Being While Doing: An Inductive Model of Mindfulness at Work

Mindfulness at work has drawn growing interest as empirical evidence increasingly supports its positive workplace impacts. Yet theory also suggests that mindfulness is a cognitive mode of “Being” that may be incompatible with the cognitive mode of “Doing” that undergirds workplace functioning. Therefore, mindfulness at work has been theorized as “being while doing,” but little is known regarding how people experience these two modes in combination, nor the influences or outcomes of this interaction. Drawing on a sample of 39 semi-structured interviews, this study explores how professionals experience being mindful at work. The relationship between Being and Doing modes demonstrated changing compatibility across individuals and experience, with two basic types of experiences and three types of transitions. We labeled experiences when informants were unable to activate Being mode while engaging Doing mode as Entanglement, and those when informants reported simultaneous co-activation of Being and Doing modes as Disentanglement. This combination was a valuable resource for offsetting important limitations of the typical reliance on the Doing cognitive mode. Overall our results have yielded an inductive model of mindfulness at work, with the core experience, outcomes, and antecedent factors unified into one system that may inform future research and practice. We did a full hour … of [mindfulness] training… My pager went off like three times. … He\u27s telling us to meditate, and everyone\u27s pager was just beeping. It was not very conducive to meditating. –medical residen

Getting one step closer to deduction: Introducing an alternative paradigm for transitive inference

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2008 Psychology Press.Transitive inference is claimed to be “deductive”. Yet every group/species ever reported apparently uses it. We asked 58 adults to solve five-term transitive tasks, requiring neither training nor premise learning. A computer-based procedure ensured all premises were continually visible. Response accuracy and RT (non-discriminative nRT) were measured as is typically done. We also measured RT confined to correct responses (cRT). Overall, very few typical transitive phenomena emerged. The symbolic distance effect never extended to premise recall and was not at all evident for nRT; suggesting the use of non-deductive end-anchor strategies. For overall performance, and particularly the critical B?D inference, our findings indicate that deductive transitive inference is far more intellectually challenging than previously thought. Contrasts of our present findings against previous findings suggest at least two distinct transitive inference modes, with most research and most computational models to date targeting an associative mode rather than their desired deductive mode. This conclusion fits well with the growing number of theories embracing a “dual process” conception of reasoning. Finally, our differing findings for nRT versus cRT suggest that researchers should give closer consideration to matching the RT measure they use to the particular conception of transitive inference they pre-held

Getting Things Done: The Science behind Stress-Free Productivity

Allen (2001) proposed the “Getting Things Done” (GTD) method for personal productivity enhancement, and reduction of the stress caused by information overload. This paper argues that recent insights in psychology and cognitive science support and extend GTD’s recommendations. We first summarize GTD with the help of a flowchart. We then review the theories of situated, embodied and distributed cognition that purport to explain how the brain processes information and plans actions in the real world. The conclusion is that the brain heavily relies on the environment, to function as an external memory, a trigger for actions, and a source of affordances, disturbances and feedback. We then show how these principles are practically implemented in GTD, with its focus on organizing tasks into “actionable” external memories, and on opportunistic, situation-dependent execution. Finally, we propose an extension of GTD to support collaborative work, inspired by the concept of stigmergy

Photon-number-resolving segmented avalanche-photodiode detectors

We investigate the feasibility and performance of photon-number-resolved photodetection employing avalanche photodiodes (APDs) with low dark counts. The main idea is to split n photons over m modes such that every mode has no more than one photon, which is detected alongside propagation by an APD. We characterize performance by evaluating the purities of positive-operator-valued measurements (POVMs), in terms of APD number and photon loss.Comment: 5 pages, 7 figures, submitted for publicatio

AI Dining Suggestion App

Trying to decide what to eat can sometimes be challenging and time-consuming for people. Google and Yelp have large scale data sets of restaurant information as well as Application Program Interfaces (APIs) for using them. This restaurant data includes time, price range, traffic, temperature, etc. The goal of this project is to build an app that eases the process of finding a restaurant to eat. This app has a Tinder-like user friendly User Interface (UI) design to change the common way that lists of restaurants are presented to users on mobile apps. It also uses the help of Artificial Intelligence (AI) with neural networks to train both supervised and unsupervised learning models that can learn from one\u27s dining pattern over time to make better suggestions at any time

A 14-mW PLL-less receiver in 0.18-μm CMOS for Chinese electronic toll collection standard

This is the accepted manuscript version of the following article: Xiaofeng He, et al., “A 14-mW PLL-less receiver in 0.18-μm CMOS for Chinese electronic toll collection standard”, IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 61(10): 763-767, August 2014. The final published version is available at: http://ieeexplore.ieee.org/document/6871304/ © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The design of a 14-mW receiver without phase-locked loop for the Chinese electronic toll collection (ETC) system in a standard 0.18-μm CMOS process is presented in this brief. Since the previously published work was mainly based on vehicle-powered systems, low power consumption was not the primary goal of such a system. In contrast, the presented system is designed for a battery-powered system. Utilizing the presented receiver architecture, the entire receiver only consumes 7.8 mA, at the supply voltage of 1.8 V, which indicates a power saving of at least 38% compared with other state-of-the-art designs for the same application. To verify the performance, the bit error rate is measured to be better than 10-6, which well satisfies the Chinese ETC standard. Moreover, the sensitivity of the designed receiver can be readjusted to -50 dBm, which is required by the standard.Peer reviewe

Icanlearn: A Mobile Application For Creating Flashcards And Social Stories\u3csup\u3etm\u3c/sup\u3e For Children With Autistm

The number of children being diagnosed with Autism Spectrum Disorder (ASD) is on the rise, presenting new challenges for their parents and teachers to overcome. At the same time, mobile computing has been seeping its way into every aspect of our lives in the form of smartphones and tablet computers. It seems only natural to harness the unique medium these devices provide and use it in treatment and intervention for children with autism. This thesis discusses and evaluates iCanLearn, an iOS flashcard app with enough versatility to construct Social StoriesTM. iCanLearn provides an engaging, individualized learning experience to children with autism on a single device, but the most powerful way to use iCanLearn is by connecting two or more devices together in a teacher-learner relationship. The evaluation results are presented at the end of the thesis