Robustness Verification for Classifier Ensembles

We give a formal verification procedure that decides whether a classifier ensemble is robust against arbitrary randomized attacks. Such attacks consist of a set of deterministic attacks and a distribution over this set. The robustness-checking problem consists of assessing, given a set of classifiers and a labelled data set, whether there exists a randomized attack that induces a certain expected loss against all classifiers. We show the NP-hardness of the problem and provide an upper bound on the number of attacks that is sufficient to form an optimal randomized attack. These results provide an effective way to reason about the robustness of a classifier ensemble. We provide SMT and MILP encodings to compute optimal randomized attacks or prove that there is no attack inducing a certain expected loss. In the latter case, the classifier ensemble is provably robust. Our prototype implementation verifies multiple neural-network ensembles trained for image-classification tasks. The experimental results using the MILP encoding are promising both in terms of scalability and the general applicability of our verification procedure

Searching Spontaneous Conversational Speech

The ACM SIGIR Workshop on Searching Spontaneous Conversational Speech was held as part of the 2007 ACM SIGIR Conference in Amsterdam.\ud The workshop program was a mix of elements, including a keynote speech, paper presentations and panel discussions. This brief report describes the organization of this workshop and summarizes the discussions

Adversarial Patch Camouflage against Aerial Detection

Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. The traditional way of hiding military assets from sight is camouflage, for example by using camouflage nets. However, large assets like planes or vessels are difficult to conceal by means of traditional camouflage nets. An alternative type of camouflage is the direct misleading of automatic object detectors. Recently, it has been observed that small adversarial changes applied to images of the object can produce erroneous output by deep learning-based detectors. In particular, adversarial attacks have been successfully demonstrated to prohibit person detections in images, requiring a patch with a specific pattern held up in front of the person, thereby essentially camouflaging the person for the detector. Research into this type of patch attacks is still limited and several questions related to the optimal patch configuration remain open. This work makes two contributions. First, we apply patch-based adversarial attacks for the use case of unmanned aerial surveillance, where the patch is laid on top of large military assets, camouflaging them from automatic detectors running over the imagery. The patch can prevent automatic detection of the whole object while only covering a small part of it. Second, we perform several experiments with different patch configurations, varying their size, position, number and saliency. Our results show that adversarial patch attacks form a realistic alternative to traditional camouflage activities, and should therefore be considered in the automated analysis of aerial surveillance imagery.Comment: 9 page

Interferon-gamma impairs maintenance and alters hematopoietic support of bone marrow mesenchymal stromal cells

Bone marrow (BM) mesenchymal stromal cells (MSCs) provide microenvironmental support to hematopoietic stem and progenitor cells (HSPCs). Culture-expanded MSCs are interesting candidates for cellular therapies due to their immunosuppressive and regenerative potential which can be further enhanced by pretreatment with interferon-gamma (IFN-γ). However, it remains unknown whether IFN-γ can also influence hematopoietic support by BM-MSCs. In this study, we elucidate the impact of IFN-γ on the hematopoietic support of BM-MSCs. We found that IFN-γ increases expression of interleukin (IL)-6 and stem cell factor by human BM-MSCs. IFN-γ-treated BM-MSCs drive HSPCs toward myeloid commitment in vitro, but impair subsequent differentiation of HSPC. Moreover, IFN-γ-ARE-Del mice with increased IFN-γ production specifically lose their BM-MSCs, which correlates with a loss of hematopoietic stem cells\u27 quiescence. Although IFN-γ treatment enhances the immunomodulatory function of MSCs in a clinical setting, we conclude that IFN-γ negatively affects maintenance of BM-MSCs and their hematopoietic support in vitro and in vivo

Rhamnolipid Biosurfactants as New Players in Animal and Plant Defense against Microbes

Rhamnolipids are known as very efficient biosurfactant molecules. They are used in a wide range of industrial applications including food, cosmetics, pharmaceutical formulations and bioremediation of pollutants. The present review provides an overview of the effect of rhamnolipids in animal and plant defense responses. We describe the current knowledge on the stimulation of plant and animal immunity by these molecules, as well as on their direct antimicrobial properties. Given their ecological acceptance owing to their low toxicity and biodegradability, rhamnolipids have the potential to be useful molecules in medicine and to be part of alternative strategies in order to reduce or replace pesticides in agriculture

Crop rotational diversity increases disease suppressive capacity of soil microbiomes

Microbiomes can aid in the protection of hosts from infection and disease, but the mechanisms underpinning these functions in complex environmental systems remain unresolved. Soils contain microbiomes that influence plant performance, including their susceptibility to disease. For example, some soil microorganisms produce antimicrobial compounds that suppress the growth of plant pathogens, which can provide benefits for sustainable agricultural management. Evidence shows that crop rotations increase soil fertility and tend to promote microbial diversity, and it has been hypothesized that crop rotations can enhance disease suppressive capacity, either through the influence of plant diversity impacting soil bacterial composition or through the increased abundance of disease suppressive microorganisms. In this study, we used a long�term field experiment to test the effects of crop diversity through time (i.e., rotations) on soil microbial diversity and disease suppressive capacity. We sampled soil from seven treatments along a crop diversity gradient (from monoculture to five crop species rotation) and a spring fallow (non�crop) treatment to examine crop diversity influence on soil microbiomes including bacteria that are capable of producing antifungal compounds. Crop diversity significantly influenced bacterial community composition, where the most diverse cropping systems with cover crops and fallow differed from bacterial communities in the 1–3 crop species diversity treatments. While soil bacterial diversity was about 4% lower in the most diverse crop rotation (corn–soybean–wheat + 2 cover crops) compared to monoculture corn, crop diversity increased disease suppressive functional group prnD gene abundance in the more diverse rotation by about 9% compared to monocultures. In addition, disease suppressive potential was significantly diminished in the (non�crop) fallow treatment compared to the most diverse crop rotation treatments. The composition of the microbial community could be more important than diversity to disease suppressive function in our study. Identifying patterns in microbial diversity and ecosystem function relationships can provide insight into microbiome management, which will require manipulating soil nutrients and resources mediated through plant diversity