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Here, the magnetic susceptibility of a triangular-graphene-quantum-dot-like system was examined by using the determinant quantum Monte Carlo method. We focused on three zigzag edge quantum dots or rings, namely, the triangular graphene quantum ring, bilayer triangular graphene quantum dot, and bilayer triangular graphene quantum ring. The triangular-graphene-quantum-dot-like system exhibited robust edge ferromagnetic behavior, which was independent of size, monolayer or bilayer, or dot or ring shape, according to the numerical results. At half filling, the edge magnetic susceptibility is increased by on-site interactions, especially in the low-temperature region. Spintronics systems may benefit from use of this system due to its robust edge ferromagnetic behavior., Comment: 9 pages, 12 figures, to be published in Physical Review B

Monitoring plants and fruits at high resolution play a key role in the future of agriculture. Accurate 3D information can pave the way to a diverse number of robotic applications in agriculture ranging from autonomous harvesting to precise yield estimation. Obtaining such 3D information is non-trivial as agricultural environments are often repetitive and cluttered, and one has to account for the partial observability of fruit and plants. In this paper, we address the problem of jointly estimating complete 3D shapes of fruit and their pose in a 3D multi-resolution map built by a mobile robot. To this end, we propose an online multi-resolution panoptic mapping system where regions of interest are represented with a higher resolution. We exploit data to learn a general fruit shape representation that we use at inference time together with an occlusion-aware differentiable rendering pipeline to complete partial fruit observations and estimate the 7 DoF pose of each fruit in the map. The experiments presented in this paper evaluated both in the controlled environment and in a commercial greenhouse, show that our novel algorithm yields higher completion and pose estimation accuracy than existing methods, with an improvement of 41% in completion accuracy and 52% in pose estimation accuracy while keeping a low inference time of 0.6s in average. Codes are available at: https://github.com/PRBonn/HortiMapping., Comment: 8 pages, IROS 2023

We investigate the ability of human 'expert' classifiers to identify strong gravitational lens candidates in Dark Energy Survey like imaging. We recruited a total of 55 people that completed more than 25$\%$ of the project. During the classification task, we present to the participants 1489 images. The sample contains a variety of data including lens simulations, real lenses, non-lens examples, and unlabeled data. We find that experts are extremely good at finding bright, well-resolved Einstein rings, whilst arcs with $g$-band signal-to-noise less than $\sim$25 or Einstein radii less than $\sim$1.2 times the seeing are rarely recovered. Very few non-lenses are scored highly. There is substantial variation in the performance of individual classifiers, but they do not appear to depend on the classifier's experience, confidence or academic position. These variations can be mitigated with a team of 6 or more independent classifiers. Our results give confidence that humans are a reliable pruning step for lens candidates, providing pure and quantifiably complete samples for follow-up studies., Comment: 16 pages, 20 Figures

Recognizing human emotions from complex, multivariate, and non-stationary electroencephalography (EEG) time series is essential in affective brain-computer interface. However, because continuous labeling of ever-changing emotional states is not feasible in practice, existing methods can only assign a fixed label to all EEG timepoints in a continuous emotion-evoking trial, which overlooks the highly dynamic emotional states and highly non-stationary EEG signals. To solve the problems of high reliance on fixed labels and ignorance of time-changing information, in this paper we propose a time-aware sampling network (TAS-Net) using deep reinforcement learning (DRL) for unsupervised emotion recognition, which is able to detect key emotion fragments and disregard irrelevant and misleading parts. Extensive experiments are conducted on three public datasets (SEED, DEAP, and MAHNOB-HCI) for emotion recognition using leave-one-subject-out cross-validation, and the results demonstrate the superiority of the proposed method against previous unsupervised emotion recognition methods.

We report the discovery of five bright strong gravitationally lensed galaxies at $3 < z < 4$: COOLJ0101$+$2055 ($z = 3.459$), COOLJ0104$-$0757 ($z = 3.480$), COOLJ0145$+$1018 ($z = 3.310$), COOLJ0516$-$2208 ($z = 3.549$), and COOLJ1356$+$0339 ($z = 3.753$). These galaxies have magnitudes of $r_{\rm AB}, z_{\rm AB} < 21.81$ mag and are lensed by galaxy clusters at $0.26 < z < 1$. This sample nearly doubles the number of known bright lensed galaxies with extended arcs at $3 < z < 4$. We characterize the lensed galaxies using ground-based grz/giy imaging and optical spectroscopy. We report model-based magnitudes and derive stellar masses, dust content, and star-formation rates via stellar population synthesis modeling. Building lens models based on ground-based imaging, we estimate source magnifications in the range $\sim$29 to $\sim$180. Combining these analyses, we derive demagnified stellar masses in the range $\rm log_{10}(M_{*}/M_{\odot}) \sim 9.69 - 10.75$ and star formation rates in the youngest age bin ranging from $\rm log_{10}(SFR/(M_{\odot}\cdot yr^{-1})) \sim 0.39 - 1.46$, placing the sample galaxies on the massive end of the star-forming main sequence in this redshift interval. In addition, three of the five galaxies have strong Ly$\alpha$ emissions, offering unique opportunities to study Ly$\alpha$ emitters at high redshift in future work., Comment: 20 pages, 10 figures

Accurate mapping of large-scale environments is an essential building block of most outdoor autonomous systems. Challenges of traditional mapping methods include the balance between memory consumption and mapping accuracy. This paper addresses the problem of achieving large-scale 3D reconstruction using implicit representations built from 3D LiDAR measurements. We learn and store implicit features through an octree-based, hierarchical structure, which is sparse and extensible. The implicit features can be turned into signed distance values through a shallow neural network. We leverage binary cross entropy loss to optimize the local features with the 3D measurements as supervision. Based on our implicit representation, we design an incremental mapping system with regularization to tackle the issue of forgetting in continual learning. Our experiments show that our 3D reconstructions are more accurate, complete, and memory-efficient than current state-of-the-art 3D mapping methods., Comment: 6+1 pages, accepted at ICRA'23

Recognition of electroencephalographic (EEG) signals highly affect the efficiency of non-invasive brain-computer interfaces (BCIs). While recent advances of deep-learning (DL)-based EEG decoders offer improved performances, the development of geometric learning (GL) has attracted much attention for offering exceptional robustness in decoding noisy EEG data. However, there is a lack of studies on the merged use of deep neural networks (DNNs) and geometric learning for EEG decoding. We herein propose a manifold attention network (mAtt), a novel geometric deep learning (GDL)-based model, featuring a manifold attention mechanism that characterizes spatiotemporal representations of EEG data fully on a Riemannian symmetric positive definite (SPD) manifold. The evaluation of the proposed MAtt on both time-synchronous and -asyncronous EEG datasets suggests its superiority over other leading DL methods for general EEG decoding. Furthermore, analysis of model interpretation reveals the capability of MAtt in capturing informative EEG features and handling the non-stationarity of brain dynamics.

We use the Auriga simulations to probe different satellite quenching mechanisms operating at different mass scales ($10^5 M_\odot \lesssim M_\star \lesssim 10^{11} M_\odot$) in Milky Way-like hosts. Our goal is to understand the origin of the satellite colour distribution and star-forming properties in both observations and simulations. We find that the satellite populations in the Auriga simulations, which was originally designed to model Milky Way-like host galaxies, resemble the populations in the Exploration of Local VolumE Satellites (ELVES) Survey and the Satellites Around Galactic Analogs (SAGA) survey in their luminosity function in the luminosity range $-12 \lesssim M_V \lesssim -15$ and resemble ELVES in their quenched fraction and colour--magnitude distribution in the luminosity range $-12 \lesssim M_g \lesssim -15$. We find that satellites transition from blue colours to red colours at the luminosity range $-15 \lesssim M_g \lesssim -12$ in both the simulations and observations and we show that this shift is driven by environmental effects in the simulations. We demonstrate also that the colour distribution in both simulations and observations can be decomposed into two statistically distinct populations based on their morphological type or star-forming status that are statistically distinct. In the simulations, these two populations also have statistically distinct infall time distributions. The comparison presented here seems to indicate that the tension between the quenched fraction in SAGA and simulations is resolved by the improved target selection of ELVES, but there are still tensions in understanding the colours of faint galaxies, of which ELVES appears to have a significant population of faint blue satellites not recovered in Auriga., Comment: 15 pages, 10 figures. Published in MNRAS on December 15, 2022

We propose to develop a wide-field and ultra-high-precision photometric survey mission, temporarily named "Earth 2.0 (ET)". This mission is designed to measure, for the first time, the occurrence rate and the orbital distributions of Earth-sized planets. ET consists of seven 30cm telescopes, to be launched to the Earth-Sun's L2 point. Six of these are transit telescopes with a field of view of 500 square degrees. Staring in the direction that encompasses the original Kepler field for four continuous years, this monitoring will return tens of thousands of transiting planets, including the elusive Earth twins orbiting solar-type stars. The seventh telescope is a 30cm microlensing telescope that will monitor an area of 4 square degrees toward the galactic bulge. This, combined with simultaneous ground-based KMTNet observations, will measure masses for hundreds of long-period and free-floating planets. Together, the transit and the microlensing telescopes will revolutionize our understandings of terrestrial planets across a large swath of orbital distances and free space. In addition, the survey data will also facilitate studies in the fields of asteroseismology, Galactic archeology, time-domain sciences, and black holes in binaries., Comment: 116 pages,79 figures