13,069 research outputs found

    Copy-paste data augmentation for domain transfer on traffic signs

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    City streets carry a lot of information that can be exploited to improve the quality of the services the citizens receive. For example, autonomous vehicles need to act accordingly to all the element that are nearby the vehicle itself, like pedestrians, traffic signs and other vehicles. It is also possible to use such information for smart city applications, for example to predict and analyze the traffic or pedestrian flows. Among all the objects that it is possible to find in a street, traffic signs are very important because of the information they carry. This information can in fact be exploited both for autonomous driving and for smart city applications. Deep learning and, more generally, machine learning models however need huge quantities to learn. Even though modern models are very good at gener- alizing, the more samples the model has, the better it can generalize between different samples. Creating these datasets organically, namely with real pictures, is a very tedious task because of the wide variety of signs available in the whole world and especially because of all the possible light, orientation conditions and con- ditions in general in which they can appear. In addition to that, it may not be easy to collect enough samples for all the possible traffic signs available, cause some of them may be very rare to find. Instead of collecting pictures manually, it is possible to exploit data aug- mentation techniques to create synthetic datasets containing the signs that are needed. Creating this data synthetically allows to control the distribution and the conditions of the signs in the datasets, improving the quality and quantity of training data that is going to be used. This thesis work is about using copy-paste data augmentation to create synthetic data for the traffic sign recognition task

    Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning

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    The spread of rumors along with breaking events seriously hinders the truth in the era of social media. Previous studies reveal that due to the lack of annotated resources, rumors presented in minority languages are hard to be detected. Furthermore, the unforeseen breaking events not involved in yesterday's news exacerbate the scarcity of data resources. In this work, we propose a novel zero-shot framework based on prompt learning to detect rumors falling in different domains or presented in different languages. More specifically, we firstly represent rumor circulated on social media as diverse propagation threads, then design a hierarchical prompt encoding mechanism to learn language-agnostic contextual representations for both prompts and rumor data. To further enhance domain adaptation, we model the domain-invariant structural features from the propagation threads, to incorporate structural position representations of influential community response. In addition, a new virtual response augmentation method is used to improve model training. Extensive experiments conducted on three real-world datasets demonstrate that our proposed model achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: AAAI 202

    The spatial decay of human capital externalities - A functional regression approach with precise geo-referenced data

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    This paper analyzes human capital externalities from high-skilled workers by applying functional regression to precise geocoded register data. Functional regression enables us to describe the concentration of high-skilled workers around workplaces as continuous curves and to efficiently estimate a spillover function determined by distance. Furthermore, our rich panel data allow us to address the sorting of workers and disentangle human capital externalities from supply effects by using an extensive set of time-varying fixed effects. Our estimates reveal that human capital externalities attenuate with increasing distance and disappear after 25 km. Externalities from the immediate neighborhood of an establishment are twice as large as externalities from surroundings 10 km away

    Anthropology in 3D: The Use of Photogrammetry in the Preservation and Dissemination of Ethnographic Art

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    Photogrammetry is an effective tool used by archaeologists in museums and organizations by creating a 3D model from overlapping photos. This project involved a collection of ethnographic artifacts from Papua New Guinea that are currently housed in the Grand Valley State University Anthropology Department. This essay reviews the process and results of this project. Artifacts were photographed and 3D models were created using the Agisoft Metashape program. Models are disseminated via the Sketchfab website with proper cultural information. Artifacts originate from Sepik River tribes and were designed originally for the tourist industry. This project shows the utility of photogrammetry in archaeology and cultural heritage preservation

    Predicting the Building Envelope in BIM Models Using Graph Convolutional Neural Networks

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    In recent years, Building Information Modeling (BIM) has become one of the leading techniques to maintain data from the lifespan of the building in the Architecture, Engineering and Construction industry. Compared to the traditional modeling methods, BIM requires less human contribution, making it an advantageous approach to represent the characteristics of buildings digitally. In addition, one interesting innovation regarding BIM is semantic enrichment in which an existing model is used to obtain new features and include them in the data entity. Despite the increasing use of BIM, the literature concerning the subject remains limited and thus, the full potential of BIM-based applications appears not to be achieved yet. In this thesis, the objective is to review the potential to employ Machine Learning solutions for BIM-related supervised prediction tasks. Beginning with the BIM dataset, different approaches to formulate 3D data are considered and based on the selected format, the experiments are made by predicting the envelope for each building using a supervised Machine Learning algorithm. Eventually, the buildings are decided to be formatted as graphs and the chosen algorithm is a Graph Convolutional Neural Network with varying architectures that emphasizes the relationships between elements in different ways. This type of graph-based approach for BIM-related classification problems is an area that has not been much examined previously. Comparing three different neural network models, the classification is performed in two different scenarios. In the first scenario, data utilized for the training and testing are from the same building whereas in the second one, both the training and testing data comprise distinct, complete buildings. In the first scenario, the Graph Convolutional Neural Network is observed to improve classification performance especially for the minor classes compared to the traditional neural network. Also in the second scenario, the accuracy is higher when employing the graph models, although this type of classification task turns out to be more challenging compared to the one in the first scenario. The results illustrate a great potential for solving BIM-related classification problems using Machine Learning algorithms. For the first prediction task, a potential application area could be dealing with missing data that occur in BIM models frequently. The second scenario, in turn, has an even higher potential to produce useful tools for semantic enrichment. These types of investigations play an important role in developing new methods to process BIM models

    In search of 'The people of La Manche': A comparative study of funerary practices in the Transmanche region during the late Neolithic and Early Bronze Age (250BC-1500BC)

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    This research project sets out to discover whether archaeological evidence dating between 2500 BC - 1500 BC from supposed funerary contexts in Kent, flanders and north-eastern Transmanche France is sufficient to make valid comparisons between social and cultural structures on either side of the short-sea Channel region. Evidence from the beginning of the period primarily comes in the form of the widespread Beaker phenomenon. Chapter 5 shows that this class of data is abundant in Kent but quite sparse in the Continental zones - most probably because it has not survived well. This problem also affects the human depositional evidence catalogued in Chapter 6, particularly in Fanders but also in north-eastern Transmanche France. This constricts comparative analysis, however, the abundant data from Kent means that general trends are still discernible. The quality and volume of data relating to the distribution, location, morphology and use of circular monuments in all three zones is far better - as demonstrated in Chapter 7 -mostly due to extensive aerial surveying over several decades. When the datasets are taken as a whole, it becomes possible to successfully apply various forms of comparative analyses. Most remarkably, this has revealed that some monuments apparently have encoded within them a sophisticated and potentially symbolically charged geometric shape. This, along with other less contentious evidence, demonstrates a level of conformity that strongly suggests a stratum of cultural homogeneity existed throughout the Transmanche region during the period 2500 BC - 1500 BC. The fact that such changes as are apparent seem to have developed simultaneously in each of the zones adds additional weight to the theory that contact throughout the Transmanche region was endemic. Even so, it may not have been continuous; there may actually have been times of relative isolation - the data is simply too course to eliminate such a possibility

    Human Rights practitioners’ approach to refugees and migrants. A therapeutic psychosocial perspective.

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    This thesis advances the argument that the best way to address the needs of involuntarily dislocated populations is to develop a combined framework that includes both psychosocial and therapeutic perspectives as well as human rights principles. Based on my professional experience as a refugee lawyer, I argue that only such a combined framework can adequately respond to the complexity of the refugee realities. Moreover, I demonstrate that, in some circumstances, the application only of human right rules can violate the same rights that they are meant to protect. I suggest that human rights practitioners are more likely to become aware of the real needs of those we help and, thus, provide them with targeted interventions, once we add a psychosocial perspective to our work. It is in this sense that our endeavours become therapeutic, which should be distinguished from offering them psychotherapy. The added therapeutic dimension also benefits refugees by rescuing them from developing victim identities. This empowering and participatory model of interaction also assists them with an awareness of their existing resources as well as of those new strengths they acquire from their exposure to adversity. Finally, they benefit from an improved level of self reflexivity and a deeper consideration of the socio-political and cultural contexts that act as background to the migratory experience. This study examines various possible applications of this proposed combined framework, ranging from the enrichment of the refugee lawyers curricula with tenets of psychosocial perspectives to the addition of a therapeutic dimension to the hearings of migration/asylum courts

    Network analysis of the cellular circuits of memory

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    Intuitively, memory is conceived as a collection of static images that we accumulate as we experience the world. But actually, memories are constantly changing through our life, shaped by our ongoing experiences. Assimilating new knowledge without corrupting pre-existing memories is then a critical brain function. However, learning and memory interact: prior knowledge can proactively influence learning, and new information can retroactively modify memories of past events. The hippocampus is a brain region essential for learning and memory, but the network-level operations that underlie the continuous integration of new experiences into memory, segregating them as discrete traces while enabling their interaction, are unknown. Here I show a network mechanism by which two distinct memories interact. Hippocampal CA1 neuron ensembles were monitored in mice as they explored a familiar environment before and after forming a new place-reward memory in a different environment. By employing a network science representation of the co-firing relationships among principal cells, I first found that new associative learning modifies the topology of the cells’ co-firing patterns representing the unrelated familiar environment. I fur- ther observed that these neuronal co-firing graphs evolved along three functional axes: the first segregated novelty; the second distinguished individual novel be- havioural experiences; while the third revealed cross-memory interaction. Finally, I found that during this process, high activity principal cells rapidly formed the core representation of each memory; whereas low activity principal cells gradually joined co-activation motifs throughout individual experiences, enabling cross-memory in- teractions. These findings reveal an organizational principle of brain networks where high and low activity cells are differentially recruited into coactivity motifs as build- ing blocks for the flexible integration and interaction of memories. Finally, I employ a set of manifold learning and related approaches to explore and characterise the complex neural population dynamics within CA1 that underlie sim- ple exploration.Open Acces

    Modeling and Implementation of Digital Twins for the Analysis of Transportation Systems

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    Transport engineers, authorities, companies, stakeholders, and all experts involved on transportation planning work every day to improve the trip experience of people, and to reduce the impacts on the collectivity. Traffic congestion, pollution and energy consumption are core problems for the transportation system. On the one hand a congested road causes an exponential increase in the energy consumption and waiting times, while a not-congested road can be more attractive and slowly become congested over time; on the other hand, the environmental capacity of road links is not perceived, thus generating high emission and distribution of pollutants. In fact, the ability of transportation planners is to avoid traffic congestion but offer at the same time pleasant, accessible and sustainable trips. This may include several planning techniques which may involve traffic calming and limitations, lane reservation, changing in the road network and geometry, as well as apply new technologies, services and means of transportation. It is worth noting that trying these features directly on a city can be very expensive and produce irreparable damages to the transportation system; also, traditional transport models are not able to adeguately simulate most of these features. Recently, it has been recently introduced the digital twin, a digital reproduction of a city to be used as a test platform for ’what if’ scenarios. In fact, transport digital twin is not just a digital reproduction of the transportation system, but consider reaction of humans to the changes applied to the system, in order to make a comparison between different scenarios. The present document studies large-scale digital twins, which are able to consider the spatial propagation of effects, and particularly focuses on advanced and time-dependent models able to simulate the door-to-door trip experience of all users and adequately model the transport features of the future
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