Effetto della temperatura di somministrazione e della percentuale di ricostituzione del succedaneo del latte sulla produzione di carne in agnelli a allattati artificialmente

In a trial of artificial rearing, carried out on 25 Sardinian lambs with two temperature (14 ÷ 16°C and 36 ÷ 38°C) and two concentration levels (15% and 20%), the Authors found a positive effect of higher temperature level, especially when associated with lower concentration

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical systems is the great difficulty in providing formal guarantees about their behavior. We present a novel, scalable, and efficient technique for verifying properties of deep neural networks (or providing counter-examples). The technique is based on the simplex method, extended to handle the non-convex Rectified Linear Unit (ReLU) activation function, which is a crucial ingredient in many modern neural networks. The verification procedure tackles neural networks as a whole, without making any simplifying assumptions. We evaluated our technique on a prototype deep neural network implementation of the next-generation airborne collision avoidance system for unmanned aircraft (ACAS Xu). Results show that our technique can successfully prove properties of networks that are an order of magnitude larger than the largest networks verified using existing methods.Comment: This is the extended version of a paper with the same title that appeared at CAV 201

I Suoli su substrati acidi in Sardegna: nota 2.: i suoli del versante nord-ovest del Massiccio del Limbara

Factors of soils formation of the NW-facing slopes of Limbara relief are described. The climatic factor is considered to be the most important factor in determining the properties of soils in upslope positions (1300-1100 m). The principal soils are Lithie Haplumbrepts. The morphology and vegetation help the development of the soils between 1100 and 800 m. They are Typic Xerumbrepts. Between 800 and 500 m the principal soils are Lithic Xerorthents. Steepness of the slope controls their development

Polimorfismi biochimici nel sangue e nel latte della capra sarda

The Authors, in a study on 990 individual samples of blood and milk collected from Sardinian goats, have found the presence of polymorphism at the loci Hb, Tf, X protein, β- Lg, αs-Cn, while the loci Alb, CA, SOD, α-La, β-Cn were monomorphic

Scelta di un modello algebrico semplice per il calcolo degli scambi energetici nelle bovine in lattazione

In order to describe the energetic exchanges in lactating cows, various mathematic models both «mechanicistic» and «empiric» types have been developed in recent years. The former type models make it possible to evaluate the elementary processes, but require both the detailed knowledge of numerous variables and the availability of suitable capacity computers. The latter type models are instead simpler to handle, but they merely reproduce the experimental data, without adding any new information: among these models, a simple algebraic one, originally devised by Wood, makes it possible, on the basis of III definited hypothesis, to foresee the milk yield, the body weight variations and the energetic requirements of lactating cows. This model has been tested on a sample of 50 Hostein Friesian cows, using a minicomputer with elementary Basic; the results show an adeguate estimate of the paramethers, based on the study of easily identified characteristics of the lactation curve

Feature-Guided Black-Box Safety Testing of Deep Neural Networks

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial examples necessitate some knowledge (architecture, parameters, etc.) of the network at hand. In this paper, we focus on image classifiers and propose a feature-guided black-box approach to test the safety of deep neural networks that requires no such knowledge. Our algorithm employs object detection techniques such as SIFT (Scale Invariant Feature Transform) to extract features from an image. These features are converted into a mutable saliency distribution, where high probability is assigned to pixels that affect the composition of the image with respect to the human visual system. We formulate the crafting of adversarial examples as a two-player turn-based stochastic game, where the first player's objective is to minimise the distance to an adversarial example by manipulating the features, and the second player can be cooperative, adversarial, or random. We show that, theoretically, the two-player game can con- verge to the optimal strategy, and that the optimal strategy represents a globally minimal adversarial image. For Lipschitz networks, we also identify conditions that provide safety guarantees that no adversarial examples exist. Using Monte Carlo tree search we gradually explore the game state space to search for adversarial examples. Our experiments show that, despite the black-box setting, manipulations guided by a perception-based saliency distribution are competitive with state-of-the-art methods that rely on white-box saliency matrices or sophisticated optimization procedures. Finally, we show how our method can be used to evaluate robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.Comment: 35 pages, 5 tables, 23 figure

Use of a glucomannan polymer to reduce the effects of mycotoxin-contaminated diets in finishing pigs

The use of feed additives with mycotoxin adsorption capacity is a common strategy for controlling negative effects of mycotoxins in swine production systems. However, adsorbents that may results very effective under experimental conditions, i.e. when feed contamination level is rather high, do not necessarily retain their efficacy when tested under field conditions feed with generally low mycotoxin contamination. In this study, the effects of diets artificially contaminated with aflatoxin B1 or ochratoxin A on fattening performance and serum chemistry of fattening pigs are investigated. Moreover, the ability of a commercial glucomannan polymer (Gm polimer) to reduce or eliminate the effects of the contaminated feeds is tested. Thirty heavy pigs (BW = 110±10.6 kg) were fed 6 diets (n = 5 pigs/diet) for 4 weeks until slaughtering. Diets were: control without toxin added (C); added with 0.02 ppm of aflatoxin B1 (AFB1); added with 0.05 ppm of ochratoxin A (OTA); other three diets as the previous but the addition of 2.0 g/kg of Gm polymer (C-GM, AFB1-GM, OTA-GM). Daily weight gain (ADG) and Feed efficiency ratio (FE) were measured every two weeks. Data were analyzed with a two-way ANOVA that included the fixed effect of diet, time and their interaction. After the first 2 weeks the ADG did not differ significantly between the diets, even if the ADG of AFB1 diet was about 20% lower than AFB1-Gm or C. In the last 2 weeks the ADG of AFB1 diet was significantly lover than the other diets (P<0.01) and was about one-half of the values reported for the same group in the first period. The contamination with ochratoxin A did not affect fattening performance of pigs during the whole experimental period. No damages were found in kidneys of all diets. Moreover, no evidence of association between observed liver damages and different diets was found. Finally, no differences between experimental diets were evidenced for the haematological parameters

Maternal and fetal fatty acid composition in ovine muscle tissues

In species characterized by a cotyledonary placenta, as sheep, the relative contribution of maternally derived and placenta synthesized fatty acids is not fully understood. For this reason, the FA composition of mother muscle and the deposition of FA in the fetal muscle were studied by gas-chromatography. Five pregnant Sarda ewes were slaughtered at approximately 145 days of pregnancy. Semitendinosus, semimembranosus and femoral biceps muscles were immediately removed from ewes and fetuses. Data were analyzed by a paired t-test, to detect differences in FA composition between fetus and mother tissues. Results showed that FA profile of fat muscle differed markedly between fetus and mother. The intramuscular fat content were 6.38% and 11.79% on DM basis in fetus and mother muscle, respectively. Linoleic (LA; 18:2n-6), and linolenic (ALA; 18:3n-3) acid were found at smaller concentrations in fetus (0.77 and 0.01 mg/100 mg total FA for LA and ALA, respectively) than in maternal muscle (5.55 and 1.04 for LA and ALA, respectively). On the contrary, a higher proportion of their long-chain polyunsaturated metabolites, like arachidonic (AA; 20:4n-6) and docoexadienoic (DHA; 22:6n-3) acid in fetus compared to mother tissue (AA, 2.32 vs 1.30; DHA, 0.83 vs 0.09 mg/100 mg total FA) indicates a preferential fetal accumulation of those FA which are important for fetuses growth and central nervous system development. The c9,t11 Conjugated linoleic acid (CLA) isomer concentration in mother tissue was 0.72 mg/100 mg FA. It was found also in fetus muscle (0.11 mg/100 mg total FA) suggesting a transplacental fatty acid passage to fetal tissues or a desaturation activity on vaccenic acid (VA; 18:1 t11) in the placental or in the fetal tissue. Surprisingly, the c9,t11-CLA was not the most abundant CLA isomer found in fetus fat. Others CLA isomers, separated but not identified, were found in concentration of 0.17, 0.15 and 0.53 mg/100 mg of FA. Concentrations of these CLA isomers were lower in mother tissue (0.12, 0.05 and 0.10 mg/100 mg of FA). The concentration of VA was 0.46 and 0.90 in fetus and mother muscle, respectively. Correlation analysis between FA profile of mothers and fetuses evidenced a close relationship only for odd-numbered long-chain fatty (r = -0.72 for C15:0 and r = -0.88 for C17:0). The results suggested a different FA metabolism in the muscle tissue of mother and fetus in dairy sheep

An Abstraction-Based Framework for Neural Network Verification

Deep neural networks are increasingly being used as controllers for safety-critical systems. Because neural networks are opaque, certifying their correctness is a significant challenge. To address this issue, several neural network verification approaches have recently been proposed. However, these approaches afford limited scalability, and applying them to large networks can be challenging. In this paper, we propose a framework that can enhance neural network verification techniques by using over-approximation to reduce the size of the network—thus making it more amenable to verification. We perform the approximation such that if the property holds for the smaller (abstract) network, it holds for the original as well. The over-approximation may be too coarse, in which case the underlying verification tool might return a spurious counterexample. Under such conditions, we perform counterexample-guided refinement to adjust the approximation, and then repeat the process. Our approach is orthogonal to, and can be integrated with, many existing verification techniques. For evaluation purposes, we integrate it with the recently proposed Marabou framework, and observe a significant improvement in Marabou’s performance. Our experiments demonstrate the great potential of our approach for verifying larger neural networks