A Robust Optimisation Perspective on Counterexample-Guided Repair of Neural Networks

Counterexample-guided repair aims at creating neural networks with mathematical safety guarantees, facilitating the application of neural networks in safety-critical domains. However, whether counterexample-guided repair is guaranteed to terminate remains an open question. We approach this question by showing that counterexample-guided repair can be viewed as a robust optimisation algorithm. While termination guarantees for neural network repair itself remain beyond our reach, we prove termination for more restrained machine learning models and disprove termination in a general setting. We empirically study the practical implications of our theoretical results, demonstrating the suitability of common verifiers and falsifiers for repair despite a disadvantageous theoretical result. Additionally, we use our theoretical insights to devise a novel algorithm for repairing linear regression models based on quadratic programming, surpassing existing approaches.Comment: Accepted at ICML 2023. 9 pages + 13 pages appendix, 8 figure

SpecAttack: Specification-Based Adversarial Training for Deep Neural Networks

Safety specification-based adversarial training aims to generate examples violating a formal safety specification and therefore provides approaches for repair. The need for maintaining high prediction accuracy while ensuring the save behavior remains challenging. Thus we present SpecAttack, a query-efficient counter-example generation and repair method for deep neural networks. Using SpecAttack allows specifying safety constraints on the model to find inputs that violate these constraints. These violations are then used to repair the neural network via re-training such that it becomes provably safe. We evaluate SpecAttack's performance on the task of counter-example generation and repair. Our experimental evaluation demonstrates that SpecAttack is in most cases more query-efficient than comparable attacks, yields counter-examples of higher quality, with its repair technique being more efficient, maintaining higher functional correctness, and provably guaranteeing safety specification compliance

Methane-carbon flow into the benthic food web at cold seeps – a case study from the Costa Rica subduction zone

Cold seep ecosystems can support enormous biomasses of free-living and symbiotic chemoautotrophic organisms that get their energy from the oxidation of methane or sulfide. Most of this biomass derives from animals that are associated with bacterial symbionts, which are able to metabolize the chemical resources provided by the seeping fluids. Often these systems also harbor dense accumulations of non-symbiotic megafauna, which can be relevant in exporting chemosynthetically fixed carbon from seeps to the surrounding deep sea. Here we investigated the carbon sources of lithodid crabs (Paralomis sp.) feeding on thiotrophic bacterial mats at an active mud volcano at the Costa Rica subduction zone. To evaluate the dietary carbon source of the crabs, we compared the microbial community in stomach contents with surface sediments covered by microbial mats. The stomach content analyses revealed a dominance of epsilonproteobacterial 16S rRNA gene sequences related to the free-living and epibiotic sulfur oxidiser Sulfurovum sp. We also found Sulfurovum sp. as well as members of the genera Arcobacter and Sulfurimonas in mat-covered surface sediments where Epsilonproteobacteria were highly abundant constituting 10% of total cells. Furthermore, we detected substantial amounts of bacterial fatty acids such as i-C15:0 and C17:1ω6c with stable carbon isotope compositions as low as −53‰ in the stomach and muscle tissue. These results indicate that the white microbial mats at Mound 12 are comprised of Epsilonproteobacteria and that microbial mat-derived carbon provides an important contribution to the crab's nutrition. In addition, our lipid analyses also suggest that the crabs feed on other 13C-depleted organic matter sources, possibly symbiotic megafauna as well as on photosynthetic carbon sources such as sedimentary detritus

Carbon release from submarine seeps at the Costa Rica fore arc: implications for the volatile cycle at the Central America convergent margin

We report total dissolved inorganic carbon (DIC) abundances and isotope ratios, as well as helium isotope ratios (3He/4He), of cold seep fluids sampled at the Costa Rica fore arc in order to evaluate the extent of carbon loss from the submarine segment of the Central America convergent margin. Seep fluids were collected over a 12 month period at Mound 11, Mound 12, and Jaco Scar using copper tubing attached to submarine flux meters operating in continuous pumping mode. The fluids show minimum 3He/4He ratios of 1.3 RA (where RA is air 3He/4He), consistent with a small but discernable contribution of mantle-derived helium. At Mound 11, δ13C∑CO2 values between −23.9‰ and −11.6‰ indicate that DIC is predominantly derived from deep methanogenesis and is carried to the surface by fluids derived from sediments of the subducting slab. In contrast, at Mound 12, most of the ascending dissolved methane is oxidized due to lower flow rates, giving extremely low δ13C∑CO2 values ranging from −68.2‰ to −60.3‰. We estimate that the carbon flux (CO2 plus methane) through submarine fluid venting at the outer fore arc is 8.0 × 105 g C km−1 yr−1, which is virtually negligible compared to the total sedimentary carbon input to the margin and the output at the volcanic front. Unless there is a significant but hitherto unidentified carbon flux at the inner fore arc, the implication is that most of the carbon being subducted in Costa Rica must be transferred to the (deeper) mantle, i.e., beyond the depth of arc magma generation

Global Patterns of Bacterial Beta-Diversity in Seafloor and Seawater Ecosystems

Background Marine microbial communities have been essential contributors to global biomass, nutrient cycling, and biodiversity since the early history of Earth, but so far their community distribution patterns remain unknown in most marine ecosystems. Methodology/Principal Findings The synthesis of 9.6 million bacterial V6-rRNA amplicons for 509 samples that span the global ocean's surface to the deep-sea floor shows that pelagic and benthic communities greatly differ, at all taxonomic levels, and share <10% bacterial types defined at 3% sequence similarity level. Surface and deep water, coastal and open ocean, and anoxic and oxic ecosystems host distinct communities that reflect productivity, land influences and other environmental constraints such as oxygen availability. The high variability of bacterial community composition specific to vent and coastal ecosystems reflects the heterogeneity and dynamic nature of these habitats. Both pelagic and benthic bacterial community distributions correlate with surface water productivity, reflecting the coupling between both realms by particle export. Also, differences in physical mixing may play a fundamental role in the distribution patterns of marine bacteria, as benthic communities showed a higher dissimilarity with increasing distance than pelagic communities. Conclusions/Significance This first synthesis of global bacterial distribution across different ecosystems of the World's oceans shows remarkable horizontal and vertical large-scale patterns in bacterial communities. This opens interesting perspectives for the definition of biogeographical biomes for bacteria of ocean waters and the seabed

Characterizing Spatial Variability of Ice Algal Chlorophyll a and Net Primary Production between Sea Ice Habitats Using Horizontal Profiling Platforms

Assessing the role of sea ice algal biomass and primary production for polar ecosystems remains challenging due to the strong spatio-temporal variability of sea ice algae. Therefore, the spatial representativeness of sea ice algal biomass and primary production sampling remains a key issue in large-scale models and climate change predictions of polar ecosystems. To address this issue, we presented two novel approaches to up-scale ice algal chl a biomass and net primary production (NPP) estimates based on profiles covering distances of 100 to 1,000 s of meters. This was accomplished by combining ice core-based methods with horizontal under-ice spectral radiation profiling conducted in the central Arctic Ocean during summer 2012. We conducted a multi-scale comparison of ice-core based ice algal chl a biomass with two profiling platforms: a remotely operated vehicle and surface and under ice trawl (SUIT). NPP estimates were compared between ice cores and remotely operated vehicle surveys. Our results showed that ice core-based estimates of ice algal chl a biomass and NPP do not representatively capture the spatial variability compared to the remotely operated vehicle-based estimates, implying considerable uncertainties for pan-Arctic estimates based on ice core observations alone. Grouping sea ice cores based on region or ice type improved the representativeness. With only a small sample size, however, a high risk of obtaining non-representative estimates remains. Sea ice algal chl a biomass estimates based on the dominant ice class alone showed a better agreement between ice core and remotely operated vehicle estimates. Grouping ice core measurements yielded no improvement in NPP estimates, highlighting the importance of accounting for the spatial variability of both the chl a biomass and bottom-ice light in order to get representative estimates. Profile-based measurements of ice algae chl a biomass identified sea ice ridges as an underappreciated component of the Arctic ecosystem because chl a biomass was significantly greater in this unique habitat. Sea ice ridges are not easily captured with ice coring methods and thus require more attention in future studies. Based on our results, we provide recommendations for designing an efficient and effective sea ice algal sampling program for the summer season

High contributions of sea ice derived carbon in polar bear (Ursus maritimus) tissue.

Polar bears (Ursus maritimus) rely upon Arctic sea ice as a physical habitat. Consequently, conservation assessments of polar bears identify the ongoing reduction in sea ice to represent a significant threat to their survival. However, the additional role of sea ice as a potential, indirect, source of energy to bears has been overlooked. Here we used the highly branched isoprenoid lipid biomarker-based index (H-Print) approach in combination with quantitative fatty acid signature analysis to show that sympagic (sea ice-associated), rather than pelagic, carbon contributions dominated the marine component of polar bear diet (72-100%; 99% CI, n = 55), irrespective of differences in diet composition. The lowest mean estimates of sympagic carbon were found in Baffin Bay bears, which were also exposed to the most rapidly increasing open water season. Therefore, our data illustrate that for future Arctic ecosystems that are likely to be characterised by reduced sea ice cover, polar bears will not only be impacted by a change in their physical habitat, but also potentially in the supply of energy to the ecosystems upon which they depend. This data represents the first quantifiable baseline that is critical for the assessment of likely ongoing changes in energy supply to Arctic predators as we move into an increasingly uncertain future for polar ecosystems

Verifying Global Neural Network Specifications using Hyperproperties

Current approaches to neural network verification focus on specifications that target small regions around known input data points, such as local robustness. Thus, using these approaches, we can not obtain guarantees for inputs that are not close to known inputs. Yet, it is highly likely that a neural network will encounter such truly unseen inputs during its application. We study global specifications that - when satisfied - provide guarantees for all potential inputs. We introduce a hyperproperty formalism that allows for expressing global specifications such as monotonicity, Lipschitz continuity, global robustness, and dependency fairness. Our formalism enables verifying global specifications using existing neural network verification approaches by leveraging capabilities for verifying general computational graphs. Thereby, we extend the scope of guarantees that can be provided using existing methods. Recent success in verifying specific global specifications shows that attaining strong guarantees for all potential data points is feasible.Comment: 10 pages, 2 figures. Accepted at FoMLAS 202

SpecRepair: Counter-Example Guided Safety Repair of Deep Neural Networks.

Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importance to certify the safety of these DNNs, i.e. that they comply with a formal safety specification. While safety certification tools exactly answer this question, they are of no help in debugging unsafe DNNs, requiring the developer to iteratively verify and modify the DNN until safety is eventually achieved. Hence, a repair technique needs to be developed that can produce a safe DNN automatically. To address this need, we present SpecRepair, a tool that efficiently eliminates counter-examples from a DNN and produces a provably safe DNN without harming its classification accuracy. SpecRepair combines specification-based counter-example search and resumes training of the DNN, penalizing counter-examples and certifying the resulting DNN. We evaluate SpecRepair’s effectiveness on the ACAS Xu benchmark, a DNN-based controller for unmanned aircraft, and two image classification benchmarks. The results show that SpecRepair is more successful in producing safe DNNs than comparable methods, has a shorter runtime, and produces safe DNNs while preserving their classification accuracy.publishe