130 research outputs found
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 () is unrealistically large (= A/) and independent on the external field (). 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 A/ 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 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 (AMC) to contrast hard positive features and
hard negative features for learning more robust skeleton representations. In
AMC, 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 AMC 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)
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