Chickens First\ud \ud Speciation by “Hopeful Monsters” in Fraternal Supertwins\ud

The idea of “hopeful monster” was proposed by Goldschmidt who envisioned that speciation could occur instantaneously via major chromosomal rearrangement in a one-step process; but he could not unravel how similar individual in the opposite sex to appear on the same time and location to generate next generation. This paper provides the answer for the challenge. \ud In this paper, a model of speciation in animals is discussed in detail. Only four steps are needed to generate a new species in sexual animals: fraternal twin zygotes, similar gross mutation on the zygotes, self-splitting of mutated zygotes into two groups of identical zygotes of both sexes, development of zygotes with birth of babies, and inbreeding when they mature. The outcome of these steps is generation of new species with chromosomal homozygosity. Viviparous animals (living young not eggs are produced) are used to explain the model. With slight modifications, other asexual organisms could be accommodated. \ud As the model provides the simplest explanation for speciation in all sexual animals, which plausibly explains many puzzles in biology; such as chicken egg, Cambrian explosion, appearance of new organs, etc. The author presents a few predictions that can be falsified. \ud This model needs only one assumption and it is consistent with many well-known observations. \u

Self-Adversarially Learned Bayesian Sampling

Scalable Bayesian sampling is playing an important role in modern machine learning, especially in the fast-developed unsupervised-(deep)-learning models. While tremendous progresses have been achieved via scalable Bayesian sampling such as stochastic gradient MCMC (SG-MCMC) and Stein variational gradient descent (SVGD), the generated samples are typically highly correlated. Moreover, their sample-generation processes are often criticized to be inefficient. In this paper, we propose a novel self-adversarial learning framework that automatically learns a conditional generator to mimic the behavior of a Markov kernel (transition kernel). High-quality samples can be efficiently generated by direct forward passes though a learned generator. Most importantly, the learning process adopts a self-learning paradigm, requiring no information on existing Markov kernels, e.g., knowledge of how to draw samples from them. Specifically, our framework learns to use current samples, either from the generator or pre-provided training data, to update the generator such that the generated samples progressively approach a target distribution, thus it is called self-learning. Experiments on both synthetic and real datasets verify advantages of our framework, outperforming related methods in terms of both sampling efficiency and sample quality.Comment: AAAI 201

A Personalized Commodities Recommendation Procedure and Algorithm Based on Association Rule Mining

The double-quick growth of EB has caused commodities overload, where our customers are not longer able to efficiently choose the products adapt to them. In order to overcome the situation that both companies and customers are facing, we present a personalized recommendation, although several recommendation systems which may have some disadvantages have been developed. In this paper, we focus on the association rule mining by EFFICIENT algorithm which can simple discovery rapidly the all association rules without any information loss. The EFFICIENT algorithm which comes of the conventional Aprior algorithm integrates the notions of fast algorithm and predigested algorithm to find the interesting association rules in a given transaction data sets. We believe that the procedure should be accepted, and experiment with real-life databases show that the proposed algorithm is efficient one

MGCN: Medical Relation Extraction Based on GCN

With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction

Myocardial Energetics in Left Ventricular Hypertrophy

The heart carries out its pumping function by converting the chemical energy stored in fatty acids and glucose into the mechanical energy of actin-myosin interaction of myofibrils. Development of congestive heart failure is usually preceded by a period of compensated left ventricular hypertrophy (LVH) and alterations in myocardial bioenergetics have been considered to play an important role in this transition. Myocardial energetic state that is reflected by the ratio of Phosphocreatine to Adenosine Triphosphate (PCr/ATP) is significantly decreased in hearts with LVH. The severity of this abnormality is linearly related to the severity of cardiac hypertrophy as well as left ventricular (LV) dysfunction, and is independent of a persistent myocardial ischemia. The decrease in PCr/ATP is accompanied by a decrease in creatine kinase flux and alterations in substrate utilization in LVH hearts. Moreover, there is a profound heterogeneity in alterations in myocardial energy metabolism in hearts with post-infarction hypertrophy with the most severe abnormality present in the inner layers of the periscar border zone (BZ). This review will discuss various aspects of myocardial energetics in animal models of three different types of LVH (pressure-overload, volume overload and post-infarction) with a brief description of myocardial energetics in humans with LVH

A novel technique for energy saving in freezing technology

In the freezing process, a single evaporating temperature is normally used in the food industry. This work proposes a new technique which uses two evaporating temperatures in freezing process. Based on the Carnot principle, the bigger the temperature difference of refrigeration, the greater the energy costs. The new technique uses a higher evaporating temperature in the first period of the new freezing process. It allows most of the refrigeration capacity to be obtained for less temperature difference. Then the second period employs a lower evaporating temperature which makes the object further cooled to the final temperature required. In this way, the energy saving can be obtained by proper operations and adjustments to refrigerating plants. The parameters of the novel freezing technique are investigated and optimized using a personal computer. The simulation program is written in Quick Basic. Optimum parameters of the new process are studied for three refrigerants (R22, R502 and R717) and different schemes of industrial refrigeration plants and various objects to be frozen. The industrial implementation of the new technique is discussed. The results show that the novel freezing technique is feasible for industrial application, and the potential of its energy saving is significant