15,012 research outputs found
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
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CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
Sleep abnormalities can have severe health consequences. Automated sleep
staging, i.e. labelling the sequence of sleep stages from the patient's
physiological recordings, could simplify the diagnostic process. Previous work
on automated sleep staging has achieved great results, mainly relying on the
EEG signal. However, often multiple sources of information are available beyond
EEG. This can be particularly beneficial when the EEG recordings are noisy or
even missing completely. In this paper, we propose CoRe-Sleep, a Coordinated
Representation multimodal fusion network that is particularly focused on
improving the robustness of signal analysis on imperfect data. We demonstrate
how appropriately handling multimodal information can be the key to achieving
such robustness. CoRe-Sleep tolerates noisy or missing modalities segments,
allowing training on incomplete data. Additionally, it shows state-of-the-art
performance when testing on both multimodal and unimodal data using a single
model on SHHS-1, the largest publicly available study that includes sleep stage
labels. The results indicate that training the model on multimodal data does
positively influence performance when tested on unimodal data. This work aims
at bridging the gap between automated analysis tools and their clinical
utility.Comment: 10 pages, 4 figures, 2 tables, journa
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
Concept Graph Neural Networks for Surgical Video Understanding
We constantly integrate our knowledge and understanding of the world to
enhance our interpretation of what we see.
This ability is crucial in application domains which entail reasoning about
multiple entities and concepts, such as AI-augmented surgery. In this paper, we
propose a novel way of integrating conceptual knowledge into temporal analysis
tasks via temporal concept graph networks. In the proposed networks, a global
knowledge graph is incorporated into the temporal analysis of surgical
instances, learning the meaning of concepts and relations as they apply to the
data. We demonstrate our results in surgical video data for tasks such as
verification of critical view of safety, as well as estimation of Parkland
grading scale. The results show that our method improves the recognition and
detection of complex benchmarks as well as enables other analytic applications
of interest
ARA-net: an attention-aware retinal atrophy segmentation network coping with fundus images
BackgroundAccurately detecting and segmenting areas of retinal atrophy are paramount for early medical intervention in pathological myopia (PM). However, segmenting retinal atrophic areas based on a two-dimensional (2D) fundus image poses several challenges, such as blurred boundaries, irregular shapes, and size variation. To overcome these challenges, we have proposed an attention-aware retinal atrophy segmentation network (ARA-Net) to segment retinal atrophy areas from the 2D fundus image.MethodsIn particular, the ARA-Net adopts a similar strategy as UNet to perform the area segmentation. Skip self-attention connection (SSA) block, comprising a shortcut and a parallel polarized self-attention (PPSA) block, has been proposed to deal with the challenges of blurred boundaries and irregular shapes of the retinal atrophic region. Further, we have proposed a multi-scale feature flow (MSFF) to challenge the size variation. We have added the flow between the SSA connection blocks, allowing for capturing considerable semantic information to detect retinal atrophy in various area sizes.ResultsThe proposed method has been validated on the Pathological Myopia (PALM) dataset. Experimental results demonstrate that our method yields a high dice coefficient (DICE) of 84.26%, Jaccard index (JAC) of 72.80%, and F1-score of 84.57%, which outperforms other methods significantly.ConclusionOur results have demonstrated that ARA-Net is an effective and efficient approach for retinal atrophic area segmentation in PM
A Design Science Research Approach to Smart and Collaborative Urban Supply Networks
Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness.
A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense.
Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice
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EEG microstates: Functional significance and short-term test-retest reliability
Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)
Desarrollo de una batería de memoria semántica para pacientes con epilepsia del lóbulo temporal
La epilepsia focal más frecuente es aquella epilepsia cuyo foco epileptógeno está localizado en el lóbulo temporal medial y es secundaria a una esclerosis con atrofia de la región amígdalo-hipocámpica, con una red epileptógena que abarca la porción anterior del lóbulo temporal. En ocasiones los pacientes requieren de un tratamiento quirúrgico que incluye la resección unilateral de ambas regiones, tanto del polo anterior, como del complejo amígdala-hipocampo. Estas estructuras han demostrado tener gran importancia para el procesamiento de la memoria semántica (región anterotemporal) y episódica (región amígdalo-hipocámpica), por lo que los pacientes que son sometidos a esta intervención suelen presentar quejas cognitivas relacionadas con ambos tipos de memoria. Sin embargo, parece que las evaluaciones neuropsicológicas que realizamos de forma rutinaria en las diferentes Unidades de Epilepsia no son capaces de detectar todos los problemas cognitivos que ocurren en estos pacientes ya que, a pesar de las dificultades expresadas por estos, las evaluaciones no muestran alteraciones. La hipótesis principal del presente trabajo es que estas quejas se deben a tipos de memoria que no están incluidos en las pruebas neuropsicológicas actuales y, por tanto, no somos capaces de identificar bien sus problemas. En primer lugar, se propone que la memoria semántica está afectada, pero solamente para palabras de baja frecuencia de uso en la vida diaria, no analizadas en las evaluaciones convencionales actuales. En segundo lugar, otros problemas no objetivados se deben a un problema de la memoria de consolidación, medida como olvido a largo plazo acelerado que se detecta cuando se amplia el periodo de evaluación del recuerdo. Además, estas alteraciones van a manifestarse con mayor intensidad en pacientes cuyo foco epileptógeno está localizado en el lóbulo temporal izquierdo. Los objetivos fundamentales de este trabajo son evaluar en pacientes con epilepsia del lóbulo temporal medial intervenidos quirúrgicamente mediante lobectomía temporal anterior con amigdalohipocampectomía la presencia de alteraciones de la memoria verbal tanto semántica como episódica, así como conocer su valor lateralizador según el hemisferio afectado. El estudio se basó en la comparación de pacientes con epilepsia del lóbulo temporal (ELT) tratados con lobectomía temporal anterior con amigdalohipocampectomía con un grupo control de personas sanas, comparables respecto a edad, nivel educativo y coeficiente intelectual (CI). Las pruebas de memoria semántica mostraron que únicamente los pacientes con ELT izquierda tenían alteraciones, especialmente para ítems de baja frecuencia y tanto en tares de expresión como de comprensión verbal. Asimismo, el tiempo de reacción fue mayor en el grupo de pacientes con ELT izquierda para todos los ítems y únicamente para las palabras o conceptos de baja frecuencia en aquellos con ELT derecha. Además, se incluyó una prueba de memoria episódica estándar (RAVLT) que en lugar de restringir la evaluación a 30 minutos, se evaluó a 7 días para medir el olvido a largo plazo. Los resultados mostraron que los dos grupos de pacientes, tanto los de ELT izquierda como aquellos con ELT derecha, desarrollaron olvido a largo plazo. Por último los resultados mostraron que la presencia de crisis epilépticas no afectó a la presencia de olvido a largo plazo acelerado
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