Q-factor mediated quasi-BIC resonances coupling in asymmetric dimer lattices

Resonance coupling in the regime of bound states in the continuum (BICs) provides an efficient method for engineering nanostructure's optical response with various lineshape while maintaining an ultra-narrow linewidth feature, where the quality factor of resonances plays a crucial role. Independent manipulation of the Q factors of BIC resonances enables full control of interaction behavior as well as both near- and far-field light engineering. In this paper, we harness reflection symmetry (RS) and translational symmetry (TS) protected BIC resonances supported in an asymmetric dimer lattice and investigate Q-factor-mediated resonance coupling behavior under controlled TS and RS perturbations. We focus on in-plane electrical dipole BIC (EDi-BIC) and magnetic dipole BIC (MD-BIC) which are protected by RS, and out-of-plane electrical dipole BIC (EDo-BIC) protected by TS. The coupling between EDi-BIC and EDo-BIC exhibits a resonance crossing behavior where the transmission spectrum at the crossing could be tuned flexibly, showing an electromagnetically induced transparency lineshape or satisfying the lattice Kerker condition with pure phase modulation capability depending on TS and RS perturbed Q factors. While the coupling between MD-BIC and EDo-BIC shows an avoided resonance crossing behavior, where the strongly coupled resonances would lead to the formation of a Friedrich-Wintgen BICs whose spectral position could also be shifted by tuning the Q factors. Our results suggest an intriguing platform to explore BIC resonance interactions with independent Q factor manipulation capability for realizing multi-functional meta-devices

Historical Occurrence of Algal Blooms in the Northern Beibu Gulf of China and Implications for Future Trends

Large-scale harmful algal blooms (HABs) occur in the coastal waters of the northern Beibu Gulf, China, and have deleterious effects on the marine ecosystem. The frequency, duration, and extent of HAB events in this region have increased over the last 30 years. However, the underlying causes of HABs and their likely future trends are unclear. To investigate, we evaluated historical data for temporal trends of HABs in the Beibu Gulf, and association with environmental factors as possible drivers. The results confirmed that HAB events had increased in frequency, from 6 reported events during the period 1985–2000, to 13 during 2001–2010, and 20 during 2011–2017. We also found that the geographic scale of algal blooms had increased from tens of km2 to hundreds of km2. There were temporal changes in HAB trigger species: prior to 2000, the cyanobacteria Microcystis aeruginosa was the dominant species, while during the period 2001–2010, blooms of cyanobacteria, dinoflagellates, and diatoms co-occurred, and during 2011–2017, the haptophyte Phaeocystis globosa became the dominant algal bloom species. Principal component analysis and variation partitioning analysis indicated that nutrient discharge, industrial development, and human activities were the key drivers of HAB events, and redundancy analysis showed that variation in the algal community tended to be driven by nutrient structure. Other factors, such as shipping activities and mariculture, also contributed to HAB events and algal succession, especially to P. globosa blooms. We speculated that the increasing severity of algal blooms in the northern Beibu Gulf reflects a more complex aquatic environment and highlights the damaging effects of anthropogenic inputs, urbanization development, and an expanding industrial marine-economy on the marine ecosystem. This research provides more insight into the increase of HABs and will aid their management in the Beibu Gulf

A Critical Evaluation of Evaluations for Long-form Question Answering

Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study of the evaluation of long-form answers, covering both human and automatic evaluation practices. We hire domain experts in seven areas to provide preference judgments over pairs of answers, along with free-form justifications for their choices. We present a careful analysis of experts' evaluation, which focuses on new aspects such as the comprehensiveness of the answer. Next, we examine automatic text generation metrics, finding that no existing metrics are predictive of human preference judgments. However, some metrics correlate with fine-grained aspects of answers (e.g., coherence). We encourage future work to move away from a single "overall score" of the answer and adopt a multi-faceted evaluation, targeting aspects such as factuality and completeness. We publicly release all of our annotations and code to spur future work into LFQA evaluation.Comment: ACL 2023 Camera Ready, Code available at https://github.com/carriex/lfqa_eva

Launching a Robust Backdoor Attack under Capability Constrained Scenarios

As deep neural networks continue to be used in critical domains, concerns over their security have emerged. Deep learning models are vulnerable to backdoor attacks due to the lack of transparency. A poisoned backdoor model may perform normally in routine environments, but exhibit malicious behavior when the input contains a trigger. Current research on backdoor attacks focuses on improving the stealthiness of triggers, and most approaches require strong attacker capabilities, such as knowledge of the model structure or control over the training process. These attacks are impractical since in most cases the attacker's capabilities are limited. Additionally, the issue of model robustness has not received adequate attention. For instance, model distillation is commonly used to streamline model size as the number of parameters grows exponentially, and most of previous backdoor attacks failed after model distillation; the image augmentation operations can destroy the trigger and thus disable the backdoor. This study explores the implementation of black-box backdoor attacks within capability constraints. An attacker can carry out such attacks by acting as either an image annotator or an image provider, without involvement in the training process or knowledge of the target model's structure. Through the design of a backdoor trigger, our attack remains effective after model distillation and image augmentation, making it more threatening and practical. Our experimental results demonstrate that our method achieves a high attack success rate in black-box scenarios and evades state-of-the-art backdoor defenses.Comment: 9 pages, 6 figure

TaCA: Upgrading Your Visual Foundation Model with Task-agnostic Compatible Adapter

Visual foundation models like CLIP excel in learning feature representations from extensive datasets through self-supervised methods, demonstrating remarkable transfer learning and generalization capabilities. A growing number of applications based on visual foundation models are emerging, including innovative solutions such as BLIP-2. These applications employ pre-trained CLIP models as upstream feature extractors and train various downstream modules to accomplish diverse tasks. In situations involving system upgrades that require updating the upstream foundation model, it becomes essential to re-train all downstream modules to adapt to the new foundation model, which is inflexible and inefficient. In this paper, we introduce a parameter-efficient and task-agnostic adapter, dubbed TaCA, that facilitates compatibility across distinct foundation models while ensuring enhanced performance for the new models. TaCA allows downstream applications to seamlessly integrate better-performing foundation models without necessitating retraining. We conduct extensive experimental validation of TaCA using different scales of models with up to one billion parameters on various tasks such as video-text retrieval, video recognition, and visual question answering. The results consistently demonstrate the emergent ability of TaCA on hot-plugging upgrades for visual foundation models. Codes and models will be available at https://github.com/TencentARC/TaCA

Deterministic Spin-Orbit Torque Switching of Mn3Sn with the Interplay between Spin Polarization and Kagome Plane

Previous studies have demonstrated spin-orbit torque (SOT) switching of Mn3Sn where the spin polarization lies in the kagome plane (configuration I). However, the critical current density ($J_{crit}$) is unrealistically large ($J_{crit}$=$10^{14}$ A/$m^2$) and independent on the external field ($H_{ext}$). The stabilized magnetic state also depends on the initial state. These features conflict with the ferromagnet (FM) switching scheme as claimed in those studies, and thus call for other explanations. Alternatively, the system with the spin polarization perpendicular to the kagome plane (configuration II) is more like the FM based system since the spin polarization is orthogonal to all magnetic moments. In this work, we show SOT switching of Mn3Sn in configuration II. Similar to the FM, Jcrit and Hext are in the order of $10^{10}$ A/$m^2$ and hundreds of Oersted, respectively. The switching result is also independent of the initial state. Interestingly, the unique spin structure of Mn3Sn also leads to distinct features from FM systems. We demonstrate that Jcrit increases linearly with Hext, and extrapolation gives ultralow $J_{crit}$ for the field-free switching system. In addition, the switching polarity is opposite to the FM. We also provide the switching phase diagram as a guideline for experimental demonstration. Our work provides comprehensive understanding for the switching mechanism in both configurations. The switching protocol proposed in this work is more advantageous in realistic spintronic applications. We also clearly reveal the fundamental difference between FM and noncollinear antiferromagnetic switching

Attack is Good Augmentation: Towards Skeleton-Contrastive Representation Learning

Contrastive learning, relying on effective positive and negative sample pairs, is beneficial to learn informative skeleton representations in unsupervised skeleton-based action recognition. To achieve these positive and negative pairs, existing weak/strong data augmentation methods have to randomly change the appearance of skeletons for indirectly pursuing semantic perturbations. However, such approaches have two limitations: 1) solely perturbing appearance cannot well capture the intrinsic semantic information of skeletons, and 2) randomly perturbation may change the original positive/negative pairs to soft positive/negative ones. To address the above dilemma, we start the first attempt to explore an attack-based augmentation scheme that additionally brings in direct semantic perturbation, for constructing hard positive pairs and further assisting in constructing hard negative pairs. In particular, we propose a novel Attack-Augmentation Mixing-Contrastive learning (A$^2$MC) to contrast hard positive features and hard negative features for learning more robust skeleton representations. In A$^2$MC, Attack-Augmentation (Att-Aug) is designed to collaboratively perform targeted and untargeted perturbations of skeletons via attack and augmentation respectively, for generating high-quality hard positive features. Meanwhile, Positive-Negative Mixer (PNM) is presented to mix hard positive features and negative features for generating hard negative features, which are adopted for updating the mixed memory banks. Extensive experiments on three public datasets demonstrate that A$^2$MC is competitive with the state-of-the-art methods

Molecular identification and toxin analysis of Alexandrium spp. in the Beibu Gulf: first report of toxic A. tamiyavanichii in Chinese coastal waters

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Xu, Y., He, X., Li, H., Zhang, T., Lei, F., Gu, H., & Anderson, D. M. Molecular identification and toxin analysis of Alexandrium spp. in the Beibu Gulf: first report of toxic A. tamiyavanichii in Chinese coastal waters. Toxins, 13(2), (2021): 161, https://doi.org/10.3390/toxins13020161.The frequency of harmful algal blooms (HABs) has increased in China in recent years. Information about harmful dinoflagellates and paralytic shellfish toxins (PSTs) is still limited in China, especially in the Beibu Gulf, where PSTs in shellfish have exceeded food safety guidelines on multiple occasions. To explore the nature of the threat from PSTs in the region, eight Alexandrium strains were isolated from waters of the Beibu Gulf and examined using phylogenetic analyses of large subunit (LSU) rDNA, small subunit (SSU) rDNA, and internal transcribed spacer (ITS) sequences. Their toxin composition profiles were also determined using liquid chromatography-tandem mass spectrometry (LC-MS/MS). All eight strains clustered in the phylogenetic tree with A. pseudogonyaulax, A. affine, and A. tamiyavanichii from other locations, forming three well-resolved groups. The intraspecific genetic distances of the three Alexandrium species were significantly smaller than interspecific genetic distances for Alexandrium species. Beibu Gulf isolates were therefore classified as A. pseudogonyaulax, A. affine, and A. tamiyavanichii. No PSTs were identified in A. pseudogonyaulax, but low levels of gonyautoxins (GTXs) 1 to 5, and saxitoxin (STX) were detected in A. tamiyavanichii (a total of 4.60 fmol/cell). The extremely low level of toxicity is inconsistent with PST detection above regulatory levels on multiple occasions within the Beibu Gulf, suggesting that higher toxicity strains may occur in those waters, but were unsampled. Other explanations including biotransformation of PSTs in shellfish and the presence of other PST-producing algae are also suggested. Understanding the toxicity and phylogeny of Alexandrium species provides foundational data for the protection of public health in the Beibu Gulf region and the mitigation of HAB events.This research was funded by the National Natural Science Foundation of China (41976155, 41506137), the Natural Science Foundation of Guangxi Province (2020GXNSFDA297001, 2016GXNSFBA380037), the Woods Hole Center for Oceans and Human Health (National Science Foundation grant OCE-1840381 and National Institutes of Health grants NIEHS-1P01-ES028938-01), the Opening Project of Guangxi Laboratory on the Study of Coral Reefs in the South China Sea (GXLSCRSCS2019002), the Opening Foundation of Key Laboratory of Environment Change and Resources Use in Beibu Gulf Ministry of Education (Nanning Normal University), and the Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation (Nanning Normal University) (GTEU-KLOP-K1803)