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Using medium-band imaging from the newly released Merian Survey, we conduct a nonparametric morphological analysis of H$\alpha$ emission maps and stellar continua for a sample of galaxies with $8\lesssim\log (M_\star/M_\odot) < 10.3$ at $0.064

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

By employing determinant quantum Monte Carlo simulations, we investigate a checkerboard lattice with next-nearest-neighbor hopping $t'$ as the frustration-control parameter, which exhibits an energetically partial flat-band in the system. Our numerical simulation identifies the dominant pairing symmetry of the checkerboard lattice Hubbard model, and we reveal the competition between the $d$-wave and $d+is$ wave in the parameter space of electron filling $\avg{n}$ and frustration control parameter $t^{\prime}/t$. To ensure the reliability and accuracy of our results, we evaluate the sign problem. We also find that the spin susceptibility, the effective pairing interactions of different pairing symmetries and the superconducting instability are enhanced as the on-site Coulomb interaction increases, demonstrating that superconductivity is driven by strong electron--electron correlation. Our work provides a further understanding of pairing symmetry in the Hubbard model and improves prospects for exploring rich correlated behaviors in frustrated systems., Comment: 9 pages and 13 figures

This paper proposes a spatial threshold GARCH-type model for dynamic spatio-temporal integer-valued data with network structure. The proposed model can simplify the parameterization by using network structure in data, and can capture the asymmetric property in dynamic volatility by adopting a threshold structure. The proposed model assumes the conditional distribution is Poisson distribution. Asymptotic theory of maximum likelihood estimation (MLE) for the spatial model is derived when both sample size and network dimension are large. We obtain asymptotic statistical inferences via investigation of the weak dependence of components of the model and application of limit theorems for weakly dependent random fields. Simulation studies and a real data example are presented to support our methodology.

As Moore's Law gains diminish, software performance and efficiency become increasingly vital. Optimizing code efficiency is challenging, even for professional programmers. However, related research remains relatively scarce, and rigorously assessing models' abilities to optimize code efficiency is fraught with difficulties. In response to this challenge, we first conduct an in-depth analysis of "code patterns" in the model training dataset, meticulously exploring human-written code. Secondly, we define a task for optimizing code efficiency and introduce the Automatic Code Efficiency Optimization Benchmark (ACEOB), which consists of 95,359 pairs of efficient-inefficient code aimed at assessing code efficiency optimization capabilities. To our knowledge, ACEOB is the first dataset specifically targeting Python code efficiency optimization. To evaluate models' ability in optimizing code efficiency, we propose two new metrics: the Isomorphic Optimal Comparison CodeBLEU (IOCCB) metric and the Normalized Performance Index (NPI) metric, to assess the efficiency of model-generated code. We also evaluate several advanced code models, such as PolyCoder and CodeT5, after fine-tuning them on ACEOB and demonstrate that the efficiency of each model improves after introducing the NPI filter. However, it was observed that even ChatGPT does not perform optimally in code efficiency optimization tasks.

Context: With the waning of Moore's Law, the software industry is placing increasing importance on finding alternative solutions for continuous performance enhancement. The significance and research results of software performance optimization have been on the rise in recent years, especially with the advancement propelled by Large Language Models(LLMs). However, traditional strategies for rectifying performance flaws have shown significant limitations at the competitive code efficiency optimization level, and research on this topic is surprisingly scarce. Objective: This study aims to address the research gap in this domain, offering practical solutions to the various challenges encountered. Specifically, we have overcome the constraints of traditional performance error rectification strategies and developed a Language Model (LM) tailored for the competitive code efficiency optimization realm. Method: We introduced E-code, an advanced program synthesis LM. Inspired by the recent success of expert LMs, we designed an innovative structure called the Expert Encoder Group. This structure employs multiple expert encoders to extract features tailored for different input types. We assessed the performance of E-code against other leading models on a competitive dataset and conducted in-depth ablation experiments. Results: Upon systematic evaluation, E-code achieved a 54.98% improvement in code efficiency, significantly outperforming other advanced models. In the ablation experiments, we further validated the significance of the expert encoder group and other components within E-code. Conclusion: The research findings indicate that the expert encoder group can effectively handle various inputs in efficiency optimization tasks, significantly enhancing the model's performance.

In this work, we investigate the hidden charm decays properties of $Y(4626)$, where $Y(4626)$ is assigned as a $S-$wave $D_{s}^{*+}D_{s1}(2536)^{-}$ molecular state with $J^{PC}=1^{--}$. The partial widths of the processes $Y(4626)\to J/\psi\eta$, $J/\psi\eta^{\prime}$, $\eta_{c}\phi$, and $ \chi_{cJ}\phi,\ (J=\{0,1,2\})$ are estimated by employing the effective Lagrangian approach. The present estimations indicate that the partial widths of the $J/\psi\eta$ and $J/\psi \eta^\prime$ channels are of the order of 1 MeV, while the one of $\chi_{c1}\phi$ is of the order of 0.1 MeV. Thus, we propose to further examine the molecular interpretation of $Y(4626)$ by searching it in the cross sections for the $e^{+}e^{-}\to J/\psi\eta^{(\prime)}$ processes, which should be accessible by the BES III and Belle II., Comment: 8 pages, 5 figures

We obtain the law of large numbers (LLN) and the central limit theorem (CLT) for weakly dependent non-stationary arrays of random fields with asymptotically unbounded moments. The weak dependence condition for arrays of random fields is proved to be inherited through transformation and infinite shift. This paves a way to prove the consistency and asymptotic normality of maximum likelihood estimation for dynamic spatio-temporal models (i.e. so-called ultra high-dimensional time series models) when the sample size and/or dimension go to infinity. Especially the asymptotic properties of estimation for network autoregression are obtained under reasonable regularity conditions.

P-wave superconductors hold immense promise for both fundamental physics and practical applications due to their unusual pairing symmetry and potential topological superconductivity. However, the exploration of the p-wave superconductors has proved to be a complex endeavor. Not only are they rare in nature but also the identification of p-wave superconductors has been an arduous task in history. For example, phase-sensitive measurement, an experimental technique which can provide conclusive evidence for unconventional pairing, has not been implemented successfully to identify p-wave superconductors. Here, we study a recently discovered family of superconductors, A2Cr3As3 (A = K, Rb, Cs), which were proposed theoretically to be a candidate of p-wave superconductors. We fabricate superconducting quantum interference devices (SQUIDs) on exfoliated K2Cr3As3, and perform the phase-sensitive measurement. We observe that such SQUIDs exhibit a pronounced second-order harmonic component sin(2{\phi}) in the current-phase relation, suggesting the admixture of 0- and {\pi}-phase. By carefully examining the magnetic field dependence of the oscillation patterns of critical current and Shapiro steps under microwave irradiation, we reveal a crossover from 0- to {\pi}-dominating phase state and conclude that the existence of the {\pi}-phase is in favor of the p-wave pairing symmetry in K2Cr3As3.

Wide-separation lensed quasars (WSLQs) are a rare class of strongly lensed quasars, magnified by foreground massive galaxy clusters, with typically large magnifications of the multiple quasar images. They are a relatively unexplored opportunity for detailed study of quasar host galaxies. The current small sample of known WSLQs has a median redshift of $z\approx 2.1$, larger than most other samples of quasar host galaxies studied to date. Here, we derive precise constraints on the properties of six WSLQs and their host galaxies, using parametric surface brightness fitting, measurements of quasar emission lines, and stellar population synthesis of host galaxies in six WSLQ systems. Our results, with significant uncertainty, indicate that these six hosts are a mixture of star-forming and quiescent galaxies. To probe for co-evolution between AGNs and host galaxies, we model the offset from the `local' ($z=0$) $M_{\rm{BH}}\unicode{x2013}M_\star$ relation as a simple power-law in redshift. Accounting for selection effects, a WSLQ-based model for evolution in the $M_{\rm{BH}}\unicode{x2013}M_\star$ relation has a power-law index of $\gamma_M=-0.42\pm0.31$, consistent with no evolution. Compared to several literature samples, which mostly probe unlensed quasars at $z<2$, the WSLQ sample shows less evolution from the local relation, at $\sim 4\sigma$. We find that selection affects and choices of $M_{\rm{BH}}$ calibration are the most important systematics in these comparisons. Given that we resolve host galaxy flux confidently even from the ground in some instances, our work demonstrates that WSLQs and highly magnified AGNs are exceptional systems for future AGN$\unicode{x2013}$host co-evolution studies., Comment: Submitted to ApJ. 25 pages + 7-page appendix, 12+4 figures. Key results are shown starting with Figure 6. Comments welcome

Affective brain-computer interfaces (aBCIs) are increasingly recognized for their potential in monitoring and interpreting emotional states through electroencephalography (EEG) signals. Current EEG-based emotion recognition methods perform well with short segments of EEG data. However, these methods encounter significant challenges in real-life scenarios where emotional states evolve over extended periods. To address this issue, we propose a Dual Attentive (DuA) transformer framework for long-term continuous EEG emotion analysis. Unlike segment-based approaches, the DuA transformer processes an entire EEG trial as a whole, identifying emotions at the trial level, referred to as trial-based emotion analysis. This framework is designed to adapt to varying signal lengths, providing a substantial advantage over traditional methods. The DuA transformer incorporates three key modules: the spatial-spectral network module, the temporal network module, and the transfer learning module. The spatial-spectral network module simultaneously captures spatial and spectral information from EEG signals, while the temporal network module detects temporal dependencies within long-term EEG data. The transfer learning module enhances the model's adaptability across different subjects and conditions. We extensively evaluate the DuA transformer using a self-constructed long-term EEG emotion database, along with two benchmark EEG emotion databases. On the basis of the trial-based leave-one-subject-out cross-subject cross-validation protocol, our experimental results demonstrate that the proposed DuA transformer significantly outperforms existing methods in long-term continuous EEG emotion analysis, with an average enhancement of 5.28%., Comment: 11 pages, 3 figures

As the world population is expected to reach 10 billion by 2050, our agricultural production system needs to double its productivity despite a decline of human workforce in the agricultural sector. Autonomous robotic systems are one promising pathway to increase productivity by taking over labor-intensive manual tasks like fruit picking. To be effective, such systems need to monitor and interact with plants and fruits precisely, which is challenging due to the cluttered nature of agricultural environments causing, for example, strong occlusions. Thus, being able to estimate the complete 3D shapes of objects in presence of occlusions is crucial for automating operations such as fruit harvesting. In this paper, we propose the first publicly available 3D shape completion dataset for agricultural vision systems. We provide an RGB-D dataset for estimating the 3D shape of fruits. Specifically, our dataset contains RGB-D frames of single sweet peppers in lab conditions but also in a commercial greenhouse. For each fruit, we additionally collected high-precision point clouds that we use as ground truth. For acquiring the ground truth shape, we developed a measuring process that allows us to record data of real sweet pepper plants, both in the lab and in the greenhouse with high precision, and determine the shape of the sensed fruits. We release our dataset, consisting of almost 7,000 RGB-D frames belonging to more than 100 different fruits. We provide segmented RGB-D frames, with camera intrinsics to easily obtain colored point clouds, together with the corresponding high-precision, occlusion-free point clouds obtained with a high-precision laser scanner. We additionally enable evaluation of shape completion approaches on a hidden test set through a public challenge on a benchmark server.

Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose a novel approach for building accurate maps of dynamic environments utilizing a sequence of LiDAR scans. To this end, we propose encoding the 4D scene into a novel spatio-temporal implicit neural map representation by fitting a time-dependent truncated signed distance function to each point. Using our representation, we extract the static map by filtering the dynamic parts. Our neural representation is based on sparse feature grids, a globally shared decoder, and time-dependent basis functions, which we jointly optimize in an unsupervised fashion. To learn this representation from a sequence of LiDAR scans, we design a simple yet efficient loss function to supervise the map optimization in a piecewise way. We evaluate our approach on various scenes containing moving objects in terms of the reconstruction quality of static maps and the segmentation of dynamic point clouds. The experimental results demonstrate that our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps, outperforming several state-of-the-art methods. Codes are available at: https://github.com/PRBonn/4dNDF, Comment: 10 pages, CVPR 2024

We conducted an in-depth analysis of candidate member stars located in the peripheries of three ultra-faint dwarf (UFD) galaxy satellites of the Milky Way: Bo\"otes I (Boo1), Bo\"otes II (Boo2), and Segue I (Seg1). Studying these peripheral stars has previously been difficult due to contamination from the Milky Way foreground. We used $u$-band photometry from the Dark Energy Camera (DECam) to derive metallicities to efficiently select UFD candidate member stars. This approach was validated on Boo1, where we identified both previously known and new candidate member stars beyond five half-light radii. We then applied a similar procedure to Boo2 and Seg1. Our findings hinted at evidence for tidal features in Boo1 and Seg1, with Boo1 having an elongation consistent with its proper motion and Seg1 showing some distant candidate stars, a few of which are along its elongation and proper motion. We find two Boo2 stars at large distances consistent with being candidate member stars. Using a foreground contamination rate derived from the \emph{Besan\c{c}on} Galaxy model, we ascribed purity estimates to each candidate member star. We recommend further spectroscopic studies on the newly identified high-purity members. Our technique offers promise for future endeavors to detect candidate member stars at large radii in other systems, leveraging metallicity-sensitive filters with the Legacy Survey of Space and Time and the new, narrow-band Ca HK filter on DECam., Comment: 25 pages, 13 figures, machine-readable Tables 3, 4, 5 in source. Submitted to ApJ. Comments welcome!

Many autonomous robotic applications require object-level understanding when deployed. Actively reconstructing objects of interest, i.e. objects with specific semantic meanings, is therefore relevant for a robot to perform downstream tasks in an initially unknown environment. In this work, we propose a novel framework for semantic-targeted active reconstruction using posed RGB-D measurements and 2D semantic labels as input. The key components of our framework are a semantic implicit neural representation and a compatible planning utility function based on semantic rendering and uncertainty estimation, enabling adaptive view planning to target objects of interest. Our planning approach achieves better reconstruction performance in terms of mesh and novel view rendering quality compared to implicit reconstruction baselines that do not consider semantics for view planning. Our framework further outperforms a state-of-the-art semantic-targeted active reconstruction pipeline based on explicit maps, justifying our choice of utilising implicit neural representations to tackle semantic-targeted active reconstruction problems.

Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation. Taking range measurements as input, our approach alternates between incremental learning of the local implicit signed distance field and the pose estimation given the current local map using a correspondence-free, point-to-implicit model registration. Our implicit map is based on sparse optimizable neural points, which are inherently elastic and deformable with the global pose adjustment when closing a loop. Loops are also detected using the neural point features. Extensive experiments validate that PIN-SLAM is robust to various environments and versatile to different range sensors such as LiDAR and RGB-D cameras. PIN-SLAM achieves pose estimation accuracy better or on par with the state-of-the-art LiDAR odometry or SLAM systems and outperforms the recent neural implicit SLAM approaches while maintaining a more consistent, and highly compact implicit map that can be reconstructed as accurate and complete meshes. Finally, thanks to the voxel hashing for efficient neural points indexing and the fast implicit map-based registration without closest point association, PIN-SLAM can run at the sensor frame rate on a moderate GPU. Codes will be available at: https://github.com/PRBonn/PIN_SLAM., Comment: 20 pages

We compute parametric measurements of the Einstein-radius-enclosed total mass for 177 cluster-scale strong gravitational lenses identified by the ChicagO Optically-selected Lenses Located At the Margins of Public Surveys (COOL-LAMPS) collaboration with lens redshifts ranging from $0.2 \lessapprox z \lessapprox 1.0$ using only two measured parameters in each lensing system: the Einstein radius, and the brightest-cluster-galaxy (BCG) redshift. We then constrain the Einstein-radius-enclosed luminosity and stellar mass by fitting parametric spectral energy distributions (SEDs) with aperture photometry from the Dark Energy Camera Legacy Survey (DECaLS) in the $g$, $r$, and $z$-band Dark Energy Camera (DECam) filters. We find that the BCG redshift, enclosed total mass, and enclosed luminosity are strongly correlated and well described by a planar relationship in 3D space. We also find that the enclosed total mass and stellar mass are correlated with a logarithmic slope of $0.443\pm0.035$, and the enclosed total mass and stellar-to-total mass fraction are correlated with a logarithmic slope of $-0.563\pm0.035$. The correlations described here can be used to validate strong lensing candidates in upcoming imaging surveys -- such as Rubin/Legacy Survey of Space and Time (LSST) -- in which an algorithmic treatment of lensing systems will be needed due to the sheer volume of data these surveys will produce., Comment: 17 pages, 5 figures, 2 tables. Submitted to The Astrophysical Journal. v3: updated authors, formatting, grammar, and references

We investigate the impact of bursty star formation on several galaxy scaling relations of dwarf galaxies using the $\texttt{GRUMPY}$ galaxy formation model. While this model reproduces the star formation rate (SFR)-stellar mass, stellar mass-gas mass, and stellar mass-metallicity relations, the scatter of these relations in the original model is smaller than observed. We explore the effects of additional stochasticity of SFR on the scaling relations using a model that reproduces the level of SFR burstiness in high-resolution zoom-in simulations. The additional SFR stochasticity increases the scatter in the SFR-stellar mass relation to a level similar to that exhibited by most nearby dwarf galaxies. The most extreme observed starbursting dwarfs, however, require higher levels of SFR stochasticity. We find that bursty star formation increases the scatter in the colour-magnitude distribution (CMD) for brighter dwarf galaxies $(M_V < -12)$ to the observed level, but not for fainter ones for which scatter remains significantly smaller than observed. This is due to the predominant old stellar populations in these faint model galaxies and their generally declining SFR over the past 10 Gyrs, rather than quenching caused by reionization. We examine the possibility that the colour scatter is due to scatter in metallicity, but show that the level of scatter required leads to an overestimation of scatter in the metallicity-mass relation. This illustrates that the scatter of observed scaling relations in the dwarf galaxy regime represents a powerful constraint on the properties of their star formation., Comment: 14 pages, 8 figures, submitted to MNRAS

Liveness Detection (LivDet) is an international competition series open to academia and industry with the objec-tive to assess and report state-of-the-art in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems. The competition serves as an important benchmark in noncontact-based fingerprint PAD, offering (a) independent assessment of the state-of-the-art in noncontact-based fingerprint PAD for algorithms and systems, and (b) common evaluation protocol, which includes finger photos of a variety of Presentation Attack Instruments (PAIs) and live fingers to the biometric research community (c) provides standard algorithm and system evaluation protocols, along with the comparative analysis of state-of-the-art algorithms from academia and industry with both old and new android smartphones. The winning algorithm achieved an APCER of 11.35% averaged overall PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%, averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68% over all smartphones tested. Four-finger systems that make individual finger-based PAD decisions were also tested. The dataset used for competition will be available 1 to all researchers as per data share protocol

Observations of high-redshift galaxies with unprecedented detail have now been rendered possible with JWST. However, accurately quantifying their morphology remains uncertain due to potential biases and uncertainties. To address this issue, we used a sample of 1816 nearby DESI galaxies, with a mass range of $10^{9.75-11.25}M_{\odot}$, to compute artificial images of galaxies of the same mass located at $0.75\leq z\leq 3$ and observed at rest-frame optical wavelength in CEERS. We analyzed the effects of cosmological redshift on the measurements of Petrosian radius ($R_p$), half-light radius ($R_{50}$), asymmetry ($A$), concentration ($C$), axis ratio ($q$), and S\'ersic index ($n$). Our results show that $R_p$ and $R_{50}$, calculated using non-parametric methods, are slightly overestimated due to PSF smoothing, while $R_{50}$, $q$, and $n$ obtained through model fitting does not exhibit significant biases. We improve the computation of $A$ by incorporating a more accurate noise effect removal procedure. Due to PSF asymmetry, there is a minor overestimation of $A$ for intrinsically symmetric galaxies. However, for intrinsically asymmetric galaxies, PSF smoothing dominates and results in an underestimation of $A$, an effect that becomes more significant with higher intrinsic $A$ or at lower resolutions. Moreover, PSF smoothing also leads to an underestimation of $C$, which is notably more pronounced in galaxies with higher intrinsic $C$ or at lower resolutions. We developed functions based on resolution level, defined as $R_p/$FWHM, for correcting these biases and the associated statistical uncertainties. Applying these corrections, we measured the bias-corrected morphology for the simulated CEERS images and we find that the derived quantities are in good agreement with their intrinsic values -- except for $A$, which is robust only for angularly large galaxies where $R_p/{\rm FWHM}\geq 5$., Comment: 21 pages, 17 figures; A&A in press

A growing number of applications depend on Machine Learning (ML) functionality and benefits from both higher quality ML predictions and better timeliness (latency) at the same time. A growing body of research in computer architecture, ML, and systems software literature focuses on reaching better latency-accuracy tradeoffs for ML models. Efforts include compression, quantization, pruning, early-exit models, mixed DNN precision, as well as ML inference accelerator designs that minimize latency and energy, while preserving delivered accuracy. All of them, however, yield improvements for a single static point in the latency-accuracy tradeoff space. We make a case for applications that operate in dynamically changing deployment scenarios, where no single static point is optimal. We draw on a recently proposed weight-shared SuperNet mechanism to enable serving a stream of queries that uses (activates) different SubNets within this weight-shared construct. This creates an opportunity to exploit the inherent temporal locality with our proposed SubGraph Stationary (SGS) optimization. We take a hardware-software co-design approach with a real implementation of SGS in SushiAccel and the implementation of a software scheduler SushiSched controlling which SubNets to serve and what to cache in real-time. Combined, they are vertically integrated into SUSHI-an inference serving stack. For the stream of queries, SUSHI yields up to 25% improvement in latency, 0.98% increase in served accuracy. SUSHI can achieve up to 78.7% off-chip energy savings., Comment: 16 pages; MLSYS 2023

Using the determinant quantum Monte Carlo method, we study the magnetic susceptibility in the parameter space of the on-site interaction $U$, temperature $T$, electron filling $\avg{n}$, and the frustration control parameter $t^{\prime}$ within the Hubbard model on a two-dimensional checkerboard lattice. It is shown that the system exhibits stable and strong ferromagnetic fluctuations about the electron filling $\avg{n}\ge1.2$ for different $t^{\prime}$, and the ferromagnetic susceptibility is strongly enhanced by the increasing interaction and decreasing tempeture. We also discuss the sign problem to clarify which parameter region is accessible and reliable. Our findings not only demonstrate important implications for modulating magnetism in the checkerboard lattice, but will also provide a theoretical platform for a flat-band model that demonstrates a variety of physical properties., Comment: 6 pages, 6 figures

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

Pulmonary respiratory motion artifacts are common in four-dimensional computed tomography (4DCT) of lungs and are caused by missing, duplicated, and misaligned image data. This paper presents a geodesic density regression (GDR) algorithm to correct motion artifacts in 4DCT by correcting artifacts in one breathing phase with artifact-free data from corresponding regions of other breathing phases. The GDR algorithm estimates an artifact-free lung template image and a smooth, dense, 4D (space plus time) vector field that deforms the template image to each breathing phase to produce an artifact-free 4DCT scan. Correspondences are estimated by accounting for the local tissue density change associated with air entering and leaving the lungs, and using binary artifact masks to exclude regions with artifacts from image regression. The artifact-free lung template image is generated by mapping the artifact-free regions of each phase volume to a common reference coordinate system using the estimated correspondences and then averaging. This procedure generates a fixed view of the lung with an improved signal-to-noise ratio. The GDR algorithm was evaluated and compared to a state-of-the-art geodesic intensity regression (GIR) algorithm using simulated CT time-series and 4DCT scans with clinically observed motion artifacts. The simulation shows that the GDR algorithm has achieved significantly more accurate Jacobian images and sharper template images, and is less sensitive to data dropout than the GIR algorithm. We also demonstrate that the GDR algorithm is more effective than the GIR algorithm for removing clinically observed motion artifacts in treatment planning 4DCT scans. Our code is freely available at https://github.com/Wei-Shao-Reg/GDR., Comment: Accepted to the journal Medical Image Analysis (MedIA)

The rapid development of autonomous driving and mobile mapping calls for off-the-shelf LiDAR SLAM solutions that are adaptive to LiDARs of different specifications on various complex scenarios. To this end, we propose MULLS, an efficient, low-drift, and versatile 3D LiDAR SLAM system. For the front-end, roughly classified feature points (ground, facade, pillar, beam, etc.) are extracted from each frame using dual-threshold ground filtering and principal components analysis. Then the registration between the current frame and the local submap is accomplished efficiently by the proposed multi-metric linear least square iterative closest point algorithm. Point-to-point (plane, line) error metrics within each point class are jointly optimized with a linear approximation to estimate the ego-motion. Static feature points of the registered frame are appended into the local map to keep it updated. For the back-end, hierarchical pose graph optimization is conducted among regularly stored history submaps to reduce the drift resulting from dead reckoning. Extensive experiments are carried out on three datasets with more than 100,000 frames collected by seven types of LiDAR on various outdoor and indoor scenarios. On the KITTI benchmark, MULLS ranks among the top LiDAR-only SLAM systems with real-time performance., Comment: Codes: https://github.com/YuePanEdward/MULLS, Accepted by ICRA 2021

Remote sensing image fusion technology (pan-sharpening) is an important means to improve the information capacity of remote sensing images. Inspired by the efficient arameter space posteriori sampling of Bayesian neural networks, in this paper we propose a Bayesian Generative Adversarial Network based on Preconditioned Stochastic Gradient Langevin Dynamics (PGSLD-BGAN) to improve pan-sharpening tasks. Unlike many traditional generative models that consider only one optimal solution (might be locally optimal), the proposed PGSLD-BGAN performs Bayesian inference on the network parameters, and explore the generator posteriori distribution, which assists selecting the appropriate generator parameters. First, we build a two-stream generator network with PAN and MS images as input, which consists of three parts: feature extraction, feature fusion and image reconstruction. Then, we leverage Markov discriminator to enhance the ability of generator to reconstruct the fusion image, so that the result image can retain more details. Finally, introducing Preconditioned Stochastic Gradient Langevin Dynamics policy, we perform Bayesian inference on the generator network. Experiments on QuickBird and WorldView datasets show that the model proposed in this paper can effectively fuse PAN and MS images, and be competitive with even superior to state of the arts in terms of subjective and objective metrics.

Point cloud registration has been one of the basic steps of point cloud processing, which has a lot of applications in remote sensing and robotics. In this report, we summarized the basic workflow of target-less point cloud registration,namely correspondence determination and transformation estimation. Then we reviewed three commonly used groups of registration approaches, namely the feature matching based methods, the iterative closest points algorithm and the randomly hypothesis and verify based methods. Besides, we analyzed the advantage and disadvantage of these methods are introduced their common application scenarios. At last, we discussed the challenges of current point cloud registration methods and proposed several open questions for the future development of automatic registration approaches., Comment: 9 pages, 14 figures, written as the final report of the geomatics seminar at ETH Zurich

Coupled quantum dots (QDs), usually referred to as artificial molecules, are important not only in exploring fundamental physics of coupled quantum objects, but also in realizing advanced QD devices. However, previous studies have been limited to artificial molecules with nonrelativistic fermions. Here, we show that relativistic artificial molecules can be realized when two circular graphene QDs are coupled to each other. Using scanning tunneling microscopy (STM) and spectroscopy (STS), we observe the formation of bonding and antibonding states of the relativistic artificial molecule and directly visualize these states of the two coupled graphene QDs. The formation of the relativistic molecular states strongly alters distributions of massless Dirac fermions confined in the graphene QDs. Because of the relativistic nature of the molecular states, our experiment demonstrates that the degeneracy of different angular-momentum states in the relativistic artificial molecule can be further lifted by external magnetic fields. Then, both the bonding and antibonding states are split into two peaks.

NuRadioMC is a Monte Carlo framework designed to simulate ultra-high energy neutrino detectors that rely on the radio detection method. This method exploits the radio emission generated in the electromagnetic component of a particle shower following a neutrino interaction. NuRadioMC simulates everything from the neutrino interaction in a medium, the subsequent Askaryan radio emission, the propagation of the radio signal to the detector and finally the detector response. NuRadioMC is designed as a modern, modular Python-based framework, combining flexibility in detector design with user-friendliness. It includes a state-of-the-art event generator, an improved modelling of the radio emission, a revisited approach to signal propagation and increased flexibility and precision in the detector simulation. This paper focuses on the implemented physics processes and their implications for detector design. A variety of models and parameterizations for the radio emission of neutrino-induced showers are compared and reviewed. Comprehensive examples are used to discuss the capabilities of the code and different aspects of instrumental design decisions., Comment: replaced with published version

In this paper, we propose a workflow that uses Terrestrial Laser Scanning(TLS) to semi-automatically monitor landslide and then test it in practice. Firstly, several groups of TLS stations are set on different time to collect the raw point cloud of the object mountain. Next, Hierarchical Merging Based Multi-view (HMMR) registration algorithm is adapted to accomplish single-phase multi-view registration.In order to analyze deformation between multiple periods, Iterative Global Similarity Point (IGSP) algorithm is applied to accomplish multiple-phase registration, which outperforms ICP in experiments. Then the cloth simulation filtering (CSF) algorithm was used together with manual post-processing to remove vegetation on the slope. After that, the mountain slope's digital terrain model (DTM) is generated for each period, and the distance between adjacent DTMs are calculated as the landslide deformation mass. Furthermore, average deformation rate of the landslide surface is calculated and analyzed.To validate the effectiveness of proposed workflow, we uses the TLS data of five periods of the landslide in the Shanhou village of northern Changshan Island from 2013 to 2015. The results indicate that the method can obtain centimeter-level deformation monitoring accuracy which can effectively monitor and analyze long-term landslide morphology and trend as well as position the significant deformation area and determine the type of landslide. In addition, the process can be automated to provide end-to-end TLS based long-term landslide monitoring applications, providing reference for monitoring and early warning of potential landslides., Comment: 10 pages, 13 figures ,3 tables

In this paper, we propose a coarse-to-fine integration solution inspired by the classical ICP algorithm, to pairwise 3D point cloud registration with two improvements of hybrid metric spaces (eg, BSC feature and Euclidean geometry spaces) and globally optimal correspondences matching. First, we detect the keypoints of point clouds and use the Binary Shape Context (BSC) descriptor to encode their local features. Then, we formulate the correspondence matching task as an energy function, which models the global similarity of keypoints on the hybrid spaces of BSC feature and Euclidean geometry. Next, we estimate the globally optimal correspondences through optimizing the energy function by the Kuhn-Munkres algorithm and then calculate the transformation based on the correspondences. Finally,we iteratively refine the transformation between two point clouds by conducting optimal correspondences matching and transformation calculation in a mutually reinforcing manner, to achieve the coarse-to-fine registration under an unified framework.The proposed method is evaluated and compared to several state-of-the-art methods on selected challenging datasets with repetitive, symmetric and incomplete structures.Comprehensive experiments demonstrate that the proposed IGSP algorithm obtains good performance and outperforms the state-of-the-art methods in terms of both rotation and translation errors., Comment: Accepted to International Conference on 3DVision (3DV) 2018 [8 pages, 6 figures and 3 tables]

Orbital angular momentum (OAM) of photons, as a new fundamental degree of freedom, has excited a great diversity of interest, because of a variety of emerging applications. Arbitrarily tunable OAM has gained much attention, but its creation remains still a tremendous challenge. We demonstrate the realization of well-controlled arbitrary OAM in both theory and experiment. We present the concept of general OAM, which extends the OAM carried by the scalar vortex field to the OAM carried by the azimuthally varying polarized vector field. The arbitrary OAM has the same characteristics as the well-defined integer OAM: intrinsic OAM, uniform local OAM and intensity ring, and propagation stability. The arbitrary OAM has unique natures: it is allowed to be flexibly tailored and the radius of the focusing ring can have various choices for a desired OAM, which are of great significance to the benefit of surprising applications of the arbitrary OAM.

In this paper, we study several structural models of amorphous silicon, and discuss structural and electronic features common to all. We note spatial correlations between short bonds, and similar correlations between long bonds. Such effects persist under a first principles relaxation of the system and at finite temperature. Next we explore the nature of the band tail states and find the states to possess a filamentary structure. We detail correlations between local geometry and the band tails., Comment: 7 pages, 11 figures, submitted to Journal of Crystalline Solids

Unlike the diversity issues in corporate governance, the diversity in top academic positions (e.g., editorial boards of academic journals in business) is rather underresearched. The editorial boards of academic marketing journals are important gatekeepers and trendsetters in the creation and dissemination of marketing knowledge. Membership on journal editorial boards usually signals scholarly stature and professional advancement. This study examines the composition of editorial boards of general marketing journals, and compares it with what it was like 15 years ago. The study also investigates the impact of the composition of editorial boards on journal quality. We find that women's participation in editorial boards generally corresponds to their presence in the profession. We also find an overall small representation of board members affiliated with nonacademic institutions. While the presence of women, practitioners, or international members does not have any relationship with journal quality, the presence of scholars affiliated with doctoral programs seems to correlate with journal quality. The number of female and international members on the boards increased, whereas practitioners' representation dropped from 1997 to 2012.