Clinical EFT (Emotional Freedom Techniques) Improves Multiple Physiological Markers of Health

Emotional Freedom Technique (EFT) is an evidence-based self-help therapeutic method and over 100 studies demonstrate its efficacy. However, information about the physiological effects of EFT is limited. The current study sought to elucidate EFTs mechanisms of action across the central nervous system (CNS) by measuring heart rate variability (HRV) and heart coherence (HC); the circulatory system using resting heart rate (RHR) and blood pressure (BP); the endocrine system using cortisol, and the immune system using salivary immunoglobulin A (SigA). The second aim was to measure psychological symptoms. Participants (N = 203) were enrolled in a 4-day training workshop held in different locations. At one workshop (n = 31), participants also received comprehensive physiological testing. Posttest, significant declines were found in anxiety (−40%), depression (−35%), posttraumatic stress disorder (−32%), pain (−57%), and cravings (−74%), all P < .000. Happiness increased (+31%, P = .000) as did SigA (+113%, P = .017). Significant improvements were found in RHR (−8%, P = .001), cortisol (−37%, P < .000), systolic BP (−6%, P = .001), and diastolic BP (−8%, P < .000). Positive trends were observed for HRV and HC and gains were maintained on follow-up, indicating EFT results in positive health effects as well as increased mental well-being

Person Recognition in Personal Photo Collections

Recognising persons in everyday photos presents major challenges (occluded faces, different clothing, locations, etc.) for machine vision. We propose a convnet based person recognition system on which we provide an in-depth analysis of informativeness of different body cues, impact of training data, and the common failure modes of the system. In addition, we discuss the limitations of existing benchmarks and propose more challenging ones. Our method is simple and is built on open source and open data, yet it improves the state of the art results on a large dataset of social media photos (PIPA).Comment: Accepted to ICCV 2015, revise

How Aspects of Self-Compassion Contribute to Wellbeing and the Effect of Age

This study investigating the differential contribution of self-compassion subcomponents to wellbeing, and examined the effect of age. A total of 275 participants (219 females) completed demographic measures, the Self-Compassion Scale, the Mental Health Index, and a Social Desirability Scale. Hierarchical Multiple Regression indicated that the self-kindness and mindfulness subcomponents predicted wellbeing, whereas the self-judgement, isolation and over- identification subcomponents predicted psychological distress. Furthermore, the negative self- compassion subcomponents accounted for additional variance in psychological wellbeing. Self- compassion was also significantly higher in older adults. This research consolidates previous findings, increases the scope of self-compassion research, and may have practical implications in treatment