Baud V Ronique

Karin Michael

V Ronique Baud
email: <veronique.baud@inserm.fr>

Michael Karin

Is NF-kappaB a good target for cancer therapy? Hopes and pitfalls

Keywords: Animals, Anticarcinogenic Agents, therapeutic use, Drug Resistance, Neoplasm, Humans, NF-kappa B, antagonists & inhibitors, metabolism, Neoplasms, drug therapy, metabolism, pathology, Signal Transduction, drug effects NF-kappaB, Signal transduction, cancer, B-cell lymphoma

NF-B transcription factors play a key role in many physiological processes, such as innate and adaptive immune responses, cell κ proliferation, cell death, and inflammation. It has become clear that aberrant regulation of NF-B and the signaling pathways that κ control its activity are involved in cancer development and progression, as well as in resistance to chemo-and radiotherapy. This review discusses recent cancer-genetics and cancer-genome evidence for the involvement of NF-B in human cancer, and discusses the κ therapeutic potential and benefit of targeting NF-B in cancer and the possible complications and pitfalls of such an approach.

Introduction

Cancer is a leading cause of death in industrialized countries . Although mortality rates have declined in recent years due to earlier 1 detection and more options in treatment, most cancers remain incurable. It is important to stress that cancer is not one but many different diseases, each with distinct characteristics and therapeutic requirements and options. The progressive sequence of mutations and epigenetic alterations of cancer genes promotes the malignant transformation of cancer progenitor cells by disrupting key processes involved in normal growth control and tissue homeostasis ( ). In addition, genomic instability is a common, if not universal, feature of advanced 2 Box 1 tumors. Thus, one major challenge for cancer researchers is to distinguish cancer-causing mutations from irrelevant alterations linked to complex cancer genotypes. In addition to such changes that are intrinsic to the malignant cell, tumor development and progression are also dependent on the microenvironment surrounding the malignant cell, which in many cases is inflammatory in nature . The critical role 3 played by the inflammatory microenvironment in the natural history and progression of most cancers offers new opportunities to therapeutic intervention based on targeting the inflammatory components of the tumor rather than the malignant cell, which is quick to mutate and acquire drug resistance , . [START_REF] Lin | A cytokine-mediated link between innate immunity, inflammation, and cancer[END_REF] 5

The current armamentarium of medical oncology includes many active anticancer agents that are applied across tumor types. None of these broadly active anticancer drugs is an ideal panacea. Most drugs have a small therapeutic index and barely discriminate between malignant and normal cells, a problem that is further amplified by the almost inevitable onset of resistance and subsequent relapse. In recent years, the focus has shifted to new molecular therapeutics that target specific signaling pathways driving inappropriate cell growth and survival, thus offering the promise of greater specificity coupled with reduced systemic toxicity. Such strategies rely on new approaches for studying cancer genetics and cancer genomes, such as gene-expression profiling using microarrays, DNA sequencing, and genome scanning for gene copy-number alterations . The successful development of a rationally designed, molecularly targeted 6-13 therapy for the treatment of a specific cancer is best illustrated by the clinical launches of antibody-based therapies for the treatment of ErbB-2-/Her2 positive breast cancer, such as Herceptin , , and the use of Gleevec (STI571/imatinib) as a kinase inhibitor for the 14 15 treatment of Bcr-Abl-positive chronic myeloid leukemias . 16

In the validation and selection of molecular targets and pathways for therapeutic intervention in cancer, the frequency with which a particular target or a pathway undergoes mutation or deregulation is a valuable indicator of its importance in the malignancy process and of the potential use of a drug that acts on that target or pathway. An important signaling pathway often altered in human cancers is the IKK/NF-B signaling module. Aberrant NF-B regulation has been observed in many cancers, including both solid and hematopoietic κ carcinogenesis and identify critical steps that drive its activation. One difficulty that may have slowed down the realization that NF-B is κ frequently altered in cancer is that in most cases NF-B is activated in malignant cells not due to intrinsic mutations but in response to κ inflammatory stimuli originating from the microenvironment . In other cases, which will be described below, the oncogenic mutations 5 reside in genes that encode upstream regulators of NF-B activity.

κ

NF-B transcription factors and their signaling pathways κ

In mammals, the NF-B family is composed of five members, RelA (p65), RelB, cRel (Rel), NF-B1 (p50 and its precursor p105) and κ κ NF-B2 (p52 and its precursor p100) . These proteins form homo-and heterodimeric complexes, the activity of which is regulated by κ 29 two major pathways. The first one, known as the classical or canonical NF-B activation pathway, mainly applies to RelA:p50 dimers, κ which, under non-stimulated conditions, are sequestered in the cytoplasm through interactions with inhibitory proteins of the I B family.

κ Following stimulation with a broad range of stimuli such as TNF-or IL-1, viruses, genotoxic agents and ionizing radiation, the I B α κ molecules are phosphorylated by the I B kinase complex (IKK) at specific serine residues, leading to their ubiquitination and degradation κ by the proteasome pathway. RelA:p50 dimers are subsequently released and free to translocate to the nucleus where they activate transcription of various target genes . This pathway plays a major role in the control of innate immunity and inflammation , . The 30 31 32 second pathway, the so-called alternative or non non-canonical NF-B signaling pathway, is stimulated by a more restricted set of κ cytokines that all belong to the TNF superfamily (e.g. BAFF, CD40L, LT ). This pathway involves the upstream kinase NF-B-inducing

β κ
kinase (NIK) which activates IKK , thereby leading to the phosphorylation and proteasome-dependent processing of p100, the main RelB α inhibitor, thus resulting in RelB:p52 and RelB:p50 nuclear translocation and DNA binding . Most importantly, all studies point out to 33-37 a crucial role for the alternative pathway in controlling the development, organization and function of secondary lymphoid organs and B-cell maturation and survival , . 32 38 The activation of the canonical as well as the alternative NF-B pathways relies on the inducible phosphorylation of I B inhibitory κ κ proteins (I B for the classical pathway and p100 for the alternative pathway) by the IKK complex and its subunits . IKK is composed of

Multiple myeloma and genetic abnormalities in NF-B pathways κ

The most dramatic and broad example for genetic alterations that lead to activation of NF-B in cancer was recently identified through κ the application of oncogenomics in multiple myeloma (MM). MM is a B cell neoplasia that accounts for 1 of all cancers and more than which have improved survival rates, MM remains an incurable disease. Thus, new treatment approaches are needed to improve the outcome of MM therapy and provide patients with longer disease-free survival. Of all the different signaling pathways constitutively activated in primary MM cells, NF-B has recently emerged as one of the most important drivers of the tumor-promoting machinery.

κ

Biochemical evidence

Constitutive nuclear NF-B activity has been described in many human MM cell lines and primary myeloma cells . Furthermore, In sum, these observations lead to the conclusion that NIK overexpression is an important marker and driver of malignant plasma B cells and that NIK-induced IKK activation is required for uncontrolled B cell growth and therefore an attractive target for therapeutic β intervention.

IAP antagonists and NF-B constitutive activation

κ NIK overexpression was detected in MM cells that have intact TRAF3 and instead posses genetic abnormalities that prevent the expression of cIAP1 and cIAP2 , . These observations provided the first clue that cIAP1 and cIAP2 play a negative role in the control 66 67 of NIK stability. As discussed above, it is now clear that cIAP1/2 are the ubiquitin ligases that target NIK for degradation, and thus accordingly IAP antagonists can induce NIK stabilization and le lead to strong induction of classical and alternative NF-B signaling (see κ below) , . However, the role of cIAP1/2 in NF-B regulation and function is more complex. NF-B suppresses apoptosis by inducing 72 73 κ κ expression of a number of genes whose products inhibit apoptosis, including cIAP1/2 . cIAP1/2 belong to the IAP family that counts 83 eight members in human: cIAP1 and cIAP2, XIAP (X-linked IAP), NAIP (neuronal IAP), ML-IAP (melanoma IAP), Ts-IAP (testis-specific IAP), BRUCE (BIR-containing ubiquitin conjugating enzyme), and survivin , . By virtue of their interaction with 84 85

TRAF2, cIAP1 and cIAP2 are recruited to TNFR1 and TNFR2 signaling complexes, where they negatively modulate caspase-8 activity , 83

. cIAP1 and cIAP2 are also RING domain-containing ubiquitin ligases capable of promoting ubiquitination and proteosomal 86-88 degradation of themselves and several of their binding partners, such as TRAF2 , , and TRAF3 . cIAP1-or cIAP2-deficient mice 84 89 90 91 did not reveal any significant developmental abnormalities or malfunctions in the apoptotic processes , , suggesting that either these 92 93 proteins are not important during normal development or that the two are fully redundant. In contrast, overexpression of cIAP1 and cIAP2 has been implicated in tumor cell survival , and genetic amplification of cIAP1 was suggested to promote tumorigenesis and sustain 85

tumor growth in a mouse model of liver cancer . 94

The model . This activity is perplexing as several recent reports established that IAP antagonists also induced the activation of both the 98 classical and alternative NF-B pathways , , a complex twist that might limit their use as cancer therapeutics. IAP antagonists induce I to a small number of unique cancer cell lines , and under many other cases these compounds would actually, activate NF-B signaling 73 κ upon NIK stabilization and at the same time can also prevent JNK activation activation . Thus the net effect may be inhibition of 91 apoptosis rather than induction of apoptosis.

What lessons can we learn from these recent data? First, the recent findings regarding the full biochemical effects of cIAP1/2 inhibition allows a better understanding of the complex mechanisms controlling NIK stability and other aspects of TNFR signaling. We propose the following model: in absence of stimuli, TRAF3 associates with cytoplasmic NIK, and subsequently targets it to E3 complexes containing TRAF2, cIAP1 and cIAP2 which mark NIK for proteosomal degradation. Upon receptor stimulation, these regulators will be sequestered at the cytoplasmic portion of the receptor, resulting in TRAF2 activation, enhanced cIAP1/2 activity and TRAF3 degradation, an and since this area of research is moving at a rapid pace, there is hope that it will be learned how to use such compounds in the clinic.

Yet one major pitfall that will have to be addressed before anti-IKK or NF-B therapies can become successful is the surprising but Collectively, it enables the targeting of individualized treatments to patients who are most likely to benefit from a given therapy.

Ackowledgements:

Our apologies to colleagues whose important contributions are not cited due to space constraints. Many thanks to H. Authier and A. Moor Moore for invaluable technical assistance. This work was supported by grants from the NIH and AACR (M.K.), and Agence Nationale pour la Recherche, Association pour la Recherche sur le Cancer, Belgian InterUniversity Attraction Pole, Minist re de la Recherche/Canc ropole IdF,

1 κ 27 κ

 127 NF-B can affect all six hallmarks of cancer through the transcriptional activation of genes associated with cell Fig. proliferation, angiogenesis, metastasis, tumor promotion, inflammation and suppression of apoptosis ( ). Various mouse models 17-22 Fig. 2 of cancer in which IKK/NF-B activation has been blocked by genetic means have highlighted the key role of NF-B as a critical promoter κ κ of inflammation-linked cancers . One of the most documented functions of NF-B is its ability to promote cell survival through 23-26 κ induction of target genes whose products inhibit the apoptotic machinery in both normal and malignant cells , . NF-B can also 20 prevent programmed necrosis by inducing genes encoding antioxidant proteins . Since tumor cells often use NF-B to achieve resistance 27 κ to anticancer drugs and radiations, inhibition of NF-B activation appears as a promising option to improve the efficacy of conventional κ anti-cancer therapies . However, only recently has cancer genomics proved useful to demonstrate the key role of NF-B in 28

%

  10 of total hematological malignancies. MM is mainly characterized by unrestrained accumulation of antibody-secreting plasma B cells % in the bone marrow, attributed to loss of apoptosis control and cell-cycle deregulation . The bone marrow microenvironment has also 40-42 been shown to contribute to the pathogenesis and progression of MM . Despite advances in chemotherapy and stem cell transplantation, 43

κ 44 - 46 MM 47 - 51 κ 54 κ 50 ( 65 κFig. 3 κ 3 κ

 4446475154506533 Fig.3

κ 66 67 NIK 73 β 67 % 80 a 66 The

 6773678066 stabilization and NF-B activation κ One major consequence of the detected mutations in MM cells is the stabilization of NIK. The high constitutive amounts of NIK protein in MM were shown to activate both classical and alternative NF-B signaling, and to be required for MM cell growth and survival κ , . Normally, NIK is rapidly degraded in non-stimulated cells and this degradation is required to keep NIK activity at a low basal level 66 67. Recent work indicates that NIK degradation in resting cells depends on its association with TRAF3, which serves as an adaptor that 68-70 recruits NIK to an ubiquitin ligase complex composed of TRAF2 and cIAP1/2 . Within this complex cIAP1/2 are directly responsible for 71 the degradative K48-linked polyubiquitination of NIK , . Receptor (CD40, LT R, BAFF-R) engagement results in recruitment of the 72 TRAF3: TRAF2: cIAP1/2 complex to the receptor, leading to rapid activation of TRAF2, which in turn ubiquitinates cIAP1/2 via a non-degradative K63-linkage and thereby enhance their ubiquitin ligase activity, which is now directed towards TRAF3 leading to its K48-linked ubiquitination and eventual degradation degradation . Once TRAF3 is degraded, NIK can no longer interact with the TRAF2: 71 cIAP1/2 complex, resulting in the stabilization of newly synthesized NIK and its eventual autophosphorylation and activation. NIK overexpression due to amplification or translocation of the locus, or enhanced stability due to amino acid substitutions that disrupt NIK NIK s ability to bind TRAF3 occur in MM , . In addition, many MM cells carry TRAF3 inactivating mutations and present ' 66 67 accumulation of NIK , . Importantly, 90 of patients with low TRAF3 amounts respond to bortezomib, with a significant increase in 66 survival . Collectively, these results suggest that stabilization of NIK, and subsequent NF-B activation, is associated with bortezomib 67κ sensitivity and increased survival. Other mutations that lead to NIK stabilization include deletions of the linked loci, and cIAP1 and cIAP2 loss of TRAF2 , . The phenotypic consequences of all of these mutations appear similar.66 67 Based on our current knowledge it was not surprising to find that NIK stabilization results in constitutive activation of the alternative NF-B pathway, and increased B cell survival. Analysis of mice, which express a non-functional NIK mutant, revealed an autonomous κ aly B cell defect, and inability to activate the alternative NF-B pathway, a defect that is also exhibited in NIK-deficient cells . κ 74-77 Moreover, induction of the alternative NF-B signaling pathway, especially in response to BAFF and CD40L, is crucial for normal B cell κ survival , , , , and TRAF3 and TRAF2 both behave as negative regulators of B cell survival downstream of BAFF-R, in part via 36 37 78 79 the suppression of NF-B2/p100 processing . Finally, it has been reported that increased expression of NIK in primary B cells, especially κ non-TRAF3-binding mutant, leads to expansion of the B-cell compartment and extended B cell survival (M. Schmidt-Supprian, personal communication). Altogether, these data suggest that in response to BAFF and CD40L, the activation of the alternative NF-B pathway via κ NIK stabilization is crucial for B cell survival. However, recent work by Staudt and colleagues revealed that inhibition of IKK , the α physiological target for NIK, had no effect on MM cell survival, whereas inhibition of IKK by the specific inhibitor MLN120B did . β control of classical NF-B signaling by NIK overexpression seems puzzling. Based on the characterization of mice as well as of κ aly -knockout mice, it was suggested that NIK does not participate in the activation of the classical NF-B pathway downstream of TNFR NIK κ in normal cells . Nonetheless, NIK overexpression is known to activate classical NF-B , and thus NIK accumulation in cancer cells 77 κ 81 results in aberrant participation of NIK in activation of the classical NF-B pathway. It should also be noted that NIK is required for the κ activation of both the classical and alternative NF-B pathways in response to CD40L and BAFF in Burkitt lymphoma-derived κ lymphoblastoid cells . 82

κ 72 73 B

 73 figure 4

  thus leading to NIK stabilization. Any mutation affecting the binding of these regulators to each other and to NIK would result in NIK stabilization. Smac mimics or cIAP antagonists have the same effect. Overexpression of NIK in turn leads to constitutive activation of the classical and alternative NF-B pathways and an increase in B cell growth and survival. The remarkable activity of bortezomib in MM κ patients, especially in those with TRAF3 inactivation, supports the key role of NF-B in MM pathogenesis. Second, since NIK κ stabilization appears to be a new marker for malignant B cells, we believe that the specific targeting of NIK may be particularly effective in the treatment of MM and potentially other B-cell malignancies. IKK inhibitors should also be effective due to its increased activity in β cells where NIK is persistently activated. Third, it is questionable whether IAP antagonists will find use as cancer therapeutics, unless they can selectively induce apoptosis in a broad spectrum of cancer cells despite their proven ability to stabilize NIK and thereby induce NF-B κ activation. Only further preclinical studies and clinical trials will define the true potential of Smac mimics as anticancer drugs.Oncogenomics and NF-B inhibitors in cancer therapy κThe discovery of genetic alterations that lead to constitutive NF NF-B activition in one fifth of MM patients strikes a strong rationale κ for the use of NF-B inhibitors as single-agent therapeutics in this malignancy. It also supports the idea that genomic technology should κ become an integral part of prospective clinical trials, helping identify which molecularly defined subsets of patients will preferentially respond to NF-B inhibition. Indeed, a major challenge for cancer researchers and clinicians is how to identify those patients that are most κ likely to benefit from IKK or NF-B inhibition. Beyond MM, cancer genetics and genomics approaches have been successful in β κ identifying a subgroup of diffuse large B-cell lymphoma ell based on the NF-B signature of gene expression profiles, and preclinical κ studies have validated NF-B as a therapeutic target in this lymphoma subtype . In addition, carboxy-terminal truncated forms of p100 κ 100 have been detected in several lymphomas and leukemias, including cutaneous T cell lymphoma and B cell chronic lymphocytic leukemia. In all cases, these mutations result in nuclear p52 accumulation, thus indicating that these types of cancer should also benefit from 101 NF-B inhibition. The question, however, is which of the two NF-B signaling pathways should be targeted in these malignancies: the κ κ classical or the alternative? The NF-B gene expression signature of patients should provide a better prediction for the choice of inhibition, κ and also allow the development of diagnostic and prognostic biomarkers of the response to NF-B inhibition. Based on MM studies, NIK κ is an attractive candidate for inhibition, but not in those cases where processing of truncated p100 variants is NIK independent. Altogether, coupling biomarkers with pathway-targeted therapeutics should ultimately allow treatment of cancer patients to be tailored to their unique genetic abnormalities ( ). Box 2 Much effort has been made by the pharmaceutical industry to develop IKK and other NF-B inhibitors inhibitors . However, IKK β κ 102 inhibitors are yet to be described. An ideal IKK/NF-B inhibitor to be used in molecularly molecularly-targeted therapy should prevent α κ NF-B activation without any effects on other signaling pathways, and be more active in malignant cells than in normal cells. One should κ also consider that excessive and prolonged NF-B inhibition can be detrimental due to its important role in innate immunity. Thus, κ although highly efficient NF-B inhibition is desired during administration of conventional anticancer drugs, NF-B inhibition should be κ κ transient and highly reversible to avoid long long-term immunosuppression. In addition, by selectively targeting specific NF-B signaling κ components involved in a particular disease, one may expect to minimize systemic toxicity and avoid broad suppression of innate immunity. Finally, the dose and schedule for delivering the compound needs to be carefully evaluated as a single agent, and its efficacy has to be determinated in combination with other anticancer agents. Several reports have documented the efficacy of IKK/NF-B inhibitors κ in triggering apoptosis in cancer cells as single agents, and in combination either with death-inducing cytokines or chemotherapeutic drugs,

κ

  pronounced ability of NF-B activation inhibitors to enhance the production of IL-1 and related cytokines due to excessive κ β inflammasome activation during bacterial infections infections . This adverse effect is due to the ability of NF-B to promote 103 κ expression of genes whose products keep IL-1 processing and secretion in check. Perhaps the only way to avoid such complications β would be to use IKK and NF-B inhibitors for only a short duration in combination with broad spectrum antibiotics under well controlled κ hospital conditions. Box 1 The six hallmarks of cancer cancera Self-sufficiency in proliferative growth signals Insensitivity to growth inhibitory signals Evasion of apoptosis Acquisition of limitless replicative potential Induction of angiogenesis Induction of invasion and metastasis Box 2 Translating basic research into new cancer therapeutics The fundamental understanding of the cancer genome and cancer signaling pathways leads to: The identification of new molecular targets driving the molecular pathology and progression of human cancer and the development of drugs acting on these targets. The identification of new and specific markers of the molecular pathology and the development of new diagnostic, pronostic and pharmacogenomics biomarkers.

Figure 1 17 Figure 2

 1172 Figure 1 Human ca cancers that have been linked to constitutive NF-B activation κ Various hematological malignancies and solid tumors that exhibit constitutive NF-B activation (adapted from ref. ).κ

Figure 3 Figure 4

 34 Figure 3 NF-B gain B gain-and loss loss-of of-function mutations in multiple myeloma κ The classical and alternative NF-B signaling pathways are represented downstream of CD40 and BAFF-R. Arrows indicate activating steps, κ and bars indicate inhibitory steps. Negative regulators subject to loss-of-function mutations are in red, and positive regulators affected by gain-of-function mutations are in green. Either type of mutations lead to NF-B constitutive activation through NIK stabilization. The κ proteasome inhibitor bortezomib (BTZ) blocks both classical and alternative NF-B signaling, whereas the IKK -specific inhibitor MLN120B κ β

  anti-apoptotic activity of IAPs is counteracted by the natural mammalian IAP antagonist SMAC/DIABLO (second mitochondrial activator of caspases/direct IAP binding protein with low pI), which is released from mitochondria into the cytoplasm upon apoptosis induction , . A number of IAP antagonists that mimic the interaction of SMAC with IAPs have been developed and are collectively95 96 called Smac mimics. These compounds promote apoptosis of various cancer cells, including MM cells, either as single agents or in synergy with other proapoptotic stimuli such as TNF-. Their efficacy was also demonstrated in a humans MM xenograft mouse

	α97-99

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]

[START_REF] Wang | Targeting loss-of-function mutations in tumor-suppressor genes as a strategy for development of cancer therapeutic agents[END_REF]


Paul Berg
email: paul.berg@univ-ubs.fr

Deise Santana
email: deise.santanamaia@univ-lille.fr

Minh-Tan Pham
email: minh-tan.pham@univ-ubs.fr

Sébastien Lefèvre
email: sebastien.lefevre@univ-ubs.fr

P.; Santana Berg

D ; Maia

M.-T ; Pham

Weakly

Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images

Keywords: marine animal monitoring, anomaly detection, deep learning, weakly supervised learning, convolutional neural networks Challenges and Current Solutions 63

Human activities in the sea, such as intensive fishing and exploitation of offshore wind 1 farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, 2 surveying, and tracking of marine animals are often performed on the sites where those activities 3 take place. Nowadays, thank to high resolution cameras and to the development of machine 4 learning techniques, tracking of wild animals can be performed remotely and the analysis of the 5 acquired images can be automatized using state-of-the-art object detection models. However, 6 most state-of-the-art detection methods require lots of annotated data to provide satisfactory 7 results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome 8 and time consuming task, we focus in this article on the weakly supervised detection of marine 9 animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.

Introduction

With the ever-growing exploitation of marine natural resources, surveying human activities in the sea has become essential [START_REF] Hooper | The implications of energy systems for ecosystem services: A detailed case study of offshore wind[END_REF]. Activities, such as the installation of offshore wind farms and intensive fishing, should be closely monitored, as they can have a serious impact on the marine mega fauna. For instance, the noise produced during the different phases of an offshore wind farm development, including the site survey, the wind farm construction and the deployment of turbines, can potentially lead to various levels of physical injury, physiological, and behavioral changes in mammals, fish, and invertebrates [START_REF] Bergström | Effects of offshore wind farms on marine wildlife-a generalized impact assessment[END_REF][START_REF] Verfuss | Review of offshore wind farm impact monitoring and mitigation with regard to marine mammals[END_REF][START_REF] Nabe-Nielsen | Predicting the impacts of anthropogenic disturbances on marine populations[END_REF][START_REF] Mooney | Acoustic impacts of offshore wind energy on fishery resources: An evolving source and varied effects across a wind farm's lifetime[END_REF]. In order to ensure that such human activities can take place without harming the marine ecosystem, different surveillance approaches have been adopted in the past years.

Nowadays, aerial surveys are among the standard non-invasive approaches for tracking the marine mega fauna [START_REF] Saqib | Real-Time Drone Surveillance and Population Estimation of Marine Animals from Aerial Imagery[END_REF][START_REF] Maire | Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach[END_REF][START_REF] Hong | Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery[END_REF][START_REF] Boudaoud | Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images[END_REF][START_REF] Kellenberger | 21 000 birds in 4.5 h: Efficient large-scale seabird detection with machine learning[END_REF]. Those surveys consist of flight sessions over the

2.

The appearance of marine animals changes as they swim deeper in the ocean,

3.

Depending on the flight altitude, animal instances are so small that they can only be 90 detected through their context. As an example, Figure 3 shows an image captured 91 during a flight session of Ifremer (Institut Français de Recherche pour l'Exploitation 92 de la Mer: https://wwz.ifremer.fr/ (accessed on 6 January 2022)). According to 93 specialists, the bright dots inside the green bounding boxes probably correspond 94 to marine animals, while that the ones inside the red box may be sun glitters. We 95 can observe that this analysis is only possible by taking into consideration the 96 proximity of each patch to the sun reflection. Although it is desirable to perform the flight sessions when the weather is favorable 98 (no rain, not too much wind, and good visibility), it is not always possible due to 99 other constraints, such as the availability of the pilot and other members of the 100 crew. For that reason, waves crests and sun glitters, which may appear similar to 101 animals (see Figure 4), are often visible in the images. Obtaining models which 102 are robust to such kind of noise is one of the most difficult challenges in marine 103 animal detection. Due to the complexity of detecting marine animals in those various scenarios, 105 research studies in the literature often limit their scope to the detection of a single animal 106 species [START_REF] Maire | Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach[END_REF][START_REF] Boudaoud | Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images[END_REF] and/or to images with high density of animal instances [START_REF] Hong | Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery[END_REF][START_REF] Kellenberger | 21 000 birds in 4.5 h: Efficient large-scale seabird detection with machine learning[END_REF]. For instance, 107 in the early work of [START_REF] Maire | Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach[END_REF], the authors tackle the detection of dugongs in aerial images 108 by combining an unsupervised region proposal method with a classification CNN.

109

On their dataset, whose number of images was not provided, the best precision and 110 recall scores were 27% and 80%, respectively. Similarly to [START_REF] Maire | Automating Marine Mammal Detection in Aerial Images Captured During Wildlife Surveys: A Deep Learning Approach[END_REF], the authors of [START_REF] Boudaoud | Marine Bird Detection Based on Deep Learning using High-Resolution Aerial Images[END_REF] targeted 111 marine bird detection through a combination of an unsupervised region proposal with a 112 classification CNN. Even though high accuracy scores (>95%) for their pre-trained CNN 113 were reported, visual results presented in the paper show the difficulty of obtaining a 114 model which is robust to sun glitters similar to the ones illustrated in Figure 4. In [START_REF] Saqib | Real-Time Drone Surveillance and Population Estimation of Marine Animals from Aerial Imagery[END_REF],

115 the authors performed an end-to-end supervised detection of dolphins and stingrays 116 in aerial images. Due to the high density and occlusion of animals in some areas, low 117 average precision scores were obtained for both species: 30% and 35% for the detection 118 of dolphins and of stingrays, respectively. In [START_REF] Hong | Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery[END_REF], both marine and terrestrial birds are 119 targeted. As a novelty, the authors were able to boost the number of birds in their 120 dataset by introducing samples of bird decoys. Using some of the state-of-the-art object 121 detection models, including Faster R-CNN [START_REF] Ren | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[END_REF] or YOLOv4 [START_REF] Bochkovskiy | Yolov4: Optimal speed and accuracy of object detection[END_REF], an average precision 122 (AP) score of over 95% was reported on a set of positive samples, i.e., samples which 123 contain at least one ground-truth bounding box.

124

In a more recent work on seabirds detection [START_REF] Kellenberger | 21 000 birds in 4.5 h: Efficient large-scale seabird detection with machine learning[END_REF], efforts were made to reduce

Unsupervised and Weakly-Supervised Object and Anomaly Detection 138

The sparse distribution of marine animals makes it hard to gather sufficient data 139 to train and test supervised models. Often, less than 5% of the images gathered during 140 a flight survey will contain animals (see Section 5.1). The differences in appearance 141 caused by the variations in animal depth shown in Figure 2 can also make it hard for 142 supervised models to learn class-specific features. To better handle these constraints is that anomalous samples will be poorly reconstructed and thus easy to detect by 169 comparing the reconstruction with the original image. The most used models are autoen-170 coders (AE) [START_REF] Chen | Outlier Detection with Autoencoder Ensembles[END_REF], variational autoencoders (VAE) [START_REF] Baur | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images[END_REF][START_REF] Bergmann | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders[END_REF] or adversarial autoencoders 171 (AAE) [START_REF] Baur | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images[END_REF][START_REF] Akcay | Semi-Supervised Anomaly Detection via Adversarial Training[END_REF]. Although easy to understand, generative models are sometimes able to

201

As first proposed in [START_REF] Cohen | Sub-Image Anomaly Detection with Deep Pyramid Correspondences[END_REF], to model the normal training set, the images are first encoded using a ResNet [START_REF] He | Deep Residual Learning for Image Recognition[END_REF] model pre-trained on the ImageNet [START_REF] Russakovsky | Imagenet large scale visual recognition challenge[END_REF] dataset. To use different semantic levels, activations from the three intermediate layers are concatenated to create a feature map as used in [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF][START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF][START_REF] Cohen | Sub-Image Anomaly Detection with Deep Pyramid Correspondences[END_REF]. Since this feature map is deep, the number of channels is often reduced using either random-dimensions selection [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF] or a semiorthogonal embedding matrix [START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF]. In practice, we found that using a semi-orthogonal embedding yields more consistent results because the random dimension selection requires to test multiple dimensions in order to find a good combination. The method [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF] then models these normal feature maps using a Gaussian distribution for each patch location. During training, only a single forward pass is necessary to encode the training set and to compute the mean vectors and covariance matrices estimating the Gaussian distribution. Both can be computed online using the formulas in Equations ( 1) and ( 2):

µ i,j = 1 N N ∑ k=1 x k,i,j (1)
Σ i,j = 1 N -1 N ∑ k=1 x k,i,j x k,i,j -N × (µ i,j µ i,j ) + I (2)
where x k,i,j is the feature vector at location i, j of the kth training sample and N is the 202 number of training samples. A regularization term I, where I is the identity matrix 203 of corresponding size, is added to the covariance matrices for numerical stability for 204 invariant patches as proposed in [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF].

205

Once a Gaussian distribution has been estimated for each patch location, the anomaly score s(x i,j ) for each patch x i,j of a test image is computed using the Mahalanobis distance:

s(x i,j ) = (x i,j -µ i,j ) Σ -1 i,j (x i,j -µ i,j ) (3)
For the Gaussian distribution, the Mahalanobis distance is proportional to the To build a spatially-invariant anomaly detection pipeline, the anomaly score should not be dependant on the patch coordinates. A simple modification could be to make the model a single Gaussian distribution fit to every patch samples of each image. However, since there are multiple patch modalities, the data may not fit a Gaussian distribution. This can be confirmed by looking at the statistical moments of the patches. Depending on the dimensionality reduction, the skewness and kurtosis of the data are not those of a Gaussian distribution. This is emphasized when using the random dimension downsampling technique proposed in [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF]. To use a Gaussian model, we propose to transform the patch distribution into a Gaussian distribution using a normalizing flow (NF). A normalizing flow consists of an invertible transformation T(•) of an unknown input distribution x = T -1 (z) to a known latent distribution z ∼ p Z . Using the change of variable formula, we can compute the likelihood of any x:

p X (x) = p Z (z) det ∂z ∂x ( 4
)
where det ∂z ∂x is the Jacobian determinant of T(•). Therefore, T is built so that its Jacobian Our model is similar to PaDiM estimated using a single shared Gaussian estimator for all 234 patches but with a learnt arbitrary complex transformation of the prior distribution p X 235 into a Gaussian distribution (see figure 5). We also experiment with using an ensemblistic 236 approach by using multiple normalizing flows in parallel and by taking the maximum 237 log-likelihood of all models for a given patch. This allows each model to specialize in a 238 type of patch. The loss function for the models is described in Equation ( 5):

239 L = 1 N × W × H ∑ n,i,j min k {-log p Z k (T k (x n,i,j ))} (5)
where k ∈ {1, . . . , K}, K is the number of models in parallel, W and H are the dimensions 240 of the patch grid, and x n,i,j corresponds to the embedding vector at location (i, j) of the between two bounding boxes and is commonly used in detection tasks.

261

The entire box proposal pipeline can be seen on Figure 6. In this section, we describe the datasets used to evaluate our proposed methods for 265 marine animal detection. Since the aerial imagery is taken with large optical sensors that 266 produce large images, the images are cut into smaller sub-patches of size 416 × 416 pixels.

267

When images are cropped into patches, it may happen that one ground-truth bounding 268 box is split into two or four patches. In that case, this ground truth is assigned only to

Experimental Setup

311

As in [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF], we use a Wide-ResNet50 [START_REF] Zagoruyko | Wide Residual Networks[END_REF] as our encoding backbone. The features 312 are then downsampled to a depth of c = 100 features using a semi-orthogonal projection 313 matrix as described in [START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF]. We use seven masked autoregressive density estimator [START_REF] Germain | Masked Autoencoder for Distribution Estimation[END_REF] 314 (MADE) layers in our MAF model. They have seven hidden units with 130 connections 315 each. The Adam [START_REF] Kingma | A Method for Stochastic Optimization[END_REF] optimizer is used with a learning rate of 0.001.

316

We compare our results with the PaDiM and OrthoAD methods from [START_REF] Defard | A Patch Distribution Modeling Framework for Anomaly Detection and Localization[END_REF][START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF] using the same parameters. We also train an adversarial convolutional variational encoder (AnoVAEGAN) similar to [START_REF] Baur | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images[END_REF] to reconstruct normal images. The anomalies are detected by comparing the image reconstruction with the original image using the structural similarity [START_REF] Wang | Image quality assessment: From error visibility to structural similarity[END_REF] (SSIM) metric. To measure the performance of the detection methods, we consider that a detection is positive if the IoU between the prediction box and the ground truth box is greater than 0.1. The F1 score, recall and precision can then be measured. They are computed as follows:

Recall = #detected #objects (6) Precision = #detected #proposals (7) F1 score = 2 × Recall × Precision Recall + Precision (8)
Because the F1 score blends information about both the recall and precision, we 317 use it as our main metric. We also evaluate the classification performance between 318 anomalous and normal images of the models by computing the area under the receiver 319 operating characteristic curve (AUROC). The anomaly score for an image is defined as 320 the maximum anomaly score among all its patches. 321

Results

322

The object detection scores for the Semmacape and Kelonia datasets are given in 323 Tables 1 and2, respectively. For all metrics, higher scores indicate better performance.

324

On both datasets, the highest F1 scores among all tested approaches were obtained by 325 one of our proposed methods. The most significant improvements were observed on 326 the Semmacape dataset, for which our method provided an improvement of 6.1% and 327 of 22.6% in terms of F1 and recall scores, respectively, with respect to the state-of-the-

328

art AnoVAEGAN [START_REF] Baur | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images[END_REF]. On this dataset, the classification of patches into anomalous 329 and normal images is also significantly improved by our method, as attested by an 330 augmentation of 12.4% of AUROC in comparison to OrthoAD [START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF]. On the other hand, 331 more modest improvements were observed on the Kelonia dataset: 1.3% and 5.2% in 332 terms of F1 and recall scores, respectively, when compared to OrthoAD [START_REF] Kim | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation[END_REF].

333

The improvement from using a normalizing flow to transform the embedding

Discussion

Our method proposes a first step in performing weakly supervised marine mammal Rocks and waves have a higher anomaly score than water, using the appropriate anomaly threshold is important for the proposed regions to be interesting.

Figure 1 .

 1 Dolphins of the Delphinus delphis (a) and Stenella coeruleoalba (b) species.

Figure 2 .

 2 Figure 2. Ground-truth bounding boxes of dolphins with low confidence levels.

Figure 3 .Figure 4 .

 34 Figure 3. Image captured during an Ifremer flight session. The bright dots inside the green boxes may correspond to marine animals while that the ones inside the red box might be sun glitters.

125

  the manual workload required to obtain annotated training data. The authors trained 126 a CNN to detect different species of seabirds, including terns and gulls, using only 127 200 training samples per class. To make up for the low number of training samples, 128 prior-knowledge about the spatial distribution of birds was introduced during post-129 processing steps, which led to high precision and recall scores of approximately 90% 130 for the most abundant class, but lower scores for the sparse classes. Though some of 131 the methods reviewed above perform well on their dataset, they require some level 132 of supervised labeling or some prior-knowledge about the distribution of the targeted 133 animals. The literature on unsupervised and weakly-supervised methods for marine 134 animal detection is still scarce, which motivated us to focus on weakly-supervised 135 detection of different kinds of marine animals, such as turtles, birds, and dolphins, 136 as described in the following sections.

143

  and to account for different weather conditions, we propose to train an object detector 144 by applying anomaly localization techniques to sea images. By training on sea images 145 without animals, our models require little to no-supervision compared to data-intensive 146 supervised techniques. Classes with a very small number of training samples should 147 offer comparable performance to that of other classes since the training data do not suffer 148 from class imbalance. In our experiments, we focus on detection only. The classification 149 of the detected animals will be left for future work. 150 Anomaly localization, as the name suggests, aims to localize the regions or area 151 of pixels from an image that diverge from the "norm", where the norm is usually 152 determined by image patterns (e.g., colors and textures) found in the training set. As a 153 result, each pixel of an image is assigned an anomaly score. The goal is to detect all 154 anomalous pixels that are different from the normal data present in the training set.

155A

  subset of this task is anomaly detection, where the goal is to classify whether an image 156 contains an anomaly or not. We refer to an image without anomalies as a normal image 157 and an image with anomalies as an anomalous image. Since marine animal detection 158 requires predicting the precise location of an animal within an image, we focus on 159 anomaly localization. 160 In the literature, most anomaly detection methods are proposed either in an indus-161 trial or in a medical context. The performance benchmarks are often made on the MVTec 162 Anomaly Detection [21] (MVTec AD) dataset which contains a variety of textures and 163 objects classes. The training set for each of these classes is composed of only normal 164 images. A variety of methods already exists to localize anomalies in images, and some 165 of them are reviewed below. 166 Reconstruction based methods train generative models to reconstruct the normal 167 images from the training data by minimizing the reconstruction loss. The intuition 168

  172 reconstruct the anomalies even though they are not part of the training set, making the 173 anomalies undetectable by standard dissimilarity measures computed from the original 174 and reconstructed images. An anomaly can also lead to a failed reconstruction larger 175 than the original anomaly making the precise anomaly localization impossible. 176 Deep embedding methods use the embedding vectors created by networks trained 177 on other tasks to model the normal data. They can use a model pre-trained on another 178 supervised dataset or on proxy tasks for a self-supervised training mode. To model the 179 training data and detect embedding vectors that are anomalous, several methods have 180 been proposed. Patch-SVDD [25] uses a proxy classifying task to encode the image and a 181 Deep-SVDD [26] one-class classifier to classify the patch as either anomalous or normal. 182 DifferNet [27] trains normalizing flows (NF) to maximize the likelihood of the training 183 set and localizes anomalies by computing the gradient of the likelihood with regard to 184 the input image. SPADE [28] compares the testing samples to the normal-only training 185 set using a K-nearest neighbors retrieval on vectors created using a model pre-trained 186 on supervised image classification. PaDiM [11] proposes to model each patch location 187 using a Gaussian distribution and then use the Mahalanobis distance to compute the 188 anomaly scores. 189 We experiment with both generative and embeddings based methods to reformulate 190 the animal detection problem as an anomaly localization problem. Leveraging the fact that a majority of the recorded aerial imagery does not contain animals, we target marine 192 animal detection models trained in a weakly-supervised setting.

193 4 .

 4 Proposed Method for Weakly-Supervised Marine Animal Detection 194 Convolutional neural networks (CNN) pre-trained on supervised tasks have proven 195 to be robust image feature extractors [28,29]. Their use in anomaly detection has al-196 ready given interesting results in state-of-the-art benchmarks [11,12,30,31]. Since the 197 benchmark datasets commonly used for anomaly detection from images are different 198 from datasets available for marine mammals detection that can be made of thousands of 199 images and involve a strong texture component, we propose to modify and adapt deep 200 feature embedding methods to tackle the marine animals detection problem.

206

  square root of the negative log-likelihood. If the Gaussian distribution hypothesis is 207 valid, detecting anomalous patches is similar to an out-of-distribution (OOD) samples 208 detection process. With this method, the learnt distributions are depending on the patch 209 location. This gives good performance on the MVTec AD dataset where the normal 210 objects are always located at the same location in the image. This means that the model is 211 not invariant to image transformations such as rotations and translations. However, such 212 geometric transformations are common in aerial imagery, while anomalies should still 213 be located. The Gaussian distribution is also a uni-modal distribution. This method is 214 able to model only one modality of the normal class. This is not a problem in the MVTec 215 AD dataset where all training samples are similar and part of the same modality. How-216 ever, for a general anomaly detection framework where normal images are composed of 217 different normal textures (sea, waves, sun glitters...), this leads to only the majority class 218 being learnt and the minority normal classes being flagged as anomalous. To address 219 these limitations, we propose a spatially-invariant anomaly localization pipeline using 220 normalizing flows to handle multi-modal normal data.

  222determinant is known and fast to compute. Usually, p Z is taken to be a centered multi-223 variate Gaussian distribution z ∼ N (0, 1). To perform the transformation T, we use 224 the Masked Autoregressive Flow[START_REF] Papamakarios | Masked Autoregressive Flow for Density Estimation[END_REF] (MAF) model which uses a series of masked 225 autoregressive dense layers, as described in[START_REF] Germain | Masked Autoencoder for Distribution Estimation[END_REF]. The masked layers and auto-regressive 226 property allow for a fast probability estimation in a single forward pass. Sampling, 227 however, requires computing a series of probabilities p(x i |x 1:i-1 ) because each x i is a 228 regression of the previous i -1 variables. In our case, we only leverage the density 229 estimation and do not make use of the sampling from the learnt distribution p X . 230 The transformation parameters can be trained by maximizing the log-likelihood of 231 the normal only training dataset. Anomaly scores for new samples can be evaluated by 232 computing the negative log-likelihood after transformation of the sample through T(•).

  233

241 nth sample inFigure 5 . 5 .

 55 Figure 5. Architecture of the multi-headed model. After extracting and downscaling the features, each model computes the negative log-likelihood of each patch and the final score for a patch is the maximum of all K = 2 predictions.Although anomaly localization models produce anomaly maps, our datasets for

(

  (a) Input image (b) Anomaly map (min-max norm) (c) Binary threshold (x ≥ 0.8) the patch which contains the center of its bounding box. In both datasets, annotations 270 were validated by specialists in marine mega fauna. 271 The Semmacape dataset comprises a set of 165 annotated aerial images acquired 272 as part the SEMMACAPE (https://semmacape.irisa.fr/ (accessed on 6 January 2022)) 273 project, which partially funded the present research and whose main objective is to 274 automatize the survey of marine animals in offshore wind-farms. The images of this 275 dataset were collected in the Gironde estuary and Pertuis sea Marine Nature Park, 276 France, during the spring of 2020. In total, it contains 165 images of 14,204 × 10,652 277 pixels with 528 ground-truth annotations belonging to one of the following classes: 278 • Dolphin (see some examples in Figures 1 and 2). A total of 258 annotations subdi-279 vided into four classes: striped dolphin ( Stenella coeruleoalba), common dolphin ( 280 Delphinus delphis), common bottlenose dolphin ( Tursiops truncatus), and a separated 281 class for dolphins whose species could not be determined; 282 • Bird (see some examples in Figure 4). A total of 270 annotations subdivided into 283 flying and landed birds belonging to four species: gannet, seagull, little shearwater 284 Puffinus assimilis), and sterna. 285 Since our focus is on marine animal detection, other classes (seaweed, jellyfish, 286 floating waste, ...) were not included from the testing dataset. The dataset contains a 287 variety of settings from homogeneous sea images to images covered with sun glitters and 288 waves, making it challenging to learn the normal distribution of the data. After filtering 289 and creating the sub-patches, the dataset is composed of 345 patches containing at least 290 one object (anomalous) and 138,544 patches without objects (normal). The percentage of 291 anomalous images is then about 0.25%. 292 The Kelonia dataset, provided by the Centre d'Etude et de Découverte des Tortues 293 Marines (CEDTM) and by the Kélonia aquarium (https://museesreunion.fr/kelonia/ 294 (accessed on 6 January 2022)), is composed of aerial images of marine turtles acquired in 295 Réunion island between 2015 and 2018. This dataset contains 1983 images with ground 296 truth bounding boxes belonging to one of these three classes: turtle, unturtle (unsure 297 annotations of turtles), and ray. In our experiments, we will consider only the turtle and 298 unturtles classes, which comprise the majority of the annotations. Unlike the Semmacape 299 dataset, the images have a larger variety of background and color settings because the 300 sea is shallower, showing the seabed. Furthermore, the training set contains images that 301 may not be representative of the normal class and are not found in the testing set. This 302 makes training on this dataset harder because the learnt distribution may not be optimal 303 for anomaly detection on the testing set. Example samples from the dataset can be seen 304 on Figure 7.

Figure 7 .

 7 Figure 7. Normal (top) and anomalous (bottom) images from the Kelonia dataset. The choice of normal images is critical for training an efficient anomaly localization

  310

Figure 8 .

 8 Figure 8. F1 score on the Semmacape dataset with different IoU thresholds. The mean performance and 95% confidence interval is reported over 3 runs.As seen on Figure9, the methods are not very sensitive to a variation in the number

Figure 9 .

 9 Figure 9. F1 score on the Semmacape dataset with different numbers of training samples. The mean performance and 95% confidence interval is reported over 3 runs.

371detection.

  As such, it requires less human intervention on the training data generation 372 process compared to supervised methods and is also able to propose individual bounding 373 boxes for each detected mammal. However, in this study we do not consider the problem 374 of classifying species. As a consequence, other anomalous floating objects such as boats 375or marine debris could be detected. In practice, the performance from our method 376 should be assessed and the predictions should be confirmed by comparing the output to 377 other population estimation methods, such as field sampling campaigns.378The proposed bounding boxes could also serve as a starting point in computer 379 assisted ecology by guiding human annotators to larger area of interest first or as an 380 initialization method for other computer assisted annotation methods which require 381 pre-trained models or active learning[START_REF] Kellenberger | Accelerating image-based ecological surveys with interactive machine learning[END_REF].

383

  By transposing the problem of anomaly localization from an industrial setting 384 to marine animals localization, we are able to provide class-agnostic bounding box 385 proposals on aerial imagery. The produced detection can either be used to speed up the 386 object discovery for new flight surveys or for direct bounding box proposal and animal 387 population density estimation. By leveraging pre-trained convolutional neural network 388features without full annotations, the proposed approach is able to detect marine animals.

Figure 10 .

 10 Figure10. Example predictions (left) and their corresponding anomaly maps (right) from the Semmacape (2 first rows) and Kelonia (2 last rows) dataset. Rocks and waves have a higher anomaly score than water, using the appropriate anomaly threshold is important for the proposed regions to be interesting.

Table 1 .

 1 Results on the Semmacape dataset. All the models have been trained using the same set of 1000 training images.

	334
	335	vector is greater on the Semmacape dataset than on the Kelonia dataset. This is due
	336	to the fact that the training dataset for Semmacape contains normal patches that are
	337	different from anomalous patches of the testing set. For the Kelonia dataset however,
	338	there are rocks and patches that are more similar to the turtles in the training set. Since
	339	the negative log-likelihood is minimized during training, some "turtle" patches will
	340	then be assigned a high likelihood because they are similar to rocks in the training set.
	341	This can lead to some failed detection because the area around the object will have a
	342	normal score. For the same reason, the generative method AnoVAEGAN performs worse
	343	on the Kelonia dataset because of training images containing rocks that are similar to
	344	turtles. The model is then able to reconstruct the turtles accurately and fails to detect
	345	them during testing. The classification performance on the Kelonia dataset can also be
	346	explained by the fact that some normal images are very dissimilar from the rest, which
		leads the model to classify them as anomalous.
		Method	F1 Score	Recall	Precision	AUROC
		PaDiM [11]	0.383	0.434	0.343	0.606
		OrthoAD [12]	0.458	0.373	0.594	0.795
		AnoVAEGAN [13]	0.469	0.531	0.420	0.697
		Ours, 1× MAF [33]	0.530	0.757	0.408	0.919
		Ours, 2× MAF [33]	0.486	0.523	0.455	0.869

347

Table 2 .

 2 Results on the Kelonia dataset. All the models have been trained using the same set of 1000 training images.

		Method	F1 Score	Recall	Precision	AUROC
		PaDiM [11]	0.504	0.443	0.586	0.431
		OrthoAD [12]	0.571	0.514	0.643	0.431
		AnoVAEGAN [13]	0.051	0.033	0.107	0.469
		Ours, 1× MAF [33]	0.568	0.559	0.578	0.410
		Ours, 2× MAF [33]	0.584	0.566	0.604	0.391
	348	When varying the IoU threshold for positive predictions (Figure 8), the F1 score
	349	decreases as the threshold increases. With an IoU threshold of 0.3, the F1 score is down
	350	to less than 0.45 for the best performing method on the Semmacape dataset. A higher
		IoU threshold means that the proposed bounding boxes must be more similar to the
	360

351

ground truth boxes in order to be counted as a positive prediction. For the PaDiM 352 method, the rate at which the performance decreases as the IoU threshold increases is 353 greater than our method. This is because large objects can sometimes be counted as 354 positive even with failed predictions when the anomaly threshold is low and a large 355 portion of the image is proposed as a region of interest. This region will then have an 356 IoU greater than the IoU threshold with the ground-truth bounding box. We can see 357 that the PaDiM method has a larger confidence interval on the F1 score because of the 358 random dimension selection which plays a role in the performance of the model and is 359 effectively sampled differently at each run.

61

//github.com/Pangoraw/

Data Availability Statement:

The Semmacape and Kélonia datasets used in the present research

Although not yet on par with supervised methods, this is a first step on enabling weakly


E Guilmineau

G B Deng

A Leroyer

P Queutey

M Visonneau

J Wackers

Influence of the Turbulence Closures for the Wake Prediction of a Marine Propeller

Keywords: Propeller, Open water, Numerical simulations, Turbulence models, DES approach, RANS models

Results of computational fluid dynamics validation for flow around a marine propeller are presented. Computations were performed for various advance coefficients following experimental conditions. The objectives of this study are to establish capabilities of various turbulent closures to predict the wake of a propeller, and to predict the instability processes in the wake. Two RANS models are used: the k-ω SST of Menter and an anisotropic two-equation Explicit Algebraic Reynolds Stress Model (EARSM). A DES approach based on the k-ω model is also used. Computational results for both global and local flow quantities are discussed and compared with experimental data. The predicted thrust and torque are in good agreement with the measured values for all turbulent closures. With the RANS turbulence models, the wake of the propeller is too dissipated and then the instabilities of the wake are not predicted. On the contrary, DES approach can allow to capture the evolution of the tip vortices and predicts the onset of instabilities in the wake. The main difference between these various turbulence closures is that the flow in the core of the vortex is characterized by rotation, streamline curvature effects which are not adequately modelled by RANS turbulence models.

INTRODUCTION

The prediction of the fluid dynamics interaction between propellers and the hull is very important for the improvement of ship performance since the interaction is directly related to vibrations, noise and propulsion performances. In this context, the demand for the improvement of performance implies a rising interest in the development and application of detailed numerical tools

The physical mechanisms that characterize the interaction between the propeller and the hull are very complex. However, even in the simpler case of an isolated propeller in a uniform flow, called open water conditions, we are confronted with several numerical and physical challenges. In the propeller field, a number of viscous phenomena can be identified including blade and hub boundary layers, flow separation on the blade, viscous-inviscid interaction, hub and tip vortices, viscous wake. Therefore, study of these flow characteristics is essential for accurate prediction of the propulsion performance. In this paper, we only focus on the open water conditions. In this case, the flow is characterized by two vortex systems: one generated by the tip of the blade and the second emanated from the hub. A comprehensive description of the state of the art can be found in [START_REF] Felli | Mechanisms of Evolution of the Propeller Wake in the Transition and Far Fields[END_REF], who experimentally investigated the flow around a propeller in water tunnel. These authors studied the mechanisms that trigger the instability of wake and investigated the dependence of the vortex pairing and grouping on the mutual vortex distance.

Based on a numerical point of view, the reliability of the such numerical predictions can be questioned. It is difficult to control numerical diffusion when intense and localized three-dimensional structures are concerned. The flow in the core of the vortex is characterized by rotation, streamline curvature effects which are not adequately modeled by classical eddy-viscosity based turbulence models. Unsteady hybrid LES turbulence closures like DES appear attractive, see [START_REF] Muscari | Modeling of Vortex Dynamics in the Wake of a Marine Propeller[END_REF].

The purpose of this paper is to conduct such a validation of the flow around an isolated propeller to compare statistical turbulence closures and an hybrid LES methodology to draw conclusions about the requirements in term of physics. The present paper is organized as follows. The test-case is presented followed by the numerical method. The CFD results are presented for comparison and validation, as well as flow field analysis. Lastly, some concluding remarks are made.

characterized by a nominally constant pitch distribution and a very low skew angle. The propeller is presented in Figures 1 and2 and the main geometrical features are reported in Table 1. The solver is based on the finite volume method to build the spatial discretization of the transport equations. The unstructured discretization is face-based. While all unknown state variables are cell-centered, the system of equations used in the implicit time stepping procedure are constructed face by face and the contribution of each face is then added to the two cells next to the face. This technique poses no specific requirements on the topology of the cells. Therefore, the grids can be completely unstructured: cells with an arbitrary number of arbitrarily-shaped faces are accepted. Pressure-velocity coupling is enforced through a Rhie & Chow SIMPLE type method: at each time step, the velocity updates come from the momentum equations and the pressure is given by the mass conservation law, transformed into a pressure equation. In the case of turbulent flows, transport equations for the variables in the turbulence model are added to the discretization Free-surface flow is simulated with a multi-phase flow approach: the water surface is captured with a conservation equation for the volume fraction of water, discretized with specific compressive discretization schemes, see [START_REF] Queutey | An Interface Capturing Method for Free-Surface Hydrodynamic Flows[END_REF]. The technique included for the 6 degrees of freedom simulation of ship motion is described by [START_REF] Leroyer | Numerical Methods for RANSE Simulations of a Self-Propelled Fish-like Body[END_REF]. Time integration of Newton's law for the ship motion is combined with analytical weighted analogy grid deformation to adapt the fluid mesh to the moving ship. To enable relative motions of appendages, propellers or bodies without having recourse to overlapping grids, a sliding grid approach has been implemented. Propellers can be modeled by actuator disc theory, by coupling with boundary element codes (RANS BEM coupling, see [START_REF] Deng | Ship Propulsion Prediction with a Coupled RANS-BEM Approach[END_REF]) or with direct discretization through e.g. the rotating frame method or sliding interface approaches

Finally, an anisotropic automatic grid refinement procedure has been developed which is controlled by various flowrelated criteria, see [START_REF] Wackers | Combined Refinement Criteria for Anisotropic Grid Refinement in Free-Surface Flow Simulations[END_REF]. Parallelization is based on domain decomposition. The grid is divided into different partitions, which contain the cells. The interface faces on the boundaries between the partitions are shared between the partitions; information on these faces is exchanged with MPI (Message Passing Interface) protocol.

The method works with the sliding grid approach and the different sub-domains can be distributed arbitrarily over the processors without any loss of generality. Moreover, the automatic grid refinement procedure is fully parallelized with a dynamic load balancing working transparently with or without sliding grids

NUMERICAL SIMULATION SET-UP

The computational domain consists of a cylindrical domain, whose diameter is 3 times the propeller diameter, and length is 9.18 times the propeller diameter. It starts 3.96R before the propeller plane and it extends until 15.4R after the propeller plane. The computational domain with the boundary conditions is presented in Figure 3. The computational mesh is created with HEXPRESS, an automatic unstructured mesh generator. This software generates meshes containing only hexahedrons. The mesh consists of 21.4×10 6 cells. The number of faces for each blade is approximately 38,100. The average wall normal resolution on the blades is y + = 0.6 with a maximum around the tip, in the order of 1.8. A box including the propeller and extending up to 6 diameters in the wake is added to capture the vortices. In this box, the cells are isotropic and the size is 0.0105R, see Figures 4 and 5 The RANS equations are written in the moving reference frame but written in terms of absolute reference quantities.

For the RANS turbulence models (k-ω SST and EARSM), the solution is a steady solution while for DES computations, this approach is unsteady and the time step is 3×10 -5 seconds. This time step corresponds to a rotation of 0.27 degrees. The time-averaged flow is obtained in approximately 9 flow-times for DES computations.

The numerical simulation is distributed on 96 processors on a SGI Altix ICE 8200. For the RANS simulations, the CPU time per processor is 23 hours while for the DES approach, it is 300 hours

Flow field in the wake

However, it is crucial to evaluate the ability of the turbulence model to reproduce some of the findings of [START_REF] Felli | Mechanisms of Evolution of the Propeller Wake in the Transition and Far Fields[END_REF]. The analysis of the flow field is carried out for three values of the advance coefficient, J = 0.71, 0.45 and 0.20. All results obtained with a RANS turbulence model are steady results while for the DES results, if not explicitly mentioned, the results are issued from an averaged flow.

J = 0.71

A general view of the wake of the propeller, for the advance coefficient J = 0.71, is given in Figure 7 which presents an isosurface of the dimensionless value λ2 = -2 of the second largest invariant of S 2 +Ω 2 (S and Ω being the symmetric and antisymmetric component of ∇u) colored by the helicity. The acceleration of the flow behind the propeller causes a slight reduction of the radial position of the vortex cores. Then, the helices formed by the tip vortices remain located on a circular cylinder. RANS model yield tip vortices but they vanish more or less rapidly in the wake depending on the turbulence model used and the level of anisotropy associated with the turbulence model. With the DES approach, the tip vortices are maintained much further in the wake. These remarks are also observed by [START_REF] Muscari | Modeling of Vortex Dynamics in the Wake of a Marine Propeller[END_REF].

In order to have a qualitative idea of the level of mesh resolution, a view of the DES function is presented in Figure 8. The blue area corresponds to F DES ≤ 1, which represents the RANS area, while F DES > 1 for the LES area, the red region in the figure. the resolution of propeller wake is suited to a LES approach Figure 9 presents a comparison of the turbulence kinetic energy (TKE) and the y-component of the vorticity for the three turbulence models used. the RANS simulations, Figure 9(a) and 9(b), predict a small value of the TKE near the tip of the blade which grows rapidly in the vortex core. The values around the vortex core increase along the filament. On the contrary, the DES approach produces a high TKE near the tip but the level vanishes quickly and the level is lower than those observed with a RANS approach.

Then, the level of vorticity, which is similar for all numerical simulations close to the propeller, decreases very quickly for the RANS simulations whereas for the DES the decay is more progressive The detailed frequency analysis performed in the experimental work illustrates the process of energy transfer from the blade harmonic to the shaft at nearly at x =7R, owing to vortex grouping. In the numerical simulation with the DES approach, the vortex grouping, marked by red ellipse, appears nearly 7.2R.

J = 0.45

The figure 11 presents an instantaneous view of the vortical structure obtained with the DES approach and the visualization in [START_REF] Felli | Mechanisms of Evolution of the Propeller Wake in the Transition and Far Fields[END_REF]. These figures are very similar and present the pairing of the vortices due to the instability of the tip vortices and the beginning of the instability of the hub vortex. The tip vortices deform from the helical path and trend to interact mutually and form a group. Then, the hub vortex undergoes a sudden deformation from a straight to a spiraling geometry. The difference between the numerical result and the experimental visualization is the spiral-to-spiral distance. In the numerical simulation, this distance is shorter than that observed in experiments. However, the numerical spiral-tospiral distance is in agreement with the numerical simulations of Musca ri et al. ( 2013). To compare all turbulence models, the averaged flow for the DES approach is presented with the RANS results in Figure 12. For the RANS results, the characteristics of the flow are the faster deformation of the wake and the stronger tip vortices. Even if the tip vortices are stronger than those predicted with the previous advance coefficient, they are resolved over a shorter distance. This trend is also confirmed by the numerical results of [START_REF] Muscari | Modeling of Vortex Dynamics in the Wake of a Marine Propeller[END_REF].

The DES model permits to predict more extended vortices and shows both the onset of the vortex instability and the start of the pairing process.

J = 0.20

The last value of the advance coefficient is J = 0.20 which corresponds to a condition of very high blade loading. The wake evolution of the tip vortices decreases quickly, even for the DES approach, see Figure 14. approach, more structures are predicted. Even if a RANS simulation yields a good prediction of forces and moments, this approach fails to give information about the wake evolution which may be important, like for the prediction of noise or if a body like another propeller or a rudder is in its wake.

CONCLUSIONS AND FUTURE WORK

The capabilities of numerical simulations with various turbulence closures (RANS and DES), using flow solver, to predict the complex flow past an isolated propeller have been presented in this paper. A marine propeller called INSEAN E779A was selected for the present study. Computations were performed for various advance coefficients following experimental conditions.

Computational results are discussed and compared with experimental data. The predicted global quantities, such as thrust and torque, obtained with various turbulence closures, are in good agreement with the measured data. For the prediction of the wake, a comparison between the RANS models and the DES approach shows that the RANS approach dissipates the tip vortices very quickly due to the high level of the turbulence kinetic energy in the core of the vortices. On the contrary, the DES approach allows to capture the evolution of the tip vortices. The initial stages of the instability pattern, with two consecutive filaments grouping their relative position, can also be reproduced and agree reasonably well with the flow visualization. The hybrid LES model performs better than the eddy-viscosity based turbulence closures since it is able to predict the relaminarization of the core of the tip vortex in the wake of the propeller.

The overall results suggest that the RANS approach is sufficient to predict the thrust and the torque. But if the prediction of the propeller wake is necessary, a DES approach should be used. Nevertheless, the cost of this approach is still prohibitive to be used as a matter of routine architecture design offices. However, for the propeller design to predict the cavitation risk, the DES approach has still to demonstrate its ability in predicting a better tracking of the ventilated tip vortex father downstream.

Figure 1 :

 1 Figure 1: Front view of the propeller model.

Figure 2 :

 2 Figure 2: Side view of the propeller model.

Figure 3 :

 3 Figure 3: Computational domain with the boundary conditions.

Figure 4 :Figure 5 :

 45 Figure 4: View of the mesh in the symmetry plane

Figure 6 :

 6 Figure 6: Open-water characteristics of the E779A propeller.

  Figure 7: J = 0.71 -Vortical structures visualisations (λ2 = -2).

Figure 8 :Figure 9 :

 89 Figure 8: J = 0.71 -Visualisation of the DES function (red: LES region, blue: RANS region).

Figure 10 :

 10 Figure 10: J = 0.71 -Instantaneous visualization of the vortical structures (λ2 = -2).

  (a) Experimental view (Fig. 8 in Felli at al. (2011)) (b) Numerical results

Figure 11 :

 11 Figure 11: J = 0.45 -Instantaneous visualization of the vortical structures.

Figure 12 :

 12 Figure 12: J = 0.45 -Vortical structures visualisations (λ 2 = -2).

  Figure 14: J = 0.20 -Vortical structures visualisations (λ 2 = -2).

Figure 15

 15 Figure 15 represents a view of the instantaneous isosurface of the invariant λ2 obtained with a DES approach. We can compare this figure with Figure 14(a) or 14(b) which represent a view with the RANS model. With the DES

Figure 14 :

 14 Figure 14: J = 0.20 -Instantaneous vortical structures visualization (λ2 = -2).

Table 1 :

 1 Propeller parameters of INSEAN E779A model.

	Diameter	D = 0.227 m
	Number of blades	Z = 4
	Pitch ratio	P/D = 1.1
	Rake	4°35' (forward)
	Expanded area ratio	0.689
	Hub ratio	0.200
	In this paper, the rotational speed of the propeller is kept
	fixed to a value of n = 25 rps and the different advance
	coefficients J = U∞/(nD) are obtained by changing the
	inflow velocity U∞. The Reynolds number Re = 1.78×10 6 is
	based on the radius of the propeller (L ref = R = 0.1135 m)
	and the velocity of the tips of the blades (U ref = nπD ≈
	17.829 m/s)

3 ISIS-CFD AT GLANCE

	The solver ISIS-CFD, available as a part of the
	FINE TM /Marine computing suite, is an incompressible
	unsteady Reynolds-averaged Navier-Stokes (URANS)
	method mainly devoted to marine hydrodynamics. The
	method features several sophisticated turbulence models:
	apart from the classical two-equation k-ε and k-ω models,
	the anisotropic two-equation Explicit Algebraic Reynolds
	Stress Model (EARSM), as well as Reynolds Stress
	Transport Models (RSM), are available, see Deng &
	Visonneau (1999) and Duvigneau et al. (2003), with or
	without rotation corrections. All models are available with
	wall-function or low-Reynolds near wall formulation.
	Hybrid LES turbulence models based on Detached Eddy
	Simulation (DES) are also implemented and have been
	validated on automotive flows characterized by large
	separations, see Guilmineau et al. (2011). Additionally,
	several cavitation models are available in the solver.

TEST-CASEThe propeller geometry is the INSEAN E779A model, i.e. a four bladed, fixed-pitch, right-handed propeller

ACKNOWLEDGMENTS

This work was granted access to the HPC resources of CINES/IDRIS under the allocation 2014-2a0129 made by GENCI.

DISCUSSION

Question from Douwe Rijjpkema

Did you already investigate the pressure inside the core of the vortex? This is of good interest when calculating cavitation.

Authors' Closure

Thank you for your question and your suggestion. Since we also consider the cavitation risk, the evolution of pressure was investigated. The evolution of pressure for J = 0.71 in the plane Y = 0 mm for is presented in Figure I. The most significant modeling effect is a high dissipation of the intensity of vortices, and of minimum pressure levels, with RANS models. With DES, the lowest pressure level is maintained further downstream. This is what we expect from the physics. However, if this trend is encouraging, it remains to validate a DES simulation including the cavitation modelling.

Question from Antoine Ducoin

You use two turbulence RANS models that predict different level of turbulent kinetic energy in the wake/tip vortex. Is it an important parameter to avoid the diffusion of the vortex in the wake? If no, how do you analyze the differences on the λ 2 plots?

Authors' Closure

Thank you for your question. For both RANS models, there is no parameter to avoid the diffusion of the vortex in the wake. Both RANS models predict an increase of the turbulence kinetic energy in the wake that destroys the tip vortex. With the DES approach, the prediction of the turbulent kinetic energy in the wake is lower than that predicted with the RANS models.

Question from Tobias Huuva

How did you model the rotation of the propeller in the computation? How will you do when including the rudder? How much longer time step for DES? For RANS?

Authors' Closure

Thank for you question. The equations are written in the moving reference frame but written in terms of absolute reference quantities. When a rudder will be present, two solutions are available. The first is the use of the sliding grid. It depends on the position of the rudder with regard to the propeller and the hull. The second option is the use of overset. The number of time steps for DES is approximately 16,200 and the CPU time per processor is 300 hours. For RANS, the number of time step is approximately 4,000 time steps and the CPU time per processor is 23 hours. All simulations are distributed on 96 processors.