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This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the LHC. In this paper, a multihead attention message passing network is used to classify the relationship between detector hits by labelling graph edges, determining whether hits were produced by the same underlying particle, and if so, the particle type. The trained model is 84% accurate overall, and performs best on the EM shower and muon track classes. The model's strengths and weaknesses are discussed, and plans for developing this technique further are summarised., Comment: 7 pages, 3 figures, submitted to the 25th International Conference on Computing in High-Energy and Nuclear Physics

Synthetic aperture imaging (SAI) is able to achieve the see through effect by blurring out the off-focus foreground occlusions and reconstructing the in-focus occluded targets from multi-view images. However, very dense occlusions and extreme lighting conditions may bring significant disturbances to the SAI based on conventional frame-based cameras, leading to performance degeneration. To address these problems, we propose a novel SAI system based on the event camera which can produce asynchronous events with extremely low latency and high dynamic range. Thus, it can eliminate the interference of dense occlusions by measuring with almost continuous views, and simultaneously tackle the over/under exposure problems. To reconstruct the occluded targets, we propose a hybrid encoder-decoder network composed of spiking neural networks (SNNs) and convolutional neural networks (CNNs). In the hybrid network, the spatio-temporal information of the collected events is first encoded by SNN layers, and then transformed to the visual image of the occluded targets by a style-transfer CNN decoder. Through experiments, the proposed method shows remarkable performance in dealing with very dense occlusions and extreme lighting conditions, and high quality visual images can be reconstructed using pure event data.

Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative. This is often related to the hyperparameter resembling the data variance. It can be shown that an inappropriate choice of this hyperparameter causes the oversmoothness in the linearly approximated case and can be empirically verified for the general cases. Moreover, determining such appropriate choice becomes infeasible if the data variance is non-uniform or conditional. Therefore, we propose VAE extensions with generalized parameterizations of the data variance and incorporate maximum likelihood estimation into the objective function to adaptively regularize the decoder smoothness. The images generated from proposed VAE extensions show improved Fr\'echet inception distance (FID) on MNIST and CelebA datasets., Comment: 35 pages with 12 figures, accepted for Neurocomputing

Microstructural materials design is one of the most important applications of inverse modeling in materials science. Generally speaking, there are two broad modeling paradigms in scientific applications: forward and inverse. While the forward modeling estimates the observations based on known parameters, the inverse modeling attempts to infer the parameters given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the parameters that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, healthcare and materials science. However, it is challenging to solve inverse problems, because they usually need to learn a one-to-many non-linear mapping, and also require significant computing time, especially for high-dimensional parameter space. Further, inverse problems become even more difficult to solve when the dimension of input (i.e. observation) is much lower than that of output (i.e. parameters). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling, and it is evaluated on a materials science dataset for microstructural materials design. Compared with baseline methods, the results demonstrate that the proposed framework can overcome the above-mentioned challenges and produce multiple promising solutions in an efficient manner.

This study focuses on reachability problems in differential games. An improved level set method for computing reachable tubes is proposed in this paper. The reachable tube is described as a sublevel set of a value function, which is the viscosity solution of a Hamilton-Jacobi equation with running cost. We generalize the concept of reachable tubes and propose a new class of reachable tubes, which are referred to as cost-limited one. In particular, a performance index can be specified for the system, and a cost-limited reachable tube is a set of initial states of the system's trajectories that can reach the target set before the performance index increases to a given admissible cost. Such a reachable tube can be obtained by specifying the corresponding running cost function for the Hamilton-Jacobi equation. Different non-zero sublevel sets of the viscosity solution of the Hamilton-Jacobi equation at a certain time point can be used to characterize the cost-limited reachable tubes with different admissible costs (or the reachable tubes with different time horizons), thus reducing the storage space consumption. Several examples are provided to illustrate the validity and accuracy of the proposed method., Comment: 9 pages, 13 figures

Spin-orbit torques (SOTs) from transition metal dichalcogenides systems (TMDs) in conjunction with ferromagnetic materials are recently attractive in spintronics for their versatile features. However, most of the previously studied crystalline TMDs are prepared by mechanical exfoliation, which limits their potentials for industrial applications. Here we show that amorphous WTe2 heterostructures deposited by magnetron sputtering possess a sizable damping-like SOT efficiency {\xi}_DL^WTe2 ~ 0.20 and low damping constant {\alpha} = 0.009/pm0.001. Only an extremely low critical switching current density J_c ~ 7.05\times10^9 A/m^2 is required to achieve SOT-driven magnetization switching. The SOT efficiency is further proved to depend on the W and Te relative compositions in the co-sputtered W_100-xTe_x samples, from which a sign change of {\xi}_DL^WTe2 is observed. Besides, the electronic transport in amorphous WTe2 is found to be semiconducting and is governed by a hopping mechanism. With the above advantages and rich tunability, amorphous and semiconducting WTe2 serves as a unique SOT source for future spintronics applications.

We revisit the problem of the gauge invariance in the Coleman-Weinberg model in which a $U(1)$ gauge symmetry is driven spontaneously broken by radiative corrections. It was noticed in previous work that masses in this model are not gauge invariant at one-loop order. In our analysis, we use the dressed propagators of scalars which include a resummation of the one-loop self-energy correction to the tree-level propagator. We calculate the one-loop self-energy correction to the vector meson using these dressed propagators. We find that the pole mass of the vector meson calculated using the dressed propagator is gauge invariant at the vacuum determined using the effective potential calculated with a resummation of daisy diagrams., Comment: 18 pages, 4 figures, version published in PRD

It is crucial that magnetic memory devices formed from magnetic heterostructures possess sizable spin-orbit torque (SOT) efficiency and high thermal stability to realize both efficient SOT control and robust storage of such memory devices. However, most previous studies on various types of magnetic heterostructures have focused on only their SOT efficiencies, whereas the thermal stabilities therein have been largely ignored. In this work, we study the temperature-dependent SOT and stability properties of two types of W-based heterostructures, namely W/CoFeB/MgO (standard) and CoFeB/W/CoFeB/MgO (field-free), from 25 ^{\circ}C (298 K) to 80 ^{\circ}C (353 K). Via temperature-dependent SOT characterization, the SOT efficacies for both systems are found to be invariant within the range of studied temperatures. Temperature-dependent current-induced SOT switching measurements further show that the critical switching current densities decrease with respect to the ambient temperature; thermal stability factors ({\Delta}) are also found to degrade as temperature increases for both standard and field-free systems. The memristive SOT switching behaviors in both systems with various pulse-widths and temperatures are also examined. Our results suggest that although the SOT efficacy is robust against thermal effects, the reduction of {\Delta} at elevated temperatures could be detrimental to standard memory as well as neuromorphic (memristive) device applications.

We study the possibility that light dark matter can be produced with right relic density by the decay of other dark sector particles. We study this possibility in a model with right-handed neutrinos, a dark sector singlet scalar and a dark sector singlet fermion. The decay of the heavier dark sector singlet scalar gives rise to the lighter dark sector singlet fermion which serves as the dark matter candidate. We show that the right dark matter relic density can be produced by the decay of the dark sector singlet scalar. We find that the mass of the dark sector singlet fermion can be GeV scale or MeV scale and the interaction of the dark sector singlet fermion is very weak. The dark sector singlet scalar can have a mass of GeV-TeV scale and can have weak scale interactions with the Standard Model(SM) particles. So, in this scenario, dark matter has an interaction with the SM much weaker than the weak interaction, but the dark sector still has weak scale interaction with the SM., Comment: 18 pages, 7 figures, version published in PLB

In this paper, a new method is proposed to compute the rolling Nash equilibrium of the time-invariant nonlinear two-person zero-sum differential games. The idea is to discretize the time to transform a differential game into a sequential game with several steps, and by introducing state-value function, transform the sequential game into a recursion consisting of several normal-form games, finally, each normal-form game is solved with action abstraction and regret matching. To improve the real-time property of the proposed method, the state-value function can be kept in memory. This method can deal with the situations that the saddle point exists or does not exist, and the analysises of the existence of the saddle point can be avoided. If the saddle point does not exist, the mixed optimal control pair can be obtained. At the end of this paper, some examples are taken to illustrate the validity of the proposed method., Comment: 18 pages, 9 figures

Beam mechanism and beam theory have attracted substantial attention from researchers, as they have been widely used in many fields such as compliant mechanisms and soft robots. The modeling of beam mechanisms becomes complicated due to the geometric nonlinearity that is proved to be significant with large deflection. A new method, called optimization algorithm-based approach (OABA), is proposed to predict the large deflection of cantilever beams, in which an optimization algorithm is exploited to find the locus of the beam tip. With the derived locus of the beam tip, the deflection curve of the cantilever beam can be calculated. The optimization algorithm in this paper is embodied in a particle swarm optimization (PSO) algorithm. Experimental results show that the proposed method can precisely predict the deflection of the uniform and non-uniform cantilever beams. The maximum error is limited to 4.35% when the normalized maximum transverse deflection reaches 0.75. Given that, the proposed OABA would be a universal approach to model the large deflection of cantilever beams subjected to tip loads.

The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training., Comment: Accepted by MICCAI DCL Workshop 2020

Binary supermassive black holes (BSBHs) are expected to be a generic byproduct from hierarchical galaxy formation. The final coalescence of BSBHs is thought to be the loudest gravitational wave (GW) siren, yet no confirmed BSBH is known in the GW-dominated regime. While periodic quasars have been proposed as BSBH candidates, the physical origin of the periodicity has been largely uncertain. Here we report discovery of a periodicity (P=1607$\pm$7 days) at 99.95% significance (with a global p-value of ~$10^{-3}$ accounting for the look elsewhere effect) in the optical light curves of a redshift 1.53 quasar, SDSS J025214.67-002813.7. Combining archival Sloan Digital Sky Survey data with new, sensitive imaging from the Dark Energy Survey, the total ~20-yr time baseline spans ~4.6 cycles of the observed 4.4-yr (restframe 1.7-yr) periodicity. The light curves are best fit by a bursty model predicted by hydrodynamic simulations of circumbinary accretion disks. The periodicity is likely caused by accretion rate modulation by a milli-parsec BSBH emitting GWs, dynamically coupled to the circumbinary accretion disk. A bursty hydrodynamic variability model is statistically preferred over a smooth, sinusoidal model expected from relativistic Doppler boost, a kinematic effect proposed for PG1302-102. Furthermore, the frequency dependence of the variability amplitudes disfavors Doppler boost, lending independent support to the circumbinary accretion variability hypothesis. Given our detection rate of one BSBH candidate from circumbinary accretion variability out of 625 quasars, it suggests that future large, sensitive synoptic surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time may be able to detect hundreds to thousands of candidate BSBHs from circumbinary accretion with direct implications for Laser Interferometer Space Antenna., Comment: Accepted to MNRAS, 17 pages, 9 figures

Periodically variable quasars have been suggested as close binary supermassive black holes. We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1.8 in a 4.6 deg$^2$ overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82). Our sample has a unique 20-year long multi-color ($griz$) light curve enabled by combining DES-SN Y6 observations with archival SDSS-S82 data. The deep imaging allows us to search for periodic light curves in less luminous quasars (down to $r{\sim}$23.5 mag) powered by less massive black holes (with masses $\gtrsim10^{8.5}M_{\odot}$) at high redshift for the first time. We find five candidates with significant (at $>$99.74% single-frequency significance in at least two bands with a global p-value of $\sim$7$\times10^{-4}$--3$\times10^{-3}$ accounting for the look-elsewhere effect) periodicity with observed periods of $\sim$3--5 years (i.e., 1--2 years in rest frame) having $\sim$4--6 cycles spanned by the observations. If all five candidates are periodically variable quasars, this translates into a detection rate of ${\sim}0.8^{+0.5}_{-0.3}$% or ${\sim}1.1^{+0.7}_{-0.5}$ quasar per deg$^2$. Our detection rate is 4--80 times larger than those found by previous searches using shallower surveys over larger areas. This discrepancy is likely caused by differences in the quasar populations probed and the survey data qualities. We discuss implications on the future direct detection of low-frequency gravitational waves. Continued photometric monitoring will further assess the robustness and characteristics of these candidate periodic quasars to determine their physical origins., Comment: 21 pages, 8 figures, MNRAS accepted

Cosmological N-body simulations rank among the most computationally intensive efforts today. A key challenge is the analysis of structure, substructure, and the merger history for many billions of compact particle clusters, called halos. Effectively representing the merging history of halos is essential for many galaxy formation models used to generate synthetic sky catalogs, an important application of modern cosmological simulations. Generating realistic mock catalogs requires computing the halo formation history from simulations with large volumes and billions of halos over many time steps, taking hundreds of terabytes of analysis data. We present fast parallel algorithms for producing halo merger trees and tracking halo substructure from a single-level, density-based clustering algorithm. Merger trees are created from analyzing the halo-particle membership function in adjacent snapshots, and substructure is identified by tracking the "cores" of merging halos -- sets of particles near the halo center. Core tracking is performed after creating merger trees and uses the relationships found during tree construction to associate substructures with hosts. The algorithms are implemented with MPI and evaluated on a Cray XK7 supercomputer using up to 16,384 processes on data from HACC, a modern cosmological simulation framework. We present results for creating merger trees from 101 analysis snapshots taken from the Q Continuum, a large volume, high mass resolution, cosmological simulation evolving half a trillion particles., Comment: 2017 IEEE 24th International Conference on High Performance Computing

Without an existing large scale coherent magnetic field in the early Universe, Population III (PopIII) stars would likely rotate at or near break-up speed. In this work, focusing on the accretion phase of PopIII stars, we investigate the possibility of generating a coherent magnetic field through large scale dynamo processes, as well as the corresponding field saturation level. Using results from hydrodynamic simulations, we demonstrate that primordial accretion disks are turbulent with a Shakura-Sunyaev disk parameter $\alpha_{ss} \gtrsim 10^{-3}$, and evidence helical turbulence with a dynamo number $\vert D_{\alpha \Omega} \vert \gg 10$. The presence of helical turbulence at these levels allows large scale dynamo modes to grow, and the saturation level is determined by the amount of net helicity remaining in the dynamo-active regions (a.k.a. the quenching problem). %We demonstrate that the magnetic field can reach approximate equipartition, with $B/B_{\rm eq} \sim 3$, indicating that the dynamo quenching problem could be alleviated through an accretion flow. We demonstrate that, if the accretion could successfully alleviate the quenching problem, the magnetic field can reach approximate equipartition with $B/B_{\rm eq} \sim 3$.

The spin-orbit torques (SOTs) generated from topological insulators (TIs) have gained increasing attention in recent years. These TIs, which are typically formed by epitaxially grown chalcogenides, possess extremely high SOT efficiencies and have great potential to be employed in the next-generation spintronics devices. However, epitaxy of these chalcogenides is required to ensure the existence of topologically-protected surface state (TSS), which limits the feasibility of using these materials in industry. In this work, we show that non-epitaxial Bi$_{x}$Te$_{1-x}$/ferromagnet heterostructures prepared by conventional magnetron sputtering possess giant SOT efficiencies even without TSS. Through harmonic voltage measurement and hysteresis loop shift measurement, we find that the damping-like SOT efficiencies originated from the bulk spin-orbit interactions of such non-epitaxial heterostructures can reach values greater than 100% at room temperature. We further demonstrate current-induced SOT switching in these Bi$_{x}$Te$_{1-x}$-based heterostructures with thermally stable ferromagnetic layers, which indicates that such non-epitaxial chalcogenide materials can be potential efficient SOT sources in future SOT magnetic memory devices.

Under rapid cooling from molecular hydrogen, the accretion disks around Population III (PopIII) stars are believed to fragment, resulting in multiple accreting cores. In this paper, we build a theoretical framework for calculating the optical depth of H$_2$ ro-vibrational line cooling based on the vertical structure in these accretion disks. Applying this physically motivated prescription for the optical depth, we find that cooling in the inner disk with $r \lesssim 10 {\rm\ AU}$ is attenuated significantly as a result of high surface density; $PdV$ heating becomes more efficient than cooling, which prevents fragmentation in the inner disk. Despite this, cooling becomes dynamically important in the outer disk, favoring fragmentation. We argue that most of the resultant fragments are initially at the outer disk, and that any surviving fragment has to migrate slower than the disk-scale photo-evaporation process. Since type I migration is fast, migration slows down as a result of gap-opening in the disk structure. Two possible processes for gap-opening are studied: (1) through a massive, densely-cored ($\rho \gtrsim 10^{-8} {\rm\ g\ cm^{-3}}$) clump able to radiate away the excess gravitational potential energy, and (2) through a fast-growing central star, with $\dot{M} \gtrsim 2 \times 10^{-3} \, M_\odot {\rm\ yr^{-1}}$, whose gravity dominates the star-disk system and favors gap opening., Comment: submitted to ApJ