Modularizing and Assembling Cognitive Map Learners via Hyperdimensional Computing

Biological organisms must learn how to control their own bodies to achieve deliberate locomotion, that is, predict their next body position based on their current position and selected action. Such learning is goal-agnostic with respect to maximizing (minimizing) an environmental reward (penalty) signal. A cognitive map learner (CML) is a collection of three separate yet collaboratively trained artificial neural networks which learn to construct representations for the node states and edge actions of an arbitrary bidirectional graph. In so doing, a CML learns how to traverse the graph nodes; however, the CML does not learn when and why to move from one node state to another. This work created CMLs with node states expressed as high dimensional vectors suitable for hyperdimensional computing (HDC), a form of symbolic machine learning (ML). In so doing, graph knowledge (CML) was segregated from target node selection (HDC), allowing each ML approach to be trained independently. The first approach used HDC to engineer an arbitrary number of hierarchical CMLs, where each graph node state specified target node states for the next lower level CMLs to traverse to. Second, an HDC-based stimulus-response experience model was demonstrated per CML. Because hypervectors may be in superposition with each other, multiple experience models were added together and run in parallel without any retraining. Lastly, a CML-HDC ML unit was modularized: trained with proxy symbols such that arbitrary, application-specific stimulus symbols could be operated upon without retraining either CML or HDC model. These methods provide a template for engineering heterogenous ML systems

Machine Learning-Based Android Malware Detection Using Manifest Permissions

The Android operating system is currently the most prevalent mobile device operating system holding roughly 54 percent of the total global market share. Due to Android’s substantial presence, it has gained the attention of those with malicious intent, namely, malware authors. As such, there exists a need for validating and improving current malware detection techniques. Automated detection methods such as anti-virus programs are critical in protecting the wide variety of Android-powered mobile devices on the market. This research investigates effectiveness of four different machine learning algorithms in conjunction with features selected from Android manifest file permissions to classify applications as malicious or benign. Case study results, on a test set consisting of 5,243 samples, produce accuracy, recall, and precision rates above 80%. Of the considered algorithms (Random Forest, Support Vector Machine, Gaussian Naïve Bayes, and K-Means), Random Forest performed the best with 82.5% precision and 81.5% accuracy

A Unified View on Solving Objective Mismatch in Model-Based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) aims to make agents more sample-efficient, adaptive, and explainable by learning an explicit model of the environment. While the capabilities of MBRL agents have significantly improved in recent years, how to best learn the model is still an unresolved question. The majority of MBRL algorithms aim at training the model to make accurate predictions about the environment and subsequently using the model to determine the most rewarding actions. However, recent research has shown that model predictive accuracy is often not correlated with action quality, tracing the root cause to the \emph{objective mismatch} between accurate dynamics model learning and policy optimization of rewards. A number of interrelated solution categories to the objective mismatch problem have emerged as MBRL continues to mature as a research area. In this work, we provide an in-depth survey of these solution categories and propose a taxonomy to foster future research

Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models

The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an urgent need for methods that reduce the energy needs of NLP and machine learning more broadly. In this article, we investigate techniques that can be used to reduce the energy consumption of common NLP applications. In particular, we focus on techniques to measure energy usage and different hardware and datacenter-oriented settings that can be tuned to reduce energy consumption for training and inference for language models. We characterize the impact of these settings on metrics such as computational performance and energy consumption through experiments conducted on a high performance computing system as well as popular cloud computing platforms. These techniques can lead to significant reduction in energy consumption when training language models or their use for inference. For example, power-capping, which limits the maximum power a GPU can consume, can enable a 15\% decrease in energy usage with marginal increase in overall computation time when training a transformer-based language model

Oligomerization but Not Membrane Bending Underlies the Function of Certain F-BAR Proteins in Cell Motility and Cytokinesis

SummaryF-BAR proteins function in diverse cellular processes by linking membranes to the actin cytoskeleton. Through oligomerization, multiple F-BAR domains can bend membranes into tubules, though the physiological importance of F-BAR-to-F-BAR assemblies is not yet known. Here, we investigate the F-BAR domain of the essential cytokinetic scaffold, Schizosaccharomyces pombe Cdc15, during cytokinesis. Challenging a widely held view that membrane deformation is a fundamental property of F-BARs, we report that the Cdc15 F-BAR binds, but does not deform, membranes in vivo or in vitro, and six human F-BAR domains—including those from Fer and RhoGAP4—share this property. Nevertheless, tip-to-tip interactions between F-BAR dimers are critical for Cdc15 oligomerization and high-avidity membrane binding, stabilization of contractile ring components at the medial cortex, and the fidelity of cytokinesis. F-BAR oligomerization is also critical for Fer and RhoGAP4 physiological function, demonstrating its broad importance to F-BAR proteins that function without membrane bending

Challenges and Strategies for Educational Virtual Reality

Virtual reality (VR) is a rich visualization and analytic platform that furthers the library’s mission of providing access to all forms of information and supporting pedagogy and scholarship across disciplines. Academic libraries are increasingly adopting VR technology for a variety of research and teaching purposes, which include providing enhanced access to digital collections, offering new research tools, and constructing new immersive learning environments for students. This trend suggests that positive technological innovation is flourishing in libraries, but there remains a lack of clear guidance in the library community on how to introduce these technologies in effective ways and make them sustainable within different types of institutions. In June 2018, the University of Oklahoma hosted the second of three forums on the use of 3D and VR for visualization and analysis in academic libraries, as part of the project Developing Library Strategy for 3D and Virtual Reality Collection Development and Reuse(LIB3DVR), funded by a grant from the Institute of Museum and Library Services. This qualitative study invited experts from a range of disciplines and sectors to identify common challenges in the visualization and analysis of 3D data, and the management of VR programs, for the purpose of developing a national library strategy