Generalized minority games with adaptive trend-followers and contrarians

We introduce a simple extension of the minority game in which the market rewards contrarian (resp. trend-following) strategies when it is far from (resp. close to) efficiency. The model displays a smooth crossover from a regime where contrarians dominate to one where trend-followers dominate. In the intermediate phase, the stationary state is characterized by non-Gaussian features as well as by the formation of sustained trends and bubbles.Comment: 4 pages, 6 figure

A nutrition mathematical model to account for dietary supply and requirements of energy and nutrients for domesticated small ruminants: the development and evaluation of the Small Ruminant Nutrition System

A mechanistic model that predicts nutrient requirements and biological values of feeds for sheep (Cornell Net Carbohydrate and Protein System; CNCPS-S) was expanded to include goats and the name was changed to the Small Ruminant Nutrition System (SRNS). The SRNS uses animal and environmental factors to predict metabolizable energy (ME) and protein, and Ca and P requirements. Requirements for goats in the SRNS are predicted based on the equations developed for CNCPS-S, modified to account for specific requirements of goats, including maintenance, lactation, and pregnancy requirements, and body reserves. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal fermentation rates, forage, concentrate and liquid passage rates, and microbial growth. The evaluation of the SRNS for sheep using published papers (19 treatment means) indicated no mean bias (MB; 1.1 g/100 g) and low root mean square prediction error (RMSPE; 3.6 g/100g) when predicting dietary organic matter digestibility for diets not deficient in ruminal nitrogen. The SRNS accurately predicted gains and losses of shrunk body weight (SBW) of adult sheep (15 treatment means; MB = 5.8 g/d and RMSPE = 30 g/d) when diets were not deficient in ruminal nitrogen. The SRNS for sheep had MB varying from -34 to 1 g/d and RSME varying from 37 to 56 g/d when predicting average daily gain (ADG) of growing lambs (42 treatment means). The evaluation of the SRNS for goats based on literature data showed accurate predictions for ADG of kids (31 treatment means; RMSEP = 32.5 g/d; r2= 0.85; concordance correlation coefficient, CCC, = 0.91), daily ME intake (21 treatment means; RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99), and energy balance (21 treatment means; RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats. In conclusion, the SRNS for sheep can accurately predict dietary organic matter digestibility, ADG of growing lambs and changes in SBW of mature sheep. The SRNS for goats is suitable for predicting ME intake and the energy balance of lactating and non-lactating adult goats and the ADG of kids of dairy, meat, and indigenous breeds. The SRNS model is available at http://nutritionmodels.tamu.edu

Nutritional Quality of Meat Analogues: Results From the Food Labelling of Italian Products (FLIP) Project

Nowadays, the interest in meat substitutes is increasing, and consumers perceive their nutritional quality better than that of the animal products they intend to resemble. Therefore, this work aimed to investigate the overall nutritional quality of these new products. Regulated information [Regulation (EU) 1169/2011], the presence/absence of nutrition or health claim and organic declarations, the gluten-free indication, and the number of ingredients were collected from the food labels of 269 commercial meat analogues currently sold on the Italian market. Nutritional information of reference animal meat products was used to compare the nutrition profile. As an indicator of the nutritional quality, the Nutri-Score of meat analogues and counterparts was also determined. Plant-based steaks showed significantly higher protein, lower energy, fats and salt contents, and better Nutri-Scores than the other analogues. All the meat analogues showed a higher fibre content than meat products, while plant-based burgers and meatballs had lower protein contents than meat counterparts. Ready-sliced meat analogues showed a lower salt content than cured meats. Overall, all these plant-based products showed a longer list of ingredients than animal meat products. Results from this survey highlighted that plant-based steaks, cutlets, and cured meats have some favourable nutritional aspects compared to animal-based products. However, they cannot be considered a “tout-court” alternative to meat products from a nutritional point of view

Entanglement swapping with photons generated on-demand by a quantum dot

Photonic entanglement swapping, the procedure of entangling photons without any direct interaction, is a fundamental test of quantum mechanics and an essential resource to the realization of quantum networks. Probabilistic sources of non-classical light can be used for entanglement swapping, but quantum communication technologies with device-independent functionalities demand for push-button operation that, in principle, can be implemented using single quantum emitters. This, however, turned out to be an extraordinary challenge due to the stringent requirements on the efficiency and purity of generation of entangled states. Here we tackle this challenge and show that pairs of polarization-entangled photons generated on-demand by a GaAs quantum dot can be used to successfully demonstrate all-photonic entanglement swapping. Moreover, we develop a theoretical model that provides quantitative insight on the critical figures of merit for the performance of the swapping procedure. This work shows that solid-state quantum emitters are mature for quantum networking and indicates a path for scaling up.Comment: The first four authors contributed equally to this work. 17 pages, 3 figure

Development of a mechanistic model to represent the dynamics of liquid flow out of the rumen and to predict the rate of passage of liquid in dairy cattle

A mechanistic and dynamic model was developed to represent the physiological aspects of liquid dynamics in the rumen and to quantitatively predict liquid flow out of the reticulorumen (RR). The model is composed of 2 inflows (water consumption and salivary secretion), one outflow (liquid flow through the reticulo-omasal orifice (ROO), and one in-and-out flow (liquid flux through the rumen wall). We assumed that liquid flow through the ROO was coordinated with the primary reticular contraction, which is characterized by its frequency, duration, and amplitude during eating, ruminating, and resting. A database was developed to predict each component of the model. A random coefficients model was used with studies as a random variable to identify significant variables. Parameters were estimated using the same procedure only if a random study effect was significant. The input variables for the model were dry matter intake, body weight, dietary dry matter, concentrate content in the diet, time spent eating, and time spent ruminating. Total water consumption (kg/d) was estimated as 4.893 x dry matter intake (kg/d), and 20% of the water consumed by drinking was assumed to bypass the RR. The salivary secretion rate was estimated to be 210 g/min during chewing. During ruminating, however, the salivation rate was assumed to be adjusted for the proportion of liquid in the rumen. Resting salivation was exponentially related to dry matter intake. Liquid efflux through the rumen wall was assumed to be the mean value in the database (4.6 kg/h). The liquid outflow rate (kg/h) was assumed to be a product of the frequency of the ROO opening, its duration per opening, and the amount of liquid passed per opening. Simulations of our model suggest that the ROO may open longer for each contraction cycle than had been previously reported (about 3 s) and that it is affected by dry matter intake, body weight, and total digesta in the rumen. When compared with 28 observations in 7 experiments, the model accounted for 40, 70, and 90% of the variation, with root mean square prediction errors of 9.25 kg, 1.84 kg/h, and 0.013 h(-1) for liquid content in the rumen, liquid outflow rate, and fractional rate of liquid passage, respectively. A sensitivity analysis showed that dry matter intake, followed by body weight and time spent eating, were the most important input variables for predicting the dynamics of liquid flow from the rumen. We conclude that this model can be used to understand the factors that affect the dynamics of liquid flow out of the rumen and to predict the fractional rate of liquid passage from the RR in dairy cattle

Evaluation of protein fractionation systems used in formulating rations for dairy cattle

Production efficiency decreases when diets are not properly balanced for protein. Sensitivity analyses of the protein fractionation schemes used by the National Research Council Nutrient Requirement of Dairy Cattle (NRC) and the Cornell Net Carbohydrate and Protein System (CNCPS) were conducted to assess the influence of the uncertainty in feed inputs and the assumptions underlying the CNCPS scheme on metabolizable protein and amino acid predictions. Monte Carlo techniques were used. Two lactating dairy cow diets with low and high protein content were developed for the analysis. A feed database provided by a commercial laboratory and published sources were used to obtain the distributions and correlations of the input variables. Spreadsheet versions of the models were used. Both models behaved similarly when variation in protein fractionation was taken into account. The maximal impact of variation on metabolizable protein from rumen-undegradable protein (RUP) was 2.5 (CNCPS) and 3.0 (NRC) kg/d of allowable milk for the low protein diet, and 3.5 (CNCPS) and 3.9 (NRC) kg/d of allowable milk for the high protein diet. The RUP flows were sensitive to ruminal degradation rates of the B protein fraction in NRC and of the B2 protein fraction in the CNCPS for protein supplements, energy concentrates, and forages. Absorbed Met and Lys flows were also sensitive to intestinal digestibility of RUP, and the CNCPS model was sensitive to acid detergent insoluble crude protein and its assumption of complete unavailability. Neither the intestinal digestibility of the RUP nor the protein degradation rates are routinely measured. Approaches need to be developed to account for their variability. Research is needed to provide better methods for measuring pool sizes and ruminal digestion rates for protein fractionation systems

A Mechanistic model for predicting the nutrient requirements and feed biological values for sheep

The Cornell Net Carbohydrate and Protein System (CNCPS), a mechanistic model that predicts nutrient requirements and biological values of feeds for cattle, was modified for use with sheep. Published equations were added for predicting the energy and protein requirements of sheep, with a special emphasis on dairy sheep, whose specific needs are not considered by most sheep-feeding systems. The CNCPS for cattle equations that are used to predict the supply of nutrients from each feed were modified to include new solid and liquid ruminal passage rates for sheep, and revised equations were inserted to predict metabolic fecal N. Equations were added to predict fluxes in body energy and protein reserves from BW and condition score. When evaluated with data from seven published studies (19 treatments), for which the CNCPS for sheep predicted positive ruminal N balance, the CNCPS for sheep predicted OM digestibility, which is used to predict feed ME values, with no mean bias (1.1 g/100 g of OM; P > 0.10) and a low root mean squared prediction error (RMSPE; 3.6 g/100 g of OM). Crude protein digestibility, which is used to predict N excretion, was evaluated with eight published studies (23 treatments). The model predicted CP digestibility with no mean bias (-1.9 g/100 g of CP; P > 0.10) but with a large RMSPE (7.2 g/100 g of CP). Evaluation with a data set of published studies in which the CNCPS for sheep predicted negative ruminal N balance indicated that the model tended to underpredict OM digestibility (mean bias of -3.3 g/100 g of OM, P > 0.10; RMSPE = 6.5 g/100 g of OM; n = 12) and to overpredict CP digestibility (mean bias of 2.7 g/100 g of CP, P > 0.10; RMSPE = 12.8 g/100 g of CP; n = 7). The ability of the CNCPS for sheep to predict gains and losses in shrunk BW was evaluated using data from six studies with adult sheep (13 treatments with lactating ewes and 16 with dry ewes). It accurately predicted variations in shrunk BW when diets had positive N balance (mean bias of 5.8 g/d; P > 0.10; RMSPE of 30.0 g/d; n = 15), whereas it markedly overpredicted the variations in shrunk BW when ruminal balance was negative (mean bias of 53.4 g/d, P < 0.05; RMSPE = 84.1 g/d; n = 14). These evaluations indicated that the Cornell Net Carbohydrate and Protein System for Sheep can be used to predict energy and protein requirements, feed biological values, and BW gains and losses in adult sheep

The Small Ruminant Nutrition System: development and evaluation of a goat submodel

The Small Ruminant Nutrition System (SRNS) is a computer model based on the structure of the Cornell Net Carbohydrate and Protein System for Sheep.A version of the SRNS for goats is under development and evaluation. In the SRNS for goats, energy and protein requirements are predicted based on the equations developed for the SRNS for sheep, modified to account for specific requirements of goats. Feed biological values are predicted based on carbohydrate and protein fractions and their ruminal degradation rates, on forage, concentrate and liquid passage rates, on microbial growth, and on physically effective fiber. The evaluation of the SRNS for goats based on literature data showed that the SRNS accurately predicted the ADG of kids (RMSEP = 32.5 g/d; r2 = 0.85; CCC = 0.91), and the daily MEI (RMSEP = 0.24 Mcal/d g/d; r2 = 0.99; CCC = 0.99) and the energy balance (RMSEP = 0.20 Mcal/d g/d; r2 = 0.87; CCC = 0.90) of goats

On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Internet Technology, https://doi.org/10.1145/3528669.The transaction-rate bottleneck built into popular proof-of-work-based cryptocurrencies, like Bitcoin and Ethereum, leads to fee markets where transactions are included according to a first-price auction for block space. Many attempts have been made to adjust and predict the fee volatility, but even well-formed transactions sometimes experience unexpected delays and evictions unless a substantial fee is offered. In this paper, we propose a novel transaction inclusion model that describes the mechanisms and patterns governing miners decisions to include individual transactions in the Bitcoin system. Using this model we devise a Machine Learning (ML) approach to predict transaction inclusion. We evaluate our predictions method using historical observations of the Bitcoin network from a five month period that includes more than 30 million transactions and 120 million entries. We find that our Machine Learning (ML) model can predict fee volatility with an accuracy of up to 91%. Our findings enable Bitcoin users to improve their fee expenses and the approval time for their transactions

Atorvastatin combined to interferon to verify the efficacy (ACTIVE) in relapsing-remitting active multiple sclerosis patients: a longitudinal controlled trial of combination therapy.

A large body of evidence suggests that, besides their cholesterol-lowering effect, statins exert anti-inflammatory action. Consequently, statins may have therapeutic potential in immune-mediated disorders such as multiple sclerosis. Our objectives were to determine safety, tolerability and efficacy of low-dose atorvastatin plus high-dose interferon beta-1a in multiple sclerosis patients responding poorly to interferon beta-1a alone. Relapsing–remitting multiple sclerosis patients, aged 18–50 years, with contrast-enhanced lesions or relapses while on therapy with interferon beta-1a 44 mg (three times weekly) for 12 months, were randomized to combination therapy (interferon+atorvastatin 20mg per day; group A) or interferon alone (group B) for 24 months. Patients underwent blood analysis and clinical assessment with the Expanded Disability Status Scale every 3 months, and brain gadolinium-enhanced magnetic resonance imaging at screening, and 12 and 24 months thereafter. Primary outcome measure was contrast-enhanced lesion number. Secondary outcome measures were number of relapses, EDSS variation and safety laboratory data. Forty-five patients were randomized to group A (n 1⁄4 21) or B (n 1⁄4 24). At 24 months, group A had significantly fewer contrast-enhanced lesions versus baseline (p 1⁄4 0.007) and significantly fewer relapses versus the two pre-randomization years (p < 0.001). At survival analysis, the risk for a 1-point EDSS increase was slightly higher in group B than in group A (p 1⁄4 0.053). Low-dose atorvastatin may be beneficial, as add-on therapy, in poor responders to high-dose interferon beta-1a alone