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Semantic image synthesis (SIS) aims to generate realistic images that match given semantic masks. Despite recent advances allowing high-quality results and precise spatial control, they require a massive semantic segmentation dataset for training the models. Instead, we propose to employ a pre-trained unconditional generator and rearrange its feature maps according to proxy masks. The proxy masks are prepared from the feature maps of random samples in the generator by simple clustering. The feature rearranger learns to rearrange original feature maps to match the shape of the proxy masks that are either from the original sample itself or from random samples. Then we introduce a semantic mapper that produces the proxy masks from various input conditions including semantic masks. Our method is versatile across various applications such as free-form spatial editing of real images, sketch-to-photo, and even scribble-to-photo. Experiments validate advantages of our method on a range of datasets: human faces, animal faces, and buildings., Comment: NeurIPS 2023, Project Page: https://hhyunn2.github.io/SIS_UncondG/

To detect and track structural changes in atomic nuclei, the systematic study of nuclear levels with firm spin-parity assignments is important. While linear polarization measurements have been applied to determine the electromagnetic character of gamma-ray transitions, the applicable range is strongly limited due to the low efficiency of the detection system. The multi-layer Cadmium-Telluride (CdTe) Compton camera can be a state-of-the-art gamma-ray polarimeter for nuclear spectroscopy with the high position sensitivity and the detection efficiency. We demonstrated the capability to operate this detector as a reliable gamma-ray polarimeter by using polarized 847-keV gamma rays produced by the $^{56}\rm{Fe}({\it p},{\it p'}\gamma)$ reaction. By combining the experimental data and simulated calculations, the modulation curve for the gamma ray was successfully obtained. A remarkably high polarization sensitivity was achieved, compatible with a reasonable detection efficiency. Based on the obtained results, a possible future gamma-ray polarimetery is discussed.

A quantum channel whose image approximates the set of separable states is called a disentangler, which plays a prominent role in the investigation of variants of the computational model called Quantum Merlin Arthur games, and has potential applications in classical and quantum algorithms for the separability testing and NP-complete problems. So far, two types of a disentangler, constructed based on $\epsilon$-nets and the quantum de Finetti theorem, have been known; however, both of them require an exponentially large input system. Moreover, in 2008, John Watrous conjectured that any disentangler requires an exponentially large input system, called the disentangler conjecture. In this paper, we show that both of the two known disentanglers can be regarded as examples of a strong disentangler, which is a disentangler approximately breaking entanglement between one output system and the composite system of another output system and the arbitrarily large environment. Note that the strong disentangler is essentially an approximately entanglement-breaking channel while the original disentangler is an approximately entanglement-annihilating channel, and the set of strong disentanglers is a subset of disentanglers. As a main result, we show that the disentangler conjecture is true for this subset, the set of strong disentanglers, for a wide range of approximation parameters without any computational hardness assumptions., Comment: 9 pages, 2 figures

Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any potential quantum advantage would be limited. There is no doubt that crosstalk is one of the main sources of noise in noisy intermediate-scale quantum (NISQ) systems, and it poses a fundamental challenge to hardware designs. Crosstalk between parallel instructions can corrupt quantum states and cause incorrect program execution. In this study, we present a necessary analysis of the crosstalk error effect on NISQ devices. Our approach is extremely straightforward and practical to estimate the crosstalk error of various multi-qubit devices. In particular, we combine the randomized benchmarking (RB) and simultaneous randomized benchmarking (SRB) protocol to estimate the crosstalk error from the correlation controlled-NOT (CNOT) gate. We demonstrate this protocol experimentally on 5-, 7-, \& 16-qubit devices. Our results demonstrate the crosstalk error model of three different IBM quantum devices over the experimental week and compare the error variation against the machine, number of qubits, quantum volume, processor, and topology. We then confirm the improvement in the circuit fidelity on different benchmarks by up to 3.06x via inserting an instruction barrier, as compared with an IBM quantum noisy device which offers near-optimal crosstalk mitigation in practice. Finally, we discuss the current system limitation, its tradeoff on fidelity and depth, noise beyond the NISQ system, and mitigation opportunities to ensure that the quantum operation can perform its quantum magic undisturbed., Comment: After careful consideration, we have decided to withdraw the manuscript due to the need for significant revisions and a change in research direction

We investigate the long-term motion of Saturn's North-Pole Hexagon and the structure of its associated eastward jet, using Cassini ISS and ground-based images from 2008 to 2014. We show that both are persistent features that have survived the long polar night, the jet profile remaining essentially unchanged. During those years the hexagon vertices showed a steady rotation period of 10 hr 39 min 23.01 $\pm$ 0.01 s. Analysis of Voyager 1 and 2 (1980-1981) and HST and ground-based (1990-91) images shows a period shorter by 3.5s, due to the presence at the time of a large anticyclone. We interpret the hexagon as a manifestation of a vertically trapped Rossby wave on the polar jet and, because of their survival and unchanged properties under the strong seasonal variations in insolation, we propose that both hexagon and jet are deep-rooted atmospheric features that could reveal the true rotation of the planet Saturn.

Purpose: To compare the accuracy with which different hadronic inelastic physics models across ten Geant4 Monte Carlo simulation toolkit versions can predict positron-emitting fragments produced along the beam path during carbon and oxygen ion therapy. Materials and Methods: Phantoms of polyethylene, gelatin or poly(methyl methacrylate) were irradiated with monoenergetic carbon and oxygen ion beams. Post-irradiation, 4D PET images were acquired and parent $^{11}$C, $^{10}$C and $^{15}$O radionuclides contributions in each voxel were determined from the extracted time activity curves. Experiments were simulated in Geant4 Monte Carlo versions 10-11.1, with three different fragmentation models: binary ion cascade (BIC), quantum molecular dynamics (QMD) and the Liege intranuclear cascade (INCL++) - 30 combinations. Total/parent isotope positron annihilation yields were compared between simulations and experiments using normalised mean squared error and Pearson cross-correlation coefficient. Depth of maximum/distal 50\% peak position yield were also compared. Results: Performance varied considerably across versions and models, with no one best predicting all positron-emitting fragments. BIC in Geant4 10.2 provided the best overall agreement with experimental results in the largest number of test cases. QMD consistently provided the best estimates of both the depth of peak positron yield (10.4 and 10.6) and the distal 50\%-of-peak point (10.2), while BIC also performed well and INCL generally performed the worst across most Geant4 versions. Conclusions: Best spatial prediction of annihilation yield and positron-emitting fragment production during carbon and oxygen ion therapy was found to be 10.2.p03 with BIC or QMD. These version/model combinations are recommended for future heavy ion therapy research., Comment: This manuscript has been submitted to IOP's Journal of Physics in Medicine and Biology, on the 5th of February 2024

The role of spin fluctuations near magnetic phase transitions is crucial for generating various exotic phenomena, including anomalies in the extraordinary Hall effect, excess spin-current generation through the spin-Hall effect (SHE), and enhanced spin-pumping, amongst others. In this study, we experimentally investigate the temperature dependence of spin-orbit torques (SOTs) generated by Mn3Ni0.35Cu0.65N (MNCN), a member of the noncollinear antiferromagnetic family that exhibits unconventional magnetotransport properties. Our work uncovers a strong and nontrivial temperature dependence of SOTs, peaking near the N\'eel temperature of MNCN, which cannot be explained by conventional intrinsic and extrinsic scattering mechanisms of the SHE. Notably, we measure a maximum SOT efficiency of 30%, which is substantially larger than that of commonly studied nonmagnetic materials such as Pt. Theoretical calculations confirm a negligible SHE and a strong orbital Hall effect that can explain the observed SOTs. We propose a previously unidentified mechanism wherein fluctuating antiferromagnetic moments trigger the generation of substantial orbital currents near the N\'eel temperature due to the emergence of scalar spin chirality. Our findings present an approach for enhancing SOTs, which holds promise for magnetic memory applications by leveraging antiferromagnetic spin fluctuations to amplify both orbital and spin currents.

We theoretically investigate the thermal Hall transport of magnon-polarons in a two-dimensional honeycomb antiferromagnetic insulator under the influence of a perpendicular magnetic field, varying in strength. The application of a perpendicular magnetic field induces a magnetic phase transition from the collinear antiferromagnetic phase to the spin-flop phase, leading to a significant alteration in Hall transport across the transition point. In this paper, our focus is on the intrinsic contribution to thermal Hall transport arising from the magnetoelastic interaction. To facilitate experimental verification of our theoretical results, we present the dependence of thermal Hall conductivity on magnetic field strength and temperature., Comment: 10 pages, 6 figures

Recent thousand-qubit processors represent a significant hardware advancement, but current limitations prevent effective quantum error correction (QEC), necessitating reliance on quantum error mitigation (QEM) to enhance result fidelity from quantum computers. Our paper introduces a noise-aware folding technique that enhances Zero-Noise Extrapolation (ZNE) by leveraging the noise characteristics of target quantum hardware to fold circuits more efficiently. Unlike traditional ZNE approaches assuming uniform error distribution, our method redistributes noise using calibration data based on hardware noise models. By employing a noise-adaptive compilation method combined with our proposed folding mechanism, we enhance the ZNE accuracy of quantum gate-based computing using superconducting quantum computers. This paper highlights the uniqueness of our method, summarizes noise accumulation, presents the scaling algorithm, and compares the reliability of our method with those of existing models using linear extrapolation model. Experimental results show that compared to existing folding methods, our approach achieved a 35% improvement on quantum computer simulators and a 31% improvement on real quantum computers, demonstrating the effectiveness of our proposed approach., Comment: 22 pages, 6 figures

A vectorial optical mode converter that can transform an orthogonal set of multiple input vector beams into another orthogonal set of vector beams is attractive for a wide range of applications in optics and photonics. While multi-plane light conversion (MPLC) and metasurface (MS) technologies have been explored to individually address multiple spatial mode conversion and polarization mode manipulation, there has been no universal methodology to simultaneously convert a set of multiple vectorial modes, having non-uniform spatial distributions in both their complex amplitude and polarization, to another set of multiple vectorial modes. In this paper, we demonstrate versatile devices based on the MPLC concept incorporating multi-layer locally birefringent MSs and present a general design formalism for complete vectorial mode conversion in arbitrary cases. The effectiveness of our proposed method is confirmed experimentally by demonstrating a 6-mode (3 spatial modes $\times$ 2 polarization modes) multiplexer, fabricated on a compact chip with a ~0.65 mm$^2$ lateral size in a folded MS configuration. Additionally, we verify its applicability to more advanced functional devices through numerical demonstration of a mode-division-multiplexed dual-polarization coherent receiver and spatial-mode-multiplexed vectorial holography. The versatility of our protocol makes it suitable for designing a myriad of multi-input-multi-output devices, providing a powerful tool for realizing universal optical mode converters for a wide range of applications., Comment: 25 pages, 12 figures

We address interconnection challenges in limited-qubit superconducting quantum computers (SQC), which often face crosstalk errors due to expanded qubit interactions during operations. Existing mitigation methods carry trade-offs, like hardware couplers or software-based gate scheduling. Our innovation, the Context-Aware COupler REconfiguration (CA-CORE) compilation method, aligns with application-specific design principles. It optimizes the qubit connections for improved SQC performance, leveraging tunable couplers. Through contextual analysis of qubit correlations, we configure an efficient coupling map considering SQC constraints. Our method reduces depth and SWAP operations by up to 18.84% and 42.47%, respectively. It also enhances circuit fidelity by 40% compared to IBM and Google's topologies. Notably, our method compiles a 33-qubit circuit in less than 1 second., Comment: 20 pages, 8 figures

We conducted 4-night multiwavelength observations of an active M-dwarf star EV Lac on 2022 October 24$-$27 with simultaneous coverage of soft X-rays (NICER; 0.2$-$12 $\mathrm{keV}$, Swift XRT; 0.2$-$10 $\mathrm{keV}$), near-ultraviolet (Swift UVOT/UVW2; 1600$-$3500 \r{A}), optical photometry (TESS; 6000$-$10000 \r{A}), and optical spectroscopy (Nayuta/MALLS; 6350$-$6800 \r{A}). During the campaign, we detected a flare starting at 12:28 UTC on October 25 with its white-light bolometric energy of $3.4 \times 10^{32}$ erg. At about 1 hour after this flare peak, our $\mathrm{H\alpha}$ spectrum showed a blue-shifted excess component at its corresponding velocity of $\sim 100 \: \mathrm{km \: s^{-1}}$. This may indicate that the prominence erupted with a 1-hour delay of the flare peak. Furthermore, the simultaneous 20-second cadence near-ultraviolet and white-light curves show gradual and rapid brightening behaviors during the rising phase at this flare. The ratio of flux in NUV to white light at the gradual brightening was $\sim 0.49$, which may suggest that the temperature of the blackbody is low ($< 9000 \: \mathrm{K}$) or the maximum energy flux of a nonthermal electron beam is less than $5\times10^{11} \: \mathrm{erg \: cm^{-2} \: s^{-1}}$. Our simultaneous observations of NUV and white-light flare raise the issue of a simple estimation of UV flux from optical continuum data by using a blackbody model., Comment: 19 pages, 10 figures. Accepted for publication in Publications of the Astronomical Society of Japan

Recent progress in single-image 3D generation highlights the importance of multi-view coherency, leveraging 3D priors from large-scale diffusion models pretrained on Internet-scale images. However, the aspect of novel-view diversity remains underexplored within the research landscape due to the ambiguity in converting a 2D image into 3D content, where numerous potential shapes can emerge. Here, we aim to address this research gap by simultaneously addressing both consistency and diversity. Yet, striking a balance between these two aspects poses a considerable challenge due to their inherent trade-offs. This work introduces HarmonyView, a simple yet effective diffusion sampling technique adept at decomposing two intricate aspects in single-image 3D generation: consistency and diversity. This approach paves the way for a more nuanced exploration of the two critical dimensions within the sampling process. Moreover, we propose a new evaluation metric based on CLIP image and text encoders to comprehensively assess the diversity of the generated views, which closely aligns with human evaluators' judgments. In experiments, HarmonyView achieves a harmonious balance, demonstrating a win-win scenario in both consistency and diversity., Comment: Project page: https://byeongjun-park.github.io/HarmonyView/

In this work, we propose a symbolic music generation model with the song structure graph analysis network. We construct a graph that uses information such as note sequence and instrument as node features, while the correlation between note sequences acts as the edge feature. We trained a Graph Neural Network to obtain node representation in the graph, then we use node representation as input of Unet to generate CONLON pianoroll image latent. The outcomes of our experimental results show that the proposed model can generate a comprehensive form of music. Our approach represents a promising and innovative method for symbolic music generation and holds potential applications in various fields in Music Information Retreival, including music composition, music classification, and music inpainting systems., Comment: 5 pages(4 pages of paper and 1 references), 3 figures

We consider optimal experimental design (OED) problems in selecting the most informative observation sensors to estimate model parameters in a Bayesian framework. Such problems are computationally prohibitive when the parameter-to-observable (PtO) map is expensive to evaluate, the parameters are high-dimensional, and the optimization for sensor selection is combinatorial and high-dimensional. To address these challenges, we develop an accurate, scalable, and efficient computational framework based on derivative-informed neural operators (DINO). We propose to use derivative-informed dimension reduction to reduce the parameter dimensions, based on which we train DINO with derivative information as an accurate and efficient surrogate for the PtO map and its derivative. Moreover, we derive DINO-enabled efficient formulations in computing the maximum a posteriori (MAP) point, the eigenvalues of approximate posterior covariance, and three commonly used optimality criteria for the OED problems. Furthermore, we provide detailed error analysis for the approximations of the MAP point, the eigenvalues, and the optimality criteria. We also propose a modified swapping greedy algorithm for the sensor selection optimization and demonstrate that the proposed computational framework is scalable to preserve the accuracy for increasing parameter dimensions and achieves high computational efficiency, with an over 1000$\times$ speedup accounting for both offline construction and online evaluation costs, compared to high-fidelity Bayesian OED solutions for a three-dimensional nonlinear convection-diffusion-reaction example with tens of thousands of parameters.

A large dataset of annotated traffic accidents is necessary to improve the accuracy of traffic accident recognition using deep learning models. Conventional traffic accident datasets provide annotations on traffic accidents and other teacher labels, improving traffic accident recognition performance. However, the labels annotated in conventional datasets need to be more comprehensive to describe traffic accidents in detail. Therefore, we propose V-TIDB, a large-scale traffic accident recognition dataset annotated with various environmental information as multi-labels. Our proposed dataset aims to improve the performance of traffic accident recognition by annotating ten types of environmental information as teacher labels in addition to the presence or absence of traffic accidents. V-TIDB is constructed by collecting many videos from the Internet and annotating them with appropriate environmental information. In our experiments, we compare the performance of traffic accident recognition when only labels related to the presence or absence of traffic accidents are trained and when environmental information is added as a multi-label. In the second experiment, we compare the performance of the training with only contact level, which represents the severity of the traffic accident, and the performance with environmental information added as a multi-label. The results showed that 6 out of 10 environmental information labels improved the performance of recognizing the presence or absence of traffic accidents. In the experiment on the degree of recognition of traffic accidents, the performance of recognition of car wrecks and contacts was improved for all environmental information. These experiments show that V-TIDB can be used to learn traffic accident recognition models that take environmental information into account in detail and can be used for appropriate traffic accident analysis., Comment: Conference paper accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023 Reason for revision: Corrected due to a missing space between sentences in the preview's abstract, which led to an unintended URL interpretation

Contact-rich manipulation tasks with stiff frictional elements like connector insertion are difficult to model with rigid-body simulators. In this work, we propose a new approach for modeling these environments by learning a quasi-static contact force model instead of a full simulator. Using a feature vector that contains information about the configuration and control, we find a linear mapping adequately captures the relationship between this feature vector and the sensed contact forces. A novel Linear Model Learning (LML) algorithm is used to solve for the globally optimal mapping in real time without any matrix inversions, resulting in an algorithm that runs in nearly constant time on a GPU as the model size increases. We validate the proposed approach for connector insertion both in simulation and hardware experiments, where the learned model is combined with an optimization-based controller to achieve smooth insertions in the presence of misalignments and uncertainty. Our website featuring videos, code, and more materials is available at https://model-based-plugging.github.io/.

Orbitronics is an emerging and fascinating field that explores the utilization of the orbital degree of freedom of electrons for information processing. An increasing number of orbital phenomena are being currently discovered, with spin-orbit coupling mediating the interplay between orbital and spin effects, thus providing a wealth of control mechanisms and device applications. In this context, the orbital analog of spin Dzyaloshinskii-Moriya interaction (DMI), i.e. orbital DMI, deserves to be explored in depth, since it is believed to be capable of inducing chiral orbital structures. Here, we unveil the main features and microscopic mechanisms of the orbital DMI in a two-dimensional square lattice using a tight-binding model of t2g orbitals in combination with the Berry phase theory. This approach allows us to investigate and transparently disentangle the role of inversion symmetry breaking, strength of orbital exchange interaction and spin-orbit coupling in shaping the properties of the orbital DMI. By scrutinizing the band-resolved contributions we are able to understand the microscopic mechanisms and guiding principles behind the orbital DMI and its anisotropy in two dimensional magnetic materials, and uncover a fundamental relation between the orbital DMI and its spin counterpart, which is currently explored very intensively. The insights gained from our work contribute to advancing our knowledge of orbitalrelated effects and their potential applications in spintronics, providing a path for future research in the field of chiral orbitronics.

From an empirical perspective, the subset chosen through active learning cannot guarantee an advantage over random sampling when transferred to another model. While it underscores the significance of verifying transferability, experimental design from previous works often neglected that the informativeness of a data subset can change over model configurations. To tackle this issue, we introduce a new experimental design, coined as Candidate Proposal, to find transferable data candidates from which active learning algorithms choose the informative subset. Correspondingly, a data selection algorithm is proposed, namely Transferable candidate proposal with Bounded Uncertainty (TBU), which constrains the pool of transferable data candidates by filtering out the presumably redundant data points based on uncertainty estimation. We verified the validity of TBU in image classification benchmarks, including CIFAR-10/100 and SVHN. When transferred to different model configurations, TBU consistency improves performance in existing active learning algorithms. Our code is available at https://github.com/gokyeongryeol/TBU., Comment: Accepted in NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World

Recent studies suggest that dark matter (DM) spikes around intermediate-mass black holes could cause observable dephasing in gravitational wave (GW) signals from Intermediate Mass Ratio Inspirals (IMRIs). Previous research primarily used non-self-consistent analytic methods to estimate the impact of DM spikes on eccentric IMRIs. Our study provides the first self-consistent treatment of this phenomenon using $N$-body simulations, incorporating Post-Newtonian effects up to the 2.5 order for accurate and robust results. Contrary to prior works, which posited that the cumulative effect of two-body encounters (dynamical friction; DF) is the primary mechanism for energy dissipation, we reveal that a three-body effect (slingshot mechanism) plays a more significant role in driving the binary system's energy loss and consequent orbital shrinkage. We find that binaries counter-rotating with respect to the DM spike merge faster, while co-rotating binaries merge slower, contrary to expectations from the DF theory. Using Fokker-Planck methods, we also assess the presence and detectability of spikes in realistic environments. When interacting with surrounding materials, DM spikes can have shallower slopes and lower densities than previously considered, leading to smaller signals and lower detection prospects via dephasing. Our results suggest that `deshifting' rather than dephasing might be a more optimistic signature, as it is more robust even in low-density environments., Comment: 21 pages, 20 figures. Accepted in MNRAS. The code used for simulations is available here: https://github.com/dipto4/falcon_dm