Hawaii Macadamia Nut Company

Owners of the Hawaii Macadamia Nut Company (HMNC) are facing an expansion opportunity. A land owner has property available that would enable the HMNC to expand its acreage and revenue by about 20%. To fully consider this opportunity the owners must decide 1) whether the expansion is strategically and financially viable, 2) how to raise capital to finance the expansion, and 3) whether they have the skills to manage the company's growth during expansion. This is a case study describing a real company facing a real opportunity in Hawaii. The names of the company and its principals have been disguised

Functional brain network architecture supporting the learning of social networks in humans

Most humans have the good fortune to live their lives embedded in richly structured social groups. Yet, it remains unclear how humans acquire knowledge about these social structures to successfully navigate social relationships. Here we address this knowledge gap with an interdisciplinary neuroimaging study drawing on recent advances in network science and statistical learning. Specifically, we collected BOLD MRI data while participants learned the community structure of both social and non-social networks, in order to examine whether the learning of these two types of networks was differentially associated with functional brain network topology. From the behavioral data in both tasks, we found that learners were sensitive to the community structure of the networks, as evidenced by a slower reaction time on trials transitioning between clusters than on trials transitioning within a cluster. From the neuroimaging data collected during the social network learning task, we observed that the functional connectivity of the hippocampus and temporoparietal junction was significantly greater when transitioning between clusters than when transitioning within a cluster. Furthermore, temporoparietal regions of the default mode were more strongly connected to hippocampus, somatomotor, and visual regions during the social task than during the non-social task. Collectively, our results identify neurophysiological underpinnings of social versus non-social network learning, extending our knowledge about the impact of social context on learning processes. More broadly, this work offers an empirical approach to study the learning of social network structures, which could be fruitfully extended to other participant populations, various graph architectures, and a diversity of social contexts in future studies

Multi-view Face Detection Using Deep Convolutional Neural Networks

In this paper we consider the problem of multi-view face detection. While there has been significant research on this problem, current state-of-the-art approaches for this task require annotation of facial landmarks, e.g. TSM [25], or annotation of face poses [28, 22]. They also require training dozens of models to fully capture faces in all orientations, e.g. 22 models in HeadHunter method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method that does not require pose/landmark annotation and is able to detect faces in a wide range of orientations using a single model based on deep convolutional neural networks. The proposed method has minimal complexity; unlike other recent deep learning object detection methods [9], it does not require additional components such as segmentation, bounding-box regression, or SVM classifiers. Furthermore, we analyzed scores of the proposed face detector for faces in different orientations and found that 1) the proposed method is able to detect faces from different angles and can handle occlusion to some extent, 2) there seems to be a correlation between dis- tribution of positive examples in the training set and scores of the proposed face detector. The latter suggests that the proposed methods performance can be further improved by using better sampling strategies and more sophisticated data augmentation techniques. Evaluations on popular face detection benchmark datasets show that our single-model face detector algorithm has similar or better performance compared to the previous methods, which are more complex and require annotations of either different poses or facial landmarks.Comment: in International Conference on Multimedia Retrieval 2015 (ICMR

Hawaii Macadamia Nut Company- A Case Study

Owners of the Hawaii Macadamia Nut Company (HMNC) are facing an expansion opportunity. A land owner has preperty available that would enable the HMNC to expand its acreage and revenue by about 20%. To fully consider this opportunity the owners must decide 1)whether the expansion is strategically and financially viable, 2)how to raise capital to finance the expansion, and 3)whether they have the skills to manage the company\u27s growth during expansion. This is a case study describing a real company facing a real opportunity in Hawaii. The names of the company and its principals have been disguised

Spreading of a Macroscopic Lattice Gas

We present a simple mechanical model for dynamic wetting phenomena. Metallic balls spread along a periodically corrugated surface simulating molecules of liquid advancing along a solid substrate. A vertical stack of balls mimics a liquid droplet. Stochastic motion of the balls, driven by mechanical vibration of the corrugated surface, induces diffusional motion. Simple theoretical estimates are introduced and agree with the results of the analog experiments, with numerical simulation, and with experimental data for microscopic spreading dynamics.Comment: 19 pages, LaTeX, 9 Postscript figures, to be published in Phy. Rev. E (September,1966

Scientific Challenges for Ensuring Clean and Reliable Water for the 21st Century

Many areas in the world are experiencing significant fresh water shortages due to drought, growing populations, increased agricultural and industrial demands, and extensive forms of pollution or water quality degradation. Many more are expected to face similar predicaments in the next 20 years. Water shortages will significantly limit economic growth, decrease the quality of life and human health for billions of people, degrade the ecologic health of natural environments, and could potentially lead to violence and conflict over securing scarce supplies of water. These concerns are not limited to the economically poor countries, of course, as many parts of the United States face similar dilemmas. These problems can be exacerbated by fluctuating imbalances between need and supply, poor water management or land use practices, social, economic, political, and trans-boundary disputes, as well as factors related to climate change. The future is one that will require significant technological advances to support the conservation, preservation, and movement of fresh water, as well as in the development of new or alternative supplies. It is also one that will also require concomitant improvements in the use of practical solutions and the ways in which the broader scientific and technical community interacts with policy-makers, water-related agencies, the educational community, as well the public in the solution process. This presentation will review several aspects of these issues and proposed or implemented solutions for new and reliable water in the context of an example water situation in the US

Routine activities and proactive police activity: a macro-scale analysis of police searches in London and New York City

This paper explored how city-level changes in routine activities were associated with changes in frequencies of police searches using six years of police records from the London Metropolitan Police Service and the New York City Police Department. Routine activities were operationalised through selecting events that potentially impacted on (a) the street population, (b) the frequency of crime or (c) the level of police activity. OLS regression results indicated that routine activity variables (e.g. day of the week, periods of high demand for police service) can explain a large proportion of the variance in search frequency throughout the year. A complex set of results emerged, revealing cross-national dissimilarities and the differential impact of certain activities (e.g. public holidays). Importantly, temporal frequencies in searches are not reducible to associations between searches and recorded street crime, nor changes in on-street population. Based on the routine activity approach, a theoretical police-action model is proposed

Brain Activity in Self- and Value-Related Regions in Response to Online Antismoking Messages Predicts Behavior Change

In this study, we combined approaches from media psychology and neuroscience to ask whether brain activity in response to online antismoking messages can predict smoking behavior change. In particular, we examined activity in subregions of the medial prefrontal cortex linked to self- and value-related processing, to test whether these neurocognitive processes play a role in message-consistent behavior change. We observed significant relationships between activity in both brain regions of interest and behavior change (such that higher activity predicted a larger reduction in smoking). Furthermore, activity in these brain regions predicted variance independent of traditional, theory-driven self-report metrics such as intention, self-efficacy, and risk perceptions. We propose that valuation is an additional cognitive process that should be investigated further as we search for a mechanistic explanation of the relationship between brain activity and media effects relevant to health behavior change

Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from noninvasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior

Time-Evolving Dynamics in Brain Networks Forecast Responses to Health Messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned forty-five adult smokers using functional magnetic resonance imaging while they viewed antismoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and one month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and frontoparietal networks also demonstrated larger increases in their intentions to quit smoking one month later. We further examined dynamics of the VMPFC, as activation in this region has been frequently related to behavior change. The degree to which VMPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly