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Vehicle platooning, one of the advanced services supported by 5G NR-V2X, improves traffic efficiency in the connected intelligent transportation systems (C-ITSs). However, the packet delivery ratio of platoon communication, especially in the out-of-coverage area, is significantly impacted by the random selection algorithms employed in the current resource allocation scheme. In this paper, we first analyze the collision probability via the random selection algorithm adopted in the current standard. Subsequently, we then investigate the deep reinforcement learning (DRL) algorithm that decreases the collision probability by letting the agent (vehicle) learn from the communication environment. Monte Carlo simulation is utilized to verify the results obtained in the analytical model and to compare the results between the two discussed algorithms. Numerical results show that the proposed DRL algorithm outperforms the random selection algorithm in terms of different vehicle density, which at least lowering the collision probability by 73% and 45% in low and high vehicle density respectively.
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Resource Allocation for Vehicle Platooning in 5G NR-V2X via Deep Reinforcement Learning
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In this article, we discuss how to carryover gravitomagnetic clock effect from classical general relativity to quantum theory and how to calculate this effect in quantum mechanics. Our calculation is valid for semi-classical regime and can be considered as the first step towards a complete gravitomagnetic quantum theory. We also show the analogy between energy levels that corresponds to the clock effect. In fact, it is argued that in quantum mechanics clock effect arises as energy level splitting in gravitomagnetic field.
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On the gravitomagnetic clock effect in quantum mechanics
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In this paper we consider the totally asymmetric simple exclusion process, with non-random initial condition having three regions of constant densities of particles. From left to right, the densities of the three regions are increasing. Consequently, there are three characteristics which meet, i.e. two shocks merge. We study the particle fluctuations at this merging point and show that they are given by a product of three (properly scaled) GOE Tracy-Widom distribution functions. We work directly in TASEP without relying on the connection to last passage percolation.
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Statistics of TASEP with three merging characteristics
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For controlling the critical electric fields of the topological phase transition in single bilayer Bi(111), we investigated topological phases in a strained system through first-principles calculations. We found a quadratic band touching semimetallic state at tensile strain $\epsilon=0.5$%. Around this strain, the topological phase can be switched to a trivial insulator by an infinitesimal electric field. The positions at which Dirac cones appear in the electric-field-induced topological phase transition changed for the strain $\epsilon>0.5$% and $\epsilon<0.5$%. Our results indicate that this topological phase transition could be applied to novel spintronic devices.
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Electric-field-induced Z2 topological phase transition in strained single bilayer Bi(111)
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The J-PET detector being developed at Jagiellonian University, is a Positron Emission Tomograph composed of the long strips of polymer scintillators. At the same time it is a detector system which will be used for studies of the decays of positronium atoms. The shape of photomultiplier signals depends on the hit-time and hit-position of the gamma quantum. In order to take advantage of this fact a dedicated sampling front-end electronics which enables to sample signals in voltage domain with the time precision of about 20 ps and novel reconstruction method based on the comparison of examined signal with the model signals stored in the library has been developed. As a measure of the similarity we use the Mahalanobis distance. The achievable position and time-resolution depends on number and values of the threshold levels at which the signal is sampled. A reconstruction method, as well as preliminary results are presented and discussed.
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Reconstruction of hit-time and hit-position of annihilation quanta in the J-PET detector using the Mahalanobis distance
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In this paper, phasor measurement unit (PMU) placement for power grid state estimation under different degrees of observability is studied. Observability degree is the depth of the buses' reachability by the placed PMUs and thus constitutes an important characteristic for PMU placement. However, the sole observability as addressed in many works still does not guarantee a good estimate for the grid state. Some existing works also considered the PMU placement for minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state. However, they ignore the observability requirements for computational tractability and thus potentially lead to artificial results such as acceptance of the estimate for an unobserved state component as its unconditional mean. In this work, the PMU placement optimization problem is considered by minimizing the mean squared error or maximizing the mutual information between the measurement output and grid state, under grid observability constraints. The provided solution is free from the mentioned fundamental drawbacks in the existing PMU placement designs. The problems are posed as binary nonlinear optimization problems, for which this paper develops efficient algorithms for computational solutions. The performance of the proposed algorithms is analyzed in detail through numerical examples on large-scale IEEE power networks.
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PMU Placement Optimization for Smart Grid Obvervability and State Estimation
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Physics dictates that cars with small mass will travel more miles per gallon (mpg) compared to massive trucks. Does this imply that small cars are more efficient machines? In this work a mileage efficiency metric is defined as a ratio of actual car mileage (mpg) to the mileage of an ideal car. This metric allows comparison of efficiencies of cars with different masses and fuel types. It is as useful to quantify efficiencies of cars as the concept of drag coefficient is to quantify the efficacy of their aerodynamic shapes. Maximum mileage and lowest CO2 emission of conventional gasoline cars, at different driving schedules, is reported based on the concept of an ideal car. This can help put government imposed standards in a rigorous context.
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Mileage efficiency and relative emission of automotive vehicles
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In gauge-Higgs unification (GHU), the 4D Higgs boson appears as a part of the fifth dimensional component of 5D gauge field. Recently, an $SO(11)$ GUT inspired $SO(5)\times U(1)\times SU(3)$ GHU model has been proposed. In the GHU, Kaluza-Klein (KK) excited states of neutral vector bosons, photon, $Z$ boson and $Z_R$ boson, appear as neutral massive vector bosons $Z'$s. The $Z'$ bosons in the GHU couple to quarks and leptons with large parity violation, which leads to distinctive polarization dependence in, e.g., cross sections and forward-backward asymmetries in $e^-e^+\to\mu^-\mu^+,q\bar{q}$ processes. In the talk, we discuss fermion pair production in $e^-e^+$ linear collider experiments with polarized $e^-$ and $e^+$ beams in the GUT inspired GHU. Deviations from the SM are shown in the early stage of planned international linear collider (ILC) with 250 GeV experiments. The deviations can be tested for the KK mass scale up to about 15 TeV. This talk is mainly based on Phys.Rev.D102(2020)015029.
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Linear Collider Signals of $Z'$ Bosons in GUT Inspired Gauge-Higgs Unification
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We establish new upper and lower bounds on the number of queries required to test convexity of functions over various discrete domains. 1. We provide a simplified version of the non-adaptive convexity tester on the line. We re-prove the upper bound $O(\frac{\log(\epsilon n)}{\epsilon})$ in the usual uniform model, and prove an $O(\frac{\log n}{\epsilon})$ upper bound in the distribution-free setting. 2. We show a tight lower bound of $\Omega(\frac{\log(\epsilon n)}{\epsilon})$ queries for testing convexity of functions $f: [n] \rightarrow \mathbb{R}$ on the line. This lower bound applies to both adaptive and non-adaptive algorithms, and matches the upper bound from item 1, showing that adaptivity does not help in this setting. 3. Moving to higher dimensions, we consider the case of a stripe $[3] \times [n]$. We construct an \emph{adaptive} tester for convexity of functions $f\colon [3] \times [n] \to \mathbb R$ with query complexity $O(\log^2 n)$. We also show that any \emph{non-adaptive} tester must use $\Omega(\sqrt{n})$ queries in this setting. Thus, adaptivity yields an exponential improvement for this problem. 4. For functions $f\colon [n]^d \to \mathbb R$ over domains of dimension $d \geq 2$, we show a non-adaptive query lower bound $\Omega((\frac{n}{d})^{\frac{d}{2}})$.
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Testing convexity of functions over finite domains
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The aim of this short note is to produce new examples of geometrical flows associated to a given Riemannian flow $g(t)$. The considered flow in covariant symmetric $2$-tensor fields will be called Ricci-Yamabe map since it involves a scalar combination of Ricci tensor and scalar curvature of $g(t)$. Due to the signs of considered scalars the Ricci-Yamabe flow can be also a Riemannian or semi-Riemannian or singular Riemannian flow. We study the associated function of volume variation as well as the volume entropy. Finally, since the two-dimensional case was the most handled situation we express the Ricci flow equation in all four orthogonal separable coordinate systems of the plane.
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Ricci-Yamabe maps for Riemannian flows and their volume variation and volume entropy
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Both Stefan-Boltzmann law and the Casimir effect, in a universe described by the FRW metric with zero curvature, are calculated. These effects are described by Thermo Field Dynamics (TFD). The gravitational energy-momentum tensor is defined in the context of Teleparallel Equivalent to General Relativity (TEGR). Each of the two effects gives a consistent prediction with what is observed on a cosmological scale. One of the effect establishes a minimum range for the deceleration parameter. While another leads to the conclusion that a possible cosmological constant has a very small order of magnitude.
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On Gravitational Stefan-Boltzmann Law and Casimir Effect in FRW Universe
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Define $QC(n)$ to be the number of quasiplatonic topological actions of the cyclic group $C_n$ on surfaces of genus at least two. We use formulas of Benim and Wootton to give an explicit formula for $QC(n)$. In addition, we relate the number of quasiplatonic topological actions of $C_n$ to the number of regular dessins d'enfants having $C_n$ as a group of automorphisms.
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Counting the Number of Quasiplatonic Topological Actions of the Cyclic Group on Surfaces
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In order to cope with the increased data volumes generated by modern radio interferometers such as LOFAR (Low Frequency Array) or SKA (Square Kilometre Array), fast and efficient calibration algorithms are essential. Traditional radio interferometric calibration is performed using nonlinear optimization techniques such as the Levenberg-Marquardt algorithm in Euclidean space. In this paper, we reformulate radio interferometric calibration as a nonlinear optimization problem on a Riemannian manifold. The reformulated calibration problem is solved using the Riemannian trust-region method. We show that calibration on a Riemannian manifold has faster convergence with reduced computational cost compared to conventional calibration in Euclidean space.
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Radio Interferometric Calibration Using a Riemannian Manifold
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We consider the problem of protecting and manipulating elections by recounting and changing ballots, respectively. Our setting involves a plurality-based election held across multiple districts, and the problem formulations are based on the model proposed recently by~[Elkind et al, IJCAI 2019]. It turns out that both of the manipulation and protection problems are NP-complete even in fairly simple settings. We study these problems from a parameterized perspective with the goal of establishing a more detailed complexity landscape. The parameters we consider include the number of voters, and the budgets of the attacker and the defender. While we observe fixed-parameter tractability when parameterizing by number of voters, our main contribution is a demonstration of parameterized hardness when working with the budgets of the attacker and the defender.
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A Parameterized Perspective on Attacking and Defending Elections
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We develop the quantization of unimodular gravity in the Plebanski and Ashtekar formulations and show that the quantum effective action defined by a formal path integral is unimodular. This means that the effective quantum geometry does not couple to terms in the expectation value of the energy-momentum tensor proportional to the metric tensor. The path integral takes the same form as is used to define spin foam models, with the additional constraint that the determinant of the four metric is constrained to be a constant by a gauge fixing term. We also review the proposal of Unruh, Wald and Sorkin- that the hamiltonian quantization yields quantum evolution in a physical time variable equal to elapsed four volume-and discuss how this may be carried out in loop quantum gravity. This then extends the results of arXiv:0904.4841 to the context of loop quantum gravity.
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Unimodular loop quantum gravity and the problems of time
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The class of local invertible operations is defined and the invariance of entanglement under such operations is established. For the quantification of entanglement, universal entanglement measures are defined, which are invariant under local invertible transformations. They quantify entanglement in a very general sense. It is shown that the Schmidt number is a universal entanglement measure, which is most important for the general amount of entanglement. For special applications, pseudo-measures are defined to quantify the useful entanglement for a certain quantum task. The entanglement quantification is further specified by operational measures, which include the accessible observables by a given experimental setup.
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The Schmidt number as a universal entanglement measure
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The discovery of gravitational waves, high-energy neutrinos or the very-high-energy counterpart of gamma-ray bursts has revolutionized the high-energy and transient astrophysics community. The development of new instruments and analysis techniques will allow the discovery and/or follow-up of new transient sources. We describe the prospects for the Cherenkov Telescope Array (CTA), the next-generation ground-based gamma-ray observatory, for multi-messenger and transient astrophysics in the decade ahead. CTA will explore the most extreme environments via very-high-energy observations of compact objects, stellar collapse events, mergers and cosmic-ray accelerators.
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Multi-messenger and transient astrophysics with the Cherenkov Telescope Array
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We propose that the search of the $B\to D^{*}_{sJ}M$ decays, $M=D$, $\pi$ and $K$, can discriminate the different theoretical postulations for the nature of the recently observed $D^{*}_{sJ}$ mesons. The ratio of the branching ratios $B(B\to D^{*}_{sJ}M)/B(B\to D^{(*)}_{s}M)\approx 1$ (0.1) supports that the $D^{*}_{sJ}$ mesons are quark-antiquark (multi-quark) bound states. The Belle measurement of the $B\to D^{*}_{sJ}D$ branching ratios seems to indicate an unconventional picture.
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Search of $D^{*}_{sJ}$ mesons in $B$ meson decays
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We implement quantum corrections for a magnetic monopole in a classically conformally invariant theory containing gravity. This yields the trace (conformal) anomaly and introduces a length scale in a natural fashion via the process of renormalization. We evaluate the one-loop effective potential and extract the vacuum expectation value (VEV) from it; spontaneous symmetry breaking is radiatively induced. The VEV is set at the renormalization scale $M$ and we exchange the dimensionless scalar coupling constant for the dimensionful VEV via dimensional transmutation. The asymptotic (background) spacetime is anti-de Sitter (AdS) and its Ricci scalar is determined entirely by the VEV. We obtain analytical asymptotic solutions to the coupled set of equations governing gravitational, gauge and scalar fields that yield the magnetic monopole in an AdS spacetime.
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Radiatively induced symmetry breaking and the conformally coupled magnetic monopole in AdS space
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We consider the Fuchsian linear differential equation obtained (modulo a prime) for $\tilde{\chi}^{(5)}$, the five-particle contribution to the susceptibility of the square lattice Ising model. We show that one can understand the factorization of the corresponding linear differential operator from calculations using just a single prime. A particular linear combination of $\tilde{\chi}^{(1)}$ and $\tilde{\chi}^{(3)}$ can be removed from $\tilde{\chi}^{(5)}$ and the resulting series is annihilated by a high order globally nilpotent linear ODE. The corresponding (minimal order) linear differential operator, of order 29, splits into factors of small orders. A fifth order linear differential operator occurs as the left-most factor of the "depleted" differential operator and it is shown to be equivalent to the symmetric fourth power of $L_E$, the linear differential operator corresponding to the elliptic integral $E$. This result generalizes what we have found for the lower order terms $\tilde{\chi}^{(3)}$ and $\tilde{\chi}^{(4)}$. We conjecture that a linear differential operator equivalent to a symmetric $(n-1)$-th power of $L_E$ occurs as a left-most factor in the minimal order linear differential operators for all $\tilde{\chi}^{(n)}$'s.
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High order Fuchsian equations for the square lattice Ising model: $\tilde{\chi}^{(5)}$
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It is known that a (concept) lattice contains an n-dimensional Boolean suborder if and only if the context contains an n-dimensional contra-nominal scale as subcontext. In this work, we investigate more closely the interplay between the Boolean subcontexts of a given finite context and the Boolean suborders of its concept lattice. To this end, we define mappings from the set of subcontexts of a context to the set of suborders of its concept lattice and vice versa and study their structural properties. In addition, we introduce closed-subcontexts as an extension of closed relations to investigate the set of all sublattices of a given lattice.
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Boolean Substructures in Formal Concept Analysis
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Entropic forces result from an increase of the entropy of a thermodynamical physical system. It has been proposed that gravity is such a phenomenon and many articles have appeared on the literature concerning this problem. Loop quantum gravity has also considered such possibility. We propose a new method in loop quantum gravity which reproduces an entropic force. By considering the interaction between a fixed gravity state space and a particle state in loop quantum gravity, we show that it leads to a mathematical description of a random walk of such particle. The random walk in special situations, can be seen as an entropic motion in such a way that the particle will move towards a location where entropy increases. This may prove that such theory can reproduce gravity as it is expected.
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Entropic Motion in Loop Quantum Gravity
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Radial basis functions are typically used when discretization sche-mes require inhomogeneous node distributions. While spawning from a desire to interpolate functions on a random set of nodes, they have found successful applications in solving many types of differential equations. However, the weights of the interpolated solution, used in the linear superposition of basis functions to interpolate the solution, and the actual value of the solution are completely different. In fact, these weights mix the value of the solution with the geometrical location of the nodes used to discretize the equation. In this paper, we used nodal radial basis functions, which are interpolants of the impulse function at each node inside the domain. This transformation allows to solve a linear hyperbolic partial differential equation using series expansion rather than the explicit computation of a matrix inverse. This transformation effectively yields an implicit solver which only requires the multiplication of vectors with matrices. Because the solver requires neither matrix inverse nor matrix-matrix products, this approach is numerically more stable and reduces the error by at least two orders of magnitude, compared to other solvers using radial basis functions directly. Further, boundary conditions are integrated directly inside the solver, at no extra cost. The method is naturally conservative, keeping the error virtually constant throughout the computation.
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A fully implicit method using nodal radial basis functions to solve the linear advection equation
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The soft behavior of the bremsstrahlung from a source is discussed in terms of classical transport models and within a non--equilibrium quantum field theory (Schwinger - Kadanoff - Baym - Keldysh) formulation.
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NON-EQUILIBRIUM DESCRIPTION OF BREMSSTRAHLUNG IN DENSE MATTER (Landau - Pomeranchuk - Migdal Effect)
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A microfabricated Fabry-Perot optical resonator has been used for atom detection and photon production with less than 1 atom on average in the cavity mode. Our cavity design combines the intrinsic scalability of microfabrication processes with direct coupling of the cavity field to single-mode optical waveguides or fibers. The presence of the atom is seen through changes in both the intensity and the noise characteristics of probe light reflected from the cavity input mirror. An excitation laser passing transversely through the cavity triggers photon emission into the cavity mode and hence into the single-mode fiber. These are first steps towards building an optical microcavity network on an atom chip for applications in quantum information processing.
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Atom detection and photon production in a scalable, open, optical microcavity
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Rydberg excitons are, with their ultrastrong mutual interactions, giant optical nonlinearities, and very high sensitivity to external fields, promising for applications in quantum sensing and nonlinear optics at the single-photon level. To design quantum applications it is necessary to know how Rydberg excitons and other excited states relax to lower-lying exciton states. Here, we present photoluminescence excitation spectroscopy as a method to probe transition probabilities from various excitonic states in cuprous oxide, and we show giant Rydberg excitons at $T=38$ mK with principal quantum numbers up to $n=30$, corresponding to a calculated diameter of 3 $\mu$m.
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Giant Rydberg excitons in Cu$_{2}$O probed by photoluminescence excitation spectroscopy
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A class of equations with exponential nonlinearities on a compact Riemannian surface is considered. More precisely, we study an asymmetric sinh-Gordon problem arising as a mean field equation of the equilibrium turbulence of vortices with variable intensities. We start by performing a blow-up analysis in order to derive some information on the local blow-up masses. As a consequence we get a compactness property in a supercritical range. We next introduce a variational argument based on improved Moser-Trudinger inequalities which yields existence of solutions for any choice of the underlying surface.
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Blow-up analysis and existence results in the supercritical case for an asymmetric mean field equation with variable intensities
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Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.
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Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence
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We show that Einstein's equations in a non-standard gauge have vacuum solutions with an asymptotically flat rotation curve as it is observed in the dark halos of galaxies. Introducing a material disk into this model we find a matter density in accordance with the Tully-Fisher relation.
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Is dark matter a phantom ?
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We report here an atomistic study of the mechanical deformation of AuxCu(1-x) atomic-size wires (NWs) by means of high resolution transmission electron microscopy (HRTEM) experiments. Molecular dynamics simulations were also carried out in order to obtain deeper insights on the dynamical properties of stretched NWs. The mechanical properties are significantly dependent on the chemical composition that evolves in time at the junction; some structures exhibit a remarkable de-alloying behavior. Also, our results represent the first experimental realization of mixed linear atomic chains (LACs) among transition and noble metals; in particular, surface energies induce chemical gradients on NW surfaces that can be exploited to control the relative LAC compositions (different number of gold and copper atoms). The implications of these results for nanocatalysis and spin transport of one-atom-thick metal wires are addressed.
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Surface Effects on the Mechanical Elongation of AuCu Nanowires: De-alloying and the Formation of Mixed Suspended Atomic Chains
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This work considers a super-resolution framework for overcomplete tensor decomposition. Specifically, we view tensor decomposition as a super-resolution problem of recovering a sum of Dirac measures on the sphere and solve it by minimizing a continuous analog of the $\ell_1$ norm on the space of measures. The optimal value of this optimization defines the tensor nuclear norm. Similar to the separation condition in the super-resolution problem, by explicitly constructing a dual certificate, we develop incoherence conditions of the tensor factors so that they form the unique optimal solution of the continuous analog of $\ell_1$ norm minimization. Remarkably, the derived incoherence conditions are satisfied with high probability by random tensor factors uniformly distributed on the sphere, implying global identifiability of random tensor factors.
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A Super-Resolution Framework for Tensor Decomposition
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Lyman series absorption features and corresponding metal lines in three high-resolution quasar spectra are fitted in the standard way with thermally broadened discrete redshift components. A candidate sample is assembled, of 10 velocity components in 7 separate complexes, which display features attributable to deuterium. Three of these components in 2 complexes were previously published; the fits to the seven others are described here in detail. To estimate the contamination of this sample by hydrogen ``interlopers'', a control sample of ``pretendium'' candidates is assembled from the same data, and in the same way in all respects but one: the control sample candidates are drawn from the red side of hydrogen absorbers, rather than the blue side where the deuterium feature appears. The control sample is therefore drawn from a population with the same statistical properties as the plausible interlopers, including their joint correlationsin velocity, column density and width. The properties of the two samples are compared statistically, using the Doppler parameters and column densities from the line fits, and are found to be significantly different. The D candidates have narrower Doppler parameters, consistent with the deuterium identification. The column densities of the D sample (but not the P sample) are consistent with a universal cosmic abundance; the weighted mean abundance for the new candidates presented here is $ < log (D/H) > = -3.75 \pm 0.51 $.
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Statistical Measurement of Primordial Deuterium Abundance
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We study the electrode polarization behaviour of different Na-Ca-phosphosilicate glasses by measuring the differential capacitance between blocking Pt electrodes. At low applied dc bias voltages, we detect a linear capacitance regime with interfacial capacitance values considerably larger than expected from double layer theories and also considerably larger than found for ionic liquids with similar ion concentrations. With increasing bias voltages, the differential capacitance of interfacial layer exhibits a maximum around 1 V and a strong drop at higher voltages. We suggest that these features are caused by pseudocapacitive processes, namely by the adsorption of mobile Na+ ions at the electrodes followed by electronic charge transfer. While pseudocapacitive processes are well known in liquid electrochemistry, more detailed studies on solid electrolytes should offer perspectives for improved energy storage in solid-state supercapacitors
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Electrode Polarization in Glassy Electrolytes: Large Interfacial Capacitance Values and Indication for Pseudocapacitive Charge Storage
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Using Monte Carlo simulations, we study the hysteresis in unzipping of a double stranded DNA whose ends are subjected to a time dependent periodic force with frequency ($\omega$) and amplitude ($G$). For the static force, i.e., $\omega \to 0$, the DNA is in equilibrium with no hysteresis. On increasing $\omega$, the area of the hysteresis loop initially increases and becomes maximum at frequency $\omega^{*}(G)$, which depends on the force amplitude $G$. If the frequency is further increased, we find that for lower amplitudes the loop area decreases monotonically to zero, but for higher amplitudes it has an oscillatory component. The height of subsequent peaks decrease and finally the loop area becomes zero at very high frequencies. The number of peaks depends on the length of the DNA. We give a simple analysis to estimate the frequencies at which maxima and minima occurs in the loop area. We find that the area of the hysteresis loop scales as $1/\omega$ in high-frequency regime whereas, it scales as $G^{\alpha} \omega^{\beta}$ with exponents $\alpha =1$ and $\beta = 5/4$ at low-frequencies. The values of the exponents $\alpha$ and $\beta$ are different from the exponents reported earlier based on the hysteresis of small hairpins.
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Unzipping DNA by a periodic force: Hysteresis loop area and its scaling
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Translating natural language into Bash Commands is an emerging research field that has gained attention in recent years. Most efforts have focused on producing more accurate translation models. To the best of our knowledge, only two datasets are available, with one based on the other. Both datasets involve scraping through known data sources (through platforms like stack overflow, crowdsourcing, etc.) and hiring experts to validate and correct either the English text or Bash Commands. This paper provides two contributions to research on synthesizing Bash Commands from scratch. First, we describe a state-of-the-art translation model used to generate Bash Commands from the corresponding English text. Second, we introduce a new NL2CMD dataset that is automatically generated, involves minimal human intervention, and is over six times larger than prior datasets. Since the generation pipeline does not rely on existing Bash Commands, the distribution and types of commands can be custom adjusted. We evaluate the performance of ChatGPT on this task and discuss the potential of using it as a data generator. Our empirical results show how the scale and diversity of our dataset can offer unique opportunities for semantic parsing researchers.
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NL2CMD: An Updated Workflow for Natural Language to Bash Commands Translation
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A brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.
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Single-trial EEG Discrimination between Wrist and Finger Movement Imagery and Execution in a Sensorimotor BCI
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In this paper, we study the Gieseker moduli space $\mathcal{M}_{1,1}^{4,3}$ of minimal surfaces with $p_g=q=1, K^2=4$ and genus 3 Albanese fibration. Under the assumption that direct image of the canonical sheaf under the Albanese map is decomposable, we find two irreducible components of $\mathcal{M}_{1,1}^{4,3}$, one of dimension 5 and the other of dimension 4.
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Algebraic Surfaces with $p_g=q=1, K^2=4$ and Genus 3 Albanese Fibration
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Due to properties such as interpolation, smoothness, and spline connections, Hermite subdivision schemes employ fast iterative algorithms for geometrically modeling curves/surfaces in CAGD and for building Hermite wavelets in numerical PDEs. In this paper we introduce a notion of generalized Hermite (dyadic) subdivision schemes and then we characterize their convergence, smoothness and underlying matrix masks with or without interpolation properties. We also introduce the notion of linear-phase moments for achieving the polynomial-interpolation property. For any given positive integer m, we constructively prove that there always exist convergent smooth generalized Hermite subdivision schemes with linear-phase moments such that their basis vector functions are spline functions in $C^m$ and have linearly independent integer shifts. As byproducts, our results resolve convergence, smoothness and existence of Lagrange, Hermite, or Birkhoff subdivision schemes. Even in dimension one our results significantly generalize and extend many known results on extensively studied univariate Hermite subdivision schemes. To illustrate the theoretical results in this paper, we provide examples of convergent generalized Hermite subdivision schemes with symmetric matrix masks having short support and smooth basis vector functions with or without interpolation property.
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Multivariate Generalized Hermite Subdivision Schemes
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We report on fundamental aspects of spin dynamics in graphene interfaced with transition metal dichalcogenides (TMDCs). By using realistic models derived from first principles we compute the spin lifetime anisotropy, defined as the ratio of lifetimes for spins pointing out of the graphene plane to those pointing in the plane. In the presence of strong intervalley scattering the anisotropy can reach unprecedented values of tens to hundreds, while it reduces to 1/2 for weak disorder. This behavior is mediated by spin-valley locking, which is strong in TMDCs and is imprinted onto graphene. Such giant spin transport anisotropy, driven by proximity effects, provides an exciting paradigm for designing novel spin device functionalities.
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Giant Spin Lifetime Anisotropy in Graphene Induced by Proximity Effects
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Spatially inhomogeneous shear flow occurs in entangled polymer solutions, both as steady state shear banding and transiently after a large step strain or during start up to a steady uniform shear rate. Steady state shear banding is a hallmark of models with a non-monotonic constitutive relation between total shear stress and applied shear rate, but transient banding is sometimes seen in fluids that do not shear band at steady state. We model this behavior using the diffusive Rolie-Poly model in a Newtonian solvent, whose constitutive behavior can be monotonic or non-monotonic depending on the degree of convected constraint release (CCR). We study monotonic constitutive behaviour. Linear stability analysis of start up to a sufficiently high shear rate shows that spatial fluctuations are unstable at early times. There is a strong correlation between this instability and the negative slope of the (time dependent) constitutive curve. If the time integral of the most unstable eigenvalue is sufficiently large then the system exhibits transient shear bands that later vanish in steady state. We show how perturbations, due to fluctuations or the inhomogeneous stresses, can trigger this instability. This transient behavior is similar to recent observations in entangled polymer solutions
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Transient shear banding in entangled polymers: a study using the Rolie-Poly model
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Using sequences of finite length with positive integer elements and the inversion statistic on such sequences, a collection of binomial and multinomial identities are extended to their $q$-analog form via combinatorial proofs. Using the major index statistic on sequences, a connection between integer partitions with kinds and finite differences of the coefficients of generalized Galois numbers is established.
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Sequences, q-Multinomial Identities, Integer Partitions with Kinds, and Generalized Galois Numbers
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Recent research has shown the potential of using available mobile fog devices (such as smartphones, drones, domestic and industrial robots) as relays to minimize communication outages between sensors and destination devices, where localized Internet-of-Things services (e.g., manufacturing process control, health and security monitoring) are delivered. However, these mobile relays deplete energy when they move and transmit to distant destinations. As such, power-control mechanisms and intelligent mobility of the relay devices are critical in improving communication performance and energy utilization. In this paper, we propose a Q-learning-based decentralized approach where each mobile fog relay agent (MFRA) is controlled by an autonomous agent which uses reinforcement learning to simultaneously improve communication performance and energy utilization. Each autonomous agent learns based on the feedback from the destination and its own energy levels whether to remain active and forward the message, or become passive for that transmission phase. We evaluate the approach by comparing with the centralized approach, and observe that with lesser number of MFRAs, our approach is able to ensure reliable delivery of data and reduce overall energy cost by 56.76\% -- 88.03\%.
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A reinforcement learning approach to improve communication performance and energy utilization in fog-based IoT
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We report on coupling between semiconducting single-wall carbon nanotubes (s-SWNT) photoluminescence and silicon microring resonators. Polyfluorene extracted s-SWNT deposited on such resonators exhibit sharp emission peaks, due to interaction with the cavity modes of the microring resonators. Ring resonators with radius of 5 {\mu}m and 10 {\mu}m were used, reaching quality factors up to 4000 in emission. These are among the highest values reported for carbon nanotubes coupled with an integrated cavity on silicon platform, which open up the possibility to build s-SWNT based efficient light source on silicon.
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Controlling carbon nanotube photoluminescence using silicon microring resonators
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In this paper, we consider the large time asymptotic behavior of solutions to systems of two cubic nonlinear Schr"odinger equations in one space dimension. It turns out that for a system there exists a small solution of which asymptotic profile is a sum of two parts oscillating in a different way. This kind of behavior seems new. Further, several examples of systems which admit solution with several types of behavior such as modified scattering, nonlinear amplification, and nonlinear dissipation, are given. We also extend our previous classification result of nonlinear cubic systems.
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Asymptotic behavior in time of solution to system of cubic nonlinear Schr"odinger equations in one space dimension
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We present an analysis of the anisotropic flow harmonics in Pb+Pb collisions at beam momenta of 30$A$ GeV/$c$ collected by the NA61/SHINE experiment in the year 2016. Directed and elliptic flow coefficients are measured relative to the spectator plane estimated with the Projectile Spectators Detector (PSD). The flow coefficients are reported as a function of transverse momentum in different classes of collision centrality. The results are compared with a new analysis of the NA49 data for Pb+Pb collisions at 40$A$ GeV using forward calorimeters (VCal and RCal) for event plane estimation.
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NA61/SHINE measurements of anisotropic flow relative to the spectator plane in Pb+Pb collisions at $30$A GeV/$c$
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For each $n\ge 3$, we construct an $O(2)\times O(n-1)$-invariant ancient Ricci flow on the $n$-sphere $S^n$ with positive curvature operator and bounded "girth". As time approaches minus infinity, it decomposes into a flat cylinder $S^1\times \mathbb{R}^{n-1}$ in the "waist" region and an $(n-2)$-plane of cigar solitons around any point in the "tip" region (both of which are of the same scale). This solution appears to be new, even in dimension three.
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Ancient Ricci pancakes of bounded girth
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A microscopic approach to macroeconomic features is intended. A model for macroeconomic behavior under heterogeneous spatial economic conditions is reviewed. A birth-death lattice gas model taking into account the influence of an economic environment on the fitness and concentration evolution of economic entities is numerically and analytically examined. The reaction-diffusion model can be also mapped onto a high order logistic map. The role of the selection pressure along various dynamics with entity diffusion on a square symmetry lattice has been studied by Monte-Carlo simulation. The model leads to a sort of phase transition for the fitness gap as a function of the selection pressure and to cycles. The control parameter is a (scalar) ''business plan''. The business plan(s) allows for spin-offs or merging and enterprise survival evolution law(s), whence bifurcations, cycles and chaotic behavior.
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A (reactive) lattice-gas approach to economic cycles
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Strongly enhanced quantum fluctuations often lead to a rich variety of quantum-disordered states. A representative case is liquid helium, in which zero-point vibrations of the helium atoms prevent its solidification at low temperatures. A similar behaviour is found for the internal degrees of freedom in electrons. Among the most prominent is a quantum spin liquid (QSL), in which localized spins are highly correlated but fluctuate even at absolute zero. Recently, a coupling of spins with other degrees of freedom has been proposed as an innovative approach to generate even more fascinating QSLs such as orbital--spin liquids. However, such ideas are limited to the internal degrees of freedom in electrons. Here, we demonstrate that a coupling of localized spins with the zero-point motion of hydrogen atoms (proton fluctuations) in a hydrogen-bonded organic Mott insulator provides a new class of QSLs. We find that a divergent dielectric behaviour towards a hydrogen-bond order is suppressed by the quantum proton fluctuations, resulting in a quantum paraelectric (QPE) state. Furthermore, our thermal-transport measurements reveal that a QSL state with gapless spin excitations rapidly emerges upon entering the QPE state. These findings indicate that the quantum proton fluctuations give rise to a novel QSL --- a quantum-disordered state of magnetic and electric dipoles --- through the coupling between the electron and proton degrees of freedom.
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Quantum-disordered state of magnetic and electric dipoles in a hydrogen-bonded Mott system
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Improving the feature representation ability is the foundation of many whole slide pathological image (WSIs) tasks. Recent works have achieved great success in pathological-specific self-supervised learning (SSL). However, most of them only focus on learning patch-level representations, thus there is still a gap between pretext and slide-level downstream tasks, e.g., subtyping, grading and staging. Aiming towards slide-level representations, we propose Slide-Level Prototypical Distillation (SLPD) to explore intra- and inter-slide semantic structures for context modeling on WSIs. Specifically, we iteratively perform intra-slide clustering for the regions (4096x4096 patches) within each WSI to yield the prototypes and encourage the region representations to be closer to the assigned prototypes. By representing each slide with its prototypes, we further select similar slides by the set distance of prototypes and assign the regions by cross-slide prototypes for distillation. SLPD achieves state-of-the-art results on multiple slide-level benchmarks and demonstrates that representation learning of semantic structures of slides can make a suitable proxy task for WSI analysis. Code will be available at https://github.com/Carboxy/SLPD.
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SLPD: Slide-level Prototypical Distillation for WSIs
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Results of photometric observations of the permanent negative superhumper TT Ari in 1961/62 and 1966 are presented. Together with data from the literature they are used to discuss the negative superhump amplitudes $A_{nSH}$ and the amplitudes $A_{beat}$ of the modulation with the beat period $P_{beat}$. Both amplitudes are shown to vary considerably from one season to another. Three correlations are found: (1) between $A_{nSH}$ and $A_{beat}$, (2) between $A_{nSH}$ and $P_{nSH}$, and (3) between $A_{beat}$ and $P_{beat}$.
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Fifty Years of TT Arietis
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Aspects of d=4, N=4 superconformal U(N) gauge theory are studied at finite temperature. Utilizing dual description of large $N$ and strong coupling limit via Type IIB string theory compactification on Schwarzschild anti-de Sitter spacetime, we study correlations of Wilson-Polyakov loops and heavy quark potential thereof. We find that the heavy quark potential is Coulomb-like and has a finite range, as expected for gauge theory in high temperature, deconfinement phase. The potential exhibits finite temperature scaling consistent with underlying conformal invariance. We also study isolated heavy quark on probe D3-brane world-volume and find supporting evidence that near extremal D3-branes are located at Schwarzschild horizon.
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Wilson-Polyakov Loop at Finite Temperature in Large N Gauge Theory and Anti-de Sitter Supergravity
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A novel approach for non-intrusive uncertainty propagation is proposed. Our approach overcomes the limitation of many traditional methods, such as generalised polynomial chaos methods, which may lack sufficient accuracy when the quantity of interest depends discontinuously on the input parameters. As a remedy we propose an adaptive sampling algorithm based on minimum spanning trees combined with a domain decomposition method based on support vector machines. The minimum spanning tree determines new sample locations based on both the probability density of the input parameters and the gradient in the quantity of interest. The support vector machine efficiently decomposes the random space in multiple elements, avoiding the appearance of Gibbs phenomena near discontinuities. On each element, local approximations are constructed by means of least orthogonal interpolation, in order to produce stable interpolation on the unstructured sample set. The resulting minimum spanning tree multi-element method does not require initial knowledge of the behaviour of the quantity of interest and automatically detects whether discontinuities are present. We present several numerical examples that demonstrate accuracy, efficiency and generality of the method.
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An adaptive minimum spanning tree multi-element method for uncertainty quantification of smooth and discontinuous responses
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We prove that the critical value of the one-dimensional Stochastic Sandpile Model is less than one. This verifies a conjecture of Rolla and Sidoravicius.
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Active Phase for the Stochastic Sandpile on Z
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A theoretical attempt to identify the physical process responsible for the afterglow emission of Gamma-Ray Bursts (GRBs) is presented, leading to the occurrence of thermal emission in the comoving frame of the shock wave giving rise to the bursts. The determination of the luminosities and spectra involves integration over an infinite number of Planckian spectra, weighted by appropriate relativistic transformations, each one corresponding to a different viewing angle in the past light cone of the observer. The relativistic transformations have been computed using the equations of motion of GRBs within our theory, giving special attention to the determination of the equitemporal surfaces. The only free parameter of the present theory is the ``effective emitting area'' in the shock wave front. A self consistent model for the observed hard-to-soft transition in GRBs is also presented. When applied to GRB 991216 a precise fit $(\chi^2\simeq 1.078)$ of the observed luminosity in the 2--10 keV band is obtained. Similarly, detailed estimates of the observed luminosity in the 50--300 keV and in the 10--50 keV bands are obtained.
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On the instantaneous spectrum of Gamma-Ray Bursts
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Spiking Neural Networks (SNNs) have received extensive academic attention due to the unique properties of low power consumption and high-speed computing on neuromorphic chips. Among various training methods of SNNs, ANN-SNN conversion has shown the equivalent level of performance as ANNs on large-scale datasets. However, unevenness error, which refers to the deviation caused by different temporal sequences of spike arrival on activation layers, has not been effectively resolved and seriously suffers the performance of SNNs under the condition of short time-steps. In this paper, we make a detailed analysis of unevenness error and divide it into four categories. We point out that the case of the ANN output being zero while the SNN output being larger than zero accounts for the largest percentage. Based on this, we theoretically prove the sufficient and necessary conditions of this case and propose an optimization strategy based on residual membrane potential to reduce unevenness error. The experimental results show that the proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets. For example, we reach top-1 accuracy of 64.32\% on ImageNet with 10-steps. To the best of our knowledge, this is the first time ANN-SNN conversion can simultaneously achieve high accuracy and ultra-low-latency on the complex dataset. Code is available at https://github.com/hzc1208/ANN2SNN\_SRP.
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Reducing ANN-SNN Conversion Error through Residual Membrane Potential
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The behavior of interacting electrons in a perfect crystal under macroscopic external electric and magnetic fields is studied. Effective Maxwell equations for the macroscopic electric and magnetic fields are derived starting from time-dependent density functional theory. Effective permittivity and permeability coefficients are obtained.
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Effective Maxwell equations from time-dependent density functional theory
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We present a new metaphysical framework for physics that is conceptually clear, ontologically parsimonious, and empirically adequate. This framework relies on the notion of self-subsisting structure, that is, a set of fundamental physical elements whose individuation and behavior are described in purely relational terms, without any need for a background spacetime. Although the specification of the fundamental elements of the ontology depends on the particular physical domain considered -- and is thus susceptible to scientific progress -- , the empirically successful structural features of the framework are preserved through theory change. The kinematics and dynamics of these self-subsisting structures are technically implemented using the theoretical framework of Pure Shape Dynamics, which provides a completely relational physical description of a system in terms of the intrinsic geometry of a suitably defined space called shape space.
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A Proposal for a Metaphysics of Self-Subsisting Structures. I. Classical Physics
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We review the progress on the determination of the CKM matrix elements |V_cs|, |V_cd|, |V_cb|, |V_ub| and heavy quark masses presented at the 6th International Workshop on the CKM Unitarity Triangle.
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CKM2010 Working Group II Summary
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We give a combinatorial characterization of Fulton's operational Chow cohomology groups of a complete rational T-variety of complexity one in terms of so called generalized Minkowsky weights. In the contraction-free case, we also describe the intersection product with Cartier invariant divisors in terms of the combinatorial data. In particular this provides a new way of computing top intersection numbers of invariant Cartier divisors combinatorially.
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Generalized Minkowski weights and Chow rings of T-varieties
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In this paper, we propose ARCH (Animatable Reconstruction of Clothed Humans), a novel end-to-end framework for accurate reconstruction of animation-ready 3D clothed humans from a monocular image. Existing approaches to digitize 3D humans struggle to handle pose variations and recover details. Also, they do not produce models that are animation ready. In contrast, ARCH is a learned pose-aware model that produces detailed 3D rigged full-body human avatars from a single unconstrained RGB image. A Semantic Space and a Semantic Deformation Field are created using a parametric 3D body estimator. They allow the transformation of 2D/3D clothed humans into a canonical space, reducing ambiguities in geometry caused by pose variations and occlusions in training data. Detailed surface geometry and appearance are learned using an implicit function representation with spatial local features. Furthermore, we propose additional per-pixel supervision on the 3D reconstruction using opacity-aware differentiable rendering. Our experiments indicate that ARCH increases the fidelity of the reconstructed humans. We obtain more than 50% lower reconstruction errors for standard metrics compared to state-of-the-art methods on public datasets. We also show numerous qualitative examples of animated, high-quality reconstructed avatars unseen in the literature so far.
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ARCH: Animatable Reconstruction of Clothed Humans
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Structural magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients. The formation of these lesions is a complex process involving inflammation, tissue damage, and tissue repair, all of which are visible on MRI. Here we characterize the lesion formation process on longitudinal, multi-sequence structural MRI from 34 MS patients and relate the longitudinal changes we observe within lesions to therapeutic interventions. In this article, we first outline a pipeline to extract voxel level, multi-sequence longitudinal profiles from four MRI sequences within lesion tissue. We then propose two models to relate clinical covariates to the longitudinal profiles. The first model is a principal component analysis (PCA) regression model, which collapses the information from all four profiles into a scalar value. We find that the score on the first PC identifies areas of slow, long-term intensity changes within the lesion at a voxel level, as validated by two experienced clinicians, a neuroradiologist and a neurologist. On a quality scale of 1 to 4 (4 being the highest) the neuroradiologist gave the score on the first PC a median rating of 4 (95% CI: [4,4]), and the neurologist gave it a median rating of 3 (95% CI: [3,3]). In the PCA regression model, we find that treatment with disease modifying therapies (p-value < 0.01), steroids (p-value < 0.01), and being closer to the boundary of abnormal signal intensity (p-value < 0.01) are associated with a return of a voxel to intensity values closer to that of normal-appearing tissue. The second model is a function-on-scalar regression, which allows for assessment of the individual time points at which the covariates are associated with the profiles. In the function-on-scalar regression both age and distance to the boundary were found to have a statistically significant association with the profiles.
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Relating multi-sequence longitudinal intensity profiles and clinical covariates in new multiple sclerosis lesions
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A supersymmetric standard model with heavier scalar particles is very interesting from various viewpoints, especially Higgs properties. If the scalar mass scale is O(10-10^4) TeV, the standard model-like Higgs with mass around 125 GeV, which is implied by the recent LHC experiments, is predicted. However this scenario is difficult to be directly tested with collider experiments. In this paper, we propose a test of this scenario by using observations of primordial gravitational waves (GWs). The future GW experiments such as DECIGO and BBO can probe the scalar mass around O(10^3-10^4) TeV, which is preferred from the Higgs mass about 125 GeV, if the primordial GWs have large amplitude.
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Gravitational Wave Probe of High Supersymmetry Breaking Scale
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A new convenient method to diagonalize the non-relativistic many-body Schroedinger equation with two-body central potentials is derived. It combines kinematic rotations (democracy transformations) and exact calculation of overlap integrals between bases with different sets of mass-scaled Jacobi coordinates, thereby allowing for a great simplification of this formidable problem. We validate our method by obtaining a perfect correspondence with the exactly solvable three-body ($N=3$) Calogero model in 1D.
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Diagonalization scheme for the many-body Schroedinger equation
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Although narrow low mass baryonic structures, observed mainly in SPES3 (Saturne) data, were not confirmed in recent experiments using lepton probes, their existence is confirmed in other data, where hadronic probes were used.
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Further evidence for narrow exotic low mass baryons
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We consider g coincident M-5-branes on top of each other, in the KK monopole background Q of multiplicity N. The worldvolume of each M-5-brane is supposed to be given by the local product of the four-dimensional spacetime and an elliptic curve. In the coincidence limit, all these curves yield a single (Seiberg-Witten) hyperelliptic curve, while the gauge symmetry is enhanced to U(N). We make this gauge symmetry enhancement manifest by considering the hypermultiplet LEEA which is given by the spacetime N=2 non-linear sigma-model (NLSM) having Q as the target space. The hyper-K"ahler manifold Q is given by the multicentre Taub-NUT space, which in the coincidence limit amounts to the multiple Eguchi-Hanson (ALE) space Q. The NLSM is most naturally described in terms of the hyper-K"ahler coset construction on SU(N,N)/U(N) in harmonic superspace, by using the auxiliary (in classical theory) N=2 vector superfields as Lagrange multipliers, with FI terms resolving the singularity. The Maldacena limit, in which the hypermultiplet LEEA becomes extended to the N=4 SYM with the gauge group U(N), arises in quantum field theory due to a dynamical generation of the N=2 vector and hypermultipet superfields, when sending the FI terms to zero.
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Making manifest the symmetry enhancement for coincident BPS branes
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In 2019, A. Lazar and M. L. Wachs conjectured that the number of cycles on $[2n]$ with only even-odd drops equals the $n$-th Genocchi number. In this paper, we restrict our attention to a subset of cycles on $[n]$ that in all drops in the cycle, the latter entry is odd. We deduce two bivariate generating functions for such a subset of cycles with an extra variable introduced to count the number of odd-odd and even-odd drops, respectively. One of the generating function identities confirms Lazar and Wachs' conjecture, while the other identity implies that the number of cycles on $[2n-1]$ with only odd-odd drops equals the $(n-2)$-th Genocchi median.
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Parity considerations for drops in cycles on $\{1,2,\ldots,n\}$
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A classical two dimensional theory of gravity which has a number of interesting features (including a Newtonian limit, black holes and gravitational collapse) is quantized using conformal field theoretic techniques. The critical dimension depends upon Newton's constant, permitting models with $d=4$. The constraint algebra and scaling properties of the model are computed.
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Two Dimensional Quantum Gravity Coupled to Matter
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The window function for protohalos in Lagrangian space is often assumed to be a tophat in real space. We measure this profile directly and find that it is more extended than a tophat but less extended than a Gaussian; its shape is well-described by rounding the edges of the tophat by convolution with a Gaussian that has a scale length about 5 times smaller. This effective window $W_{\rm eff}$ is particularly simple in Fourier space, and has an analytic form in real space. Together with the excursion set bias parameters, $W_{\rm eff}$ describes the scale-dependence of the Lagrangian halo-matter cross correlation up to $kR_{\rm Lag} \sim 10 $, where $R_{\rm Lag}$ is the Lagrangian size of the protohalo. Moreover, with this $W_{\rm eff}$, all the spectral moments of the power spectrum are finite, allowing a straightforward estimate of the excursion set peak mass function. This estimate requires a prescription of the critical overdensity enclosed within a protohalo if it is to collapse, which we calibrate from simulations. We find that the resulting estimate of halo abundances is only accurate to about 20%, and we discuss why: A tophat in `infall time' towards the protohalo center need not correspond to a tophat in the initial spatial distribution, so models in which infall rather than smoothed overdensity is the relevant variable may be more accurate.
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Effective Window Function for Lagrangian Halos
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We analyze the effect of the Sun's gravitational field on a flow of cold dark matter (CDM) through the solar system in the limit where the velocity dispersion of the flow vanishes. The exact density and velocity distributions are derived in the case where the Sun is a point mass. The results are extended to the more realistic case where the Sun has a finite size spherically symmetric mass distribution. We find that regions of infinite density, called caustics, appear. One such region is a line caustic on the axis of symmetry, downstream from the Sun, where the flow trajectories cross. Another is a cone-shaped caustic surface near the trajectories of maximum scattering angle. The trajectories forming the conical caustic pass through the Sun's interior and probe the solar mass distribution, raising the possibility that the solar mass distribution may some day be measured by a dark matter detector on Earth. We generalize our results to the case of flows with continuous velocity distributions, such as that predicted by the isothermal model of the Milky Way halo.
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Solar Wakes of Dark Matter Flows
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Let ${\mathcal {B}}$ be a reducible reduced plane curve. We introduce a new point of view to study the topology of $(\PP^2, {\mathcal {B}})$ via Galois covers and Alexander polynomials. We show its effectiveness through examples of Zariski $N$-plets for conic and conic-quartic configurations.
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On the topology of the complements of reducible plane curves via Galois covers
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In this note we study solitary wave solutions of a class of Whitham-Boussinesq systems which includes the bi-directional Whitham system as a special example. The travelling wave version of the evolution system can be reduced to a single evolution equation, similar to a class of equations studied by Ehrnstr\"om, Groves and Wahl\'en. In that paper the authors prove the existence of solitary wave solutions using a constrained minimization argument adapted to noncoercive functionals, developed by Buffoni, Groves and Wahl\'en, together with the concentration-compactness principle.
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Solitary wave solutions to a class of Whitham-Boussinesq systems
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We investigate theoretically the Landau levels (LLs) and magneto-transport properties of phosphorene under a perpendicular magnetic field within the framework of the effective \textbf{\emph{k$\cdot$p}} Hamiltonian and tight-binding (TB) model. At low field regime, we find that the LLs linearly depend both on the LL index $n$ and magnetic field $B$, which is similar with that of conventional semiconductor two-dimensional electron gas. The Landau splittings of conduction and valence band are different and the wavefunctions corresponding to the LLs are strongly anisotropic due to the different anisotropic effective masses. An analytical expression for the LLs in low energy regime is obtained via solving the decoupled Hamiltonian, which agrees well with the numerical calculations. At high magnetic regime, a self-similar Hofstadter butterfly (HB) spectrum is obtained by using the TB model. The HB spectrum is consistent with the Landau level fan calculated from the effective \textbf{\emph{k$\cdot$p}} theory in a wide regime of magnetic fields. We find the LLs of phosphorene nanoribbon depend strongly on the ribbon orientation due to the anisotropic hopping parameters. The Hall and the longitudinal conductances (resistances) clearly reveal the structure of LLs.
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Landau levels and magneto-transport property of monolayer phosphorene
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Prenatal stress (PS) impacts early postnatal behavioural and cognitive development. This process of 'fetal programming' is mediated by the effects of the prenatal experience on the developing hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS). The HPA axis is a dynamic system regulating homeostasis, especially the stress response, and is highly sensitive to adverse early life experiences. We review the evidence for the effects of PS on fetal programming of the HPA axis and the ANS. We derive a multi-scale multi-species approach to devising preclinical and clinical studies to identify early non-invasively available pre- and postnatal biomarkers of these programming effects. Such approach would identify adverse postnatal brain developmental trajectories, a prerequisite for designing therapeutic interventions. The multiple scales include the biomarkers reflecting changes in the brain epigenome, metabolome, microbiome and the ANS activity gauged via an array of advanced non-invasively obtainable properties of fetal heart rate fluctuations. The proposed framework has the potential to reveal mechanistic links between maternal stress during pregnancy and changes across these physiological scales. Such biomarkers may hence be useful as early and non-invasive predictors of neurodevelopmental trajectories influenced by the PS. We conclude that studies into PS effects must be conducted on multiple scales derived from concerted observations in multiple animal models and human cohorts performed in an interactive and iterative manner and deploying machine learning for data synthesis, identification and validation of the best non-invasive biomarkers.
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Non-invasive biomarkers of fetal brain development reflecting prenatal stress: an integrative multi-scale multi-species perspective on data collection and analysis
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We present both time-averaged and time-resolved transport measurements of a two-dimensional electron (Wigner) crystal on the surface of superfluid helium confined in a narrow microchannel. We find that the field-current characteristics of the driven crystal obtained by the time-averaged measurements exhibit oscillations and negative differential conductivity. This unusual transport behavior was observed previously by Glasson et al. [Phys. Rev. Lett. 87, 176802 (2001)] and was attributed to a nonequilibrium transition of the electron system to a novel dynamically ordered phase of current filaments aligned along the channels. Contrarily to this explanation, our time-resolved transport measurements reveal that oscillating field-current characteristics appear due to dynamical decoupling (slipping) and recoupling (sticking) of the uniform electron crystal to the liquid helium substrate. Our result demonstrates that this unusual non-linear transport effect is intrinsic, does not depend on the device geometry, and is associated with the dynamical interaction of Wigner crystal with the surface excitations of the liquid substrate.
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On dynamical ordering in a 2D electron crystal confined in a narrow channel geometry
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An important challenge in loop quantum gravity is to find semiclassical states - states that are as close to classical as quantum theory allows. This is difficult because the states in the Hilbert space used in LQG are excitations over a vacuum in which geometry is highly degenerate. Additionally, fluctuations are distributed very unevenly between configuration and momentum variables. Coherent states that have been proposed to balance the uncertainties more evenly can, up to now, only do this for finitely many degrees of freedom. Our work is motivated by the desire to obtain Gaussian states that encompass all degrees of freedom. To obtain a toy-model we reformulate the U(1) holonomy-flux algebra in any dimension as a Weyl algebra, and discuss generalisations to SU(2). We then define and investigate a new class of states on these algebras which behave like quasifree states on the momentum variables.
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Towards Gaussian states for loop quantum gravity
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Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double estimator, performance of clipped Double Q-learning may be degraded in some stochastic environments. In this paper, in order to reduce the underestimation bias, we propose an action candidate based clipped double estimator for Double Q-learning. Specifically, we first select a set of elite action candidates with the high action values from one set of estimators. Then, among these candidates, we choose the highest valued action from the other set of estimators. Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value. Theoretically, the underestimation bias in our clipped Double Q-learning decays monotonically as the number of the action candidates decreases. Moreover, the number of action candidates controls the trade-off between the overestimation and underestimation biases. In addition, we also extend our clipped Double Q-learning to continuous action tasks via approximating the elite continuous action candidates. We empirically verify that our algorithm can more accurately estimate the maximum expected action value on some toy environments and yield good performance on several benchmark problems.
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Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks
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Recently, it has been observed that the non-Abelian action associated with lattice monopoles and vortices is ultraviolet divergent, at least at presently available lattices. On the other hand, the total length of the monopole trajectories and area of the vortices scale in physical units. Coexistence of the two different scales, infrared and ultraviolet, for the same vacuum fluctuations represents a fine tuning. To check consistency of the newly emerging picture of non--perturbative fluctuations we consider constraints from the continuum theory on the ultraviolet behaviour of the monopoles and vortices. The constraints turn to be satisfied by the data in a highly non-trivial way. Namely, it is crucial that the monopoles populate not the whole of the four dimensional space but a two-dimensional subspace of it.
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Fine Tuning in Lattice SU(2) Gluodynamics vs Continuum-Theory Constraints
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We consider simple polytopes $P=vc^{k}(\Delta^{n_{1}}\times\ldots\times\Delta^{n_{r}})$, for $n_1\ge\ldots\ge n_r\ge 1,r\ge 1,k\ge 0$, that is, $k$-vertex cuts of a product of simplices, and call them {\emph{generalized truncation polytopes}}. For these polytopes we describe the cohomology ring of the corresponding moment-angle manifold $\mathcal Z_P$ and explore some topological consequences of this calculation. We also examine minimal non-Golodness for their Stanley--Reisner rings and relate it to the property of $\mathcal Z_P$ being a connected sum of sphere products.
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Stanley--Reisner rings of generalised truncation polytopes and their moment-angle manifolds
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We construct Chern-Simons bundles as $\mathrm{Aut}^{+}P$-equivariant $U(1)$ -bundles with connection over the space of connections $\mathcal{A}_{P}$ on a principal $G$-bundle $P\rightarrow M$. We show that the Chern-Simons bundles are determined up to an isomorphisms by means of its equivariant holonomy. The space of equivariant holonomies is shown to coincide with the space of equivariant differential characteres of second order. Furthermore, we prove that the Chern-Simons theory provides, in a natural way, an equivariant differential character that determines the Chern-Simons bundles. Our construction can be applied in the case in which $M$ is a compact manifold of even dimension and for arbitrary bundle $P$ and group $G$. The results are also generalized to the case of the action of diffeomorphisms on the space of Riemannian metrics. In particular, in dimension $2$ a Chern-Simons bundle over the Teichm\"{u}ller space is obtained.
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Equivariant differential characters and Chern-Simons bundles
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A sequence $s_1,s_2,...,s_k,s_1,s_2,...,s_k$ is a repetition. A sequence $S$ is nonrepetitive, if no subsequence of consecutive terms of $S$ form a repetition. Let $G$ be a vertex colored graph. A path of $G$ is nonrepetitive, if the sequence of colors on its vertices is nonrepetitive. If $G$ is a plane graph, then a facial nonrepetitive vertex coloring of $G$ is a vertex coloring such that any facial path is nonrepetitive. Let $\pi_f(G)$ denote the minimum number of colors of a facial nonrepetitive vertex coloring of $G$. Jendro\vl and Harant posed a conjecture that $\pi_f(G)$ can be bounded from above by a constant. We prove that $\pi_f(G)\le 24$ for any plane graph $G$.
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Vertex coloring of plane graphs with nonrepetitive boundary paths
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We determine the properties of the binary star V106 in the old open cluster NGC6791. We identify the system to be a blue straggler cluster member by using a combination of ground-based and Kepler photometry and multi-epoch spectroscopy. The properties of the primary component are found to be $M_{\rm p}\sim1.67 \rm M_{\odot}$, more massive than the cluster turn-off, with $R_{\rm p}\sim1.91 \rm R_{\odot}$ and $T_{\rm eff}=7110\pm100$ K. The secondary component is highly oversized and overluminous for its low mass with $M_{\rm s}\sim0.182 \rm M_{\odot}$, $R_{\rm s}\sim0.864 \rm R_{\odot}$ and $T_{\rm eff}=6875\pm200$ K. We identify this secondary star as a bloated (proto) extremely low-mass helium white dwarf. These properties of V106 suggest that it represents a typical Algol-paradox system and that it evolved through a mass-transfer phase which provides insight into its past evolution. We present a detailed binary stellar evolution model for the formation of V106 using the MESA code and find that the mass-transfer phase only ceased about 40 Myr ago. Due to the short orbital period (P=1.4463 d) another mass-transfer phase is unavoidable once the current primary star evolves towards the red giant phase. We argue that V106 will evolve through a common-envelope phase within the next 100 Myr and merge to become a single over-massive giant. The high mass will make it appear young for its true age, which is revealed by the cluster properties. Therefore, V106 is potentially a prototype progenitor of old field giants masquerading as young.
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The blue straggler V106 in NGC6791: A prototype progenitor of old single giants masquerading as young
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This article investigates the weak approximation towards the invariant measure of semi-linear stochastic differential equations (SDEs) under non-globally Lipschitz coefficients. For this purpose, we propose a linear-theta-projected Euler (LTPE) scheme, which also admits an invariant measure, to handle the potential influence of the linear stiffness. Under certain assumptions, both the SDE and the corresponding LTPE method are shown to converge exponentially to the underlying invariant measures, respectively. Moreover, with time-independent regularity estimates for the corresponding Kolmogorov equation, the weak error between the numerical invariant measure and the original one can be guaranteed with convergence of order one. In terms of computational complexity, the proposed ergodicity preserving scheme with the nonlinearity explicitly treated has a significant advantage over the ergodicity preserving implicit Euler method in the literature. Numerical experiments are provided to verify our theoretical findings.
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Linear implicit approximations of invariant measures of semi-linear SDEs with non-globally Lipschitz coefficients
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In this paper we report the evaluation of an optical lattice clock based on neutral mercury down to a relative uncertainty of $1.7\times 10^{-16}$. Comparing this characterized frequency standard to a Cs atomic fountain we determine the absolute frequency of the $^1S_0 \rightarrow \phantom{}^3P_0$ transition of $^{199}$Hg as $\nu_{\mathrm{Hg}} = 1 128\,575\,290\,808\,154.62\,$Hz $\pm\,0.19\,$Hz (statistical) $\pm\,0.38\,$Hz (systematic), limited solely by the realization of the SI second. Furthermore, by comparing the mercury optical lattice clock to a Rb atomic fountain, we determine for the first time to our knowledge the ratio between the $^{199}$Hg clock transition and the $^{87}$Rb ground state hyperfine transition. Finally we present a direct optical to optical measurement of the $^{199}$Hg/$^{87}$Sr frequency ratio. The obtained value of $\nu_{\mathrm{Hg}}/\nu_{\mathrm{Sr}}=2.629\,314\,209\,898\,909\,15$ with a fractional uncertainty of $1.8\times10^{-16}$ is in excellent agreement with the same measurement obtained by Yamanaka et al. (arXiv:1503.07941). This makes this frequency ratio one of the few physical quantities agreed upon by different laboratories to this level of uncertainty. Frequency ratio measurements of the kind of those reported in this paper have a strong impact for frequency metrology but also for fundamental physics as they can be used to monitor putative variations of fundamental constants.
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Comparing a mercury optical lattice clock with microwave and optical frequency standards
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We exploit the high sensitivity and moderate spectral resolution of the $HST$-Cosmic Origins Spectrograph to detect far-ultraviolet spectral features of carbon monoxide (CO) present in the inner regions of protoplanetary disks for the first time. We present spectra of the classical T Tauri stars HN Tau, RECX-11, and V4046 Sgr, representative of a range of CO radiative processes. HN Tau shows CO bands in absorption against the accretion continuum. We measure a CO column density and rotational excitation temperature of N(CO) = 2 +/- 1 $\times$ 10$^{17}$ cm$^{-2}$ and T_rot(CO) 500 +/- 200 K for the absorbing gas. We also detect CO A-X band emission in RECX-11 and V4046 Sgr, excited by ultraviolet line photons, predominantly HI LyA. All three objects show emission from CO bands at $\lambda$ $>$ 1560 \AA, which may be excited by a combination of UV photons and collisions with non-thermal electrons. In previous observations these emission processes were not accounted for due to blending with emission from the accretion shock, collisionally excited H$_{2}$, and photo-excited H2; all of which appeared as a "continuum" whose components could not be separated. The CO emission spectrum is strongly dependent upon the shape of the incident stellar LyA emission profile. We find CO parameters in the range: N(CO) 10$^{18-19}$ cm$^{-2}$, T_{rot}(CO) > 300 K for the LyA-pumped emission. We combine these results with recent work on photo- and collisionally-excited H$_{2}$ emission, concluding that the observations of ultraviolet-emitting CO and H2 are consistent with a common spatial origin. We suggest that the CO/H2 ratio in the inner disk is ~1, a transition between the much lower interstellar value and the higher value observed in solar system comets today, a result that will require future observational and theoretical study to confirm.
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The Far-Ultraviolet "Continuum" in Protoplanetary Disk Systems II: CO Fourth Positive Emission and Absorption
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In this paper we first show that on projective manifolds (M, {\omega}), there are holomorphic determinant bundles (in the sense of Knusden-Mumford used by Bismut, Gillet, Soule) which play the role of the geometric quantum bundle, namely one for each input data of a Hermitian holomorphic line bundle L of non-trivial Chern class on a compact Kahler manifold Z (with Todd genus non-zero) and a choice of a geometric quantization of (M, {\omega}). Next we further study the generalization of the vortex equations on Kahler 4-manifold which has been studied earlier by Bradlow. We show that when the Kahler 4-manifold avoids some obstructions then the regular part of the moduli space is a Kahler manifold and admit a pull back of a Quillen determinant bundle as the quantum line bundle, i.e. the curvature is proportional to the Kahler form. Thus they can be quantized geometrically. In fact we show that the moduli space of the usual vortex equations on a projective Kahler 4-manifold is projective when the moduli space is smooth. Since in Kahler 4-manifold the vortex moduli and the Seiberg Witten moduli coincide our effort gives a quantization of Seiberg Witten moduli by determinant bundles
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Determinat Bundles and Geometric Quantization Of Vortex Moduli Spaces ON Compact Kahler Surfaces
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Despite several indirect confirmations of the existence of dark matter, the properties of a new dark matter particle are still largely unknown. Several experiments are currently searching for this particle underground in direct detection, in space and on earth in indirect detection and at the LHC. A confirmed signal could select a model for dark matter among the many extensions of the standard model. In this paper we present a short review of the public codes for computation of dark matter observables.
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Tools for Dark Matter in Particle and Astroparticle Physics
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We demonstrate that if consciousness is relevant for the temporal evolution of a system's states -- that is, if it is dynamically relevant -- then AI systems cannot be conscious. That is because AI systems run on CPUs, GPUs, TPUs or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. The design and verification preclude or suppress, in particular, potential consciousness-related dynamical effects, so that if consciousness is dynamically relevant, AI systems cannot be conscious.
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If consciousness is dynamically relevant, artificial intelligence isn't conscious
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Spreadsheets provide a flexible and easy to use software development environment, but that leads to error proneness. Work has been done to prevent errors in spreadsheets, including using models to specify distinct parts of a spreadsheet as it is done with model-driven software development. Previous model languages for spreadsheets offer a limited expressiveness, and cannot model several features present in most real world spreadsheets. In this paper, the modeling language Tabula is introduced. It extends previous spreadsheet models with features like type constraints and nested classes with repetitions. Tabula is not only more expressive than other models but it can also be extended with more features. Moreover, Tabula includes a bidirectional transformation engine that guarantees synchronization after an update either in the model or spreadsheet.
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Tabula: A Language to Model Spreadsheet Tables
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Recent theoretical research proposes that computational complexity can be seen as an ultimate constraint that allows for open-ended biological evolution on finite static fitness landscapes. Whereas on easy fitness landscapes, evolution will quickly converge to a local fitness peaks, on hard fitness landscapes this computational constraints prevents evolution from reaching any local fitness peak in polynomial time. Valued constraint satisfaction problems (VCSPs) can be used to represent both easy and hard fitness landscapes. Thus VCSPS can be seen as a natural way of linking the theory of evolution with notions of computer science to better understand the features that make landscapes hard. However, there are currently no simulators that study VCSP-structured fitness landscapes. This report describes the design and build of an evolution simulator for VCSP-structured fitness landscapes. The platform is used for simulating various instances of easy and hard fitness landscapes. In particular, we look at evolution under more realistic assumptions than fittest mutant strong-selection weak mutation dynamics on the winding semismooth fitness landscape. The results obtained match with the theoretical expectations, while also providing new information about the limits of evolution. The last part of the report introduces a mathematical model for smooth fitness landscapes and uses it to better understand why these landscapes are easy.
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Simulating Evolution on Fitness Landscapes represented by Valued Constraint Satisfaction Problems
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Approaches based on refinement operators have been successfully applied to class expression learning on RDF knowledge graphs. These approaches often need to explore a large number of concepts to find adequate hypotheses. This need arguably stems from current approaches relying on myopic heuristic functions to guide their search through an infinite concept space. In turn, deep reinforcement learning provides effective means to address myopia by estimating how much discounted cumulated future reward states promise. In this work, we leverage deep reinforcement learning to accelerate the learning of concepts in $\mathcal{ALC}$ by proposing DRILL -- a novel class expression learning approach that uses a convolutional deep Q-learning model to steer its search. By virtue of its architecture, DRILL is able to compute the expected discounted cumulated future reward of more than $10^3$ class expressions in a second on standard hardware. We evaluate DRILL on four benchmark datasets against state-of-the-art approaches. Our results suggest that DRILL converges to goal states at least 2.7$\times$ faster than state-of-the-art models on all benchmark datasets. We provide an open-source implementation of our approach, including training and evaluation scripts as well as pre-trained models.
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DRILL-- Deep Reinforcement Learning for Refinement Operators in $\mathcal{ALC}$
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Active matter encompasses systems whose individual consituents dissipate energy to exert propelling forces on their environment. This rapidly developing field harbors a dynamical phenomenology with no counterpart in passive systems. The extent to which this is rooted in the breaking of time-reversibility has recently triggered an important theoretical and experimental activity which is the focus of this review. Building on recent progress in the field, we disentangle the respective roles of the arrow of time and of the non-Boltzmann nature of steady-state fluctuations in single- and many-body active systems. We show that effective time-reversible descriptions of active systems may be found at all scales, and discuss how interactions, either between constituents or with external operators, may reveal the non-equilibrium nature of the microscopic source of energy. At a time when the engineering of active materials appears within our reach, this allows us to discuss to which extent methods stemming from equilibrium statistical mechanics may guide us in their design.
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Time-(ir)reversibility in active matter: from micro to macro
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In many real-world machine learning applications, unlabeled data can be easily obtained, but it is very time-consuming and/or expensive to label them. So, it is desirable to be able to select the optimal samples to label, so that a good machine learning model can be trained from a minimum amount of labeled data. Active learning (AL) has been widely used for this purpose. However, most existing AL approaches are supervised: they train an initial model from a small amount of labeled samples, query new samples based on the model, and then update the model iteratively. Few of them have considered the completely unsupervised AL problem, i.e., starting from zero, how to optimally select the very first few samples to label, without knowing any label information at all. This problem is very challenging, as no label information can be utilized. This paper studies unsupervised pool-based AL for linear regression problems. We propose a novel AL approach that considers simultaneously the informativeness, representativeness, and diversity, three essential criteria in AL. Extensive experiments on 14 datasets from various application domains, using three different linear regression models (ridge regression, LASSO, and linear support vector regression), demonstrated the effectiveness of our proposed approach.
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Unsupervised Pool-Based Active Learning for Linear Regression
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We discuss the parametric oscillatory instability in a Fabry-Perot cavity of the Einstein Telescope. Unstable combinations of elastic and optical modes for two possible configurations of gravitational wave third-generation detector are deduced. The results are compared with the results for gravita- tional wave interferometers LIGO and LIGO Voyager.
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Parametric Oscillatory Instability in a Fabry-Perot Cavity of the Einstein Telescope with different mirror's materials
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A novel refinement of the conventional treatment of Kadanoff--Baym equations is suggested. Besides the Boltzmann equation another differential equation is used for calculating the evolution of the non-equilibrium two-point function. Although it was usually interpreted as a constraint on the solution of the Boltzmann equation, we argue that its dynamics is relevant to the determination and resummation of the particle production cut contributions. The differential equation for this new contribution is illustrated in the example of the cubic scalar model. The analogue of the relaxation time approximation is suggested. It results in the shift of the threshold location and in smearing out of the non-analytic threshold behaviour of the spectral function. Possible consequences for the dilepton production are discussed.
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Generalized Boltzmann equations for on-shell particle production in a hot plasma
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In this note, based on a conference talk, we show how a 3 dimensional topological field theory leads to an algebraic gadget roughly equivalent to a quantum group. This is an expository version of some material in hep-th/9212115 (where we also carry out computations for a specific finite example). We also explain how to incorporate the central extensions usually explained via ``framings'', and we show how to recover invariants of framed tangles. This paper is written using AMSTeX 2.1, which can be obtained via ftp from the American Mathematical Society (instructions included). 2 encapsulated postscript files were submitted separately in uuencoded tar-compressed format.
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Extended Structures in Topological Quantum Field Theory
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We consider fast deterministic algorithms to identify the "best" linearly independent terms in multivariate mixtures and use them to compute, up to a user-selected accuracy, an equivalent representation with fewer terms. One algorithm employs a pivoted Cholesky decomposition of the Gram matrix constructed from the terms of the mixture to select what we call skeleton terms and the other uses orthogonalization for the same purpose. Importantly, the multivariate mixtures do not have to be a separated representation of a function. Both algorithms require $O(r^2 N + p(d) r N) $ operations, where $N$ is the initial number of terms in the multivariate mixture, $r$ is the number of selected linearly independent terms, and $p(d)$ is the cost of computing the inner product between two terms of a mixture in $d$ variables. For general Gaussian mixtures $p(d) \sim d^3$ since we need to diagonalize a $d\times d$ matrix, whereas for separated representations $p(d) \sim d$. Due to conditioning issues, the resulting accuracy is limited to about one half of the available significant digits for both algorithms. We also describe an alternative algorithm that is capable of achieving higher accuracy but is only applicable in low dimensions or to multivariate mixtures in separated form. We describe a number of initial applications of these algorithms to solve partial differential and integral equations and to address several problems in data science. For data science applications in high dimensions,we consider the kernel density estimation (KDE) approach for constructing a probability density function (PDF) of a cloud of points, a far-field kernel summation method and the construction of equivalent sources for non-oscillatory kernels (used in both, computational physics and data science) and, finally, show how to use the new algorithm to produce seeds for subdividing a cloud of points into groups.
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Reduction of multivariate mixtures and its applications
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We present OptEx, a closed-form model of job execution on Apache Spark, a popular parallel processing engine. To the best of our knowledge, OptEx is the first work that analytically models job completion time on Spark. The model can be used to estimate the completion time of a given Spark job on a cloud, with respect to the size of the input dataset, the number of iterations, the number of nodes comprising the underlying cluster. Experimental results demonstrate that OptEx yields a mean relative error of 6% in estimating the job completion time. Furthermore, the model can be applied for estimating the cost optimal cluster composition for running a given Spark job on a cloud under a completion deadline specified in the SLO (i.e., Service Level Objective). We show experimentally that OptEx is able to correctly estimate the cost optimal cluster composition for running a given Spark job under an SLO deadline with an accuracy of 98%.
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OptEx: A Deadline-Aware Cost Optimization Model for Spark
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There are a lot of on going efforts in the research community as well as industry around providing privacy-preserving and secure storage for personal data. Although, over time it has adopted many tag lines such as Personal Information Hub [12], personal container [8], DataBox [4], Personal Data Store (PDS) [3] and many others, these are essentially reincarnations of a simple idea: provide a secure way and place for users to store their information and allow them to provision who has access to that information. In this paper, we would like to discuss a way to facilitate access control mechanism (AC) in the various "personal cloud" proposals.
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Immutable Views -- Access control (to your information) for masses
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Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.
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Blockchain-based Secure Client Selection in Federated Learning
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The proliferation of connected devices through Internet connectivity presents both opportunities for smart applications and risks to security and privacy. It is vital to proactively address these concerns to fully leverage the potential of the Internet of Things. IoT services where one data owner serves multiple clients, like smart city transportation, smart building management and healthcare can offer benefits but also bring cybersecurity and data privacy risks. For example, in healthcare, a hospital may collect data from medical devices and make it available to multiple clients such as researchers and pharmaceutical companies. This data can be used to improve medical treatments and research but if not protected, it can also put patients' personal information at risk. To ensure the benefits of these services, it is important to implement proper security and privacy measures. In this paper, we propose a symmetric searchable encryption scheme with dynamic updates on a database that has a single owner and multiple clients for IoT environments. Our proposed scheme supports both forward and backward privacy. Additionally, our scheme supports a decentralized storage environment in which data owners can outsource data across multiple servers or even across multiple service providers to improve security and privacy. Further, it takes a minimum amount of effort and costs to revoke a client's access to our system at any time. The performance and formal security analyses of the proposed scheme show that our scheme provides better functionality, and security and is more efficient in terms of computation and storage than the closely related works.
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A Multi-Client Searchable Encryption Scheme for IoT Environment
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