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We characterize gap-opening mechanisms in the topological heavy fermion (THF) model of magic-angle twisted bilayer graphene (MATBG), with and without electron-phonon coupling, using dynamical mean-field theory (DMFT) with the numerical renormalization group (NRG) impurity solver. In the presence of symmetry breaking associated with valley-orbital ordering (time-reversal-symmetric or Kramers intervalley coherent, or valley polarized), spin anti-Hund and orbital-angular-momentum Hund couplings, induced by the dynamical Jahn-Teller effect, result in a robust pseudogap at filling $2 \lesssim |\nu| \lesssim 2.5$. We also find that Hundness enhances the pairing susceptibilities for $1.6 \lesssim |\nu| \lesssim 2.8$, which might be a precursor to the superconducting phases neighboring $|\nu| = 2$.

Confidential Computing (CC) has received increasing attention in recent years as a mechanism to protect user data from untrusted operating systems (OSes). Existing CC solutions hide confidential memory from the OS and/or encrypt it to achieve confidentiality. In doing so, they render OS memory optimization unusable or complicate the trusted computing base (TCB) required for optimization. This paper presents our results toward overcoming these limitations, synthesized in a CC design named Blindfold. Like many other CC solutions, Blindfold relies on a small trusted software component running at a higher privilege level than the kernel, called Guardian. It features three techniques that can enhance existing CC solutions. First, instead of nesting page tables, Guardian mediates how the OS accesses memory and handles exceptions by switching page and interrupt tables. Second, Blindfold employs a lightweight capability system to regulate the kernel semantic access to user memory, unifying case-by-case approaches in previous work. Finally, Blindfold provides carefully designed secure ABI for confidential memory management without encryption. We report an implementation of Blindfold that works on ARMv8-A/Linux. Using Blindfold prototype, we are able to evaluate the cost of enabling confidential memory management by the untrusted Linux kernel. We show Blindfold has a smaller runtime TCB than related systems and enjoys competitive performance. More importantly, we show that the Linux kernel, including all of its memory optimizations except memory compression, can function properly for confidential memory. This requires only about 400 lines of kernel modifications.

Transition metal dichalcogenides (TMDs) are a novel type of quantum materials that could be used in spintronics, optoelectronics, valleytronics and opto-valleytronics. The phenomena of spinto-charge conversion at the interface between sputtered magnetic materials and transition metal dichalcogenides have gained attention for designing fast and ultra-low power consumption devices that are applicable for spintronic-based applications. Here, we report a remarkable enhancement of spin-to-charge conversion efficiency in MoS$_2$/CoFeB heterostructures via Cu intercalation. In this context, magneto-optic Kerr effect-based microscopy reveals the absence of substantial domain modification due to Cu which indicates that Cu successfully detaches MoS2 from CoFeB. This shows that Cu interlayer is protecting the spin orbit coupling (SOC) of MoS$_2$ from exchange interaction with CoFeB, since the proximity with local magnetization can alter the electronic structure and hence the SOC. Further, via first-principles calculations, it is found that notable improvements in spin Berry curvature and spin Hall conductivity occur via the Cu intercalation which in turn increases the spin Hall angle. We have shown here that one can modify interfaces of ferromagnet and transition metal dichalcogenides-based heterostructures to achieve higher efficiencies of spin-tocharge conversion across them for extended functionalities and its relevance in spin orbit torquebased applications.

Traditional panel data causal inference frameworks, such as difference-in-differences and synthetic control methods, rely on pre-intervention data to estimate counterfactuals, which may not be available in real-world settings when interventions are implemented in response to sudden events. In this paper, we introduce two data fusion methods for causal inference from panel data in scenarios where pre-intervention data is unavailable. These methods leverage auxiliary reference domains with related panel data to estimate causal effects in the target domain, overcoming the limitations imposed by the absence of pre-intervention data. We show the efficacy of these methods by obtaining converging bounds on the absolute bias as well as through simulations, showing their robustness in a variety of panel data settings. Our findings provide a framework for applying causal inference in urgent and data-constrained environments, such as public health crises or epidemiological shocks.

The pursuit of quantum spin liquid (QSL) states in condensed matter physics has drawn attention to kagome antiferromagnets (AFM) where a two-dimensional corner-sharing network of triangles frustrates conventional magnetic orders. While quantum kagome AFMs based on Cu$^{2+}$ (3d$^9$, $s=\frac{1}{2}$) ions have been extensively studied, there is so far little work beyond copper-based systems. Here we present our bulk magnetization, specific heat and neutron scattering studies on single crystals of a new titanium fluoride Cs$_8$RbK$_3$Ti$_{12}$F$_{48}$ where Ti$^{3+}$ (3d$^1$, $s = \frac{1}{2}$) ions form a modulated quantum kagome antiferromagnet that does not order magnetically down to 1.5 K. Our comprehensive map of the dynamic response function $S(Q,\hbar\omega)$ acquired at 1.5 K where the heat capacity is T-linear reveals a dispersive continuum emanating from soft lines that extend along (100). The data indicate fractionalized spinon-like excitations with quasi-one-dimensional dispersion within a quasi-two-dimensional spin system.

Accurately detecting objects at long distances remains a critical challenge in 3D object detection when relying solely on LiDAR sensors due to the inherent limitations of data sparsity. To address this issue, we propose the LiDAR-Camera Augmentation Network (LCANet), a novel framework that reconstructs LiDAR point cloud data by fusing 2D image features, which contain rich semantic information, generating additional points to improve detection accuracy. LCANet fuses data from LiDAR sensors and cameras by projecting image features into the 3D space, integrating semantic information into the point cloud data. This fused data is then encoded to produce 3D features that contain both semantic and spatial information, which are further refined to reconstruct final points before bounding box prediction. This fusion effectively compensates for LiDAR's weakness in detecting objects at long distances, which are often represented by sparse points. Additionally, due to the sparsity of many objects in the original dataset, which makes effective supervision for point generation challenging, we employ a point cloud completion network to create a complete point cloud dataset that supervises the generation of dense point clouds in our network. Extensive experiments on the KITTI and Waymo datasets demonstrate that LCANet significantly outperforms existing models, particularly in detecting sparse and distant objects., Comment: 7 pages

LaTeX is suitable for creating specially formatted documents in science, technology, mathematics, and computer science. Although the use of mathematical expressions in LaTeX format along with language models is increasing, there are no proper evaluation matrices to evaluate them. In this study, we propose TeXBLEU, a metric for evaluating mathematical expressions in the LaTeX format built on the n-gram-based BLEU metric widely used in translation tasks. The proposed TeXBLEU consists of a predefined tokenizer trained on the arXiv paper dataset and a fine-tuned embedding model with positional encoding. The TeXBLEU score was calculated by replacing BLUE's modified precision score with the similarity of n-gram-based tokens. TeXBLEU showed improvements of 86\%, 121\%, and 610\% over traditional evaluation metrics, such as BLEU, sacreBLEU, and Rouge, respectively, on the MathBridge dataset with 1,000 data points. The code is available at https://github.com/KyuDan1/TeXBLEU., Comment: 5 pages, 4 figures

We conjecture a precise relationship between Lusztig $q$-weight multiplicities for type $C$ and Kirillov-Reshetikhin crystals. We also define $\mathfrak{gl}_n$-version of $q$-weight multiplicity for type $C$ and conjecture the positivity., Comment: 7 pages

The interplay between cosmology and strongly coupled dynamics can yield transient spectral features that vanish at late times, but which may leave behind phenomenological signatures in the spectrum of primordial fluctuations. Of particular interest are strongly coupled extensions of the standard model featuring approximate conformal invariance. In flat space, the spectral density for a scalar operator in a conformal field theory is characterized by a continuum with scaling law governed by the dimension of the operator, and is otherwise featureless. AdS/CFT arguments suggest that for large $N$, in an inflationary background with Hubble rate $H$, this continuum is gapped. We demonstrate that there can be additional peak structures that become sharp and particle-like at phenomenologically interesting regions in parameter space, and we estimate their contribution to cosmological observables. We find phenomena that are potentially observable in future experiments that are unique to these models, including displaced oscillatory features in the squeezed limit of the bi-spectrum. These particles can be either fundamental, and localized to a UV brane, or composite at the Hubble scale, $H$, and bound to a horizon in the bulk of the 5D geometry. We comment on how stabilization of conformal symmetry breaking vacua can be correlated with these spectral features and their phenomenology., Comment: 31 pages, 11 figures

The goal of image cropping is to identify visually appealing crops within an image. Conventional methods rely on specialized architectures trained on specific datasets, which struggle to be adapted to new requirements. Recent breakthroughs in large vision-language models (VLMs) have enabled visual in-context learning without explicit training. However, effective strategies for vision downstream tasks with VLMs remain largely unclear and underexplored. In this paper, we propose an effective approach to leverage VLMs for better image cropping. First, we propose an efficient prompt retrieval mechanism for image cropping to automate the selection of in-context examples. Second, we introduce an iterative refinement strategy to iteratively enhance the predicted crops. The proposed framework, named Cropper, is applicable to a wide range of cropping tasks, including free-form cropping, subject-aware cropping, and aspect ratio-aware cropping. Extensive experiments and a user study demonstrate that Cropper significantly outperforms state-of-the-art methods across several benchmarks.

We investigated the Gilbert damping in La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) and La$_{0.7}$Sr$_{0.3}$MnO$_3$/Pt (LSMO/Pt) heterostructures using first-principles calculations and Wannier interpolation techniques. Our work is motivated by recent experimental observations showing smaller Gilbert damping in LSMO/Pt films compared to their reference single-layer LSMO films, despite expectations of enhanced spin-pumping effects in the former. We analyze the electronic structures and transport behaviors, finding that LSMO thin films have a high spin Hall angle ($|{\theta_{\mathrm{SH}}}|$). However, in LSMO/Pt, the presence of platinum significantly increases longitudinal conductivity, reducing $|\theta_{\mathrm{SH}}|$. Despite the lower $|\theta_{\mathrm{SH}}|$, LSMO/Pt shows a notable anti-damping contribution to Gilbert damping due to a larger spin diffusion length. In contrast, pure LSMO films with large $|\theta_{\mathrm{SH}}|$ exhibit higher damping due to efficient spin-to-charge conversion via a self-induced inverse spin Hall effect (ISHE), as reported in a recent experiment. Finally, this work demonstrates that by fine-tuning the ratio of spin Hall conductivity to longitudinal charge conductivity, it is possible to engineer heterostructures with desired spin-to-charge or charge-to-spin conversion efficiencies even with weaker spin-orbit couplings.

We propose a fault-tolerant quantum computation scheme in a measurement-based manner with finite-sized entangled resource states and encoded fusion scheme with linear optics. The encoded-fusion is an entangled measurement devised to enhance the fusion success probability in the presence of losses and errors based on a quantum error-correcting code. We apply an encoded-fusion scheme, which can be performed with linear optics and active feedforwards to implement the generalized Shor code, to construct a fault-tolerant network configuration in a three-dimensional Raussendorf-Harrington-Goyal lattice based on the surface code. Numerical simulations show that our scheme allows us to achieve up to 10 times higher loss thresholds than nonencoded fusion approaches with limited numbers of photons used in fusion. Our scheme paves an efficient route toward fault-tolerant quantum computing with finite-sized entangled resource states and linear optics., Comment: 14 pages including 5 Appendices, 14 figures

Recent developments in the magnetization dynamics in spin textures, particularly skyrmions, offer promising new directions for magnetic storage technologies and spintronics. Skyrmions, characterized by their topological protection and efficient mobility at low current density, are increasingly recognized for their potential applications in next-generation logic and memory devices. This study investigates the dynamics of skyrmion magnetization, focusing on the manipulation of their topological states as a basis for bitwise data storage through a modified Landau-Lifshitz-Gilbert equation (LLG). We introduce spin-polarized electrons from a topological ferromagnet that induce an electric dipole moment that interacts with the electric gauge field within the skyrmion domain. This interaction creates an effective magnetic field that results in a torque that can dynamically change the topological state of the skyrmion. In particular, we show that these torques can selectively destroy and create skyrmions, effectively writing and erasing bits, highlighting the potential of using controlled electron injection for robust and scalable skyrmion-based data storage solutions.

Due to the rare occurrence of anomalous events, a typical approach to anomaly detection is to train an autoencoder (AE) with normal data only so that it learns the patterns or representations of the normal training data. At test time, the trained AE is expected to well reconstruct normal but to poorly reconstruct anomalous data. However, contrary to the expectation, anomalous data is often well reconstructed as well. In order to further separate the reconstruction quality between normal and anomalous data, we propose creating pseudo anomalies from learned adaptive noise by exploiting the aforementioned weakness of AE, i.e., reconstructing anomalies too well. The generated noise is added to the normal data to create pseudo anomalies. Extensive experiments on Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the effectiveness and generic applicability of our approach in improving the discriminative capability of AEs for anomaly detection., Comment: SharedIt link: https://rdcu.be/dGOrh

We compute the genus $0$ free energy for the $2$-matrix model with quartic interactions, which acts as a generating function for the Ising model's partition function on a random, $4$-regular, planar graph. This rigorously confirms the predictions of V.A. Kazakov and D.V. Boulatov on this model, and provides a new parametric formula for the free energy. We also give a characterization of the phase space of the model. Our analysis is based on a steepest descent Riemann-Hilbert analysis of the associated biorthogonal polynomials and the corresponding isomonodromic $\tau$-function. A key ingredient in the analysis is a parametrization of the spectral curve., Comment: Version 1: 83 pages, 17 figures. Comments welcome!

The Euler class characterizes the topology of two real bands isolated from other bands in two-dimensions. Despite various intriguing topological properties predicted up to now, the candidate real materials hosting electronic Euler bands are extremely rare. Here, we show that in a quantum spin Hall insulator with two-fold rotation $C_{2z}$ about the $z$-axis, a pair of bands with nontrivial $\mathbb{Z}_2$ invariant turn into magnetic Euler bands under in-plane Zeeman field or in-plane ferromagnetic ordering. The resulting magnetic insulator generally carries a nontrivial second Stiefel-Whitney invariant. In particular, when the topmost pair of occupied bands carry a nonzero Euler number, the corresponding magnetic insulator can be called a magnetic Euler insulator. Moreover, the topological phase transition between a trivial magnetic insulator and a magnetic Stiefel-Whitney or Euler insulator is mediated by a stable topological semimetal phase in which Dirac nodes carrying non-Abelian topological charges exhibit braiding processes across the transition. Using the first-principles calculations, we propose various candidate materials hosting magnetic Euler bands. We especially show that ZrTe$_5$ bilayers under in-plane ferromagnetism are a candidate system for magnetic Stiefel-Whitney insulators in which the non-Abelian braiding-induced topological phase transitions can occur under pressure.

Variational quantum eigensolver (VQE), which combines quantum systems with classical computational power, has been arisen as a promising candidate for near-term quantum computing applications. However, the experimental resources such as the number of measurements to implement VQE rapidly increases as the Hamiltonian problem size grows. Applying entanglement measurements to reduce the number of measurement setups has been proposed to address this issue, however, entanglement measurements themselves can introduce additional resource demands. Here, we apply entanglement measurements to the photonic VQE utilizing polarization and path degrees of freedom of a single-photon. In our photonic VQE, entanglement measurements can be deterministically implemented using linear optics, so it takes full advantage of introducing entanglement measurements without additional experimental demands. Moreover, we show that such a setup can mitigate errors in measurement apparatus for a certain Hamiltonian., Comment: Comments are welcome

We study dynamical scaling associated with a Kondo-breakdown quantum critical point (KB-QCP) of the periodic Anderson model, treated by two-site cellular dynamical mean-field theory (2CDMFT). In the quantum critical region, the staggered spin exhibits SYK-like slow dynamics and its dynamical susceptibility shows $\omega/T$ scaling. We propose a scaling Ansatz that describes this behavior. It also implies Planckian dissipation for the longest-lived excitations. The current susceptibility follows the same scaling ansatz, leading to strange-metal scaling. This demonstrates that the KB-QCP described by 2CDMFT is an intrinsic (i.e., disorder-free) strange-metal fixed point. Surprisingly, the SYK-like dynamics and scaling are driven by strong vertex contributions to the susceptibilities. Our results for the optical conductivity match experimental observations on YbRh${}_2$Si${}_2$ and CeCoIn${}_5$., Comment: 16 pages, 13 figures, comments are welcome!

Aerial unperching of multirotors has received little attention as opposed to perching that has been investigated to elongate operation time. This study presents a new aerial robot capable of both perching and unperching autonomously on/from a ferromagnetic surface during flight, and a switching controller to avoid rotor saturation and mitigate overshoot during transition between free-flight and perching. To enable stable perching and unperching maneuvers on/from a vertical surface, a lightweight ($\approx$ $1$ \si{kg}), fully actuated tiltrotor that can hover at $90^\circ$ pitch angle is first developed. We design a perching/unperching module composed of a single servomotor and a magnet, which is then mounted on the tiltrotor. A switching controller including exclusive control modes for transitions between free-flight and perching is proposed. Lastly, we propose a simple yet effective strategy to ensure robust perching in the presence of measurement and control errors and avoid collisions with the perching site immediately after unperching. We validate the proposed framework in experiments where the tiltrotor successfully performs perching and unperching on/from a vertical surface during flight. We further show effectiveness of the proposed transition mode in the switching controller by ablation studies where large overshoot and even collision with a perching site occur. To the best of the authors' knowledge, this work presents the first autonomous aerial unperching framework using a fully actuated tiltrotor., Comment: 7 pages, 10 figures, 2024 IEEE International Conference on Robotics and Automation (ICRA) accepted

Two-particle response functions are a centerpiece of both experimental and theoretical quantum many-body physics. Yet, due to their size and discontinuity structure, they are challenging to handle numerically. Recently, two advances were made to tackle this problem: first, the overcomplete intermediate representation (OIR), which provides a highly efficient compression of Green's functions in imaginary frequency, and second, partial spectral functions (PSFs), which allow for an efficient evaluation in real frequency. We show that there is a two-to-one correspondence between PSFs and OIR coefficients and exploit this fact to construct the OIR for three-or-more-particle propagators. We then use OIR to fit and compress imaginary-frequency data obtained from the numerical renormalization group (NRG), reaching a compression ratio of more than 400. Finally, we attempt to match the OIR data to partial Green's functions from NRG.Due to the overcompleteness, we achieve only qualitative agreement., Comment: 9 pages, 4 figures

Eddy current shielding by a Faraday cage is an effective way to shield alternating-current (AC) magnetic fields in scientific instrumentation. In a strong static magnetic field, however, the eddy current in the conductive shield is subject to the Lorentz force which causes the shield to vibrate. In addition to mechanical issues, such vibration induces motional eddy current in the shield that can dominate the original, electromagnetic eddy current to undermine the conductor's shielding capability. In this work we investigate a method to control motional eddy current by making cut-out patterns in the conductor that follow the electromagnetic eddy current image. This effectively limits the surface current of the plate to a single mode and prevents proliferation of uncontrolled motion-induced surface currents that disrupts eddy current shielding. Implications of our results for improved shielding of gradient fields in high-field MRI are discussed.

In this study, we present an advanced convolutional neural network (CNN) architecture for ship classification based on optical satellite imagery, which significantly enhances performance through the integration of a convolutional block attention module (CBAM) and additional architectural innovations. Building upon the foundational ResNet50 model, we first incorporated a standard CBAM to direct the model's focus toward more informative features, achieving an accuracy of 87% compared to 85% of the baseline ResNet50. Further augmentations involved multiscale feature integration, depthwise separable convolutions, and dilated convolutions, culminating in an enhanced ResNet model with improved CBAM. This model demonstrated a remarkable accuracy of 95%, with precision, recall, and F1 scores all witnessing substantial improvements across various ship classes. In particular, the bulk carrier and oil tanker classes exhibited nearly perfect precision and recall rates, underscoring the enhanced capability of the model to accurately identify and classify ships. Attention heatmap analyses further validated the efficacy of the improved model, revealing more focused attention on relevant ship features regardless of background complexities. These findings underscore the potential of integrating attention mechanisms and architectural innovations into CNNs for high-resolution satellite imagery classification. This study navigates through the class imbalance and computational costs and proposes future directions for scalability and adaptability in new or rare ship-type recognition. This study lays the groundwork for applying advanced deep learning techniques in remote sensing, offering insights into scalable and efficient satellite image classification., Comment: Submitted to IEEE Access on August 16, 2024

We introduce HyperCLOVA X, a family of large language models (LLMs) tailored to the Korean language and culture, along with competitive capabilities in English, math, and coding. HyperCLOVA X was trained on a balanced mix of Korean, English, and code data, followed by instruction-tuning with high-quality human-annotated datasets while abiding by strict safety guidelines reflecting our commitment to responsible AI. The model is evaluated across various benchmarks, including comprehensive reasoning, knowledge, commonsense, factuality, coding, math, chatting, instruction-following, and harmlessness, in both Korean and English. HyperCLOVA X exhibits strong reasoning capabilities in Korean backed by a deep understanding of the language and cultural nuances. Further analysis of the inherent bilingual nature and its extension to multilingualism highlights the model's cross-lingual proficiency and strong generalization ability to untargeted languages, including machine translation between several language pairs and cross-lingual inference tasks. We believe that HyperCLOVA X can provide helpful guidance for regions or countries in developing their sovereign LLMs., Comment: 44 pages; updated authors list and fixed author names