1,808 research outputs found
Entropic contribution to phenotype fitness
All possible phenotypes are not equally accessible to evolving populations.
In fact, only phenotypes of large size, i.e. those resulting from many
different genotypes, are found in populations of sequences, presumably because
they are easier to discover and maintain. Genotypes that map to these
phenotypes usually form mostly connected genotype networks that percolate the
space of sequences, thus guaranteeing access to a large set of alternative
phenotypes. Within a given environment, where specific phenotypic traits become
relevant for adaptation, the replicative ability of a phenotype and its overall
fitness (in competition experiments with alternative phenotypes) can be
estimated. Two primary questions arise: how do phenotype size, reproductive
capability and topology of the genotype network affect the fitness of a
phenotype? And, assuming that evolution is only able to access large
phenotypes, what is the range of unattainable fitness values? In order to
address these questions, we quantify the adaptive advantage of phenotypes of
varying size and spectral radius in a two-peak landscape. We derive analytical
relationships between the three variables (size, topology, and replicative
ability) which are then tested through analysis of genotype-phenotype maps and
simulations of population dynamics on such maps. Finally, we analytically show
that the fraction of attainable phenotypes decreases with the length of the
genotype, though its absolute number increases. The fact that most phenotypes
are not visible to evolution very likely forbids the attainment of the highest
peak in the landscape. Nevertheless, our results indicate that the relative
fitness loss due to this limited accessibility is largely inconsequential for
adaptation.Comment: 25 pages, 10 figures, uses iopart.cls, iopart10.clo, iopart12.clo,
iopams.sty, setstack.st
Learning with a Mole: Transferable latent spatial representations for navigation without reconstruction
Agents navigating in 3D environments require some form of memory, which
should hold a compact and actionable representation of the history of
observations useful for decision taking and planning. In most end-to-end
learning approaches the representation is latent and usually does not have a
clearly defined interpretation, whereas classical robotics addresses this with
scene reconstruction resulting in some form of map, usually estimated with
geometry and sensor models and/or learning. In this work we propose to learn an
actionable representation of the scene independently of the targeted downstream
task and without explicitly optimizing reconstruction. The learned
representation is optimized by a blind auxiliary agent trained to navigate with
it on multiple short sub episodes branching out from a waypoint and, most
importantly, without any direct visual observation. We argue and show that the
blindness property is important and forces the (trained) latent representation
to be the only means for planning. With probing experiments we show that the
learned representation optimizes navigability and not reconstruction. On
downstream tasks we show that it is robust to changes in distribution, in
particular the sim2real gap, which we evaluate with a real physical robot in a
real office building, significantly improving performance
POVNav: A Pareto-Optimal Mapless Visual Navigator
Mapless navigation has emerged as a promising approach for enabling
autonomous robots to navigate in environments where pre-existing maps may be
inaccurate, outdated, or unavailable. In this work, we propose an image-based
local representation of the environment immediately around a robot to parse
navigability. We further develop a local planning and control framework, a
Pareto-optimal mapless visual navigator (POVNav), to use this representation
and enable autonomous navigation in various challenging and real-world
environments. In POVNav, we choose a Pareto-optimal sub-goal in the image by
evaluating all the navigable pixels, finding a safe visual path, and generating
actions to follow the path using visual servo control. In addition to providing
collision-free motion, our approach enables selective navigation behavior, such
as restricting navigation to select terrain types, by only changing the
navigability definition in the local representation. The ability of POVNav to
navigate a robot to the goal using only a monocular camera without relying on a
map makes it computationally light and easy to implement on various robotic
platforms. Real-world experiments in diverse challenging environments, ranging
from structured indoor environments to unstructured outdoor environments such
as forest trails and roads after a heavy snowfall, using various image
segmentation techniques demonstrate the remarkable efficacy of our proposed
framework
From Vision-Language Multimodal Learning Towards Embodied Agents
To build machine agents with intelligent capabilities mimicking human perception and cognition, vision and language stand out as two essential modalities and foster computer vision and natural language processing. Advances in such realms stimulate research in vision-language multimodal learning that allows optical and linguistic inputs and outputs. Due to the innate difference between the two modalities and the lack of large-scale fine-grained annotations, multimodal agents tend to inherit unimodal shortcuts. In this thesis, we develop various solutions to intervene unimodal shortcuts for multimodal generation and reasoning. For visual shortcuts, we introduce a linguistic prior and devise a syntax-aware action targeting module for dynamic description to rectify the correlation between subject and object in a sentence. We apply concept hierarchy and propose a visual superordinate abstraction framework for unbiased concept learning to reduce the correlation among different attributes of an object. For linguistic shortcuts, we disentangle the topic and syntax to reduce the repetition in generated paragraph descriptions for a given image. With the ubiquity of large-scale pre-trained models, we leverage self-supervised learning in finetuning process to increase the robustness of multimodal reasoning.
The rapid development in multimodal learning promises embodied agents capable of interacting with physical environments. This thesis studies the typical embodied task vision-and-language navigation in discrete scenarios and proposes an episodic scene memory (ESceme) mechanism to balance generalization and efficiency. We figure out one desirable instantiation of the mechanism, namely candidate enhancing, and validate its superiority in various settings. Without extra time and computational cost before inference, ESceme improves performance in unseen environments by a large margin. We hope our findings can inspire more practical explorations on episodic memory in embodied AI
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Student Citizens: Whiteness, Inequality, and Social Reproduction in Marketized Music Education
Music education policy and administration attempts to shape the musical sensibilities of young people. Yet the logics of music education from a socioeconomic standpoint are inadequately understood. This dissertation focuses on the relationship between music education nonprofits and public schools and on the public and private policies that have shaped the formation and perpetuation of these relationships. I analyze the logics of policy documents alongside the discourses and narratives of private organizations that support music education within the specific contexts of New Jersey, a state that mandates music education access for all students, and the COVID-19 pandemic, which has exacerbated societal inequalities, to illuminate how policy makers and administrators shape student experiences in the proto-democratic space of the classroom.
I use policy analysis and institutional ethnography, approaching data primarily through the lenses of neoliberal critiques of marketization, critical whiteness studies, and analyses of the intersection of class and race, which I outline in chapter one. I also consider the design of music education programs within the theoretical framework of culturally relevant pedagogy. Education systems are adapting to shifting racial discourses as schools continue to construct citizens within racialized and classed hierarchies. Music historically has been invoked in the construction of societal stratifications to mark ethnic and cultural boundaries.
In chapter two, I examine these narratives that have shaped the formation of music education in the United States as a culturally hegemonizing force and persist in debates around the purpose of music education in under-resourced schools that mainly serve students from minoritized communities. Music education remains a site at which policy makers, administrators, educators, and community members negotiate the role of culture in shaping new citizens. State music education policy in New Jersey specifically struggles to support the progressive vision it professes as it continues to suggest a strongly hegemonic curriculum and perpetually underfunds music programs in schools.
Within this context, the third chapter considers how funders and advocacy groups are so frequently focused on short-term funding needs that they persistently struggle to address systemic issues in music education, such as issues with administrations that do not represent the communities being served, colonial content and pedagogy, and unsustainable funding solutions. As such, the limited services and non-democratic leadership of privately funded music education programs in public schools reinforce the role of public schools as gate-keepers of exclusionary citizenship norms. At the same time, privatization has also opened opportunities for non-normative, anti-oppressive forms of music pedagogy to enter public schools. In the fourth chapter, I investigate how, though their very existence reinforces the marketizing trends that rank and exclude, some nonprofits do attempt to serve students in culturally relevant ways within this environment, and can even work in ways that support publicly funded programs.
Altogether, my research provides insight into the role that the privatization of public spaces within neoliberalism plays in the formation and reproduction of classed and raced citizens, as policy makers, funders, and program administrators determine which young people are given access to which forms of education
Dynamic processes on networks and higher-order structures
Higher-order interactions are increasingly recognized as a critical aspect in the modeling of complex systems. Higher-order networks provide a framework for studying the relationship between the structure of higher-order interactions and the function of the complex system. However, little is known about how higher-order interactions affect dynamic processes. In this thesis, we develop general frameworks of percolation aiming at understanding the interplay between higher-order network structures and the critical properties of dynamics. We reveal that degree correlations strongly affect the percolation threshold on higher-order networks and interestingly, the effect of correlations is different on ordinary percolation and higher-order percolation. We further elucidate the mechanisms responsible for the emergence of discontinuous transitions on higher-order networks. Moreover, we show that triadic regulatory interaction, as a general type of higher-order interaction found widely in nature, can turn percolation into a fully-fledged dynamic process that exhibits period doubling and a route to chaos. As an important example of dynamic processes, we further investigate the role of network topology on epidemic spreading. We show that higher-order interactions can induce a non-linear infection kernel in a pandemic, which results in a discontinuous phase transition, hysteresis, and superexponential spreading. Finally, we propose an epidemic model to evaluate the role of automated contact-and-tracing with mobile apps as a new containment measure to mitigate a pandemic. We reveal the non-linear effect on the reduction of the incidence provided by a certain fraction of app adoption in the population and we propose the optimal strategy to mitigate the pandemic with limited resources. Altogether, the thesis provides new insights into the interplay between the topology of higher-order networks and their dynamics. The results obtained may shed light on the research in other areas of interest such as brain functions and epidemic spreading
Congressional Power, Public Rights, and Non-Article III Adjudication
When can Congress vest in administrative agencies or other non–Article III federal courts the power to adjudicate any of the nine types of “Cases” or “Controversies” listed in Article III of the United States Constitution? The core doctrine holds that Congress may employ non–Article III adjudicators in territorial courts, in military courts, and for decision of matters of public right. Scholars have criticized this so-called “public rights” doctrine as incoherent but have struggled to offer a more cogent answer.
This Article provides a new, overarching explanation of when and why Congress may use non–Article III federal officials to adjudicate matters of public right as well as matters in territorial and military courts. We reorganize the traditional categories into three overlapping spheres where such non–Article III adjudication may occur: (1) a case occurs in a physical space beyond the control of the states and therefore does not implicate preexisting state decisional primacy over matters of private right (e.g., territorial courts); (2) a case lies within the national government’s operational space, in which Congress and the executive cooperate to manage the government’s internal affairs (e.g., via courts martial) and to administer statutorily created rights or benefits (e.g., a grant of a land or invention patent); or (3) a case involves a claim against a private party brought by the government or another private party within a properly bounded enforcement space of a federal regulatory scheme (e.g., NLRB adjudication of labor-management disputes). Our account of the public-rights doctrine is functionally grounded but also deeply rooted in history. This account both explains the caselaw and squares the doctrine with the modern ubiquity of non–Article III adjudication
Climate Change and the Expansion of Arctic Shipping
The potential economic benefits from increased Arctic shipping are enormous. As well as changing the logistics of global trade, Arctic shipping routes have the potential to generate inward investment flows benefiting the Northern and Indigenous communities, supporting regional development, and enhancing economic and social sustainability. At the same time, however, unregulated economic expansion carries a significant risk. In such a fragile region the ecological consequences might be severe and potentially irreversible. Likewise, the social, economic, and cultural consequences of such ecological damage would likely cause serious disruptions, affecting life across the region, as well as having global impacts in terms of climate change. For this reason, during this early, preparatory period, well before any large-scale commercial exploitation, it is imperative that the community of nations and other stakeholders establish a clear and effective set of standards along with an effective governance regime to regulate Arctic shipping to ensure contributions to long-lasting wellbeing. In the case of the largely pristine Arctic environment, it is crucially important that we understand the ecological and biophysical limits of increased exploitation to avoid irreversible impacts whereby disruption and crisis will outweigh stability and development.
This research seeks to understand the potential expansion of commercial Arctic shipping in the wake of the anticipated decline of Arctic sea ice. In the first phase of a longer-term doctoral project, my goal has been a preliminary review and synthesis of the literature relating to medium- and long-term costs and benefits with respect to sustainable development in the region. This scoping project has been designed as the first step toward the development of a Sustainable Arctic Shipping Standard (SASS). The research centers on the analysis of literature from a diverse range of scientific fields to create a comprehensive picture of the potential threats arising from increased Arctic sea shipping.
This research shows that anticipating the overall impact of arctic shipping on regional sustainability is extremely complex. While the environmental impact might be more negative, there are some serious economic advantages, that are often opposed to the environmental losses. The effects on local communities are also ambiguous and further research is needed to have a more defined conclusion. Nevertheless, the key areas of concern and opportunity are usefully identified
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