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With the advent of large language models (LLMs), managing scientific literature via LLMs has become a promising direction of research. However, existing approaches often overlook the rich structural and semantic relevance among scientific literature, limiting their ability to discern the relationships between pieces of scientific knowledge, and suffer from various types of hallucinations. These methods also focus narrowly on individual downstream tasks, limiting their applicability across use cases. Here we propose LitFM, the first literature foundation model designed for a wide variety of practical downstream tasks on domain-specific literature, with a focus on citation information. At its core, LitFM contains a novel graph retriever to integrate graph structure by navigating citation graphs and extracting relevant literature, thereby enhancing model reliability. LitFM also leverages a knowledge-infused LLM, fine-tuned through a well-developed instruction paradigm. It enables LitFM to extract domain-specific knowledge from literature and reason relationships among them. By integrating citation graphs during both training and inference, LitFM can generalize to unseen papers and accurately assess their relevance within existing literature. Additionally, we introduce new large-scale literature citation benchmark datasets on three academic fields, featuring sentence-level citation information and local context. Extensive experiments validate the superiority of LitFM, achieving 28.1% improvement on retrieval task in precision, and an average improvement of 7.52% over state-of-the-art across six downstream literature-related tasks, Comment: 18 pages, 12 figures

Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios, where each node and edge are associated with text descriptions, and both the graph structure and text descriptions evolve over time. Despite their broad applicability, there is a notable scarcity of benchmark datasets tailored to DyTAGs, which hinders the potential advancement in many research fields. To address this gap, we introduce Dynamic Text-attributed Graph Benchmark (DTGB), a collection of large-scale, time-evolving graphs from diverse domains, with nodes and edges enriched by dynamically changing text attributes and categories. To facilitate the use of DTGB, we design standardized evaluation procedures based on four real-world use cases: future link prediction, destination node retrieval, edge classification, and textual relation generation. These tasks require models to understand both dynamic graph structures and natural language, highlighting the unique challenges posed by DyTAGs. Moreover, we conduct extensive benchmark experiments on DTGB, evaluating 7 popular dynamic graph learning algorithms and their variants of adapting to text attributes with LLM embeddings, along with 6 powerful large language models (LLMs). Our results show the limitations of existing models in handling DyTAGs. Our analysis also demonstrates the utility of DTGB in investigating the incorporation of structural and textual dynamics. The proposed DTGB fosters research on DyTAGs and their broad applications. It offers a comprehensive benchmark for evaluating and advancing models to handle the interplay between dynamic graph structures and natural language. The dataset and source code are available at https://github.com/zjs123/DTGB., Comment: 28 pages, 13 figures

Minifilaments are widespread small-scale structures in the solar atmosphere. To better understand their formation and eruption mechanisms, we investigate the entire life of a sigmoidal minifilament located below a large quiescent filament observed by BBSO/GST on 2015 August 3. The H{\alpha} structure initially appears as a group of arched threads, then transforms into two J-shaped arcades, and finally forms a sigmoidal shape. SDO/AIA observations in 171{\AA} show that two coronal jets occur around the southern footpoint of the minifilament before the minifilament eruption. The minifilament eruption starts from the southern footpoint, then interacts with the overlying filament and fails. The aforementioned observational changes correspond to three episodes of flux cancellations observed by SDO/HMI. Unlike previous studies, the flux cancellation occurs between the polarity where southern footpoint of the minifilament is rooted in and an external polarity. We construct two magnetic field models before the eruption using the flux rope insertion method, and find an hyperbolic flux tube (HFT) above the flux cancellation site. The observation and modeling results suggest that the eruption is triggered by the external magnetic reconnection between the core field of the minifilament and the external fields due to flux cancellations. This study reveals a new triggering mechanism for minifilament eruptions and a new relationship between minifilament eruptions and coronal jets.

Time series forecasting has attracted significant attention in recent decades. Previous studies have demonstrated that the Channel-Independent (CI) strategy improves forecasting performance by treating different channels individually, while it leads to poor generalization on unseen instances and ignores potentially necessary interactions between channels. Conversely, the Channel-Dependent (CD) strategy mixes all channels with even irrelevant and indiscriminate information, which, however, results in oversmoothing issues and limits forecasting accuracy. There is a lack of channel strategy that effectively balances individual channel treatment for improved forecasting performance without overlooking essential interactions between channels. Motivated by our observation of a correlation between the time series model's performance boost against channel mixing and the intrinsic similarity on a pair of channels, we developed a novel and adaptable Channel Clustering Module (CCM). CCM dynamically groups channels characterized by intrinsic similarities and leverages cluster identity instead of channel identity, combining the best of CD and CI worlds. Extensive experiments on real-world datasets demonstrate that CCM can (1) boost the performance of CI and CD models by an average margin of 2.4% and 7.2% on long-term and short-term forecasting, respectively; (2) enable zero-shot forecasting with mainstream time series forecasting models; (3) uncover intrinsic time series patterns among channels and improve interpretability of complex time series models., Comment: 20 pages, 6 figures

Considering the noise level limit, one crucial aspect for quantum machine learning is to design a high-performing variational quantum circuit architecture with small number of quantum gates. As the classical neural architecture search (NAS), quantum architecture search methods (QAS) employ methods like reinforcement learning, evolutionary algorithms and supernet optimiza-tion to improve the search efficiency. In this paper, we propose a novel qubit-wise architec-ture search (QWAS) method, which progres-sively search one-qubit configuration per stage, and combine with Monte Carlo Tree Search al-gorithm to find good quantum architectures by partitioning the search space into several good and bad subregions. The numerical experimental results indicate that our proposed method can balance the exploration and exploitation of cir-cuit performance and size in some real-world tasks, such as MNIST, Fashion and MOSI. As far as we know, QWAS achieves the state-of-art re-sults of all tasks in the terms of accuracy and circuit size.

The high-resolution time series classification problem is essential due to the increasing availability of detailed temporal data in various domains. To tackle this challenge effectively, it is imperative that the state-of-the-art attention model is scalable to accommodate the growing sequence lengths typically encountered in high-resolution time series data, while also demonstrating robustness in handling the inherent noise prevalent in such datasets. To address this, we propose to hierarchically encode the long time series into multiple levels based on the interaction ranges. By capturing relationships at different levels, we can build more robust, expressive, and efficient models that are capable of capturing both short-term fluctuations and long-term trends in the data. We then propose a new time series transformer backbone (KronTime) by introducing Kronecker-decomposed attention to process such multi-level time series, which sequentially calculates attention from the lower level to the upper level. Experiments on four long time series datasets demonstrate superior classification results with improved efficiency compared to baseline methods.

The newly discovered high-$T_c$ nickelate superconductor La$_3$Ni$_2$O$_7$ has generated significant research interest. To uncover the pairing mechanism, it is essential to investigate the intriguing interplay between the two $e_g$, i.e., $d_{x^2-y^2}$ and $d_{z^2}$ orbitals. Here we perform an infinite projected entangled-pair state (iPEPS) study of the bilayer $t$-$J$ model, directly in the thermodynamic limit and with orbitally selective parameters for $d_{x^2-y^2}$ and $d_{z^2}$ orbitals, respectively. The $d_{x^2-y^2}$ electrons exhibit significant intralayer hopping $t_\parallel$ and spin couplings $J_\parallel$, with interlayer $J_\perp$ passed from the $d_{z^2}$ electrons. However, the interlayer $t_\perp$ is negligible in this case. In contrast, the $d_{z^2}$ orbital demonstrates strong interlayer $t_\perp$ and $J_\perp$, while the inherent intralayer $t_\parallel$ and $J_\parallel$ are small. Based on the iPEPS results, we find clear orbital-selective behaviors in La$_3$Ni$_2$O$_7$. The $d_{x^2-y^2}$ orbitals exhibit robust superconductive (SC) order driven by the interlayer coupling $J_\perp$; while the $d_{z^2}$ band shows relatively weak SC order as a result of small $t_\parallel$ (lack of coherence) but large $t_\perp$ (strong Pauli blocking). Furthermore, by substituting rare-earth element Pm or Sm with La, we find an enhanced SC order, which opens up a promising avenue for discovering nickelate superconductors with even higher $T_c$., Comment: 7+4 pages, 4+5 figures

Graph convolutions have been a pivotal element in learning graph representations. However, recursively aggregating neighboring information with graph convolutions leads to indistinguishable node features in deep layers, which is known as the over-smoothing issue. The performance of graph neural networks decays fast as the number of stacked layers increases, and the Dirichlet energy associated with the graph decreases to zero as well. In this work, we introduce a framelet system into the analysis of Dirichlet energy and take a multi-scale perspective to leverage the Dirichlet energy and alleviate the over-smoothing issue. Specifically, we develop a Framelet Augmentation strategy by adjusting the update rules with positive and negative increments for low-pass and high-passes respectively. Based on that, we design the Energy Enhanced Convolution (EEConv), which is an effective and practical operation that is proved to strictly enhance Dirichlet energy. From a message-passing perspective, EEConv inherits multi-hop aggregation property from the framelet transform and takes into account all hops in the multi-scale representation, which benefits the node classification tasks over heterophilous graphs. Experiments show that deep GNNs with EEConv achieve state-of-the-art performance over various node classification datasets, especially for heterophilous graphs, while also lifting the Dirichlet energy as the network goes deeper.

Graph Neural Networks (GNNs) achieve state-of-the-art performance in various graph-related tasks. However, the black-box nature often limits their interpretability and trustworthiness. Numerous explainability methods have been proposed to uncover the decision-making logic of GNNs, by generating underlying explanatory substructures. In this paper, we conduct a comprehensive review of the existing explanation methods for GNNs from the perspective of graph generation. Specifically, we propose a unified optimization objective for generative explanation methods, comprising two sub-objectives: Attribution and Information constraints. We further demonstrate their specific manifestations in various generative model architectures and different explanation scenarios. With the unified objective of the explanation problem, we reveal the shared characteristics and distinctions among current methods, laying the foundation for future methodological advancements. Empirical results demonstrate the advantages and limitations of different explainability approaches in terms of explanation performance, efficiency, and generalizability.

Temporal graphs are widely used to model dynamic systems with time-varying interactions. In real-world scenarios, the underlying mechanisms of generating future interactions in dynamic systems are typically governed by a set of recurring substructures within the graph, known as temporal motifs. Despite the success and prevalence of current temporal graph neural networks (TGNN), it remains uncertain which temporal motifs are recognized as the significant indications that trigger a certain prediction from the model, which is a critical challenge for advancing the explainability and trustworthiness of current TGNNs. To address this challenge, we propose a novel approach, called Temporal Motifs Explainer (TempME), which uncovers the most pivotal temporal motifs guiding the prediction of TGNNs. Derived from the information bottleneck principle, TempME extracts the most interaction-related motifs while minimizing the amount of contained information to preserve the sparsity and succinctness of the explanation. Events in the explanations generated by TempME are verified to be more spatiotemporally correlated than those of existing approaches, providing more understandable insights. Extensive experiments validate the superiority of TempME, with up to 8.21% increase in terms of explanation accuracy across six real-world datasets and up to 22.96% increase in boosting the prediction Average Precision of current TGNNs., Comment: Accepted at NeurIPS 2023, Camera Ready Version

The widespread deployment of Graph Neural Networks (GNNs) sparks significant interest in their explainability, which plays a vital role in model auditing and ensuring trustworthy graph learning. The objective of GNN explainability is to discern the underlying graph structures that have the most significant impact on model predictions. Ensuring that explanations generated are reliable necessitates consideration of the in-distribution property, particularly due to the vulnerability of GNNs to out-of-distribution data. Unfortunately, prevailing explainability methods tend to constrain the generated explanations to the structure of the original graph, thereby downplaying the significance of the in-distribution property and resulting in explanations that lack reliability. To address these challenges, we propose D4Explainer, a novel approach that provides in-distribution GNN explanations for both counterfactual and model-level explanation scenarios. The proposed D4Explainer incorporates generative graph distribution learning into the optimization objective, which accomplishes two goals: 1) generate a collection of diverse counterfactual graphs that conform to the in-distribution property for a given instance, and 2) identify the most discriminative graph patterns that contribute to a specific class prediction, thus serving as model-level explanations. It is worth mentioning that D4Explainer is the first unified framework that combines both counterfactual and model-level explanations. Empirical evaluations conducted on synthetic and real-world datasets provide compelling evidence of the state-of-the-art performance achieved by D4Explainer in terms of explanation accuracy, faithfulness, diversity, and robustness., Comment: Accepted at NeurIPS 2023, Camera Ready Version

Motivated by the recent discovery of an intermediate quantum critical phase between the antiferromagnetic order and the Fermi liquid in the frustrated Kondo lattice CePdAl, we study here a Kondo-Heisenberg chain with frustrated $J_1$-$J_2$ XXZ interactions among local spins using the density matrix renormalization group method. Our simulations reveal a global phase diagram with rich ground states including the antiferromagnetic order, the valence-bond-solid and bond-order-wave orders, the pair density wave state, the uniform superconducting state, and the Luttinger liquid state. We show that both the pair density wave and uniform superconductivity belong to the family of Luther-Emery liquids and may arise from pair instability of an intermediate quantum critical phase with medium Fermi volume in the presence of strong quantum fluctuations, while the Luttinger liquid has a large Fermi volume. This suggests a deep connection between the pair density wave, the unconventional superconductivity, and the non-Fermi liquid quantum critical phase., Comment: 12 pages, 13 figures

The recently discovered nickelate superconductor La$_3$Ni$_2$O$_7$ has a high transition temperature near 80 K under pressure, which offers additional avenues of unconventional superconductivity. Here with state-of-the-art tensor-network methods, we study a bilayer $t$-$J$-$J_\perp$ model for La$_3$Ni$_2$O$_7$ and find a robust $s$-wave superconductive (SC) order mediated by interlayer magnetic couplings. Large-scale density matrix renormalization group calculations find algebraic pairing correlations with Luttinger parameter of $K_{\rm SC} \simeq 1$. Infinite projected entangled-pair state method obtains a nonzero SC order directly in the thermodynamic limit, and estimates a strong pairing strength $\bar{\Delta}_z \sim \mathcal{O}(0.1)$. Tangent-space tensor renormalization group simulations further determine a high SC temperature $T_c^*/J \sim \mathcal{O}(0.1)$ and clarify the temperature evolution of SC order. Because of the intriguing orbital selective behaviors and strong Hund's rule coupling in the compound, $t$-$J$-$J_\perp$ model has strong interlayer spin exchange (while negligible interlayer hopping), which greatly enhances the SC pairing in the bilayer system. Such a magnetically mediated strong pairing has also been observed recently in the optical lattice of ultracold atoms. Our accurate and comprehensive tensor-network calculations reveal robust SC order in the bilayer $t$-$J$-$J_\perp$ model and shed light on the high-$T_c$ superconductivity in the pressurized nickelate La$_3$Ni$_2$O$_7$., Comment: 5 + 6 pages, 4 + 8 figures

Machine reading comprehension (MRC) poses new challenges over logical reasoning, which aims to understand the implicit logical relations entailed in the given contexts and perform inference over them. Due to the complexity of logic, logical relations exist at different granularity levels. However, most existing methods of logical reasoning individually focus on either entity-aware or discourse-based information but ignore the hierarchical relations that may even have mutual effects. In this paper, we propose a holistic graph network (HGN) which deals with context at both discourse level and word level, as the basis for logical reasoning, to provide a more fine-grained relation extraction. Specifically, node-level and type-level relations, which can be interpreted as bridges in the reasoning process, are modeled by a hierarchical interaction mechanism to improve the interpretation of MRC systems. Experimental results on logical reasoning QA datasets (ReClor and LogiQA) and natural language inference datasets (SNLI and ANLI) show the effectiveness and generalization of our method, and in-depth analysis verifies its capability to understand complex logical relations., Comment: Accepted at COLING 2022, 9 pages

Accessing the low-energy non-equilibrium dynamics of materials and their polaritons with simultaneous high spatial and temporal resolution has been a bold frontier of electron microscopy in recent years. One of the main challenges lies in the ability to retrieve extremely weak signals while simultaneously disentangling amplitude and phase information. Here, we present Free-Electron Ramsey Imaging (FERI), a microscopy approach based on light-induced electron modulation that enables coherent amplification of optical near-fields in electron imaging. We provide simultaneous time-, space-, and phase-resolved measurements of a micro-drum made from a hexagonal boron nitride membrane visualizing the sub-cycle dynamics of 2D polariton wavepackets therein. The phase-resolved measurements reveals vortex-anti-vortex singularities on the polariton wavefronts, together with an intriguing phenomenon of a traveling wave mimicking the amplitude profile of a standing wave. Our experiments show a 20-fold coherent amplification of the near-field signal compared to conventional electron near-field imaging, resolving peak field intensities in the order of ~W/cm2, corresponding to field amplitudes of a few kV/m. As a result, our work paves the way for spatio-temporal electron microscopy of biological specimens and quantum materials, exciting yet delicate samples that are currently difficult to investigate.

Free-electron radiation offers an enticing route to create light emission at arbitrary spectral regime. However, this type of light emission is generally weak, which is intrinsically limited by the weak particle-matter interaction and unavoidably impedes the development of many promising applications, such as the miniaturization of free-electron radiation sources and high-energy particle detectors. Here we reveal a mechanism to enhance the particle-matter interaction by exploiting the pseudo-Brewster effect of gain materials - presenting an enhancement of at least four orders of magnitude for the light emission. This mechanism is enabled by the emergence of an unprecedented phase diagram that maps all phenomena of free-electron radiation into three distinct phases in a gain-thickness parameter space, namely the conventional, intermediate, and Brewster phases, when an electron penetrates a dielectric slab with a modest gain and a finite thickness. Essentially, our revealed mechanism corresponds to the free-electron radiation in the Brewster phase, which also uniquely features ultrahigh directionality, always at the Brewster angle, regardless of the electron velocity. Counterintuitively, we find that the intensity of this free-electron Brewster radiation is insensitive to the Fabry-Perot resonance condition and thus the variation of slab thickness, and moreover, a weaker gain could lead to a stronger enhancement for the light emission. The scheme of free-electron Brewster radiation, especially along with its compatibility with low-energy electrons, may enable the development of high-directionality high-intensity light sources at any frequency.

Directionally molding the near-field and far-field radiation lies at the heart of nanophotonics and is crucial for applications such as on-chip information processing and chiral quantum networks. The most fundamental model for radiating structures is a dipolar source located inside a homogeneous matter. However, the influence of matter on the directionality of dipolar radiation is oftentimes overlooked, especially for the near-field radiation. We show that the dipole-matter interaction is intrinsically asymmetric and does not fulfill the duality principle, originating from the inherent asymmetry of Maxwell equations, i.e., electric charge and current are ubiquitous but their magnetic counterparts are non-existent to elusive. Moreover, we find that the asymmetric dipole-matter interaction could offer an enticing route to reshape the directionality of not only the near-field radiation but also the far-field radiation. As an example, both the near-field and far-field radiation directionality of Huygens dipole (located close to a dielectric-metal interface) would be reversed, if the dipolar position is changed from the dielectric region to the metal region.

Transition radiation is a fundamental process of light emission and occurs whenever a charged particle moves across an inhomogeneous region. One feature of transition radiation is that it can create light emission at arbitrary frequency under any particle velocity. Therefore, transition radiation is of significant importance to both fundamental science and practical applications. In this paper, we provide a brief historical review of transition radiation and its recent development. Moreover, we pay special attention to four typical applications of transition radiation, namely the detection of high-energy particles, coherent radiation sources, beam diagnosis, and excitation of surface waves. Finally, we give an outlook for the research tendency of transition radiation, especially its flexible manipulation by exploiting artificially-engineered materials and nanostructures, such as gain materials, metamaterials, spatial-temporal materials, meta-boundaries, and layered structures with a periodic or non-periodic stacking.

When a charged particle penetrates through an optical interface, photon emissions emerge - a phenomenon known as transition radiation. Being paramount to fundamental physics, transition radiation has enabled many applications from high-energy particle identification to novel light sources. A rule of thumb in transition radiation is that the radiation intensity generally decreases with the particle velocity v; as a result, low-energy particles are not favored in practice. Here we find that there exist situations where transition radiation from particles with extremely low velocities (e.g. v/c<0.001) exhibits comparable intensity as that from high-energy particles (e.g. v/c=0.999), where c is light speed in free space. The comparable radiation intensity implies an extremely high photon extraction efficiency from low-energy particles, up to eight orders of magnitude larger than that from high-energy particles. This exotic phenomenon of low-velocity-favored transition radiation originates from the excitation of Ferrell-Berreman modes in epsilon-near-zero materials. Our findings may provide a promising route towards the design of integrated light sources based on low-energy electrons and specialized detectors for beyond-standard-model particles., Comment: 13 pages, 4 figures

We investigate the formation and eruption of hot channels associated with the M6.5 class flare (SOL2015-06-22T18:23) occurring in NOAA AR 12371 on 2015 June 22. Two flare precursors are observed before the flare main phase. Observations in 94 {\AA} and 131 {\AA} by SDO/AIA have revealed the early morphology of the first hot channel as a group of hot loops, which is termed as seed hot channel. A few seed hot channels are formed above the polarity inversion line (PIL) and the formation is associated with footpoint brightenings' parallel motion along the PIL, which proceeds into the early stage of the flare main phase. During this process, seed hot channels build up and rise slowly, being accelerated at the peak of the second precursor. They merge in the process of acceleration forming a larger hot channel, which then forms an "inverted {\gamma}" shape kinking structure. Before the flare peak, the second kinking hot channel with negative crossing appears near the first kinking hot channel that has erupted. The eruption of these two hot channels produce two peaks on the main flare's GOES light curve. The footpoint brightenings' propagation along the PIL indicate that the first kinking hot channel may be formed due to zipper reconnection. The occurrence of merging between seed hot channels observed by AIA is supported by the extrapolated nonlinear force-free field models. The observed writhing motion of the first kinking hot channel may be driven by the Lorentz force.

The judicious design of electromagnetic boundary provides a crucial route to control light-matter interactions, and it is thus fundamental to basic science and practical applications. General design approaches rely on the manipulation of bulk properties of superstrate or substrate and on the modification of boundary geometries. Due to the recent advent of metasurfaces and low-dimensional materials, the boundary can be flexibly featured with a surface conductivity, which can be rather complex but provide an extra degree of freedom to regulate the propagation of light. In this perspective, we denote the boundary with a non-zero surface conductivity as the meta-boundary. The meta-boundaries are categorized into four types, namely isotropic, anisotropic, biisotropic and bianisotropic meta-boundaries, according to the electromagnetic boundary conditions. Accordingly, the latest development for these four kinds of meta-boundaries are reviewed. Finally, an outlook on the research tendency of meta-boundaries is provided, particularly on the manipulation of light-matter interactions by simultaneously exploiting meta-boundaries and metamaterials.

This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals. The spherical needlet transform is generalized from $\mathbb{S}^2$ onto the SO(3) group, which decomposes a spherical signal to approximate and detailed spectral coefficients by a set of tight framelet operators. The spherical signal during the decomposition and reconstruction achieves rotation invariance. Based on needlet transforms, we form a Needlet approximate Equivariance Spherical CNN (NES) with multiple SO(3) needlet convolutional layers. The network establishes a powerful tool to extract geometric-invariant features of spherical signals. The model allows sufficient network scalability with multi-resolution representation. A robust signal embedding is learned with wavelet shrinkage activation function, which filters out redundant high-pass representation while maintaining approximate rotation invariance. The NES achieves state-of-the-art performance for quantum chemistry regression and Cosmic Microwave Background (CMB) delensing reconstruction, which shows great potential for solving scientific challenges with high-resolution and multi-scale spherical signal representation.

Motivated by the recent discovery of ferromagnetic quantum criticality in the heavy fermion compound CeRh$_6$Ge$_4$, we develop a numerical algorithm of infinite projected entangled pair states for the anisotropic ferromagnetic Kondo-Heisenberg model in two dimensions and study the ferromagnetic quantum phase transitions with varying magnetic and hopping anisotropy. Our calculations reveal a continuous ferromagnetic quantum phase transition in the large anisotropic region and first-order quantum phase transitions for smaller anisotropy. Our results highlight the importance of magnetic anisotropy on ferromagnetic quantum criticality in Kondo lattice systems and provide a possible explanation for the experimental observation in CeRh$_6$Ge$_4$ with a quasi-one-dimensional magnetic structure. Our work opens the avenue for future studies of the rich Kondo lattice physics using state-of-the-art tensor network approaches., Comment: 7 pages, 6 figures

We investigate the failed partial eruption of a filament system in NOAA AR 12104 on 2014 July 5, using multiwavelength EUV, magnetogram, and H$\alpha$ observations, as well as magnetic field modeling. The filament system consists of two almost co-spatial segments with different end points, both resembling a C shape. Following an ejection and a precursor flare related to flux cancellation, only the upper segment rises and then displays a prominent twisted structure, while rolling over toward its footpoints. The lower segment remains undisturbed, indicating that the system possesses a double-decker structure. The erupted segment ends up with a reverse-C shape, with material draining toward its footpoints, while losing its twist. Using the flux rope insertion method, we construct a model of the source region that qualitatively reproduces key elements of the observed evolution. At the eruption onset, the model consists of a flux rope atop a flux bundle with negligible twist, which is consistent with the observational interpretation that the filament possesses a double-decker structure. The flux rope reaches the critical height of the torus instability during its initial relaxation, while the lower flux bundle remains in stable equilibrium. The eruption terminates when the flux rope reaches a dome-shaped quasi-separatrix layer that is reminiscent of a magnetic fan surface, although no magnetic null is found. The flux rope is destroyed by reconnection with the confining overlying flux above the dome, transferring its twist in the process., Comment: 40 pages, 10 figures

Moire superlattices-twisted van der Waals (vdW) structures with small angles-are attracting increasing attention in condensed matter physics, due to important phenomena revealed therein, including unconventional superconductivity, correlated insulating states, and ferromagnetism. Moire superlattices are typically comprised of atomic layers of vdW materials where the exotic physics arises from the quantum electronic coupling between adjacent atomic layers. Recently, moire electronics has motivated their photonic counterparts. In addition to vdW materials, twisted photonic systems can also be comprised of metamaterials, metasurfaces, and photonic crystals, mediated by interlayer electromagnetic coupling instead. The interplay between short-ranged interlayer quantum and long-ranged electromagnetic coupling in twisted structures are expected to yield rich phenomena in nano-optics. This perspective reviews recent progress in twisted structures for nanophotonics and outlooks emerging topics, opportunities, fundamental challenges, and potential applications., Comment: 23 pages, 5 figures The following article has been accepted by Applied Physics Letters. After it is published, it will be found at https://aip.scitation.org/journal/apl

An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpoint changes. To remedy this issue, this work proposes the Coated Adversarial Camouflage (CAC) to attack the detectors in arbitrary viewpoints. Unlike the patch trained in the 2D space, our camouflage generated by a conceptually different training framework consists of 3D rendering and dense proposals attack. Specifically, we make the camouflage perform 3D spatial transformations according to the pose changes of the object. Based on the multi-view rendering results, the top-n proposals of the region proposal network are fixed, and all the classifications in the fixed dense proposals are attacked simultaneously to output errors. In addition, we build a virtual 3D scene to fairly and reproducibly evaluate different attacks. Extensive experiments demonstrate the superiority of CAC over the existing attacks, and it shows impressive performance both in the virtual scene and the real world. This poses a potential threat to the security-critical computer vision systems.