28 research outputs found
Trust Repair in Human-Swarm Teams+
Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trust-repair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions
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Flexible and Transparent High-Dielectric-Constant Polymer Films Based on Molecular Ferroelectric-Modified Poly(Vinyl Alcohol)
One-shot ultraspectral imaging with reconfigurable metasurfaces
One-shot spectral imaging that can obtain spectral information from thousands
of different points in space at one time has always been difficult to achieve.
Its realization makes it possible to get spatial real-time dynamic spectral
information, which is extremely important for both fundamental scientific
research and various practical applications. In this study, a one-shot
ultraspectral imaging device fitting thousands of micro-spectrometers (6336
pixels) on a chip no larger than 0.5 cm, is proposed and demonstrated.
Exotic light modulation is achieved by using a unique reconfigurable
metasurface supercell with 158400 metasurface units, which enables 6336
micro-spectrometers with dynamic image-adaptive performances to simultaneously
guarantee the density of spectral pixels and the quality of spectral
reconstruction. Additionally, by constructing a new algorithm based on
compressive sensing, the snapshot device can reconstruct ultraspectral imaging
information (/~0.001) covering a broad (300-nm-wide)
visible spectrum with an ultra-high center-wavelength accuracy of 0.04-nm
standard deviation and spectral resolution of 0.8 nm. This scheme of
reconfigurable metasurfaces makes the device can be directly extended to almost
any commercial camera with different spectral bands to seamlessly switch the
information between image and spectral image, and will open up a new space for
the application of spectral analysis combining with image recognition and
intellisense
MemDA: Forecasting Urban Time Series with Memory-based Drift Adaptation
Urban time series data forecasting featuring significant contributions to
sustainable development is widely studied as an essential task of the smart
city. However, with the dramatic and rapid changes in the world environment,
the assumption that data obey Independent Identically Distribution is
undermined by the subsequent changes in data distribution, known as concept
drift, leading to weak replicability and transferability of the model over
unseen data. To address the issue, previous approaches typically retrain the
model, forcing it to fit the most recent observed data. However, retraining is
problematic in that it leads to model lag, consumption of resources, and model
re-invalidation, causing the drift problem to be not well solved in realistic
scenarios. In this study, we propose a new urban time series prediction model
for the concept drift problem, which encodes the drift by considering the
periodicity in the data and makes on-the-fly adjustments to the model based on
the drift using a meta-dynamic network. Experiments on real-world datasets show
that our design significantly outperforms state-of-the-art methods and can be
well generalized to existing prediction backbones by reducing their sensitivity
to distribution changes.Comment: Accepted by CIKM 202
GW26-e4518 The diversity of BMI and WC on cardiac damage in patients from a cardiac rehabilitation program after acute coronary syndrome
Development and applications of chromosome-specific BAC-FISH probes in Pacific abalone (Haliotis discus hannai)
Pacific abalone (Haliotis discus hannai) is an economically important marine shellfish for aquaculture and is distributed throughout eastern Asia. Although a lot of genetic breeding work has been carried out, chromosome identification in abalone is still a challenging task. Here, we developed a set of BACs to be chromosome-specific probes in Pacific abalone, and to study chromosome evolution in the related species. Through BAC paired-end sequencing and sequence alignment, we were able to in silico anchor 168 BACs onto 18 pseudochromosomes of Pacific abalone genome. After selecting 42 BACs that contained DNA inserts with minimal repetitive sequences, we validated them through PCR and Fluorescence in situ hybridization (FISH) test. As a result, We obtained specific FISH signals for 26 clones on the chromosomes of Pacific abalone with at least one BAC mapped per chromosome. We also applied the chromosome-specific BAC-FISH probes to a close relative of Pacific abalone, Xishi abalone (H. gigantea), which revealed that chromosome 13 and 15 between the two species underwent a chromosomes rearrangement event. This study provides the first set of chromosome-specific probes for the family Haliotidae, which can serve as an important tool for future cytogenetics and genomics research