23,624 research outputs found

    Scaling up integrated photonic reservoirs towards low-power high-bandwidth computing

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    Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control

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    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

    Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory

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    Mining data streams is one of the main studies in machine learning area due to its application in many knowledge areas. One of the major challenges on mining data streams is concept drift, which requires the learner to discard the current concept and adapt to a new one. Ensemble-based drift detection algorithms have been used successfully to the classification task but usually maintain a fixed size ensemble of learners running the risk of needlessly spending processing time and memory. In this paper we present improvements to the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for regression that employs social networks theory. In order to detect concept drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show improvements in accuracy, especially in concept drift situations and better performance compared to other state-of-the-art algorithms in both real and synthetic data

    RAFEN -- Regularized Alignment Framework for Embeddings of Nodes

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    Learning representations of nodes has been a crucial area of the graph machine learning research area. A well-defined node embedding model should reflect both node features and the graph structure in the final embedding. In the case of dynamic graphs, this problem becomes even more complex as both features and structure may change over time. The embeddings of particular nodes should remain comparable during the evolution of the graph, what can be achieved by applying an alignment procedure. This step was often applied in existing works after the node embedding was already computed. In this paper, we introduce a framework -- RAFEN -- that allows to enrich any existing node embedding method using the aforementioned alignment term and learning aligned node embedding during training time. We propose several variants of our framework and demonstrate its performance on six real-world datasets. RAFEN achieves on-par or better performance than existing approaches without requiring additional processing steps.Comment: ICCS 202

    Concept Graph Neural Networks for Surgical Video Understanding

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    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

    EMPRESS. XI. SDSS and JWST Search for Local and z~4-5 Extremely Metal-Poor Galaxies (EMPGs): Clustering and Chemical Properties of Local EMPGs

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    We search for local extremely metal-poor galaxies (EMPGs), selecting photometric candidates by broadband color excess and machine-learning techniques with the SDSS photometric data. After removing stellar contaminants by shallow spectroscopy with Seimei and Nayuta telescopes, we confirm that three candidates are EMPGs with 0.05--0.1 Z⊙Z_\odot by deep Magellan/MagE spectroscopy for faint {\sc[Oiii]}λ\lambda4363 lines. Using a statistical sample consisting of 105 spectroscopically-confirmed EMPGs taken from our study and the literature, we calculate cross-correlation function (CCF) of the EMPGs and all SDSS galaxies to quantify environments of EMPGs. Comparing another CCF of all SDSS galaxies and comparison SDSS galaxies in the same stellar mass range (107.0−108.4M⊙10^{7.0}-10^{8.4} M_\odot), we find no significant (>1σ>1\sigma) difference between these two CCFs. We also compare mass-metallicity relations (MZRs) of the EMPGs and those of galaxies at z∼z\sim 0--4 with a steady chemical evolution model and find that the EMPG MZR is comparable with the model prediction on average. These clustering and chemical properties of EMPGs are explained by a scenario of stochastic metal-poor gas accretion on metal-rich galaxies showing metal-poor star formation. Extending the broadband color-excess technique to a high-zz EMPG search, we select 17 candidates of z∼z\sim 4--5 EMPGs with the deep (≃30\simeq30 mag) near-infrared JWST/NIRCam images obtained by ERO and ERS programs. We find galaxy candidates with negligible {\sc[Oiii]}λλ\lambda\lambda4959,5007 emission weaker than the local EMPGs and known high-zz galaxies, suggesting that some of these candidates may fall in 0--0.01 Z⊙Z_\odot, which potentially break the lowest metallicity limit known to date

    Neural Architecture Search: Insights from 1000 Papers

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    In the past decade, advances in deep learning have resulted in breakthroughs in a variety of areas, including computer vision, natural language understanding, speech recognition, and reinforcement learning. Specialized, high-performing neural architectures are crucial to the success of deep learning in these areas. Neural architecture search (NAS), the process of automating the design of neural architectures for a given task, is an inevitable next step in automating machine learning and has already outpaced the best human-designed architectures on many tasks. In the past few years, research in NAS has been progressing rapidly, with over 1000 papers released since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized and comprehensive guide to neural architecture search. We give a taxonomy of search spaces, algorithms, and speedup techniques, and we discuss resources such as benchmarks, best practices, other surveys, and open-source libraries

    Quantifying the retention of emotions across story retellings

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    Story retelling is a fundamental medium for the transmission of information between individuals and among social groups. Besides conveying factual information, stories also contain affective information. Though natural language processing techniques have advanced considerably in recent years, the extent to which machines can be trained to identify and track emotions across retellings is unknown. This study leverages the powerful RoBERTa model, based on a transformer architecture, to derive emotion-rich story embeddings from a unique dataset of 25,728 story retellings. The initial stories were centered around five emotional events (joy, sadness, embarrassment, risk, and disgust—though the stories did not contain these emotion words) and three intensities (high, medium, and low). Our results indicate (1) that RoBERTa can identify emotions in stories it was not trained on, (2) that the five emotions and their intensities are preserved when they are transmitted in the form of retellings, (3) that the emotions in stories are increasingly well-preserved as they experience additional retellings, and (4) that among the five emotions, risk and disgust are least well-preserved, compared with joy, sadness, and embarrassment. This work is a first step toward quantifying situation-driven emotions with machines

    Prompt Detection of Fast Optical Bursts with the Vera C. Rubin Observatory

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    The transient optical sky has remained largely unexplored on very short timescales. While there have been some experiments searching for optical transients from minutes to years, none have had the capability to distinguish millisecond Fast Optical Bursts (FOB). Such very fast transients could be the optical counterparts of Fast Radio Bursts (FRB), the prompt emission from γ\gamma-Ray Bursts (GRB), or other previously unknown phenomena. Here, we investigate a novel approach to the serendipitous detection of FOBs, which relies on searching for anomalous spatial images. In particular, due to their short duration, the seeing distorted images of FOBs should look characteristically different than those of steady sources in a standard optical exposure of finite duration. We apply this idea to simulated observations with the Vera C. Rubin Observatory, produced by tracing individual photons through a turbulent atmosphere, and down through the optics and camera of the Rubin telescope. We compare these simulated images to steady-source star simulations in 15 s integrations, the nominal Rubin exposure time. We report the classification accuracy results of a Neural Network classifier for distinguishing FOBs from steady sources. From this classifier, we derive constraints in duration-intensity parameter space for unambiguously identifying FOBs in Rubin observations. We conclude with estimates of the total number of detections of FOB counterparts to FRBs expected during the 10-year Rubin Legacy Survey of Space and Time (LSST).Comment: 7 pages, 4 figures, submitted to the Astrophysical Journa

    OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI

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    Contrails (condensation trails) are line-shaped ice clouds caused by aircraft and are likely the largest contributor of aviation-induced climate change. Contrail avoidance is potentially an inexpensive way to significantly reduce the climate impact of aviation. An automated contrail detection system is an essential tool to develop and evaluate contrail avoidance systems. In this paper, we present a human-labeled dataset named OpenContrails to train and evaluate contrail detection models based on GOES-16 Advanced Baseline Imager (ABI) data. We propose and evaluate a contrail detection model that incorporates temporal context for improved detection accuracy. The human labeled dataset and the contrail detection outputs are publicly available on Google Cloud Storage at gs://goes_contrails_dataset
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