Deep Unsupervised Similarity Learning using Partially Ordered Sets

Unsupervised learning of visual similarities is of paramount importance to computer vision, particularly due to lacking training data for fine-grained similarities. Deep learning of similarities is often based on relationships between pairs or triplets of samples. Many of these relations are unreliable and mutually contradicting, implying inconsistencies when trained without supervision information that relates different tuples or triplets to each other. To overcome this problem, we use local estimates of reliable (dis-)similarities to initially group samples into compact surrogate classes and use local partial orders of samples to classes to link classes to each other. Similarity learning is then formulated as a partial ordering task with soft correspondences of all samples to classes. Adopting a strategy of self-supervision, a CNN is trained to optimally represent samples in a mutually consistent manner while updating the classes. The similarity learning and grouping procedure are integrated in a single model and optimized jointly. The proposed unsupervised approach shows competitive performance on detailed pose estimation and object classification.Comment: Accepted for publication at IEEE Computer Vision and Pattern Recognition 201

Boosting Latent Diffusion with Flow Matching

Recently, there has been tremendous progress in visual synthesis and the underlying generative models. Here, diffusion models (DMs) stand out particularly, but lately, flow matching (FM) has also garnered considerable interest. While DMs excel in providing diverse images, they suffer from long training and slow generation. With latent diffusion, these issues are only partially alleviated. Conversely, FM offers faster training and inference but exhibits less diversity in synthesis. We demonstrate that introducing FM between the Diffusion model and the convolutional decoder offers high-resolution image synthesis with reduced computational cost and model size. Diffusion can then efficiently provide the necessary generation diversity. FM compensates for the lower resolution, mapping the small latent space to a high-dimensional one. Subsequently, the convolutional decoder of the LDM maps these latents to high-resolution images. By combining the diversity of DMs, the efficiency of FMs, and the effectiveness of convolutional decoders, we achieve state-of-the-art high-resolution image synthesis at $1024^2$ with minimal computational cost. Importantly, our approach is orthogonal to recent approximation and speed-up strategies for the underlying DMs, making it easily integrable into various DM frameworks

A Graph Theoretic Approach for Object Shape Representation in Compositional Hierarchies Using a Hybrid Generative-Descriptive Model

A graph theoretic approach is proposed for object shape representation in a hierarchical compositional architecture called Compositional Hierarchy of Parts (CHOP). In the proposed approach, vocabulary learning is performed using a hybrid generative-descriptive model. First, statistical relationships between parts are learned using a Minimum Conditional Entropy Clustering algorithm. Then, selection of descriptive parts is defined as a frequent subgraph discovery problem, and solved using a Minimum Description Length (MDL) principle. Finally, part compositions are constructed by compressing the internal data representation with discovered substructures. Shape representation and computational complexity properties of the proposed approach and algorithms are examined using six benchmark two-dimensional shape image datasets. Experiments show that CHOP can employ part shareability and indexing mechanisms for fast inference of part compositions using learned shape vocabularies. Additionally, CHOP provides better shape retrieval performance than the state-of-the-art shape retrieval methods.Comment: Paper : 17 pages. 13th European Conference on Computer Vision (ECCV 2014), Zurich, Switzerland, September 6-12, 2014, Proceedings, Part III, pp 566-581. Supplementary material can be downloaded from http://link.springer.com/content/esm/chp:10.1007/978-3-319-10578-9_37/file/MediaObjects/978-3-319-10578-9_37_MOESM1_ESM.pd

Nature-based solutions can help reduce the impact of natural hazards: A global analysis of NBS case studies

The knowledge derived from successful case studies can act as a driver for the implementation and upscaling of nature-based solutions (NBS). This work reviewed 547 case studies to gain an overview of NBS practices and their role in reducing the adverse impact of natural hazards and climate change. The majority (60 %) of case studies are situated in Europe compared with the rest of the world where they are poorly represented. Of 547 case studies, 33 % were green solutions followed by hybrid (31 %), mixed (27 %), and blue (10 %) approaches. Approximately half (48 %) of these NBS interventions were implemented in urban (24 %), and river and lake (24 %) ecosystems. Regarding the scale of intervention, 92 % of the case studies were operationalised at local (50 %) and watershed (46 %) scales while very few (4 %) were implemented at the landscape scale. The results also showed that 63 % of NBS have been used to deal with natural hazards, climate change, and loss of biodiversity, while the remaining 37 % address socio-economic challenges (e.g., economic development, social justice, inequality, and cohesion). Around 88 % of NBS implementations were supported by policies at the national level and the rest 12 % at local and regional levels. Most of the analysed cases contributed to Sustainable Development Goals 15, 13, and 6, and biodiversity strategic goals B and D. Case studies also highlighted the co-benefits of NBS: 64 % of them were environmental co-benefits (e.g., improving biodiversity, air and water qualities, and carbon storage) while 36 % were social (27 %) and economic (9 %) co-benefits. This synthesis of case studies helps to bridge the knowledge gap between scientists, policymakers, and practitioners, which can allow adopting and upscaling of NBS for disaster risk reduction and climate change adaptation and enhance their preference in decision-making processes

Neuropathic pain caused by miswiring and abnormal end organ targeting

Nerve injury leads to chronic pain and exaggerated sensitivity to gentle touch (allodynia) as well as a loss of sensation in the areas in which injured and non-injured nerves come together1-3. The mechanisms that disambiguate these mixed and paradoxical symptoms are unknown. Here we longitudinally and non-invasively imaged genetically labelled populations of fibres that sense noxious stimuli (nociceptors) and gentle touch (low-threshold afferents) peripherally in the skin for longer than 10 months after nerve injury, while simultaneously tracking pain-related behaviour in the same mice. Fully denervated areas of skin initially lost sensation, gradually recovered normal sensitivity and developed marked allodynia and aversion to gentle touch several months after injury. This reinnervation-induced neuropathic pain involved nociceptors that sprouted into denervated territories precisely reproducing the initial pattern of innervation, were guided by blood vessels and showed irregular terminal connectivity in the skin and lowered activation thresholds mimicking low-threshold afferents. By contrast, low-threshold afferents-which normally mediate touch sensation as well as allodynia in intact nerve territories after injury4-7-did not reinnervate, leading to an aberrant innervation of tactile end organs such as Meissner corpuscles with nociceptors alone. Genetic ablation of nociceptors fully abrogated reinnervation allodynia. Our results thus reveal the emergence of a form of chronic neuropathic pain that is driven by structural plasticity, abnormal terminal connectivity and malfunction of nociceptors during reinnervation, and provide a mechanistic framework for the paradoxical sensory manifestations that are observed clinically and can impose a heavy burden on patients.The research leading to these results has received funding from the following sources: an ERC Advanced Investigator grant to R.K. (Pain Plasticity 294293); grants from the Deutsche Forschungsgemeinschaft to R.K. (SFB1158, projects B01, B06), to T.K. (SFB1158, project B08), to S.G.L. (SFB1158, project A01) and to V.G. (SFB1158, project A03); a grant to B.O. (project number 371923335); and grant CIDEGENT/2020/052 from Generalitat Valenciana to F.J.T. R.K. is a member of the Molecular Medicine Partnership Unit of the European Molecular Biology Laboratory and Medical Faculty Heidelberg. V.G. and T.A.N. were partially supported by a post-doctoral fellowship and physician scientist fellowship, respectively, from the Medical Faculty Heidelberg. D.M. was partially supported by a post-doctoral fellowship from Excellence Cluster CellNetworks. We acknowledge support from the Interdisciplinary Neurobehavioral Core (INBC) for the behavioural experiments, the data storage service SDS@hd and bwMLS&WISO HPC supported by the state of Baden-Württemberg and the German Research Foundation (DFG) through grants INST 35/1314-1 FUGG and INST 35/1134-1 FUGG, respectively.Peer reviewe

'Thinking like a fish': adaptive strategies for coping with vulnerability and variability emerging from a relational engagement with kob

Based on ethnographic fieldwork amongst a group of commercial handline fishers in the town of Stilbaai in South Africa's southern Cape region, this paper presents a range of flexible, adaptive and evolving strategies through which fishers negotiate constantly shifting variability in weather patterns, fish stocks, fisheries policies, and economic conditions. These variabilities constitute a diverse set of vulnerabilities to which fishers must respond in order to sustain their livelihoods. In this context, the act of 'thinking like a fish' on the part of the fishers provides them with an effective means of adapting to variability and uncertainty. Findings of ethnographic research in 2010-11 suggest that a number of the fishers who participated in the research actively work towards achieving a balance between profit and sustainability. 'Thinking like a fish' is an embodied, interactive way of knowing that emerges from interactions between fishers and fish, offering an ethical and ecological outlook which is a valuable resource for fisheries and conservation management in the region. We suggest that the deeply embodied interactional component of 'thinking like a fish' results from a desire to understand the life world of fish and to think from their perspective in order to more effectively target them while sustaining the species and ecosystem

Localized practices and globalized futures: challenges for Alaska coastal community youth

An article from Maritime Studies (2015) 14:

Shallow waters: social science research in South Africa's marine environment

This paper provides an overview of social science research in the marine environment of South Africa for the period 1994–2012. A bibliography based on a review of relevant literature and social science projects funded under the SEAChange programme of the South African Network for Coastal and Oceanic Research (SANCOR) was used to identify nine main themes that capture the knowledge generated in the marine social science field. Within these themes, a wide diversity of topics has been explored, covering a wide geographic area. The review suggests that there has been a steady increase in social science research activities and outputs over the past 18 years, with a marked increase in postgraduate dissertations in this field. The SEAChange programme has contributed to enhancing understanding of certain issues and social interactions in the marine environment but this work is limited. Furthermore, there has been limited dissemination of these research results amongst the broader marine science community and incorporation of this information into policy and management decisions has also been limited. However, marine scientists are increasingly recognising the importance of taking a more holistic and integrated approach to management, and are encouraging further social science research, as well as interdisciplinary research across the natural and social sciences. Possible reasons for the lack of communication and coordination amongst natural and social scientists, as well as the limited uptake of research results in policy and management decisions, are discussed and recommendations are proposed.Web of Scienc