA on Ross broiler lasted a week longer than on Cobb broilers

Full-View Coverage Problems in Camera Sensor Networks

Camera Sensor Networks (CSNs) have emerged as an information-rich sensing modality with many potential applications and have received much research attention over the past few years. One of the major challenges in research for CSNs is that camera sensors are different from traditional scalar sensors, as different cameras from different positions can form distinct views of the object in question. As a result, simply combining the sensing range of the cameras across the field does not necessarily form an effective camera coverage, since the face image (or the targeted aspect) of the object may be missed. The angle between the object\u27s facing direction and the camera\u27s viewing direction is used to measure the quality of sensing in CSNs instead. This distinction makes the coverage verification and deployment methodology dedicated to conventional sensor networks unsuitable. A new coverage model called full-view coverage can precisely characterize the features of coverage in CSNs. An object is full-view covered if there is always a camera to cover it no matter which direction it faces and the camera\u27s viewing direction is sufficiently close to the object\u27s facing direction. In this dissertation, we consider three areas of research for CSNS: 1. an analytical theory for full-view coverage; 2. energy efficiency issues in full-view coverage CSNs; 3. Multi-dimension full-view coverage theory. For the first topic, we propose a novel analytical full-view coverage theory, where the set of full-view covered points is produced by numerical methodology. Based on this theory, we solve the following problems. First, we address the full-view coverage holes detection problem and provide the healing solutions. Second, we propose $k$-Full-View-Coverage algorithms in camera sensor networks. Finally, we address the camera sensor density minimization problem for triangular lattice based deployment in full-view covered camera sensor networks, where we argue that there is a flaw in the previous literature, and present our corresponding solution. For the second topic, we discuss lifetime and full-view coverage guarantees through distributed algorithms in camera sensor networks. Another energy issue we discuss is about object tracking problems in full-view coverage camera sensor networks. Next, the third topic addresses multi-dimension full-view coverage problem where we propose a novel 3D full-view coverage model, and we tackle the full-view coverage optimization problem in order to minimize the number of camera sensors and demonstrate a valid solution. This research is important due to the numerous applications for CSNs. Especially some deployment can be in remote locations, it is critical to efficiently obtain accurate meaningful data