Pion-Nucleon Sigma Term from Lattice QCD.

We present an analysis of the pion-nucleon σ-term σ_{πN} using six ensembles with 2+1+1-flavor highly improved staggered quark action generated by the MILC Collaboration. The most serious systematic effect in lattice calculations of nucleon correlation functions is the contribution of excited states. We estimate these using chiral perturbation theory (χPT) and show that the leading contribution to the isoscalar scalar charge comes from Nπ and Nππ states. Therefore, we carry out two analyses of lattice data to remove excited-state contamination, the standard one and a new one including Nπ and Nππ states. We find that the standard analysis gives σ_{πN}=41.9(4.9) MeV, consistent with previous lattice calculations, while our preferred χPT-motivated analysis gives σ_{πN}=59.6(7.4) MeV, which is consistent with phenomenological values obtained using πN scattering data. Our data on one physical pion mass ensemble were crucial for exposing this difference, therefore, calculations on additional physical mass ensembles are needed to confirm our result and resolve the tension between lattice QCD and phenomenology.

Axial Vector Form Factors from Lattice QCD that Satisfy the PCAC Relation.

Previous lattice QCD calculations of axial vector and pseudoscalar form factors show significant deviation from the partially conserved axial current (PCAC) relation between them. Since the original correlation functions satisfy PCAC, the observed deviations from the operator identity cast doubt on whether all of the systematics in the extraction of form factors from the correlation functions are under control. We identify the problematic systematic as a missed excited state, whose energy as a function of the momentum transfer squared Q^{2} is determined from the analysis of the three-point functions themselves. Its energy is much smaller than those of the excited states previously considered, and including it impacts the extraction of all of the ground state matrix elements. The form factors extracted using these mass and energy gaps satisfy PCAC and another consistency condition, and they validate the pion-pole dominance hypothesis. We also show that the extraction of the axial charge g_{A} is very sensitive to the value of the mass gaps of the excited states used, and current lattice data do not provide an unambiguous determination of these, unlike the Q^{2}≠0 case. To highlight the differences and improvement between the conventional vs the new analysis strategy, we present a comparison of results obtained on a physical pion mass ensemble at a≈0.0871 fm. With the new strategy, we find g_{A}=1.30(6) and axial charge radius r_{A}=0.74(6) fm, both extracted using the z expansion to parametrize the Q^{2} behavior of G_{A}(Q^{2}), and g_{P}^{*}=8.06(44), obtained using the pion-pole dominance ansatz to fit the Q^{2} behavior of the induced pseudoscalar form factor G[over ˜]_{P}(Q^{2}). These results are consistent with current phenomenological values.

Neutron Electric Dipole Moment and Tensor Charges from Lattice QCD.

We present lattice QCD results on the neutron tensor charges including, for the first time, a simultaneous extrapolation in the lattice spacing, volume, and light quark masses to the physical point in the continuum limit. We find that the "disconnected" contribution is smaller than the statistical error in the "connected" contribution. Our estimates in the modified minimal subtraction scheme at 2 GeV, including all systematics, are g_{T}^{d-u}=1.020(76), g_{T}^{d}=0.774(66), g_{T}^{u}=-0.233(28), and g_{T}^{s}=0.008(9). The flavor diagonal charges determine the size of the neutron electric dipole moment (EDM) induced by quark EDMs that are generated in many new scenarios of CP violation beyond the standard model. We use our results to derive model-independent bounds on the EDMs of light quarks and update the EDM phenomenology in split supersymmetry with gaugino mass unification, finding a stringent upper bound of d_{n}<4×10^{-28} e cm for the neutron EDM in this scenario.

The Effects of Spatial Complexity on Narrative Experience in Space-Adaptive AR Storytelling.

A critical yet unresolved challenge in designing space-adaptive narratives for Augmented Reality (AR) is to provide consistently immersive user experiences anywhere, regardless of physical features specific to a space. For this, we present a comprehensive analysis on a series of user studies investigating how the size, density, and layout of real indoor spaces affect users playing Fragments, a space-adaptive AR detective game. Based on the studies, we assert that moderate levels of traversability and visual complexity afforded in counteracting combinations of size and complexity are beneficial for narrative experience. To confirm our argument, we combined the experimental data of the studies (n=112) to compare how five different spatial complexity conditions impact narrative experience when applied to contrasting room sizes. Results show that whereas factors of narrative experience are rated significantly higher in relatively simple settings for a small space, they are less affected by complexity in a large space. Ultimately, we establish guidelines on the design and placement of space-adaptive augmentations in location-independent AR narratives to compensate for the lack or excess of affordances in various real spaces and enhance user experiences therein.

Visualizing Hand Force with Wearable Muscle Sensing for Enhanced Mixed Reality Remote Collaboration.

In this paper, we present a prototype system for sharing a user's hand force in mixed reality (MR) remote collaboration on physical tasks, where hand force is estimated using wearable surface electromyography (sEMG) sensor. In a remote collaboration between a worker and an expert, hand activity plays a crucial role. However, the force exerted by the worker's hand has not been extensively investigated. Our sEMG-based system reliably captures the worker's hand force during physical tasks and conveys this information to the expert through hand force visualization, overlaid on the worker's view or on the worker's avatar. A user study was conducted to evaluate the impact of visualizing a worker's hand force on collaboration, employing three distinct visualization methods across two view modes. Our findings demonstrate that sensing and sharing hand force in MR remote collaboration improves the expert's awareness of the worker's task, significantly enhances the expert's perception of the collaborator's hand force and the weight of the interacting object, and promotes a heightened sense of social presence for the expert. Based on the findings, we provide design implications for future mixed reality remote collaboration systems that incorporate hand force sensing and visualization.

Effects of Avatar Transparency on Social Presence in Task-Centric Mixed Reality Remote Collaboration.

Despite the importance of avatar representation on user experience for Mixed Reality (MR) remote collaboration involving various device environments and large amounts of task-related information, studies on how controlling visual parameters for avatars can benefit users in such situations have been scarce. Thus, we conducted a user study comparing the effects of three avatars with different transparency levels (Nontransparent, Semi-transparent, and Near-transparent) on social presence for users in Augmented Reality (AR) and Virtual Reality (VR) during task-centric MR remote collaboration. Results show that avatars with a strong visual presence are not required in situations where accomplishing the collaborative task is prioritized over social interaction. However, AR users preferred more vivid avatars than VR users. Based on our findings, we suggest guidelines on how different levels of avatar transparency should be applied based on the context of the task and device type for MR remote collaboration.

Kaon B parameter from improved staggered fermions in N(f)=2+1 QCD.

We present a calculation of the kaon B parameter B(K) using lattice QCD. We use improved staggered valence and sea fermions, the latter generated by the MILC Collaboration with N(f)=2+1 light flavors. To control discretization errors, we use four different lattice spacings ranging down to a≈0.045 fm. The chiral and continuum extrapolations are done using SU(2) staggered chiral perturbation theory. Our final result is B(K)=0.727±0.004(stat)±0.038(syst), where the dominant systematic error is from our use of truncated (one-loop) matching factors.

Lossy compression of statistical data using quantum annealer.

We present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variables. The algorithm finds a set of basis vectors and their binary coefficients that precisely reconstruct the original data. The optimization for the basis vectors is performed classically, while binary coefficients are retrieved through both simulated and quantum annealing for comparison. A bias correction procedure is also presented to estimate and eliminate the error and bias introduced from the inexact reconstruction of the lossy compression for statistical data analyses. The compression algorithm is demonstrated on two different datasets of lattice quantum chromodynamics simulations. The results obtained using simulated annealing show 3-3.5 times better compression performance than the algorithm based on neural-network autoencoder. Calculations using quantum annealing also show promising results, but performance is limited by the integrated control error of the quantum processing unit, which yields large uncertainties in the biases and coupling parameters. Hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system. Our study shows that the Advantage system is more likely to obtain low-energy solutions for the problems than the 2000Q.

A machine learning approach for efficient multi-dimensional integration.

Many physics problems involve integration in multi-dimensional space whose analytic solution is not available. The integrals can be evaluated using numerical integration methods, but it requires a large computational cost in some cases, so an efficient algorithm plays an important role in solving the physics problems. We propose a novel numerical multi-dimensional integration algorithm using machine learning (ML). After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculated to correct the bias in the approximation of the integral induced by ML prediction errors. Because of the bias correction, the final estimate of the integral is unbiased and has a statistically correct error estimation. Three ML models of multi-layer perceptron, gradient boosting decision tree, and Gaussian process regression algorithms are investigated. The performance of the proposed algorithm is demonstrated on six different families of integrands that typically appear in physics problems at various dimensions and integrand difficulties. The results show that, for the same total number of integrand evaluations, the new algorithm provides integral estimates with more than an order of magnitude smaller uncertainties than those of the VEGAS algorithm in most of the test cases.

A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer.

We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer. In this regression algorithm, we concatenate the independent and dependent variables as a combined vector, and encode the high-order correlations between them into a dictionary optimized for sparse reconstruction. On a test dataset, the dependent variable is initialized to its average value and then a sparse reconstruction of the combined vector is obtained in which the dependent variable is typically shifted closer to its true value, as in a standard inpainting or denoising task. Here, a quantum annealer, which can presumably exploit a fully entangled initial state to better explore the complex energy landscape, is used to solve the highly non-convex sparse coding optimization problem. The regression algorithm is demonstrated for a lattice quantum chromodynamics simulation data using a D-Wave 2000Q quantum annealer and good prediction performance is achieved. The regression test is performed using six different values for the number of fully connected logical qubits, between 20 and 64. The scaling results indicate that a larger number of qubits gives better prediction accuracy.

Multiple Resonance Metamaterial Emitter for Deception of Infrared Emission with Enhanced Energy Dissipation.

Artificial camouflage surfaces for assimilating with the environment have been utilized for controlling optical properties. Especially, the optical properties of infrared (IR) camouflage materials should be satisfied with two requirements: deception of IR signature in a detected band through reduced emissive energy and dissipation of reduced emissive energy for preventing thermal instability through an undetected band. Most reported articles suggest the reduction of emissive energy in the detected band; however, broadband emission for enough energy dissipation through the undetected band simultaneously is still a challenging issue. Here, we demonstrate the multiresonance emitter for broadband emission with IR camouflage utilizing the electromagnetic properties of dielectric material. We reveal that the interaction between the magnetic resonance and dielectric layer's property in a metal-dielectric-metal structure induces the multiple resonance at the specific band. We present an IR camouflage behavior of multiresonance emitter on a curved surface through the IR camera (8-14 µm). We evaluate the energy dissipation in the undetected band, which is 1613% higher than metal and 26% higher than conventional selective emitters. This study paves the way to develop broadband emitters for radiative cooling and thermophotovoltaic applications.