5,868 research outputs found
Bion Theory: an answer to the question Why is there Something rather than Nothing?
Why is there something rather than nothing? This paper explores one particular argument in favor of the answer that 'the existence of nothing' would amount to a logical contradiction. This argument consists of positing the existence of a novel entity, called a bion, of which all contingent things can be composed yet itself is non-contingent. First an overview of historical attempts to compile a systematic and exhaustive list of answers to the question is presented as context. Then follows an analysis of how the antropic principle would manifest itself in a world that consists of information and at the same time conforms to modal realism. Next, a thought experiment introduces bions as the foundation of such a world, showing how under these circumstances the ultimate origin of all existing things would be explained. The non-contingent nature of bions themselves is subsequently argued via a discussion of the principle of non-contradiction. Finally, this theory centered on the existence of bions is integrated into the worldview of Popperian metaphysics. According to the latter's criteria, I conclude that bion theory provides an integral answer to why there is something rather than nothing
Sellars on Functionalism and Normativity
The term ‘functionalism’ is usually heard in connection with the philosophy of mind or cognition. The functionalism of Wilfrid Sellars, however, is in the first instance as response to the worries about the metaphysics not of mental states, but of meaning. Only late in his career did Sellars explore the possibility of extending his functionalism into an account of cognition. It has been suggested, though, that Sellars’ extension of his functionalist theory into subpersonal territory is not successful. In particular, there is a worry abroad that in order to be a functionalist about cognitive states, Sellars must succumb to a special form of the Myth of the Given. In this essay I will review and elucidate what I take to be the structure of Sellars’ functionalism, defending it from this worry. I will suggest a resolution of some apparent textual contradictions based in part on the chronology of Sellars’ writing, with the assumption that later writings express Sellars’ more nuanced views.
Draft of 2009
Development of Semantic Scene Conversion Model for Image-based Localization at Night
Developing an autonomous vehicle navigation system invariant to illumination change is one of the biggest challenges in vision-based localization field due to the fact that the appearance of an image becomes inconsistent under different light conditions even with the same location. In particular, the night scene images have greatest change in appearance compared to the according day scenes. Moreover, the night images do not have enough information in Image-based localization. To deal with illumination change, image conversion methods have been researched. However, these methods could lose the detail of objects and add fake objects into the output images. In this thesis, we proposed the semantic objects conversion model using the change of local semantic objects by categories at night. This enables the proposed model to obtain the detail of local semantic objects in image conversion. As a result, it is expected that the proposed model has a better result in image-based localization. Our model uses local semantic objects (i.e., traffic signs and street lamps) as categories. The model is composed of two phases as (1) instance segmentation and (2) semantic objects conversion. Instance segmentation is utilized as a detector for local semantic objects. In translation phase, the detected local semantic objects are translated from the appearance of the night image to day image. In evaluation, we prove that models using a set of paired images show higher accuracy compared to the models using a set of unpaired images. Our proposed method will be compared with pix2pix and ToDayGAN. Moreover, the result quantitatively evaluates the best matching score with a query image and the converted images using ORB matching descriptor
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Recently significant progress has been made in human action recognition and
behavior prediction using deep learning techniques, leading to improved
vision-based semantic understanding. However, there is still a lack of
high-quality motion datasets for small bio-robotics, which presents more
challenging scenarios for long-term movement prediction and behavior control
based on third-person observation. In this study, we introduce RatPose, a
bio-robot motion prediction dataset constructed by considering the influence
factors of individuals and environments based on predefined annotation rules.
To enhance the robustness of motion prediction against these factors, we
propose a Dual-stream Motion-Scenario Decoupling (\textit{DMSD}) framework that
effectively separates scenario-oriented and motion-oriented features and
designs a scenario contrast loss and motion clustering loss for overall
training. With such distinctive architecture, the dual-branch feature flow
information is interacted and compensated in a decomposition-then-fusion
manner. Moreover, we demonstrate significant performance improvements of the
proposed \textit{DMSD} framework on different difficulty-level tasks. We also
implement long-term discretized trajectory prediction tasks to verify the
generalization ability of the proposed dataset.Comment: Rat, Video Position Predictio
DERIVING SURFACE FUELS FROM UAS IMAGERY FORFIRE MODELS
Understanding how fuel, weather, and terrain interact to produce fire behavior continues to motivate fire science andhas resulted in development of new physics-based fire behavior models that place increased demands on input data such as fuels. Recent technological advancements in computing, unmanned aerial systems (UAS), and sensors (RGB, multispectral, thermal, and hyperspectral cameras) can provide new opportunities for land managers and scientists to advance knowledge of fuels and fire behavior and their interactions on the landscape. In this study, imagery from high resolution multispectral cameras mounted on UAS were used to build orthomosaics and point clouds of surface fuelbeds in grass, litter, and shrub fuels of the Sycan Marsh Preserve in Oregon. The purpose of this effort was to develop useful inputs to a fuels translator called STANDFIRE that prepares fuels data for use in physics-based fire models. Fuel type polygons were delineated using traditional photo-interpretation for nine 1 ha plots that were ultimately treated with fire. Each fuel polygon was attributed from field-collected data based on their dominant fuel type. Differences between fuel type polygons were assessed statistically to document the distinctiveness of each fuel type, to overcome field sample-size limitations, and to provide logic for merging fuel types that were similar. Additionally, 3D point clouds and orthomosaics were examined to better understand their information content for more detailed characterizations of fuels. In this latter part of the research, shrub height, width, and cover were extracted from the point clouds and compared to field measurements. The findings were as follows: Defensible fuel type classes were easily delineated using photo-interpretation, resulting in 21.4% of the cumulative plot area classified as litter, 65.3% as grass and 10.3% as shrub fuels. Effective attribution of fuel polygons was dependent on how and where field data were collected and differed by year. Lack of sufficient sample sizes in some fuel type polygons required aggregation of field data from all plots within the Brattain burn unit in 2018. These shortcomings were overcome in 2019 by acquiring rapid-look imagery prior to field sampling that enabled more balanced samples across the range of variability, along with utilization of precision GPS. Within the point-clouds, shrub height was underestimated while width was over-estimated. Shrub cover was also under-predicted from the point cloud and was better enumerated using a conventional dot-grid approach on the orthomosaic. Improvements in data collection methods from 2017-2019 have resulted in a stable workflow that produces consistent fuels data formatted for STANDFIRE. The polygon-based approach is suitable for use in fire model validation due to its ability to rationally integrate sparse field data, because STANDFIRE is designed to work easily with polygons, and because there is insufficient evidence that model validation is at a point where it will benefit from use of more complex pixel or object-based inputs. Automated approaches to polygon delineation via region-growing, machine learning, and segmentation are a logical next step, with the caveat that the inputs derived in this study should be tested in the modeling environment first
Revitalization Approaches to Maximize Heritage Urban DNA Characteristics in Declined Cities: Foah City as a Case Study
Revitalization is an important process in action area planning, especially in the heritage sites located within urban area contexts. Varied techniques and tools of revitalization are applied at various spatial levels, some are suitable for the urban scope, and others suit the architectural building scope. Urban DNA is a term used academically to reflect social, economic, and urban characteristics but has a different interpretation that depends on the spatial scale and context. In action areas, urban DNA refers to the essential visual, social, economic, and physical characteristics that preserve the vital structure of an urban area. Heritage areas are vital in a city structure, in the journey of maximizing the urban DNA chrematistics of heritage sites, sometimes the urban DNA is lost in the process. This paper identifies and encapsulates the importance of Urban DNA in heritage site considerations in the revitalization process within heritage urban context to maximize the socio-economic and visual impacts, especially in declined cities such as Foah City the case study in the Nile Delta region in Egypt. The results pinpoint the most effective urban DNA structure for the declined Foah Heritage Center, despite the city's importance as a ranked third of heritage cities in the country
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