196 research outputs found
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 Ă·
16°C and 36
Ă·
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
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