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

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

Robustness Verification of Support Vector Machines

We study the problem of formally verifying the robustness to adversarial examples of support vector machines (SVMs), a major machine learning model for classification and regression tasks. Following a recent stream of works on formal robustness verification of (deep) neural networks, our approach relies on a sound abstract version of a given SVM classifier to be used for checking its robustness. This methodology is parametric on a given numerical abstraction of real values and, analogously to the case of neural networks, needs neither abstract least upper bounds nor widening operators on this abstraction. The standard interval domain provides a simple instantiation of our abstraction technique, which is enhanced with the domain of reduced affine forms, which is an efficient abstraction of the zonotope abstract domain. This robustness verification technique has been fully implemented and experimentally evaluated on SVMs based on linear and nonlinear (polynomial and radial basis function) kernels, which have been trained on the popular MNIST dataset of images and on the recent and more challenging Fashion-MNIST dataset. The experimental results of our prototype SVM robustness verifier appear to be encouraging: this automated verification is fast, scalable and shows significantly high percentages of provable robustness on the test set of MNIST, in particular compared to the analogous provable robustness of neural networks

Insect farming for feed and food production from a circular business model perspective

The studies focused on the use of insects have outlined numerous reasons for using insects as food and feed as an important method to increase food opportunities for consumers. Insects have been emphasized as a food source with a low environmental impact due to the limited requirement for arable land and water, low ecological cost, and high-quality protein provision. In Europe andWestern countries, insect farming is a growing business in which, however, some critical economic aspects must be recognized. The sector needs to be adequately promoted to rationally exploit the huge amount of potential. As such, the aim of this study was to analyze the recent research on economic aspects related to insect farming for feed and food production with the purpose of providing evidence of the critical economic points in this emerging sector. The focus was mainly oriented to understanding how insect farming can foster virtuous circular economic processes, specifically considering economic aspects on the basis of the limited literature currently available and the circular economic principles. A circular business model approach was proposed to address the entire insect-based feed and food supply chain from a circular economic perspective. In our opinion, the findings underline some economic research questions that need to be addressed in the near future, and the conceptual approach can be individualized to help increase cost- and eco-effectiveness from a circular economic perspective

The economic and environmental sustainability dimensions of agriculture: a trade-off analysis of Italian farms

Crop and livestock farms are central to achieving the 2030 Agenda goals and a sustainable agri-food system. However, the transition toward a sustainable agri-food system requires optimizing several economic and environmental farm targets that, interacting with one another, would lead to win-win opportunities, at least as desired by the European Union (EU) policies. Indeed, in recent years, the EU has fostered sustainable development in a logic of synergy between farms’ environmental and economic performances. This work fits into the agricultural sustainability assessment with the aim of improving our understanding of the existence of synergy or a trade-off between the economic and environmental dimensions at a crop and livestock field and farm scale. Specifically, using a set of appropriate agricultural economic and environmental indicators, two composite indexes were created and used to perform trade-off analysis on 7.891 farms that participated in 2019 and 2020 in the Italian Farm Accountancy Data Network. The findings showed a trade-off between economic and environmental dimensions in all livestock sub-sectors and the cereals sector, while a synergy in the horticulture sector. Considering the new European sustainability policies on agriculture and global scenarios, the study significantly contributes to policymakers, practitioners, and academic debate on sustainability in agriculture

Analysis of the impact of length of stay on the quality of service experience, satisfaction and loyalty

Although length of stay is a relevant variable in destination management, little research has been produced connecting it with tourists' post-consumption behaviour. This research compares the post-consumption behaviour of same-day visitors with overnight tourists in a sample of 398 domestic vacationers at two Mediterranean heritage-and-beach destinations. Although economic research on length of stay posits that there are destination benefits in longer stays, same-day visitors score higher in most of the post-consumption variables under study. Significant differences arise in hedonic aspects of the tourist experience and destination loyalty. Thus, we propose that length of stay can be used as a segmentation variable. Furthermore, destination management organisations need to consider length of stay when designing tourism policies. The tourist product and communication strategies might be adapted to different vacation durations

α5β1 Integrin-Mediated Adhesion to Fibronectin Is Required for Axis Elongation and Somitogenesis in Mice

The arginine-glycine-aspartate (RGD) motif in fibronectin (FN) represents the major binding site for α5β1 and αvβ3 integrins. Mice lacking a functional RGD motif in FN (FNRGE/RGE) or α5 integrin develop identical phenotypes characterized by embryonic lethality and a severely shortened posterior trunk with kinked neural tubes. Here we show that the FNRGE/RGE embryos arrest both segmentation and axis elongation. The arrest is evident at about E9.0, corresponding to a stage when gastrulation ceases and the tail bud-derived presomitic mesoderm (PSM) induces α5 integrin expression and assumes axis elongation. At this stage cells of the posterior part of the PSM in wild type embryos are tightly coordinated, express somitic oscillator and cyclic genes required for segmentation, and form a tapered tail bud that extends caudally. In contrast, the posterior PSM cells in FNRGE/RGE embryos lost their tight associations, formed a blunt tail bud unable to extend the body axis, failed to induce the synchronised expression of Notch1 and cyclic genes and cease the formation of new somites. Mechanistically, the interaction of PSM cells with the RGD motif of FN is required for dynamic formation of lamellipodia allowing motility and cell-cell contact formation, as these processes fail when wild type PSM cells are seeded into a FN matrix derived from FNRGE/RGE fibroblasts. Thus, α5β1-mediated adhesion to FN in the PSM regulates the dynamics of membrane protrusions and cell-to-cell communication essential for elongation and segmentation of the body axis

Algorithm Portfolios for Noisy Optimization: Compare Solvers Early

International audienceNoisy optimization is the optimization of objective functions corrupted by noise. A portfolio of algorithms is a set of algorithms equipped with an algorithm selection tool for distributing the compu- tational power among them. We study portfolios of noisy optimization solvers, show that different settings lead to dramatically different perfor- mances, obtain mathematically proved adaptivity by an ad hoc selection algorithm dedicated to noisy optimization. A somehow surprising result is that it is better to compare solvers with some lag; i.e., recommend the current recommendation of the best solver, selected from a comparison based on their recommendations earlier in the run

Fibronectin is a stress responsive gene regulated by HSF1 in response to geldanamycin

Fibronectin is an extracellular matrix glycoprotein with key roles in cell adhesion and migration. Hsp90 binds directly to fibronectin and Hsp90 depletion regulates fibronectin matrix stability. Where inhibition of Hsp90 with a C-terminal inhibitor, novobiocin, reduced the fibronectin matrix, treatment with an N-terminal inhibitor, geldanamycin, increased fibronectin levels. Geldanamycin treatment induced a stress response and a strong dose and time dependent increase in fibronectin mRNA via activation of the fibronectin promoter. Three putative heat shock elements (HSEs) were identified in the fibronectin promoter. Loss of two of these HSEs reduced both basal and geldanamycin-induced promoter activity, as did inhibition of the stress-responsive transcription factor HSF1. Binding of HSF1 to one of the putative HSE was confirmed by ChIP under basal conditions, and occupancy shown to increase with geldanamycin treatment. These data support the hypothesis that fibronectin is stress-responsive and a functional HSF1 target gene. COLA42 and LAMB3 mRNA levels were also increased with geldanamycin indicating that regulation of extracellular matrix (ECM) genes by HSF1 may be a wider phenomenon. Taken together, these data have implications for our understanding of ECM dynamics in stress-related diseases in which HSF1 is activated, and where the clinical application of N-terminal Hsp90 inhibitors is intended