Self-Referential Noise as a Fundamental Aspect of Reality

Noise is often used in the study of open systems, such as in classical Brownian motion and in Quantum Dynamics, to model the influence of the environment. However generalising results from G\"{o}del and Chaitin in mathematics suggests that systems that are sufficiently rich that self-referencing is possible contain intrinsic randomness. We argue that this is relevant to modelling the universe, even though it is by definition a closed system. We show how a three-dimensional process-space may arise, as a Prigogine dissipative structure, from a non-geometric order-disorder model driven by, what is termed, self-referential noise.Comment: 7 pages, Latex, 3 ps figures. Contribution to the 2nd International Conference on Unsolved Problems of Noise, Adelaide 199

IEST: WASSA-2018 Implicit Emotions Shared Task

Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion - the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at http://implicitemotions.wassa2018.com.Comment: Accepted at Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysi

Autologous Fat Grafting Reduces Pain in Irradiated Breast: A Review of Our Experience

Introduction. Pain syndromes affect women after conservative and radical breast oncological procedures. Radiation therapy influences their development. We report autologous fat grafting therapeutical role in treating chronic pain in irradiated patients. Materials and Methods. From February 2006 to November 2014, we collect a total of 209 patients who meet the definition of "Postmastectomy Pain Syndrome" (PMPS) and had undergone mastectomy with axillary dissection (113 patients) or quadrantectomy (96 patients). Both procedures were followed by radiotherapy. We performed fat grafting following Coleman's procedure. Mean amount of adipose tissue injected was 52\u2009cc (\ub18.9\u2009cc) per breast. Seventy-eight in 209 patients were not treated surgically and were considered as control group. Data were gathered through preoperative and postoperative VAS questionnaires; analgesic drug intake was recorded. Results. The follow-up was at 12 months (range 11.7-13.5 months). In 120 treated patients we detected pain decrease (mean \ub1 SD point reduction, 3.19 \ub1 2.86). Forty-eight in 59 patients stopped their analgesic drug therapy. Controls reported a mean \ub1 SD decrease of pain of 1.14 \ub1 2.72. Results showed that pain decreased significantly in patients treated (p < 0.005, Wilcoxon rank-sum test). Conclusion. Our 8-year experience confirms fat grafting effectiveness in decreasing neuropathic pain