Instance-based Deep Transfer Learning

Deep transfer learning recently has acquired significant research interest. It makes use of pre-trained models that are learned from a source domain, and utilizes these models for the tasks in a target domain. Model-based deep transfer learning is probably the most frequently used method. However, very little research work has been devoted to enhancing deep transfer learning by focusing on the influence of data. In this paper, we propose an instance-based approach to improve deep transfer learning in a target domain. Specifically, we choose a pre-trained model from a source domain and apply this model to estimate the influence of training samples in a target domain. Then we optimize the training data of the target domain by removing the training samples that will lower the performance of the pre-trained model. We later either fine-tune the pre-trained model with the optimized training data in the target domain, or build a new model which is initialized partially based on the pre-trained model, and fine-tune it with the optimized training data in the target domain. Using this approach, transfer learning can help deep learning models to capture more useful features. Extensive experiments demonstrate the effectiveness of our approach on boosting the quality of deep learning models for some common computer vision tasks, such as image classification.Comment: Accepted to WACV 2019. This is a preprint versio

Separation of Visual and Motor Workspaces During Targeted Reaching Results in Limited Generalization of Visuomotor Adaptation

Separating visual and proprioceptive information in terms of workspace locations during reaching movement has been shown to disturb transfer of visuomotor adaptation across the arms. Here, we investigated whether separating visual and motor workspaces would also disturb generalization of visuomotor adaptation across movement conditions within the same arm. Subjects were divided into four experimental groups (plus three control groups). The first two groups adapted to a visual rotation under a “dissociation” condition in which the targets for reaching movement were presented in midline while their arm performed reaching movement laterally. Following that, they were tested in an “association” condition in which the visual and motor workspaces were combined in midline or laterally. The other two groups first adapted to the rotation in one association condition (medial or lateral), then were tested in the other association condition. The latter groups demonstrated complete transfer from the training to the generalization session, whereas the former groups demonstrated substantially limited transfer. These findings suggest that when visual and motor workspaces are separated, two internal models (vision-based one, proprioception-based one) are formed, and that a conflict between the two disrupts the development of an overall representation that underlies adaptation to a novel visuomotor transform

Soil Respiration Measurements Reveal High Retention of Organic Carbon from Corn Residue Derived High-Lignin Fermentation Byproduct Enabling Sustainable Lignocellulosic Biofuel Production

While 2G biofuel production can utilize non-edible, lignocellulosic feedstocks such as agricultural residues to produce liquid fuel, harvesting crop residues is unsustainable without careful management of the soil underneath. By harvesting a fraction of the crop residues left in the field after harvest, soil health can diminish and critically, the soil organic carbon (SOC) stored in agricultural fields can decrease. Currently, in the most popular 2G process models published, the issue of soil degradation remains unresolved with residue harvest strategies receiving considerable attention in the literature and other SOC management strategies receiving far less. Specifically, the strategy of returning the high lignin fermentation byproduct (HLFB) from ethanol production to soil has been sparsely modelled and only tested experimentally once. Our study endeavors to expand on this literature by evaluating the SOC storage potential of various HLFBs and anaerobic digestates and comparing them to their unprocessed corn stover feedstocks using soil incubation experiments, isotope analysis, and simple modelling techniques. For both a 267-day and a 135-day incubation experiment, we measured the amount of carbon lost through microbial respiration and the amount of carbon remaining at the end. We found that in all but one case, for the same initial amounts of substrate inputs, the incubated digestate and HLFBs respired away less carbon and persisted longer in the soil than the incubated corn stover. Then, by applying multi-pool exponential decay models to our data, we found that the incubated corn stover respired away to completion substantially quicker than the biologically processed materials in our projected timespan of 100 years. We then approximated the steady-state SOC levels for a scenario in which the same bioprocessed materials were annually re-added to an incubation with our preliminary results indicating that the biologically processed materials formed .95-4.8 more SOC than their unprocessed counterparts. Emboldened by our experimental results and tenuously strengthened by our preliminary modelling results, we believe that our work supports the feasibility of returning HLFB to soil to restore SOC and opens the door to the increased circularity and viability of biofuels in a future low carbon economy

Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging

The human brain is estimated to contain 100 billion or so neurons and 10 thousand times as many connections. Neurons never function in isolation: each of them is connected to 10, 000 others and they interact extensively every millisecond. Brain cells are organized into neural circuits often in a dynamic way, processing specific types of information and providing th

Using the Virtual Reality-Cognitive Rehabilitation Approach to Improve Contextual Processing in Children with Autism

Background. This pilot study investigated the efficacy of a novel virtual reality-cognitive rehabilitation (VR-CR) intervention to improve contextual processing of objects in children with autism. Previous research supports that children with autism show deficits in contextual processing, as well as deficits in its elementary components: abstraction and cognitive flexibility. Methods. Four children with autism participated in a multiple-baseline, single-subject study. The children were taught how to see objects in context by reinforcing attention to pivotal contextual information. Results. All children demonstrated statistically significant improvements in contextual processing and cognitive flexibility. Mixed results were found on the control test and changes in context-related behaviours. Conclusions. Larger-scale studies are warranted to determine the effectiveness and usability in comprehensive educational programs