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Direct imaging of exoplanets allows us to measure positions and chemical signatures of exoplanets. Given the limited resources for space observations where the atmosphere is absent, we want to make these measurements from the ground. However, it is difficult from the ground because it requires an adaptive optics system to provide an extremely well corrected wavefront to enable coronographic techniques. Currently only natural guide star AO systems have demonstrated the necessary wavefront correction for direct imaging of exoplanets. However, using a stellar source as the guide star for wavefront sensing limits the number of exoplanet systems we can directly image because it requires a relatively bright V~10 mag star. To increase the number of observable targets, we need to push the limit of natural guide stars to fainter magnitudes with high Strehl ratio correction. We propose to combine laser guide star (LGS) and natural guide star (NGS) wavefront sensing to achieve the high Strehl correction with fainter natural guide stars. We call this approach Hybrid Atmospheric Phase Analysis (HAPA); 'hapa' in Hawaiian means 'half' or 'of mixed ethnic heritage'. The relatively bright LGS is used for higher order correction, whereas the NGS is used for high accuracy lower order correction. We focus on demonstrating this approach using Robo-AO-2 at the UH 2.2m telescope on Maunakea with a UV Rayleigh laser at 355 nm. The laser focuses at 10 km altitude and has an equivalent magnitude of m_U~8. In this report specifically, we present simulated results of HAPA employed at Robo-AO-2, with the LGS system having a single configuration of 16x16 subaperture Shack-Hartmann wavefront sensor and the NGS system having 6 different configurations -- 16x16, 8x8, 5x5, 4x4, 2x2 and 1x1. We also discuss the on-sky experiments we plan to carry out with HAPA at the UH 2.2m telescope., Comment: 11 pages, 4 figures, 8 tables, SPIE Astronomical Telescopes + Instrumentation 2024

We performed closed-loop lab testing of large-format deformable mirrors (DMs) with hybrid variable reluctance actuators. TNO has been developing the hybrid variable reluctance actuators in support for a new generation of adaptive secondary mirrors (ASMs), which aim to be more robust and reliable. Compared to the voice coil actuators, this new actuator technology has a higher current to force efficiency, and thus can support DMs with thicker facesheets. Before putting this new technology on-sky, it is necessary to understand how to control it and how it behaves in closed-loop. We performed closed-loop tests with the Shack-Hartmann wavefront sensor with three large-format deformable mirrors that use the TNO actuators: DM3, FLASH, and IRTF-ASM-1 ASM. The wavefront sensor and the real-time control systems were developed for the NASA Infrared Telescope Facility (IRTF) and the UH 2.2-meter telescope ASMs. We tested IRTF-ASM-1 on-sky and proved that it meets all of our performance requirements. This work presents our lab setup for the experiments, the techniques we have employed to drive these new ASMs, the results of our closed-loop lab tests for FLASH and IRTF-ASM-1, and the on-sky closed-loop results of IRTF-ASM-1 ASM., Comment: 15 pages, 12 figures, SPIE Astronomical Telescopes + Instrumentation 2024

We are developing an adaptive secondary mirror (ASM) that uses a new actuator technology created by the Netherlands Organization for Applied Scientific Research (TNO). The TNO hybrid variable reluctance actuators have more than an order of magnitude better efficiency over the traditional voice coil actuators that have been used on existing ASMs and show potential for improving the long-term robustness and reliability of ASMs. To demonstrate the performance, operations, and serviceability of TNO's actuators in an observatory, we have developed a 36-actuator prototype ASM for the NASA Infrared Telescope Facility (IRTF) called IRTF-ASM-1. IRTF-ASM-1 provides the first on-sky demonstration of this approach and will help us evaluate the long-term performance and use of this technology in an astronomical facility environment. We present calibration and performance results with the ASM in a Meniscus Hindle Sphere lens setup as well as preliminary on-sky results on IRTF. IRTF-ASM-1 achieved stable closed-loop performance on-sky with H-band Strehl ratios of 35-40% in long-exposure images under a variety of seeing conditions., Comment: 15 pages, 12 figures, SPIE Astronomical Telescopes + Instrumentation: Adaptive Optics Systems IX

We installed the next-generation automated laser adaptive optics system, Robo-AO-2, on the University of Hawaii 2.2-m telescope on Maunakea in 2023. We engineered Robo-AO-2 to deliver robotic, diffraction-limited observations at visible and near-infrared wavelengths in unprecedented numbers. This new instrument takes advantage of upgraded components, manufacturing techniques and control; and includes a parallel reconfigurable natural guide star wavefront sensor with which to explore hybrid wavefront sensing techniques. We present the results of commissioning in 2023 and 2024., Comment: Proceedings of SPIE, 13097-15

Adversarial examples pose a security threat to many critical systems built on neural networks. Given that deterministic robustness often comes with significantly reduced accuracy, probabilistic robustness (i.e., the probability of having the same label with a vicinity is $\ge 1-\kappa$) has been proposed as a promising way of achieving robustness whilst maintaining accuracy. However, existing training methods for probabilistic robustness still experience non-trivial accuracy loss. It is unclear whether there is an upper bound on the accuracy when optimising towards probabilistic robustness, and whether there is a certain relationship between $\kappa$ and this bound. This work studies these problems from a Bayes error perspective. We find that while Bayes uncertainty does affect probabilistic robustness, its impact is smaller than that on deterministic robustness. This reduced Bayes uncertainty allows a higher upper bound on probabilistic robust accuracy than that on deterministic robust accuracy. Further, we prove that with optimal probabilistic robustness, each probabilistically robust input is also deterministically robust in a smaller vicinity. We also show that voting within the vicinity always improves probabilistic robust accuracy and the upper bound of probabilistic robust accuracy monotonically increases as $\kappa$ grows. Our empirical findings also align with our results.

Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there is a certain fundamental limit on achieving robustness whilst maintaining accuracy. In this work, we offer a novel perspective based on Bayes errors. By adopting Bayes error to robustness analysis, we investigate the limit of certified robust accuracy, taking into account data distribution uncertainties. We first show that the accuracy inevitably decreases in the pursuit of robustness due to changed Bayes error in the altered data distribution. Subsequently, we establish an upper bound for certified robust accuracy, considering the distribution of individual classes and their boundaries. Our theoretical results are empirically evaluated on real-world datasets and are shown to be consistent with the limited success of existing certified training results, e.g., for CIFAR10, our analysis results in an upper bound (of certified robust accuracy) of 67.49\%, meanwhile existing approaches are only able to increase it from 53.89\% in 2017 to 62.84\% in 2023., Comment: accepted by CAV 2024

This paper presents Visual Evaluative AI, a decision aid that provides positive and negative evidence from image data for a given hypothesis. This tool finds high-level human concepts in an image and generates the Weight of Evidence (WoE) for each hypothesis in the decision-making process. We apply and evaluate this tool in the skin cancer domain by building a web-based application that allows users to upload a dermatoscopic image, select a hypothesis and analyse their decisions by evaluating the provided evidence. Further, we demonstrate the effectiveness of Visual Evaluative AI on different concept-based explanation approaches., Comment: 4 pages

By systematic theoretical calculations, we have revealed an excitonic insulator (EI) in the Ta2Pd3Te5 monolayer. The bulk Ta2Pd3Te5 is a van der Waals (vdW) layered compound, whereas the vdW layer can be obtained through exfoliation or molecular-beam epitaxy. First-principles calculations show that the monolayer is a nearly zero-gap semiconductor with the modified Becke-Johnson functional. Due to the same symmetry of the band-edge states, the two-dimensional polarization $\alpha_{2D}$ would be finite as the band gap goes to zero, allowing for an EI state in the compound. Using the first-principles many-body perturbation theory, the GW plus Bethe-Salpeter equation calculation reveals that the exciton binding energy is larger than the single-particle band gap, indicating the excitonic instability. The computed phonon spectrum suggests that the monolayer is dynamically stable without lattice distortion. Our findings suggest that the Ta2Pd3Te5 monolayer is an excitonic insulator without structural distortion., Comment: 6 pages, 4 figures

We examine the relationship between metallicity and $J-K$ color for 64 benchmark late-M and L dwarfs, all of which are wide companions to higher mass stars, and 6 of which are new discoveries. We assess the correlation between the $\Delta(J-K)$ color anomaly (the difference of an object's $J-K$ color with the median color for field objects of the same spectral type) and the host star metallicity to investigate how metallicity affects ultracool photospheres. Using Spearman's rank correlation test and Student's t test, the late-M dwarf (L dwarf) sample's $\Delta(J-K)$ and metallicity show a positive correlation with 95\% (90\%) confidence level. A linear fit to color anomaly as a function of metallicity finds a slope of $0.17\pm0.07$ for the late-M dwarfs and a slope of $0.20^{+0.07}_{-0.08}$ for the L dwarfs. We also computed the $\Delta(J-K)$ versus metallicity relationship predicted by multi-metallicity model spectra generated using Drift-Phoenix. The modeled late-M dwarfs show a slope of 0.202$\pm$0.03, which is close to our observational results, but the modeled L dwarfs show a slope of 0.493$\pm$0.02, steeper than our observational results. Both our empirical results and the models indicate that more metal-rich objects should appear redder photometrically. We speculate that higher metallicity drives more condensate formation in these atmospheres, thus making these ultracool dwarfs appear redder.

Adversarial examples pose a security threat to many critical systems built on neural networks (such as face recognition systems, and self-driving cars). While many methods have been proposed to build robust models, how to build certifiably robust yet accurate neural network models remains an open problem. For example, adversarial training improves empirical robustness, but they do not provide certification of the model's robustness. On the other hand, certified training provides certified robustness but at the cost of a significant accuracy drop. In this work, we propose a novel approach that aims to achieve both high accuracy and certified probabilistic robustness. Our method has two parts, i.e., a probabilistic robust training method with an additional goal of minimizing variance in terms of divergence and a runtime inference method for certified probabilistic robustness of the prediction. The latter enables efficient certification of the model's probabilistic robustness at runtime with statistical guarantees. This is supported by our training objective, which minimizes the variance of the model's predictions in a given vicinity, derived from a general definition of model robustness. Our approach works for a variety of perturbations and is reasonably efficient. Our experiments on multiple models trained on different datasets demonstrate that our approach significantly outperforms existing approaches in terms of both certification rate and accuracy.

Based on first-principles calculations, we demonstrate that 1H/2H-phase transition metal dichalcogenides MX2 (M=Nb,Ta; X=S,Se,Te) are unconventional metals, which have an empty-site band of $A_1'@1e$ elementary band representation at the Fermi level. The computed phonon dispersions indicate the stability of the system at high temperatures, while the presence of the soft phonon mode suggests a phase transition to the charge density wave state at low temperatures. Based on the Bardeen-Cooper-Schrieffer theory and computed electron-phonon coupling, our calculations show that the superconductivity (SC) in NbSe2 is mainly attributed to the soft phonon mode due to the half filling of the empty-site band. Accordingly, the SC has been predicted in unconventional metals TaNS monolayer and 2H-TaN2 bulk with computed $T_C=$ 10 K and 26 K respectively. These results demonstrate that the unconventional metals with partial filling of the empty-site band offer an attractive platform to search for superconductors., Comment: 5 pages, 5 figures,42 references

Based on the elementary band representations (EBR), some topologically trivial materials are classified as unconventional ones (obstructed atomic limit), where the EBR decomposition of electronic states is not consistent with the atomic valence-electron band representations. In the work, we identify that the unconventional nature can also exist in phonon spectra, where the EBR decomposition of the phonon modes is not consistent with atomic vibration band representations (ABR). The unconventionality has two types: type I is on an empty site; type II is on an atom site with non-atomic vibration orbitals. Our detailed calculations show that black phosphorus (BP) and 1H-MoSe2 have unconventional both phonon spectra and electronic band structures. The BP has the type-I unconventional phonon spectrum, while 1H-MoSe2 has the type-II one. The obstructed phonon modes are obtained for two types of unconventional phonon spectra., Comment: 5 pages, 5 figures

Topological materials with boundary (surface/edge/hinge) states have attracted tremendous research interest. Besides, unconventional (obstructed atomic) materials have recently drawn lots of attention owing to their obstructed boundary states. Experimentally, Josephson junctions (JJs) constructed on materials with boundary states produce the peculiar boundary supercurrent, which was utilized as a powerful diagnostic approach. Here, we report the observations of conspicuous hinge supercurrent in NiTe$_2$-based JJs. Particularly, applying an in-plane magnetic field along the Josephson current could rapidly suppress the bulk supercurrent and retain the nearly pure hinge supercurrent, namely the magnetic field filtering of supercurrent. Further systematic comparative analysis and theoretical calculations demonstrate the existence of unconventional nature and obstructed hinge states in NiTe$_2$. Our results revealed the unique hinge states in unconventional metal NiTe$_2$, and demonstrated in-plane magnetic field as an efficient method to filter out the futile bulk contributions and thereby to highlight the hinge states hidden in topological/unconventional materials., Comment: 20+6 pages,6+5 figures

The recently discovered vanadium-based Kagome metals $A$V$_{3}$Sb$_{5}$ ($A = \text{K, Rb, Cs}$) are of great interest with the interplay of charge density wave (CDW) order, band topology and superconductivity. In this paper, by identifying elementary band representations (EBRs), we construct a two-EBR graphene-Kagome model to capture the two low-energy van-Hove-singularity dispersions and, more importantly, the nontrivial band topology in these Kagome metals. This model consists of $A_g@3g$ (V-$d_{x^2-y^2/z^2}$, Kagome sites) and $A_2''@2d$ EBRs (Sb1-$p_z$, honeycomb sites). We have investigated the Fermi surface instability by calculating the electronic susceptibility $\chi(\mathbf{q})$. Prominent Fermi-surface nesting peaks are obtained at three L points, where the $z$ component of the nesting vector shows intimate relationship with the anticrossing point along M--L. The nesting peaks at L are consistent with the $2\times 2\times 2$ CDW reconstruction in these compounds. In addition, the sublattice-resolved bare susceptibility is calculated and similar sharp peaks are observed at the L points, indicating a strong antiferromagnetic fluctuation. Assuming a bulk $s$-wave superconducting pairing, helical surface states and nontrivial superconducting gap are obtained on the (001) surface. In analogous to FeTe$_{1-x}$Se$_{x}$ superconductor, our results establish another material realization of a stoichiometric superconductor with nontrivial band topology, providing a promising platform for studying exotic Majorana physics in condensed matter

Recently, unconventional materials (or obstructed atomic insulators) have attracted much attention owing to the unconventional feature of mismatch between Wannier centers and atomic positions. In this paper, we demonstrate that the trigonal selenium and tellurium host an unconventional nature in both electronic and phonon spectra. In electronic band structures, the band representation (BR) decomposition for occupied bands has to contain the essential BR of $A@3b$, and the real-space invariant is $\delta_1@3b=-1$. The $\pi$ Zak phase suggests that the one-dimensional Se/Te chain is a chiral Su-Schrieffer-Heeger chain. The effective magnetism can be induced by $p$ states at ends. More importantly, a large shift current is obtained in Se quantum well. In addtion, in phonon spectra, three sets of phonon bands are well separated and assigned to $B@3b$, $B@3a$, and $A@3b$ BRs, respectively. Thus, the obstructed phonon states are predicted on the (0001) surface. As the prototypes of unconventional materials in both electronic and phonon spectra, our findings could create much interest in the study of obstructed surface electronic and phonon states in these novel materials.

Conventional event detection models under supervised learning settings suffer from the inability of transfer to newly-emerged event types owing to lack of sufficient annotations. A commonly-adapted solution is to follow a identify-then-classify manner, which first identifies the triggers and then converts the classification task via a few-shot learning paradigm. However, these methods still fall far short of expectations due to: (i) insufficient learning of discriminative representations in low-resource scenarios, and (ii) trigger misidentification caused by the overlap of the learned representations of triggers and non-triggers. To address the problems, in this paper, we propose a novel Hybrid Contrastive Learning method with a Task-Adaptive Threshold (abbreviated as HCLTAT), which enables discriminative representation learning with a two-view contrastive loss (support-support and prototype-query), and devises a easily-adapted threshold to alleviate misidentification of triggers. Extensive experiments on the benchmark dataset FewEvent demonstrate the superiority of our method to achieve better results compared to the state-of-the-arts. All the code and data of this paper will be available for online public access., Comment: This paper has been accepted by Findings of EMNLP 2022

A growing body of research in natural language processing (NLP) and natural language understanding (NLU) is investigating human-like knowledge learned or encoded in the word embeddings from large language models. This is a step towards understanding what knowledge language models capture that resembles human understanding of language and communication. Here, we investigated whether and how the affect meaning of a word (i.e., valence, arousal, dominance) is encoded in word embeddings pre-trained in large neural networks. We used the human-labeled dataset as the ground truth and performed various correlational and classification tests on four types of word embeddings. The embeddings varied in being static or contextualized, and how much affect specific information was prioritized during the pre-training and fine-tuning phase. Our analyses show that word embedding from the vanilla BERT model did not saliently encode the affect information of English words. Only when the BERT model was fine-tuned on emotion-related tasks or contained extra contextualized information from emotion-rich contexts could the corresponding embedding encode more relevant affect information.

Two-dimensional (2D) materials have gained lots of attention due to the potential applications. In this work, we propose that based on first-principles calculations, the (2$\times$2) patterned PtTe$_2$ monolayer with kagome lattice formed by the well-ordered Te vacancy (PtTe$_{1.75}$) hosts large spin Hall conductivity (SHC) and excellent hydrogen evolution reaction (HER) activity. The unconventional nature relies on the $A1@1b$ band representation (BR) of the highest valence band without SOC. The large SHC comes from the Rashba spin-orbit coupling (SOC) in the noncentrosymmetric structure induced by the Te vacancy. Even though it has a metallic SOC band structure, the $\mathbb Z_2$ invariant is well defined due to the existence of the direct band gap and is computed to be nontrivial. The calculated SHC is as large as 1.25$\times 10^3 \frac{\hbar}{e} (\Omega~cm)^{-1}$ at the Fermi level ($E_F$). By tuning the chemical potential from $E_F-0.3$ to $E_F+0.3$ eV, it varies rapidly and monotonically from $-1.2\times 10^3$ to 3.1$\times 10^3 \frac{\hbar}{e} (\Omega~cm)^{-1}$. In addition, we also find the Te vacancy in the patterned monolayer can induce excellent HER activity. Our results not only offer a new idea to search 2D materials with large SHC, i.e., by introducing inversion-symmetry breaking vacancies in large SOC systems, but also provide a feasible system with tunable SHC (by applying gate voltage) and excellent HER activity.

Most of the state-of-the-art semantic segmentation reported in recent years is based on fully supervised deep learning in the medical domain. How?ever, the high-quality annotated datasets require intense labor and domain knowledge, consuming enormous time and cost. Previous works that adopt semi?supervised and unsupervised learning are proposed to address the lack of anno?tated data through assisted training with unlabeled data and achieve good perfor?mance. Still, these methods can not directly get the image annotation as doctors do. In this paper, inspired by self-training of semi-supervised learning, we pro?pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR). The MIPR combines image-editing and pseudo-label technology to obtain labeled data. As the number of iterations increases, the edited image is similar to the original image, and the labeled result is similar to the doctor annotation. Therefore, the MIPR is to get labeled pairs of data directly from amounts of unlabled data with pixel rearrange?ment, which is implemented with a designed conditional Generative Adversarial Networks and a segmentation network. Experiments on the ISIC18 show that the effect of the data annotated by our method for segmentation task is is equal to or even better than that of doctors annotations

Instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying a pre-trained model optimized on diverse training data or conducting domain translation and image segmentation as two independent steps. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation jointly using a unified framework. Besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we introduce additional self-supervised and segmentation-based adversarial objectives to improve the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. Our CySGAN outperforms both pretrained generalist models and the baselines that sequentially conduct image translation and segmentation. Our implementation and the newly collected, densely annotated ExM nuclei dataset, named NucExM, are available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html., Comment: 13 pages with appendix

The medical datasets are usually faced with the problem of scarcity and data imbalance. Moreover, annotating large datasets for semantic segmentation of medical lesions is domain-knowledge and time-consuming. In this paper, we propose a new object-blend method(short in soft-CP) that combines the Copy-Paste augmentation method for semantic segmentation of medical lesions offline, ensuring the correct edge information around the lession to solve the issue above-mentioned. We proved the method's validity with several datasets in different imaging modalities. In our experiments on the KiTS19[2] dataset, Soft-CP outperforms existing medical lesions synthesis approaches. The Soft-CP augementation provides gains of +26.5% DSC in the low data regime(10% of data) and +10.2% DSC in the high data regime(all of data), In offline training data, the ratio of real images to synthetic images is 3:1., Comment: 9 pages, 6 figures, 1 table

Let $\mathfrak A$ be a type 1 subdiagonal algebra in a $\sigma$-finite von Neumann algebra $\mathcal M$ with respect to a faithful normal conditional expectation $\Phi$. We consider a Riesz type factorization theorem in noncommutative $H^p$ spaces associated with $\mathfrak A$. It is shown that if $1\leq r,p,q<\infty$ such that $\frac1r=\frac1p+\frac1q$, then for any $h\in H^r$, there exist $h_p\in H^p$ and $h_q\in H^q$ such that $h=h_ph_q$. Beurling type invariant subspace theorem for noncommutative $L^p(1< p<\infty)$ space is obtained. Furthermore, we show that a $\sigma$-weakly closed subalgebra containing $\mathfrak A$ of $\mathcal M$ is also a type 1 subdiagonal algebra. As an application, We prove that the relative invariant subspace lattice $Lat_{\mathcal M}\mathfrak A$ of $\mathfrak A$ in $\mathcal M$ is commutative., Comment: arXiv admin note: text overlap with arXiv:2003.12966