Multi-view constrained clustering with an incomplete mapping between views

Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via the partial mapping. It uses co-EM to iteratively estimate the propagation within each view based on the current clustering model, transfer the constraints across views, and then update the clustering model. By alternating the learning process between views, this approach produces a unified clustering model that is consistent with all views. We show that this approach significantly improves clustering performance over several other methods for transferring constraints and allows multi-view clustering to be reliably applied when given a limited mapping between the views. Our evaluation reveals that the propagated constraints have high precision with respect to the true clusters in the data, explaining their benefit to clustering performance in both single- and multi-view learning scenarios

Historicity and Ecological Restoration

Traditional ecological restoration often relies on ideals of reversibility and balance of nature. I suggest that we should change these for a path-dependent view of natural processes. This conceptual shift also invites for philosophical and methodological revisions, such as favouring “futuristic” dynamic goals and alternative state models

Ecological Historicity, Functional Goals, and Novelty in the Anthropocene

While many recognize that rigid historical and compositional goals are inadequate in a world where climate and other global systems are undergoing unprecedented changes, others contend that promoting ecosystem services and functions encourages practices that can ultimately lower the bar of ecological management. These worries are foregrounded in discussions about Novel Ecosystems (NEs); where some researchers and conservationists claim that NEs provide a license to trash nature as long as some ecosystem services are provided. This criticism arises from what we call the “anything goes” problem created by the release of historical conditions. After explaining the notion of NE, we identify numerous substantive motivations for worrying about the anything-goes-problem and then go on to show the problem can be solved by correcting two mistaken assumptions. In short, we argue that the problem is a product of adopting an overly sparse functional perspective and one that assumes an unrealistically high degree of convergence in the trajectories of natural processes. Our analysis illuminates why such assumptions are unwarranted. Finally, we further argue that adopting an appropriate ethical framework is essential to overcoming the anything-goes-problem and suggest that a certain virtue ethics conception of ecological management provides useful resources for framing and resolving the problem

Ecological Historicity, Functional Goals, and Novelty in the Anthropocene

While many recognize that rigid historical and compositional goals are inadequate in a world where climate and other global systems are undergoing unprecedented changes, others contend that promoting ecosystem services and functions encourages practices that can ultimately lower the bar of ecological management. These worries are foregrounded in discussions about Novel Ecosystems (NEs); where some researchers and conservationists claim that NEs provide a license to trash nature as long as some ecosystem services are provided. This criticism arises from what we call the “anything goes” problem created by the release of historical conditions. After explaining the notion of NE, we identify numerous substantive motivations for worrying about the anything-goes-problem and then go on to show the problem can be solved by correcting two mistaken assumptions. In short, we argue that the problem is a product of adopting an overly sparse functional perspective and one that assumes an unrealistically high degree of convergence in the trajectories of natural processes. Our analysis illuminates why such assumptions are unwarranted. Finally, we further argue that adopting an appropriate ethical framework is essential to overcoming the anything-goes-problem and suggest that a certain virtue ethics conception of ecological management provides useful resources for framing and resolving the problem

Algorithms for Learning Preferences for Sets of Objects

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features

Lockable Knee Implants and Related Methods

Total knee replacements for hinged knee implants include a tibial member, a femoral member, a hinge assembly having a laterally extending axle configured to hingedly attach the femoral member to the tibial member, and a lock mechanism in communication with the hinge assembly. The lock mechanism is configured to (i) lock the femoral member in alignment with the tibial member for a full extension or other defined stabile walking configuration to thereby allow an arthrodesis or stiff knee gait and (ii) unlock to allow the femoral and tibial members to pivot relative to each other for flexion or bending when not ambulating

Lockable Implants

Total joint replacements for implants include a first member configured to attach to a first bone, a second member configured to reside in an adjacent second bone and a locking mechanism. The locking mechanism is configured to (i) lock the first and second members in alignment for full extension or other defined stabilized configuration and (ii) unlock to allow the first and second members to pivot relative to each other for flexion or bending

Lockable Knee Implants and Related Methods

Total knee replacements for hinged knee implants include a tibial member, a femoral member, a hinge assembly having a laterally extending axle configured to hingedly attach the femoral member to the tibial member, and a lock mechanism in communication with the hinge assembly. The lock mechanism is configured to (i) lock the femoral member in alignment with the tibial member for a full extension or other defined stabile walking configuration to thereby allow an arthrodesis or stiff knee gait and (ii) unlock to allow the femoral and tibial members to pivot relative to each other for flexion or bending when not ambulating

Environmental Grain, Organism Fitness, and Type Fitness

Abstract Natural selection is the result of organisms' interactions with their environment, but environments vary in space and time, sometimes in extreme ways. Such variation is generally thought to play an important role in evolution by natural selection, maintaining genetic variation within and between populations, increasing the chance of speciation, selecting for plasticity of responses to the environment, and selecting for behaviors such as habitat selection and niche construction. Are there different roles that environmental variation plays in natural selection? When biologists make choices about how to divide up an environment for the sake of modeling or empirical research, are there any constraints on these choices? Since diverse evolutionary models relativize fitnesses to component environments within a larger environment, it would be useful to understand when such practices capture real aspects of evolutionary processes, and when they count as mere modeling conveniences. In this paper, I try to provide a general framework for thinking about how fitness and natural selection depend on environmental variation. I'll give an account of how the roles of environmental conditions in natural selection differ depending the probability of being experienced repeatedly by organisms, and how environmental conditions combine probabilistically to help determine fitness. My view has implications for what fitness is, and suggests that some authors have misconceived its nature