Spectral Characterization of functional MRI data on voxel-resolution cortical graphs

The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. We study graph spectral energy metrics associated to fMRI data of 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs' Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as experimental conditions within each task.Comment: Fixed two typos in the equations; (1) definition of L in section 2.1, paragraph 1. (2) signal de-meaning and normalization in section 2.4, paragraph

Graph Spectral Characterization of Brain Cortical Morphology

The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral metrics derived from these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the metrics are studied in the context of hemispheric asymmetry as well as gender dependent discrimination of cortical morphology.Comment: arXiv admin note: substantial text overlap with arXiv:1810.1033

Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems

Majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augmenting topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. However, most NEAT implementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. To address these gaps, this paper develops a multi-objective neuro-evolution algorithm. While adopting the basic elements of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning applications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance maximization alone. The effectiveness of the single-objective (optimizing performance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios

Fertilizer use on smallholder farms in Eastern Province, Zambia:

Fertilizers Zambia Eastern Province., Farms, Small Zambia Eastern Province.,

Preparation and Characterization of Kenaf Cellulosepolyethylene Glycol- Polyethylene Biocomposites

The possibility of using cellulose as natural fiber for the production of bicomposites was investigated in this study that included two stages. The first stage involved the extraction of cellulose from the cell walls of kenaf (Hibiscus cannabinus L.), an annual herbaceous crop with many environmental advantages and good mechanical properties. It was done from the bast part of the crop by chemical treatments. Then, mixture of different weights of low density polyethylene (LDPE) and high density polyethylene (HDPE), as a matrix, with the obtained cellulose, and polyethylene glycol (PEG) were blended in order to produce a biocomposite material suitable for food packaging. For the second stage, the characterization of LDPE- and HDPE-kenaf cellulose biocomposites was performed in order to develop the optimal blends with optimized thermo-mechanical properties and propensity to environmental degradation. Therefore, the mechanical properties including tensile strength, flexural and unnotched Izod Impact tests were performed using Instron Universal Testing Machine and Izod Impact Tester, respectively. Thermal properties, biodegradability and water absorption of biocomposites were investigated as well. In addition, a scanning electron microscope (SEM) was used to observe the surface morphology of the tensile fracture surface of the samples before and after biodegradation test. The results showed that the mechanical properties of the LDPE and HDPEcellulose composites decreased slightly as the cellulose content increased from 0 to 50 wt % in the biocomposite formulation. It is interesting to note that in all treatments, the mechanical behavior of biocomposites retained in an acceptable level of strength except of HDPE composites with 50% cellulose. In general, there is a good homogeneity between samples with PEG that help to find reasonable and acceptable properties. These findings were confirmed by the SEM study. Thermal analysis of composites is necessary for determining their end use. Therefore, thermal properties of biocomposites were obtained by a thermogravimetry analysis (TGA) and a differential scanning calorimetry (DSC). Addition of cellulose fillers improves the thermal resistance of these biocomposites. The results also showed that PEG has positive role in thermal behavior of composites. This finding gives a good indication that the addition of kenaf cellulose into the body of LDPE and HDPE was capable to increase their thermal degradation properties. Biodegradability of these biocomposites was performed based on soil burial test to investigate their degradation during 120 days. The findings illustrated that there is a clear trend of degradation during burial time. The degradability increased as cellulose content was raised in the composite’s formulation. Finally, water absorption was done for biocomposites. The results showed that water absorption value for both composites was higher than those of LDPE and HDPE polymers. Addition of PEG to the formulations reduced the water absorption of the composites. Generally, it seems that the results of this research may lead to a development of a new type of biocomposites using kenaf cellulose as a natural fiber that can be used to replace plastics for food packaging in the near future

IMAGE RETRIEVAL BASED ON COMPLEX DESCRIPTIVE QUERIES

The amount of visual data such as images and videos available over web has increased exponentially over the last few years. In order to efficiently organize and exploit these massive collections, a system, apart from being able to answer simple classification based questions such as whether a specific object is present (or absent) in an image, should also be capable of searching images and videos based on more complex descriptive questions. There is also a considerable amount of structure present in the visual world which, if effectively utilized, can help achieve this goal. To this end, we first present an approach for image ranking and retrieval based on queries consisting of multiple semantic attributes. We further show that there are significant correlations present between these attributes and accounting for them can lead to superior performance. Next, we extend this by proposing an image retrieval framework for descriptive queries composed of object categories, semantic attributes and spatial relationships. The proposed framework also includes a unique multi-view hashing technique, which enables query specification in three different modalities - image, sketch and text. We also demonstrate the effectiveness of leveraging contextual information to reduce the supervision requirements for learning object and scene recognition models. We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding. Within this framework we introduce new kinds of labeling questions that are designed to collect appearance as well as contextual information and which mimic the way in which humans actively learn about their environment. Furthermore we explicitly model the contextual interactions between the regions within an image and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy)