112 research outputs found
Successfully Applying Lottery Ticket Hypothesis to Diffusion Model
Despite the success of diffusion models, the training and inference of
diffusion models are notoriously expensive due to the long chain of the reverse
process. In parallel, the Lottery Ticket Hypothesis (LTH) claims that there
exists winning tickets (i.e., aproperly pruned sub-network together with
original weight initialization) that can achieve performance competitive to the
original dense neural network when trained in isolation. In this work, we for
the first time apply LTH to diffusion models. We empirically find subnetworks
at sparsity 90%-99% without compromising performance for denoising diffusion
probabilistic models on benchmarks (CIFAR-10, CIFAR-100, MNIST). Moreover,
existing LTH works identify the subnetworks with a unified sparsity along
different layers. We observe that the similarity between two winning tickets of
a model varies from block to block. Specifically, the upstream layers from two
winning tickets for a model tend to be more similar than the downstream layers.
Therefore, we propose to find the winning ticket with varying sparsity along
different layers in the model. Experimental results demonstrate that our method
can find sparser sub-models that require less memory for storage and reduce the
necessary number of FLOPs. Codes are available at
https://github.com/osier0524/Lottery-Ticket-to-DDPM
Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning
Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about âwho eats whom.â Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change
Ecological plasticity governs ecosystem services in multilayer networks
Agriculture is under pressure to achieve sustainable development goals for biodiversity and ecosystem services. Services in agro-ecosystems are typically driven by key species, and changes in the community composition and species abundance can have multifaceted effects. Assessment of individual services overlooks co-variance between different, but related, services coupled by a common group of species. This partial view ignores how effects propagate through an ecosystem. We conduct an analysis of 374 agricultural multilayer networks of two related services of weed seed regulation and gastropod mollusc predation delivered by carabid beetles. We found that weed seed regulation increased with the herbivore predation interaction frequency, computed from the network of trophic links between carabids and weed seeds in the herbivore layer. Weed seed regulation and herbivore interaction frequencies declined as the interaction frequencies between carabids and molluscs in the carnivore layer increased. This suggests that carabids can switch to gastropod predation with community change, and that link turnover rewires the herbivore and carnivore network layers affecting seed regulation. Our study reveals that ecosystem services are governed by ecological plasticity in structurally complex, multi-layer networks. Sustainable management therefore needs to go beyond the autecological approaches to ecosystem services that predominate, particularly in agriculture
Ecological networks reveal resilience of agro-ecosystems to changes in farming management
International audienc
Recommended from our members
Safeguarding pollinators and their values to human well-being
Wild and managed pollinators provide a wide range of benefits to society in terms of contributions to food security, farmer
and beekeeper livelihoods, social and cultural values, as well as the maintenance of wider biodiversity and ecosystem
stability. Pollinators face numerous threats, including changes in land-use and management intensity, climate change,
pesticides and genetically modified crops, pollinator management and pathogens, and invasive alien species. There are
well-documented declines in some wild and managed pollinators in several regions of the world. However, many effective
policy and management responses can be implemented to safeguard pollinators and sustain pollination services
An ecological future for weed science to sustain crop production and the environment. A review
Sustainable strategies for managing weeds are critical to meeting agriculture's potential to feed the world's population while conserving the ecosystems and biodiversity on which we depend. The dominant paradigm of weed management in developed countries is currently founded on the two principal tools of herbicides and tillage to remove weeds. However, evidence of negative environmental impacts from both tools is growing, and herbicide resistance is increasingly prevalent. These challenges emerge from a lack of attention to how weeds interact with and are regulated by the agroecosystem as a whole. Novel technological tools proposed for weed control, such as new herbicides, gene editing, and seed destructors, do not address these systemic challenges and thus are unlikely to provide truly sustainable solutions. Combining multiple tools and techniques in an Integrated Weed Management strategy is a step forward, but many integrated strategies still remain overly reliant on too few tools. In contrast, advances in weed ecology are revealing a wealth of options to manage weedsat the agroecosystem levelthat, rather than aiming to eradicate weeds, act to regulate populations to limit their negative impacts while conserving diversity. Here, we review the current state of knowledge in weed ecology and identify how this can be translated into practical weed management. The major points are the following: (1) the diversity and type of crops, management actions and limiting resources can be manipulated to limit weed competitiveness while promoting weed diversity; (2) in contrast to technological tools, ecological approaches to weed management tend to be synergistic with other agroecosystem functions; and (3) there are many existing practices compatible with this approach that could be integrated into current systems, alongside new options to explore. Overall, this review demonstrates that integrating systems-level ecological thinking into agronomic decision-making offers the best route to achieving sustainable weed management
Culture or Biology? If this sounds interesting, you might be confused
Culture or Biology? The question can seem deep and important. Yet, I argue in this chapter, if you are enthralled by questions about our biological differences, then you are probably confused. My goal is to diagnose the confusion. In debates about the role of biology in the social world it is easy to ask the wrong questions, and it is easy to misinterpret the scientific research. We are intuitively attracted to what is called psychological essentialism, and therefore interpret what is biological as what can be traced to âessencesâ. On this interpretation, it would be deep and important to know what about, say, the differences between the genders is biological: it would correspond to what is essential to being a man or being a woman, and be opposed to what is a mere accidental feature that some women or some men have. Yet, the psychological essentialist understanding of âbiological differencesâ is deeply mistaken about biology. It has the wrong conception of biological kinds, of biological heritability, and of how genes and hormones work. Those who argue for an important role of âbiologyâ in the explanation of human differences often see âthe scienceâ on their side. But this is false â on the interpretation of âbiological differencesâ that is most intuitive and that makes the question appear to be most interesting. Defenders of âbiologyâ often have the science against them. What is often called âbiologyâ is a myth: a myth created by an intuitive tendency that grotesquely distorts real biological research
- âŠ