Measuring Group Personality with Swarm AI

The aggregation of individual personality assessments to predict team performance is widely accepted in management theory but has significant limitations: the isolated nature of individual personality surveys fails to capture much of the team dynamics that drive realworld team performance. Artificial Swarm Intelligence (ASI)—a technology that enables networked teams to think together in real-time and answer questions as a unified system—promises a solution to these limitations by enabling teams to collectively complete a personality assessment, whereby the team uses ASI to converge upon answers that best represent the group’s disposition. In the present study, the group personality of 94 small teams was assessed by having teams take a standard Big Five Inventory (BFI) assessment both as individuals, and as a realtime system enabled by an ASI technology known as Swarm AI. The predictive accuracy of each personality assessment method was assessed by correlating the BFI personality traits to a range of real-world performance metrics. The results showed that assessments of personality generated using Swarm AI were far more predictive of team performance than the traditional aggregation methods, showing at least a 91.8% increase in average correlation with the measured outcome variables, and in no case showing a significant decrease in predictive performance. This suggests that Swarm AI technology may be used as a highly effective team personality assessment tool that more accurately predicts future team performance than traditional survey approaches.

Analysis of an open source, closed-loop, realtime system for hippocampal sharp-wave ripple disruption

Transient neural activity pervades hippocampal electrophysiological activity. During more quiescent states, brief ≈100 ms periods comprising large ≈150–250 Hz oscillations known as sharp-wave ripples (SWR) which co-occur with ensemble bursts of spiking activity, are regularly found in local field potentials recorded from area CA1. SWRs and their concomitant neural activity are thought to be important for memory consolidation, recall, and memory-guided decision making. Temporally-selective manipulations of hippocampal neural activity upon online hippocampal SWR detection have been used as causal evidence of the importance of SWR for mnemonic process as evinced by behavioral and/or physiological changes. However, though this approach is becoming more wide spread, the performance trade-offs involved in building a SWR detection and disruption system have not been explored, limiting the design and interpretation of SWR detection experiments. We present an open source, plug-and-play, online ripple detection system with a detailed performance characterization. Our system has been constructed to interface with an open source software platform, Trodes, and two hardware acquisition platforms, Open Ephys and SpikeGadgets. We show that our in vivo results — approximately 80% detection latencies falling in between ≈20–66 ms with ≈2 ms closed-loop latencies while maintaining <10 false detections per minute — are dependent upon both algorithmic trade-offs and acquisition hardware. We discuss strategies to improve detection accuracy and potential limitations of online ripple disruptions. By characterizing this system in detail, we present a template for analyzing other closed-loop neural detection and perturbation systems. Thus, we anticipate our modular, open source, realtime system will facilitate a wide range of carefully-designed causal closed-loop neuroscience experiments.

Mixed-mode Operating System for Real-time Performance

The purpose of the mixed-mode system research is to handle devices with the accuracy of real-time systems and at the same time, having all the benefits and facilities of a matured Graphic User Interface (GUI) operating system which is typically nonreal-time. This mixed-mode operating system comprising of a real-time portion and a non-real-time portion was studied and implemented to identify the feasibilities and performances in practical applications (in the context of scheduled the real-time events). In this research an i8751 microcontroller-based hardware was used to measure the performance of the system in real-time-only as well as non-real-time-only configurations. The real-time portion is an 486DX-40 IBM PC system running under DOS-based realtime kernel and the non-real-time portion is a Pentium III based system running under Windows NT. It was found that mixed-mode systems performed as good as a typical realtime system and in fact, gave many additional benefits such as simplified/modular programming and load tolerance.

Windows High-Speed Drawing Technology Research

The display speed of image and large real-time data processing is a huge challenge for realtime system. This paper completed a thorough research on existing drawing technology on the platform of windows; analyzed adaptive characteristics of using the general high-speed drawing techniques for high speed drawing and its merits and demerits. Finally, through a lot of experiments and simulations of high speed drawing process after optimization and combination, tested their drawing performance and efficiency in order to select an appropriate drawing method to develop a high-speed graphics engine for large real-time data.