23,624 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
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
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
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
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
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 by deep Magellan/MagE
spectroscopy for faint {\sc[Oiii]}4363 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 (), we find no significant ()
difference between these two CCFs. We also compare mass-metallicity relations
(MZRs) of the EMPGs and those of galaxies at 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- EMPG search, we select 17 candidates of
4--5 EMPGs with the deep ( mag) near-infrared JWST/NIRCam
images obtained by ERO and ERS programs. We find galaxy candidates with
negligible {\sc[Oiii]}4959,5007 emission weaker than the local
EMPGs and known high- galaxies, suggesting that some of these candidates may
fall in 0--0.01 , which potentially break the lowest metallicity limit
known to date
Neural Architecture Search: Insights from 1000 Papers
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
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
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
-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
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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