The ventral striatum mediates goal-directed behaviors based, in part, on inputs from the amygdala. However, striatal areas caudal to the ventral striatum also receive inputs from the amygdala. In primates, the amygdala projects to the central ventral putamen, lateral amygdalostriatal area, and caudal ventral putamen, suggesting that these regions are also "limbic-related." The anterior insula, which integrates sensory and amygdaloid inputs, projects to the classic ventral striatum. We used retrograde and anterograde tract tracing techniques to determine the extent to which specific subdivisions of the insula influence the caudal ventral striatum in the primate. The anterior (agranular and rostral dysgranular) insula has significant inputs to caudal ventral striatal regions that receive projections from the amygdala. In contrast, the posterior (granular) insula has sparse projections. Within the agranular insula, the posteromedial agranular (Iapm), lateral agranular (Ial), and posterolateral agranular (Iapl) subdivisions have the strongest inputs. These subdivisions mediate olfactory, gustatory, and visceral information processing (Carmichael and Price JL [1996b] J. . In contrast, the intermediate agranular subdivision (Iai) is relatively devoid of visceral/gustatory inputs and has few inputs. In summary, caudal ventral striatal areas that receive amygdaloid inputs also receive significant innervation by agranular and dysgranular insula subdivisions that are themselves connected with the amygdala. Within this projection, the Ial, Iapm, and Iapl make the strongest contribution, suggesting that highly processed visceral/autonomic information, taste, and olfaction influence behavioral responses mediated by the caudal ventral striatum. Keywordslimbic; gustatory; caudate; putamen; amygdalostriatal area; amygdala; tract tracingThe rostral ventral striatum is considered a substrate for goal-directed behaviors based on its inputs from multiple brain regions mediating motivation and reward (Mogenson et al., 1980;Nauta, 1986;Ferry et al., 2000;Haber et al., 2000). However, it is increasingly recognized that caudal ventromedial striatal regions also receive inputs from several "limbic" brain regions, including the amygdala and anterior cingulate cortex (Russchen et al., 1985;Selemon and Goldman-Rakic, 1985;Nauta, 1986;McDonald et al., 1999;Ferry et al., 2000; ShammahLagnado et al., 2001;Fudge et al., 2004). We have previously defined the primate "caudal ventral striatum" based on amygdaloid afferent inputs and histochemical markers typical of the ventral striatum (Fudge and Haber, 2002;Fudge et al., 2004). Based on these criteria, the NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript caudal ventral striatum includes the ventromedial putamen surrounding, and caudal to, the decussation of the anterior commissure, the lateral amygdalostriatal area, and the medial tail of the caudate nucleus.The anterior insula is involved in emotional and multisensory processing (Lane et al., 1997;Phillips et a...

Abstract

Multi- and many-core processors are becoming increasingly popular in embedded systems. Many of these processors now feature hardware virtualization capabilities, as found on the ARM Cortex A15 and x86 architectures with Intel VT-x or AMD-V support. Hardware virtualization provides a way to partition physical resources, including processor cores, memory, and I/O devices, among guest virtual machines (VMs). Each VM is then able to host tasks of a specific criticality level, as part of a mixed-criticality system with different timing and safety requirements. However, traditional virtual machine systems are inappropriate for mixed-criticality computing. They use hypervisors to schedule separate VMs on physical processor cores. The costs of trapping into hypervisors to multiplex and manage machine physical resources on behalf of separate guests are too expensive for many time-critical tasks. Additionally, traditional hypervisors have memory footprints that are often too large for many embedded computing systems. In this article, we discuss the design of the Quest-V separation kernel, which partitions services of different criticality levels across separate VMs, or sandboxes . Each sandbox encapsulates a subset of machine physical resources that it manages without requiring intervention from a hypervisor. In Quest-V, a hypervisor is only needed to bootstrap the system, recover from certain faults, and establish communication channels between sandboxes. This not only reduces the memory footprint of the most privileged protection domain but also removes it from the control path during normal system operation, thereby heightening security.

Legacy Building Information Modelling (BIM) systems are not designed to process the high-volume, high-velocity data emitted by in-building Internet-of-Things (IoT) sensors. Historical lack of consideration for the real-time nature of such data means that outputs from such BIM systems typically lack the timeliness necessary for enacting decisions as a result of patterns emerging in the sensor data. Similarly, as sensors are increasingly deployed in buildings, antiquated Building Management Systems (BMSs) struggle to maintain functionality as interoperability challenges increase. In combination these motivate the need to fill an important gap in smart buildings research, to enable faster adoption of these technologies, by combining BIM, BMS and sensor data. This paper describes the data architecture of the Adaptive City Platform, designed to address these combined requirements by enabling integrated BIM and real-time sensor data analysis across both time and space. CCS CONCEPTS• Computer systems organization → Real-time system architecture; • Information systems → Information integration.

Much of mathematics' use in science revolves around measurements of physical quantities, both abstractly and concretely. Such measurements are naturally classified by their dimension, i.e. whether the measurement is of distance, energy, time, and so on. Dimensionality is further refined by the units-of-measure (or units for short) of a measurement e.g., metres, Joules, seconds; thereby, units-of-measure distinguish magnitudes from each other, giving additional meaning. Despite their extensive use in the practice of science, units-of-measure do not see widespread adoption in tools for scientific computing. Here we demonstrate how our freely available and open-source tool, named CamFort, provides a low-effort and automated way of detecting mismatched units-ofmeasure in code. This feature of CamFort is an example of a lightweight, non-binding specification and analysis tool that can help find bugs in programs before they strike. We hope that, in general, these kinds of program analysis tool will become more widely used by scientists to save time and reduce grief during the process of development, as well as increasing confidence in results of numerical models.