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

The Application of a Readiness-Based Sparing Model to Foreign Military Sales

Current Foreign Military Sales FMS models provide stock levels that result in a very low system availability or a funding requirement that exceeds the overall budget. The purpose of this research was to determine if an inventory model exists that can be used in FMS reparable sparing to provide a more efficient and economical inventory purchase. The Aircraft Sustainability Model ASM is such a model, providing the most aircraft availability possible from a given inventory investment by computing the optimal number of spare parts to buy for each item. FMS data was obtained from two sources - the International Data System IDS and the International Weapon Item Projection System IWIPS. Both systems are currently used in FMS reparable sparing to provide stock level requirements to customer countries. The data obtained from these FMS systems included part data, program data, and actual recommended stock level quantities calculated by the respective FMS systems. The ASM when compared to the current FMS models computed reasonable stock levels and provided better aircraft availability for a given level of expenditure. The comparison verified that the ASM is preferable to current FMS reparable sparing techniques in the computation of stock level requirements

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

Universal energy-speed-accuracy trade-offs in driven nonequilibrium systems

Physical systems driven away from equilibrium by an external controller dissipate heat to the environment; the excess entropy production in the thermal reservoir can be interpreted as a "cost" to transform the system in a finite time. The connection between measure theoretic optimal transport and dissipative nonequilibrium dynamics provides a language for quantifying this cost and has resulted in a collection of "thermodynamic speed limits", which argue that the minimum dissipation of a transformation between two probability distributions is directly proportional to the rate of driving. Thermodynamic speed limits rely on the assumption that the target probability distribution is perfectly realized, which is almost never the case in experiments or numerical simulations. Here, we address the ubiquitous situation in which the external controller is imperfect. As a consequence, we obtain a lower bound for the dissipated work in generic nonequilibrium control problems that 1) is asymptotically tight and 2) matches the thermodynamic speed limit in the case of optimal driving. We illustrate these bounds on analytically solvable examples and also develop a strategy for optimizing minimally dissipative protocols based on optimal transport flow matching, a generative machine learning technique. This latter approach ensures the scalability of both the theoretical and computational framework we put forth. Crucially, we demonstrate that we can compute the terms in our bound numerically using efficient algorithms from the computational optimal transport literature and that the protocols that we learn saturate the bound