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

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

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

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

Associations between Coherent Neural Activity

Objective: Worldwide, tobacco use is the leading cause of preventable death and illness. One common strategy for reducing the prevalence of cigarette smoking and other health risk behaviors is the use of graphic warning labels (GWLs). This has led to widespread interest from the perspective of health psychology in understanding the mechanisms of GWL effectiveness. Here we investigated differences in how the brain responds to negative, graphic warning label-inspired antismoking ads and neutral control ads, and we probed how this response related to future behavior. Method: A group of smokers (N = 45) viewed GWL-inspired and control antismoking ads while undergoing fMRI, and their smoking behavior was assessed before and one month after the scan. We examined neural coherence between two regions in the brain’s valuation network, the medial prefrontal cortex (MPFC) and ventralstriatum (VS). Results: We found that greater neural coherence in the brain’s valuation network during GWL ads (relative to control ads) preceded later smoking reduction. Conclusions: Our results suggest that the integration of information about message value may be key for message influence. Understanding how the brain responds to health messaging and relates to future behavior could ultimately contribute to the design of effective messaging campaigns, as well as more broadly to theories of message effects and persuasion across domains

Absence of Street Lighting May Prevent Vehicle Crime, but Spatial and Temporal Displacement Remains a Concern

OBJECTIVES: This paper estimates the effect of changes in street lighting at night on levels of crime at street-level. Analyses investigate spatial and temporal displacement of crime into adjacent streets. METHODS: Offense data (burglaries, robberies, theft of and theft from vehicles, and violent crime) were obtained from Thames Valley Police, UK. Street lighting data (switching lights off at midnight, dimming, and white light) were obtained from local authorities. Monthly counts of crime at street-level were analyzed using a conditional fixed-effects Poisson regression model, adjusting for seasonal and temporal variation. Two sets of models analyzed: (1) changes in night-time crimes adjusting for changes in day-time crimes and (2) changes in crimes at all times of the day. RESULTS: Switching lights off at midnight was strongly associated with a reduction in night-time theft from vehicles relative to daytime (rate ratio RR 0.56; 0.41–0.78). Adjusted for changes in daytime, night-time theft from vehicles increased (RR 1.55; 1.14–2.11) in adjacent roads where street lighting remained unchanged. CONCLUSION: Theft from vehicle offenses reduced in streets where street lighting was switched off at midnight but may have been displaced to better-lit adjacent streets. Relative to daytime, night-time theft from vehicle offenses reduced in streets with dimming while theft from vehicles at all times of the day increased, thus suggesting temporal displacement. These findings suggest that the absence of street lighting may prevent theft from vehicles, but there is a danger of offenses being temporally or spatially displaced

Individual Differences in Learning Social and Non-Social Network Structures

How do people acquire knowledge about which individuals belong to different cliques or communities? And to what extent does this learning process differ from the process of learning higher-order information about complex associations between non-social bits of information? Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network. We examined individual differences in the ability to learn community structure of networks composed of social versus non-social stimuli. Although participants were able to learn community structure of both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. In addition, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social community structure but not the learning of non-social community structure. Taken together, our results suggest that the process of learning higher-order community structure in social networks is partially distinct from the process of learning higher-order community structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes

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

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