531 research outputs found

    Mass spectrometry imaging for plant biology: A review

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    Mass spectrometry imaging (MSI) is a developing technique to measure the spatio-temporal distribution of many biomolecules in tissues. Over the preceding decade, MSI has been adopted by plant biologists and applied in a broad range of areas, including primary metabolism, natural products, plant defense, plant responses to abiotic and biotic stress, plant lipids and the developing field of spatial metabolomics. This review covers recent advances in plant-based MSI, general aspects of instrumentation, analytical approaches, sample preparation and the current trends in respective plant research

    Semantics-Empowered Communication: A Tutorial-cum-Survey

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    Along with the springing up of the semantics-empowered communication (SemCom) research, it is now witnessing an unprecedentedly growing interest towards a wide range of aspects (e.g., theories, applications, metrics and implementations) in both academia and industry. In this work, we primarily aim to provide a comprehensive survey on both the background and research taxonomy, as well as a detailed technical tutorial. Specifically, we start by reviewing the literature and answering the "what" and "why" questions in semantic transmissions. Afterwards, we present the ecosystems of SemCom, including history, theories, metrics, datasets and toolkits, on top of which the taxonomy for research directions is presented. Furthermore, we propose to categorize the critical enabling techniques by explicit and implicit reasoning-based methods, and elaborate on how they evolve and contribute to modern content & channel semantics-empowered communications. Besides reviewing and summarizing the latest efforts in SemCom, we discuss the relations with other communication levels (e.g., conventional communications) from a holistic and unified viewpoint. Subsequently, in order to facilitate future developments and industrial applications, we also highlight advanced practical techniques for boosting semantic accuracy, robustness, and large-scale scalability, just to mention a few. Finally, we discuss the technical challenges that shed light on future research opportunities.Comment: Submitted to an IEEE journal. Copyright might be transferred without further notic

    Machine Learning for Microcontroller-Class Hardware -- A Review

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    The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

    Social-ecological soundscapes: examining aircraft-harvester-caribou conflict in Arctic Alaska

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    Thesis (M.S.) University of Alaska Fairbanks, 2017As human development expands across the Arctic, it is crucial to carefully assess the impacts to remote natural ecosystems and to indigenous communities that rely on wild resources for nutritional and cultural wellbeing. Because indigenous communities and wildlife populations are interdependent, assessing how human activities impact traditional harvest practices can advance our understanding of the human dimensions of wildlife management. Indigenous communities across Arctic Alaska have expressed concern over the last four decades that low-flying aircraft interfere with their traditional harvest practices. For example, communities often have testified that aircraft disturb caribou (Rangifer tarandus) and thereby reduce harvest opportunities. Despite this longstanding concern, little research exists on the extent of aircraft activity in Arctic Alaska and on how aircraft affect the behavior and perceptions of harvesters. Therefore, the overarching goal of my research was to highlight the importance of aircraft-harvester conflict in Arctic Alaska and begin to address the issue using a scientific and community-driven approach. In Chapter 1, I demonstrated that conflict between aircraft and indigenous harvesters in Arctic Alaska is a widespread, understudied, and complex issue. By conducting a meta-analysis of the available literature, I quantified the deficiency of scientific knowledge about the impacts of aircraft on rural communities and traditional harvest practices in the Arctic. My results indicated that no peer-reviewed literature has addressed the conflict between low-flying aircraft and traditional harvesters in Arctic Alaska. I speculated that the scale over which aircraft, rural communities, and wildlife interact limits scientists' ability to determine causal relationships and therefore detracts from their interest in researching the human dimension of this social-ecological system. Innovative research approaches like soundscape ecology could begin to quantify interactions and provide baseline data that may foster mitigation discourses among stakeholders. In Chapter 2, I employed a soundscape-ecology approach to address concerns about aircraft activity expressed by the Alaska Native community of Nuiqsut. Nuiqsut faces the greatest volume of aircraft activity of any community in Arctic Alaska because of its proximity to intensive oil and gas activity. However, information on when and where these aircraft are flying is unavailable to residents, managers, and researchers. I worked closely with Nuiqsut residents to deploy acoustic monitoring systems along important caribou harvest corridors during the peak of caribou harvest, from early June through late August 2016. This method successfully captured aircraft sound and the community embraced my science for addressing local priorities. I found aircraft activity levels near Nuiqsut and surrounding oil developments (12 daily events) to be approximately six times greater than in areas over 30 km from the village (two daily events). Aircraft sound disturbance was 26 times lower in undeveloped areas (Noise Free Interval =13 hrs) than near human development (NFI = 0.5 hrs). My study provided baseline data on aircraft activity and noise levels. My research could be used by stakeholders and managers to develop conflict avoidance agreements and minimize interference with traditional harvest practices. Soundscape methods could be adapted to rural regions across Alaska that may be experiencing conflict with aircraft or other sources of noise that disrupt human-wildlife interactions. By quantifying aircraft activity using a soundscape approach, I demonstrated a novel application of an emerging field in ecology and provided the first scientific data on one dimension of a larger social-ecological system. Future soundscape studies should be integrated with research on both harvester and caribou behaviors to understand how the components within this system are interacting over space and time. Understanding the long-term impacts to traditional harvest practices will require integrated, cross-disciplinary efforts that collaborate with communities and other relevant stakeholders. Finally, my research will likely spark efforts to monitor and mitigate aircraft impacts to wildlife populations and traditional harvest practices across Alaska, helping to inform a decision-making process currently hindered by an absence of objective data

    Iris Recognition Approach for Preserving Privacy in Cloud Computing

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    Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy

    Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

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    This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

    Commonsense knowledge acquisition and applications

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    Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen

    Commonsense knowledge acquisition and applications

    Get PDF
    Computers are increasingly expected to make smart decisions based on what humans consider commonsense. This would require computers to understand their environment, including properties of objects in the environment (e.g., a wheel is round), relations between objects (e.g., two wheels are part of a bike, or a bike is slower than a car) and interactions of objects (e.g., a driver drives a car on the road). The goal of this dissertation is to investigate automated methods for acquisition of large-scale, semantically organized commonsense knowledge. Prior state-of-the-art methods to acquire commonsense are either not automated or based on shallow representations. Thus, they cannot produce large-scale, semantically organized commonsense knowledge. To achieve the goal, we divide the problem space into three research directions, constituting our core contributions: 1. Properties of objects: acquisition of properties like hasSize, hasShape, etc. We develop WebChild, a semi-supervised method to compile semantically organized properties. 2. Relationships between objects: acquisition of relations like largerThan, partOf, memberOf, etc. We develop CMPKB, a linear-programming based method to compile comparative relations, and, we develop PWKB, a method based on statistical and logical inference to compile part-whole relations. 3. Interactions between objects: acquisition of activities like drive a car, park a car, etc., with attributes such as temporal or spatial attributes. We develop Knowlywood, a method based on semantic parsing and probabilistic graphical models to compile activity knowledge. Together, these methods result in the construction of a large, clean and semantically organized Commonsense Knowledge Base that we call WebChild KB.Von Computern wird immer mehr erwartet, dass sie kluge Entscheidungen treffen können, basierend auf Allgemeinwissen. Dies setzt voraus, dass Computer ihre Umgebung, einschließlich der Eigenschaften von Objekten (z. B. das Rad ist rund), Beziehungen zwischen Objekten (z. B. ein Fahrrad hat zwei Räder, ein Fahrrad ist langsamer als ein Auto) und Interaktionen von Objekten (z. B. ein Fahrer fährt ein Auto auf der Straße), verstehen können. Das Ziel dieser Dissertation ist es, automatische Methoden für die Erfassung von großmaßstäblichem, semantisch organisiertem Allgemeinwissen zu schaffen. Dies ist schwierig aufgrund folgender Eigenschaften des Allgemeinwissens. Es ist: (i) implizit und spärlich, da Menschen nicht explizit das Offensichtliche ausdrücken, (ii) multimodal, da es über textuelle und visuelle Inhalte verteilt ist, (iii) beeinträchtigt vom Einfluss des Berichtenden, da ungewöhnliche Fakten disproportional häufig berichtet werden, (iv) Kontextabhängig, und hat aus diesem Grund eine eingeschränkte statistische Konfidenz. Vorherige Methoden, auf diesem Gebiet sind entweder nicht automatisiert oder basieren auf flachen Repräsentationen. Daher können sie kein großmaßstäbliches, semantisch organisiertes Allgemeinwissen erzeugen. Um unser Ziel zu erreichen, teilen wir den Problemraum in drei Forschungsrichtungen, welche den Hauptbeitrag dieser Dissertation formen: 1. Eigenschaften von Objekten: Erfassung von Eigenschaften wie hasSize, hasShape, usw. Wir entwickeln WebChild, eine halbüberwachte Methode zum Erfassen semantisch organisierter Eigenschaften. 2. Beziehungen zwischen Objekten: Erfassung von Beziehungen wie largerThan, partOf, memberOf, usw. Wir entwickeln CMPKB, eine Methode basierend auf linearer Programmierung um vergleichbare Beziehungen zu erfassen. Weiterhin entwickeln wir PWKB, eine Methode basierend auf statistischer und logischer Inferenz welche zugehörigkeits Beziehungen erfasst. 3. Interaktionen zwischen Objekten: Erfassung von Aktivitäten, wie drive a car, park a car, usw. mit temporalen und räumlichen Attributen. Wir entwickeln Knowlywood, eine Methode basierend auf semantischem Parsen und probabilistischen grafischen Modellen um Aktivitätswissen zu erfassen. Als Resultat dieser Methoden erstellen wir eine große, saubere und semantisch organisierte Allgemeinwissensbasis, welche wir WebChild KB nennen
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