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We theoretically analyse the equation of topological solitons in a chain of particles interacting via a repulsive power-law potential and confined by a periodic lattice. Starting from the discrete model, we perform a gradient expansion and obtain the kink equation in the continuum limit for a power law exponent $n \ge 1$. The power-law interaction modifies the sine-Gordon equation, giving rise to a rescaling of the coefficient multiplying the second derivative (the kink width) and to an additional integral term. We argue that the integral term does not affect the local properties of the kink, but it governs the behaviour at the asymptotics. The kink behaviour at the center is dominated by a sine-Gordon equation and its width tends to increase with the power law exponent. When the interaction is the Coulomb repulsion, in particular, the kink width depends logarithmically on the chain size. We define an appropriate thermodynamic limit and compare our results with existing studies performed for infinite chains. Our formalism allows one to systematically take into account the finite-size effects and also slowly varying external potentials, such as for instance the curvature in an ion trap.
|
condensed matter
|
Fermi arcs are disconnected contour of Fermi surface, which can be observed in the pseudo-gap phase of high temperature superconductors. Aiming to understand this pseudo-gap phenomena, we study a holographic Fermionic system coupled with a massive scalar field in an AdS black hole background. Depending on the boundary condition on the scalar field mode, we discuss two possible scenarios. When the scalar condenses below a critical temperature $T_c$, the Fermi surface undergoes a transition from normal phase to pseudo-gap phase. Hence $T_c$ can be the reminiscent of well-known cross over temperature $T^*$ in cuprate superconductor, below which pseudo-gap appears at constant doping. In the second scenario, the bulk scalar develops a non-normalizable profile at arbitrary temperature for non-zero source at the boundary. Therefore, we can tune the Fermi spectrum by tuning a dual source at the boundary. The dual source for this case can be the reminiscent of hole doping in the real cuprate superconductor. For both the cases we have studied Fermi spectrum and observed anisotropic gap in the spectral function depending on the model parameter and studied the properties of Fermi arcs across different phases.
|
high energy physics theory
|
In this paper, we consider the wave equation on an n-dimensional simplex with Dirichlet boundary conditions. Our main result is an asymptotic observability identity from any one face of the simplex. The novel aspects of the result are that it is a large-time asymptotic rather than an estimate, and it requires no dynamical assumptions on the billiard flow. The proof uses mainly integrations by parts.
|
mathematics
|
We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on $\mathbb{R}^p$ and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate the curse of dimensionality. Our main symmetry assumption is that the super-level sets of the density are $K$-homothetic (i.e. scalar multiples of a convex body $K \subseteq \mathbb{R}^p$). When $K$ is known, we prove that the $K$-homothetic log-concave maximum likelihood estimator based on $n$ independent observations from such a density has a worst-case risk bound with respect to, e.g., squared Hellinger loss, of $O(n^{-4/5})$, independent of $p$. Moreover, we show that the estimator is adaptive in the sense that if the data generating density admits a special form, then a nearly parametric rate may be attained. We also provide worst-case and adaptive risk bounds in cases where $K$ is only known up to a positive definite transformation, and where it is completely unknown and must be estimated nonparametrically. Our estimation algorithms are fast even when $n$ and $p$ are on the order of hundreds of thousands, and we illustrate the strong finite-sample performance of our methods on simulated data.
|
mathematics
|
We predict the preservation of temporal indistinguishability of photons propagating through helical coupled-resonator optical waveguides (H-CROWs). H-CROWs exhibit a pseudospin-momentum locked dispersion, which we show suppresses onsite disorder-induced backscattering and group velocity fluctuations. We simulate numerically the propagation of two-photon wavepackets, demonstrating that they exhibit almost perfect Hong-Ou-Mandel dip visibility and then can preserve their quantum coherence even in the presence of moderate disorder, in contrast to regular CROWs which are highly sensitive to disorder. As indistinguishability is the most fundamental resource of quantum information processing, H-CROWs may find applications for the implementation of robust optical links and delay lines in the emerging quantum photonic communication and computational platforms.
|
quantum physics
|
Using the Landau-Ginzburg-Devonshire phenomenological approach, we perform finite element modeling of the electric polarization, electric field, and elastic stresses and strains in a core-shell nanoparticle, where the ferroelectric core has a shape of prolate cylinder. Calculations reveal the quadrupolar-type diffuse domain structure consisting of two oppositely oriented diffuse axial domains located near the cylinder ends, which are separated by a region with a zero-axial polarization; we have termed this "flexon" to underline the flexoelectric nature of its axial polarization. Analytical calculations and FEM results have proven that a change of the flexoelectric coefficient sign leads to a reorientation of the flexon axial polarization; as well as an anisotropy of the flexoelectric coupling critically influences the flexon formation and related domain morphology. The flexon polarization forms a drop-shaped region near the ends of the cylinder, with a distinct chiral structure that is determined by the sign of the flexoelectric coupling constant. Its rounded shape, combined with its distinct chiral properties and the localization nature near the surface are reminiscent of a those of Chiral Bobber structures in magnetism. In the azimuthal plane, the flexon displays the polarization state of a meron. We show that this new type of chiral polarization structure is stabilized by an anisotropic flexoelectric coupling. It is important to note that the Lifshitz invariant describing the flexoelectric effect, which couples the electric polarization and elastic strain gradients, plays a determining role in the stabilization of these chiral states. It thereby provides an energetic interaction that, similar to the recently predicted ferroelectric Dyzaloshinskii-Moryia interaction, can lead to the formation of chiral polarization states, and, by extension, ferroelectric skyrmions.
|
condensed matter
|
This paper discusses the boundary feedback stabilization of a reaction-diffusion equation with Robin boundary conditions and in the presence of a time-varying state-delay. The proposed control design strategy is based on a finite-dimensional truncated model obtained via a spectral decomposition. By an adequate selection of the number of modes of the original infinite-dimensional system, we show that the design performed on the finite-dimensional truncated model achieves the exponential stabilization of the original infinite-dimensional system. In the presence of distributed disturbances, we show that the closed-loop system is exponentially input-to-state stable with fading memory.
|
mathematics
|
This paper presents a novel AI-based approach for maximizing time-series available transfer capabilities (ATCs) via autonomous topology control considering various practical constraints and uncertainties. Several AI techniques including supervised learning and deep reinforcement learning (DRL) are adopted and improved to train effective AI agents for achieving the desired performance. First, imitation learning (IL) is used to provide a good initial policy for the AI agent. Then, the agent is trained by DRL algorithms with a novel guided exploration technique, which significantly improves the training efficiency. Finally, an Early Warning (EW) mechanism is designed to help the agent find good topology control strategies for long testing periods, which helps the agent to determine action timing using power system domain knowledge; thus, effectively increases the system error-tolerance and robustness. Effectiveness of the proposed approach is demonstrated in the "2019 Learn to Run a Power Network (L2RPN)" global competition, where the developed AI agents can continuously and safely control a power grid to maximize ATCs without operator's intervention for up to 1-month's operation data and eventually won the first place in both development and final phases of the competition. The winning agent has been open-sourced on GitHub.
|
electrical engineering and systems science
|
High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a non-linear dimensionality reduction technique, to a sample of spectra from the Sloan Digital Sky Survey. In contrast to Principal Component Analysis (PCA), a widely used technique, VAEs can capture non-linear relationships between latent parameters and the data. We find that a VAE can reconstruct the SDSS spectra well with only six latent parameters, outperforming PCA with the same number of components. Different galaxy classes are naturally separated in this latent space, without class labels having been given to the VAE. The VAE latent space is interpretable because the VAE can be used to make synthetic spectra at any point in latent space. For example, making synthetic spectra along tracks in latent space yields sequences of realistic spectra that interpolate between two different types of galaxies. Using the latent space to find outliers may yield interesting spectra: in our small sample, we immediately find unusual data artifacts and stars misclassified as galaxies. In this exploratory work, we show that VAEs create compact, interpretable latent spaces that capture non-linear features of the data. While a VAE takes substantial time to train (~1 day for 48000 spectra), once trained, VAEs can enable the fast exploration of large astronomical data sets.
|
astrophysics
|
QCD Gaussian sum-rules are used to explore the vector ($J^{PC}=1^{--}$) strangeonium hybrid interpretation of the $Y(2175)$. Using a two-resonance model consisting of the $Y(2175)$ and an additional resonance, we find that the relative resonance strength of the $Y(2175)$ in the Gaussian sum-rules is less than 5\% that of a heavier 2.9 GeV state. This small relative strength presents a challenge to a dominantly-hybrid interpretation of the $Y(2175)$.
|
high energy physics phenomenology
|
We consider the following nonlinear Schr\"{o}dinger equation of derivative type: \begin{equation}i \partial_t u + \partial_x^2 u +i |u|^{2} \partial_x u +b|u|^4u=0 , \quad (t,x) \in \mathbb{R}\times\mathbb{R}, \ b \in\mathbb{R}. \end{equation} If $b=0$, this equation is known as a gauge equivalent form of well-known derivative nonlinear Schr\"{o}dinger equation (DNLS), which is mass critical and completely integrable. The equation can be considered as a generalized equation of DNLS while preserving mass criticality and Hamiltonian structure. For DNLS it is known that if the initial data $u_0\in H^1(\mathbb{R})$ satisfies the mass condition $\| u_0\|_{L^2}^2 <4\pi$, the corresponding solution is global and bounded. In this paper we first establish the mass condition on the equation for general $b\in\mathbb{R}$, which is exactly corresponding to $4\pi$-mass condition for DNLS, and then characterize it from the viewpoint of potential well theory. We see that the mass threshold value gives the turning point in the structure of potential wells generated by solitons. In particular, our results for DNLS give a characterization of both $4\pi$-mass condition and algebraic solitons.
|
mathematics
|
Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which makes them more challenging to model and control. The impact of this added complexity to autonomous learning has not been thoroughly explored. This is especially relevant to tendon-driven limbs whose cables and tendons are inevitably elastic. Here, we explored the efficacy of autonomous learning and control on a simulated bio-plausible tendon-driven leg across different tendon stiffness values. We demonstrate that increasing stiffness of the simulated muscles can require more iterations for the inverse map to converge but can then perform more accurately, especially in discrete tasks. Moreover, the system is robust to subsequent changes in muscle stiffnesses and can adapt on-the-go within 5 attempts. Lastly, we test the system for the functional task of locomotion, and found similar effects of muscle stiffness to learning and performance. Given that a range of stiffness values led to improved learning and maximized performance, we conclude the robot bodies and autonomous controllers---at least for tendon-driven systems---can be co-developed to take advantage of elastic elements. Importantly, this opens also the door to development efforts that recapitulate the beneficial aspects of the co-evolution of brains and bodies in vertebrates.
|
computer science
|
The human visual system uses numerous cues for depth perception, including disparity, accommodation, motion parallax and occlusion. It is incumbent upon virtual-reality displays to satisfy these cues to provide an immersive user experience. Multifocal displays, one of the classic approaches to satisfy the accommodation cue, place virtual content at multiple focal planes, each at a di erent depth. However, the content on focal planes close to the eye do not occlude those farther away; this deteriorates the occlusion cue as well as reduces contrast at depth discontinuities due to leakage of the defocus blur. This paper enables occlusion-aware multifocal displays using a novel ConeTilt operator that provides an additional degree of freedom -- tilting the light cone emitted at each pixel of the display panel. We show that, for scenes with relatively simple occlusion con gurations, tilting the light cones provides the same e ect as physical occlusion. We demonstrate that ConeTilt can be easily implemented by a phase-only spatial light modulator. Using a lab prototype, we show results that demonstrate the presence of occlusion cues and the increased contrast of the display at depth edges.
|
electrical engineering and systems science
|
We present a calibration component for the Murchison Widefield Array All-Sky Virtual Observatory (MWA ASVO) utilising a newly developed PostgreSQL database of calibration solutions. Since its inauguration in 2013, the MWA has recorded over thirty-four petabytes of data archived at the Pawsey Supercomputing Centre. According to the MWA Data Access policy, data become publicly available eighteen months after collection. Therefore, most of the archival data are now available to the public. Access to public data was provided in 2017 via the MWA ASVO interface, which allowed researchers worldwide to download MWA uncalibrated data in standard radio astronomy data formats (CASA measurement sets or UV FITS files). The addition of the MWA ASVO calibration feature opens a new, powerful avenue for researchers without a detailed knowledge of the MWA telescope and data processing to download calibrated visibility data and create images using standard radio-astronomy software packages. In order to populate the database with calibration solutions from the last six years we developed fully automated pipelines. A near-real-time pipeline has been used to process new calibration observations as soon as they are collected and upload calibration solutions to the database, which enables monitoring of the interferometric performance of the telescope. Based on this database we present an analysis of the stability of the MWA calibration solutions over long time intervals.
|
astrophysics
|
We consider dark matter (DM) with very weak couplings to the standard model (SM), such that its self-annihilation cross section is much smaller than the canonical one, $\langle\sigma v\rangle_{\chi\chi} \ll 10^{-26}\mathrm{cm}^3/\mathrm{s}$. In this case DM self-annihilation is negligible for the dynamics of freeze-out and DM dilution is solely driven by efficient annihilation of heavier accompanying dark sector particles provided that DM maintains chemical equilibrium with the dark sector. This chemical equilibrium is established by conversion processes which require much smaller couplings to be efficient than annihilation. The chemical decoupling of DM from the SM can either be initiated by the freeze-out of annihilation, resembling a co-annihilation scenario, or of conversion processes, leading to the scenario of conversion-driven freeze-out. We focus on the latter and discuss its distinct phenomenology.
|
high energy physics phenomenology
|
Early detection of head and neck tumors is crucial for patient survival. Often, diagnoses are made based on endoscopic examination of the larynx followed by biopsy and histological analysis, leading to a high inter-observer variability due to subjective assessment. In this regard, early non-invasive diagnostics independent of the clinician would be a valuable tool. A recent study has shown that hyperspectral imaging (HSI) can be used for non-invasive detection of head and neck tumors, as precancerous or cancerous lesions show specific spectral signatures that distinguish them from healthy tissue. However, HSI data processing is challenging due to high spectral variations, various image interferences, and the high dimensionality of the data. Therefore, performance of automatic HSI analysis has been limited and so far, mostly ex-vivo studies have been presented with deep learning. In this work, we analyze deep learning techniques for in-vivo hyperspectral laryngeal cancer detection. For this purpose we design and evaluate convolutional neural networks (CNNs) with 2D spatial or 3D spatio-spectral convolutions combined with a state-of-the-art Densenet architecture. For evaluation, we use an in-vivo data set with HSI of the oral cavity or oropharynx. Overall, we present multiple deep learning techniques for in-vivo laryngeal cancer detection based on HSI and we show that jointly learning from the spatial and spectral domain improves classification accuracy notably. Our 3D spatio-spectral Densenet achieves an average accuracy of 81%.
|
electrical engineering and systems science
|
The controllability of the linearized KdV equation with right Neumann control is studied in the pioneering work of Rosier [25]. However, the proof is by contradiction arguments and the value of the observability constant remains unknown, though rich mathematical theories are built on this totally unknown constant. We introduce a constructive method that gives the quantitative value of this constant.
|
mathematics
|
Heckman selection model is perhaps the most popular econometric model in the analysis of data with sample selection. The analyses of this model are based on the normality assumption for the error terms, however, in some applications, the distribution of the error term departs significantly from normality, for instance, in the presence of heavy tails and/or atypical observation. In this paper, we explore the Heckman selection-t model where the random errors follow a bivariate Student's-t distribution. We develop an analytically tractable and efficient EM-type algorithm for iteratively computing maximum likelihood estimates of the parameters, with standard errors as a by-product. The algorithm has closed-form expressions at the E-step, that rely on formulas for the mean and variance of the truncated Student's-t distributions. Simulations studies show the vulnerability of the Heckman selection-normal model, as well as the robustness aspects of the Heckman selection-t model. Two real examples are analyzed, illustrating the usefulness of the proposed methods. The proposed algorithms and methods are implemented in the new R package HeckmanEM.
|
statistics
|
A graphical model is an undirected network representing the conditional independence properties between random variables. Graphical modeling has become part and parcel of systems or network approaches to multivariate data, in particular when the variable dimension exceeds the observation dimension. rags2ridges is an R package for graphical modeling of high-dimensional precision matrices. It provides a modular framework for the extraction, visualization, and analysis of Gaussian graphical models from high-dimensional data. Moreover, it can handle the incorporation of prior information as well as multiple heterogeneous data classes. As such, it provides a one-stop-shop for graphical modeling of high-dimensional precision matrices. The functionality of the package is illustrated with an example dataset pertaining to blood-based metabolite measurements in persons suffering from Alzheimer's Disease.
|
statistics
|
We study the one-band Hubbard model on the honeycomb lattice using a combination of quantum Monte Carlo (QMC) simulations and static as well as dynamical mean-field theory (DMFT). This model is known to show a quantum phase transition between a Dirac semi-metal and the antiferromagnetic insulator. The aim of this article is to provide a detailed comparison between these approaches by computing static properties, notably ground-state energy, single-particle gap, double occupancy, and staggered magnetization, as well as dynamical quantities such as the single-particle spectral function. At the static mean-field level local moments cannot be generated without breaking the SU(2) spin symmetry. The DMFT approximation accounts for temporal fluctuations, thus captures both the evolution of the double occupancy and the resulting local moment formation in the paramagnetic phase. As a consequence, the DMFT approximation is found to be very accurate in the Dirac semi-metallic phase where local moment formation is present and the spin correlation length small. However, in the vicinity of the fermion quantum critical point the spin correlation length diverges and the spontaneous SU(2) symmetry breaking leads to low-lying Goldstone modes in the magnetically ordered phase. The impact of these spin fluctuations on the single-particle spectral function -- \textit{waterfall} features and narrow spin-polaron bands -- is only visible in the lattice QMC approach.
|
condensed matter
|
The thermal structure of protoplanetary disks is a fundamental characteristic of the system that has wide reaching effects on disk evolution and planet formation. In this study, we constrain the 2D thermal structure of the protoplanetary disk TW Hya structure utilizing images of seven CO lines. This includes new ALMA observations of 12CO J=2-1 and C18O J=2-1 as well as archival ALMA observations of 12CO J=3-2, 13CO J=3-2, 6-5, C18O J= 3-2, 6-5. Additionally, we reproduce a Herschel observation of the HD J=1-0 line flux, the spectral energy distribution, and utilize a recent quantification of CO radial depletion in TW Hya. These observations were modeled using the thermochemical code RAC2D, and our best fit model reproduces all spatially resolved CO surface brightness profiles. The resulting thermal profile finds a disk mass of 0.025 Msun and a thin upper layer of gas depleted of small dust with a thickness of approx 1.2% of the corresponding radius. Using our final thermal structure, we find that CO alone is not a viable mass tracer as its abundance is degenerate with the total H2 surface density. Different mass models can readily match the spatially resolved CO line profiles with disparate abundance assumptions. Mass determination requires additional knowledge and, in this work, HD provides the additional constraint to derive the gas mass and supports the inference of CO depletion in the TW Hya disk. Our final thermal structure confirms the use of HD as a powerful probe of protoplanetary disk mass. Additionally, the method laid out in this paper is an employable strategy for extraction of disk temperatures and masses in the future.
|
astrophysics
|
We consider the problem of estimating causal DAG models from a mix of observational and interventional data, when the intervention targets are partially or completely unknown. This problem is highly relevant for example in genomics, since gene knockout technologies are known to have off-target effects. We characterize the interventional Markov equivalence class of DAGs that can be identified from interventional data with unknown intervention targets. In addition, we propose a provably consistent algorithm for learning the interventional Markov equivalence class from such data. The proposed algorithm greedily searches over the space of permutations to minimize a novel score function. The algorithm is nonparametric, which is particularly important for applications to genomics, where the relationships between variables are often non-linear and the distribution non-Gaussian. We demonstrate the performance of our algorithm on synthetic and biological datasets. Links to an implementation of our algorithm and to a reproducible code base for our experiments can be found at https://uhlerlab.github.io/causaldag/utigsp.
|
statistics
|
We find a new class of N=1 no-scale supergravity models with F- and D-term supersymmetry breaking, using a new Fayet-Iliopoulos term. The minimal setup contains one U(1) vector multiplet and one neutral chiral multiplet parametrizing SL(2,R)/U(1) manifold, with constant superpotential and linear gauge kinetic function. In our construction the FI term is field-dependent, and one can obtain flat vanishing potential (Minkowski vacuum) with broken SUSY, and global SL(2,R) invariance (self-duality) of the bosonic equations of motion. The spectrum of the model includes a massive spin-1/2 field as well as a vector, a scalar, and a pseudo-scalar -- all classically massless. We discuss several modifications/extensions of the model as well as the introduction of matter fields. We also find a two-field extension of already existing no-scale model.
|
high energy physics theory
|
We compare several different ways of measuring the energy resolution for meV-resolved inelastic x-ray scattering (IXS): using scattering from poly(methyl methacrylate), PMMA, using scattering from borosilicate glass (Tempax), and using powder diffraction from aluminum. All of these methods provide a reasonable first approximation to the energy resolution, but, also, in all cases, inelastic contributions appear over some range of energy transfers. Over a range of +-15 meV energy transfer there is good agreement between the measurements of PMMA and Tempax at low temperature, and room temperature powder diffraction from aluminum so we consider this to be a good indication of the true resolution of our ~1.3 meV spectrometer. We self-consistently determine the resolution over a wider energy range using the temperature, momentum and sample dependence of the measured response. We then quantitatively investigate the inelastic contributions from the PMMA and Tempax, and their dependence on momentum transfer and temperature. The resulting data allows us to determine the resolution of our multi-analyzer array efficiently using a single scan. We demonstrate the importance of this procedure by showing that the results of the analysis of a spectrum from a glass are changed by using the properly deconvolved resolution function. We also discuss the impact of radiation damage on the scattering from PMMA and Tempax.
|
condensed matter
|
This paper shows the susceptibility of spectrogram-based audio classifiers to adversarial attacks and the transferability of such attacks to audio waveforms. Some commonly used adversarial attacks to images have been applied to Mel-frequency and short-time Fourier transform spectrograms, and such perturbed spectrograms are able to fool a 2D convolutional neural network (CNN). Such attacks produce perturbed spectrograms that are visually imperceptible by humans. Furthermore, the audio waveforms reconstructed from the perturbed spectrograms are also able to fool a 1D CNN trained on the original audio. Experimental results on a dataset of western music have shown that the 2D CNN achieves up to 81.87% of mean accuracy on legitimate examples and such performance drops to 12.09% on adversarial examples. Likewise, the 1D CNN achieves up to 78.29% of mean accuracy on original audio samples and such performance drops to 27.91% on adversarial audio waveforms reconstructed from the perturbed spectrograms.
|
computer science
|
For each nonzero $h\in \mathbb{F}[x]$, where $\mathbb{F}$ is a field, let $\mathsf{A}_h$ be the unital associative algebra generated by elements $x,y$, satisfying the relation $yx-xy = h$. This gives a parametric family of subalgebras of the Weyl algebra $\mathsf{A}_1$, containing many well-known algebras which have previously been studied independently. In this paper, we give a full description the Hochschild cohomology $\mathsf{HH}^\bullet(\mathsf{A}_h)$ over a field of arbitrary characteristic. In case $\mathbb{F}$ has positive characteristic, the center of $\mathsf{A}_h$ is nontrivial and we describe $\mathsf{HH}^\bullet(\mathsf{A}_h)$ as a module over its center. The most interesting results occur when $\mathbb{F}$ has characteristic $0$. In this case, we describe $\mathsf{HH}^\bullet(\mathsf{A}_h)$ as a module over the Lie algebra $\mathsf{HH}^1(\mathsf{A}_h)$ and find that this action is closely related to the intermediate series modules over the Virasoro algebra. We also determine when $\mathsf{HH}^\bullet(\mathsf{A}_h)$ is a semisimple $\mathsf{HH}^1(\mathsf{A})$-module.
|
mathematics
|
Transition metal (TM) defects in silicon carbide have favorable spin coherence properties and are suitable as quantum memory for quantum communication. To characterize TM defects as quantum spin-photon interfaces, we model defects that have one active electron with spin 1/2 in the atomic $D$ shell. The spin structure, as well as the magnetic and optical resonance properties of the active electron emerge from the interplay of the crystal potential and spin-orbit coupling and are described by a general model derived using group theory. We find that the spin-orbit coupling leads to additional allowed transitions and a modification of the $g$-tensor. To describe the dependence of the Rabi frequency on the magnitude and direction of the static and driving fields, we derive an effective Hamiltonian. This theoretical description can also be instrumental to perform and optimize spin control in TM defects.
|
condensed matter
|
Large scale discrete uniform and homogeneous $P$-values often arise in applications with multiple testing. For example, this occurs in genome wide association studies whenever a nonparametric one-sample (or two-sample) test is applied throughout the gene loci. In this paper we consider $q$-values for such scenarios based on several existing estimators for the proportion of true null hypothesis, $\pi_0$, which take the discreteness of the $P$-values into account. The theoretical guarantees of the several approaches with respect to the estimation of $\pi_0$ and the false discovery rate control are reviewed. The performance of the discrete $q$-values is investigated through intensive Monte Carlo simulations, including location, scale and omnibus nonparametric tests, and possibly dependent $P$-values. The methods are applied to genetic and financial data for illustration purposes too. Since the particular estimator of $\pi_0$ used to compute the $q$-values may influence the power, relative advantages and disadvantages of the reviewed procedures are discussed. Practical recommendations are given.
|
statistics
|
Flexible and tensile wavelength-tunable micrometer-sized random lasers in the form of microporous polymer fiber are demonstrated. The fibers are fabricated by directly drawing from an aqueous mixture and subsequently selective chemical etching in a green solvent. Size-dependent lasing threshold and spectrum are investigated. Owing to mechanical flexibility, wavelength-tuning of 5.5 nm is achieved by stretching the fiber.
|
physics
|
Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect of building a privacy-centric non-intrusive smart assistant. In this paper, we address the task of false trigger mitigation (FTM) using a novel approach based on analyzing automatic speech recognition (ASR) lattices using graph neural networks (GNN). The proposed approach uses the fact that decoding lattice of a falsely triggered audio exhibits uncertainties in terms of many alternative paths and unexpected words on the lattice arcs as compared to the lattice of a correctly triggered audio. A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant. We deploy two variants of GNNs in this paper based on 1) graph convolution layers and 2) self-attention mechanism respectively. Our experiments demonstrate that GNNs are highly accurate in FTM task by mitigating ~87% of false triggers at 99% true positive rate (TPR). Furthermore, the proposed models are fast to train and efficient in parameter requirements.
|
electrical engineering and systems science
|
A central problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce a new, data-driven method for parameterizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local \emph{gradient} of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.
|
physics
|
Motivated by the conjectures formulated in 2003 by Tun\c{c}el et al., we study interlacing properties of the eigenvalues of $A\otimes B + B\otimes A$ for pairs of $n$-by-$n$ matrices $A, B$. We prove that for every pair of symmetric matrices (and skew-symmetric matrices) with one of them at most rank two, the \emph{odd spectrum} (those eigenvalues determined by skew-symmetric eigenvectors) of $A\otimes B + B\otimes A$ interlaces its \emph{even spectrum} (those eigenvalues determined by symmetric eigenvectors). Using this result, we also show that when $n \leq 3$, the odd spectrum of $A\otimes B + B\otimes A$ interlaces its even spectrum for every pair $A, B$. The interlacing results also specify the structure of the eigenvectors corresponding to the extreme eigenvalues. In addition, we identify where the conjecture(s) and some interlacing properties hold for a number of structured matrices. We settle the conjectures of Tun\c{c}el et al. and show they fail for some pairs of symmetric matrices $A, B$, when $n\geq 4$ and the ranks of $A$ and $B$ are at least $3$.
|
mathematics
|
In observational studies, we are usually interested in estimating causal effects between treatments and outcomes. When some covariates are not observed, an unbiased estimator usually cannot be obtained. In this paper, we focus on instrumental variable (IV) methods. By using IVs, an unbiased estimator for causal effects can be estimated even if there exists some unmeasured covariates. Constructing a linear combination of IVs solves weak IV problems, however, there are risks estimating biased causal effects by including some invalid IVs. In this paper, we use Negative Control Outcomes as auxiliary variables to select valid IVs. By using NCOs, there are no necessity to specify not only the set of valid IVs but also invalid one in advance: this point is different from previous methods. We prove that the estimated causal effects has the same asymptotic variance as the estimator using Generalized Method of Moments that has the semiparametric efficiency. Also, we confirm properties of our method and previous methods through simulations.
|
statistics
|
We investigate the exotic $\Omega\Omega$ dibaryon states with $J^P=0^+$ and $2^+$ in a molecular picture. We construct the scalar and tensor $\Omega$$\Omega$ molecular interpolating currents and calculate their masses within the method of QCD sum rules. Our results indicate that the mass of the scalar dibaryon state is $m_{\Omega\Omega, \, 0^+}=(3.33\pm0.22) \,\unit$, which is about $15 \,\mathrm{MeV}$ below the $2m_\Omega$ threshold. This result suggests the existence of a loosely bound molecular state of the $J^P=0^+$ scalar $\Omega\Omega$ dibaryon with a small binding energy around 15 MeV. The mass of the tensor dibaryon is predicted to be $m_{\Omega\Omega,\, 2^+}=(3.24\pm0.23)\, \mbox{GeV}$, which may imply a deeper molecular state of the tensor $\Omega\Omega$ dibaryon than the scalar channel. These exotic strangeness $S=-6$ and doubly-charged $\Omega\Omega$ dibaryon states may be identified in the heavy-ion collision processes.
|
high energy physics phenomenology
|
We present spectro-polarimetric analysis of \thisgrb\ using data from \asat, \fermi, and \swift, to provide insights into the physical mechanisms of the prompt radiation and the jet geometry. Prompt emission from \thisgrb\ was very bright (fluence $>10^{-4}$~ergs~cm$^{-2}$) and had a complex structure composed of the superimposition of several pulses. The energy spectra deviate from the typical Band function to show a low energy peak $\sim 15$~keV --- which we interpret as a power-law with two breaks, with a synchrotron origin. Alternately, the prompt spectra can also be interpreted as Comptonized emission, or a blackbody combined with a Band function. Time-resolved analysis confirms the presence of the low energy component, while the peak energy is found to be confined in the range of 100--200~keV. Afterglow emission detected by \fermi-LAT is typical of an external shock model, and we constrain the initial Lorentz factor using the peak time of the emission. \swift-XRT measurements of the afterglow show an indication for a jet break, allowing us to constrain the jet opening angle to $>$ 6$\degr$. Detection of a large number of Compton scattered events by \asat-CZTI provides an opportunity to study hard X-ray polarization of the prompt emission. We find that the burst has high, time-variable polarization, with the emission {\bf have higher polarization} at energies above the peak energy. We discuss all observations in the context of GRB models and polarization arising due to {\bf due to physical or geometric effects:} synchrotron emission from multiple shocks with ordered or random magnetic fields, Poynting flux dominated jet undergoing abrupt magnetic dissipation, sub-photospheric dissipation, a jet consisting of fragmented fireballs, and the Comptonization model.
|
astrophysics
|
The Quantum Approximate Optimization Algorithm can naturally be applied to combinatorial search problems on graphs. The quantum circuit has p applications of a unitary operator that respects the locality of the graph. On a graph with bounded degree, with p small enough, measurements of distant qubits in the state output by the QAOA give uncorrelated results. We focus on finding big independent sets in random graphs with dn/2 edges keeping d fixed and n large. Using the Overlap Gap Property of almost optimal independent sets in random graphs, and the locality of the QAOA, we are able to show that if p is less than a d-dependent constant times log n, the QAOA cannot do better than finding an independent set of size .854 times the optimal for d large. Because the logarithm is slowly growing, even at one million qubits we can only show that the algorithm is blocked if p is in single digits. At higher p the algorithm "sees" the whole graph and we have no indication that performance is limited.
|
quantum physics
|
In this paper we present an approach to determine the smallest possible number of neurons in a layer of a neural network in such a way that the topology of the input space can be learned sufficiently well. We introduce a general procedure based on persistent homology to investigate topological invariants of the manifold on which we suspect the data set. We specify the required dimensions precisely, assuming that there is a smooth manifold on or near which the data are located. Furthermore, we require that this space is connected and has a commutative group structure in the mathematical sense. These assumptions allow us to derive a decomposition of the underlying space whose topology is well known. We use the representatives of the $k$-dimensional homology groups from the persistence landscape to determine an integer dimension for this decomposition. This number is the dimension of the embedding that is capable of capturing the topology of the data manifold. We derive the theory and validate it experimentally on toy data sets.
|
statistics
|
The Swampland de Sitter conjecture in combination with upper limits on the tensor-to-scalar ratio $r$ derived from observations of the cosmic microwave background endangers the paradigm of slow-roll single field inflation. This conjecture constrains the first and the second derivatives of the inflationary potential in terms of two ${\cal O} (1)$ constants $c$ and $c'$. In view of these restrictions we reexamine single-field inflationary potentials with $S$-duality symmetry, which ameliorate the unlikeliness problem of the initial condition. We compute $r$ at next-to-leading order in slow-roll parameters for the most general form of $S$-dual potentials and confront model predictions to constraints imposed by the de Sitter conjecture. We find that $c \sim {\cal O} (10^{-1})$ and $c' \sim {\cal O} (10^{-2})$ can accommodate the 95\% CL upper limit on $r$. By imposing at least 50 $e$-folds of inflation with the effective field theory description only valid over a field displacement ${\cal O} (1)$ when measured as a distance in the target space geometry, we further restrict $c \sim {\cal O} (10^{-2})$, while the constraint on $c'$ remains unchanged. We comment on how to accommodate the required small values of $c$ and $c'$.
|
high energy physics theory
|
Novel categories of electronic devices and quantum materials are obtained by pipelining the unitary evolution of electron quantum states as described by Schroedinger's equation with non-unitary processes that interrupt the coherent propagation of electrons. These devices and materials reside in the fascinating transition regime between quantum mechanics and classical physics. The devices are designed such that a nonreciprocal unitary state evolution is achieved by means of a broken inversion symmetry, for example as induced at material interfaces. This coherent state evolution is interrupted by individual inelastic scattering events caused by defects coupled to an environment. Two-terminal non-unitary quantum devices, for example, feature nonreciprocal conductance in linear response. Thus, they are exemptions to Onsager's reciprocal relation, and they challenge the second law of thermodynamics. Implementing the device function into the unit cells of materials or meta-materials yields novel functionalities in 2D and 3D materials, at interfaces, and in heterostructures.
|
condensed matter
|
Interacting Bosons, loaded in artificial lattices, have emerged as a modern platform to explore collective manybody phenomena, quantum phase transitions and exotic phases of matter as well as to enable advanced on chip simulators. Such experiments strongly rely on well-defined shaping the potential landscape of the Bosons, respectively Bosonic quasi-particles, and have been restricted to cryogenic, or even ultra-cold temperatures. On chip, the GaAs-based exciton-polariton platform emerged as a promising system to implement and study bosonic non-linear systems in lattices, yet demanding cryogenic temperatures. In our work, we discuss the first experiment conducted on a polaritonic lattice at ambient conditions: We utilize fluorescent proteins as an excitonic gain material, providing ultra-stable Frenkel excitons. We directly take advantage of their soft nature by mechanically shaping them in the photonic one-dimensional lattice. We demonstrate controlled loading of the condensate in distinct orbital lattice modes of different symmetries, and finally explore, as an illustrative example, the formation of a gap solitonic mode, driven by the interplay of effective interaction and negative effective mass in our lattice. The observed phenomena in our open dissipative system are comprehensively scrutinized by a nonequilibrium model of polariton condensation. We believe, that this work is establishing the organic polariton platform as a serious contender to the well-established GaAs platform for a wide range of applications relying on coherent Bosons in lattices, given its unprecedented flexibility, cost effectiveness and operation temperature.
|
condensed matter
|
Coherent errors in a quantum system can, in principle, build up much more rapidly than incoherent errors, accumulating as the square of the number of qubits in the system rather than linearly. I show that only channels dominated by a unitary rotation can display such behavior. A maximally sensitive set of states is a set such that if a channel is capable of quadratic error scaling, then it is present for at least one sequence of states in the set. I show that the GHZ states in the X, Y, and Z bases form a maximally sensitive set of states, allowing a straightforward test to identify coherent errors in a system. This allows us to identify coherent errors in gates and measurements to within a constant fraction of the maximum possible sensitivity to such errors. A related protocol with simpler circuits but less sensitivity can also be used to test for coherent errors in state preparation or if the noise in a particular circuit is accumulating coherently or not.
|
quantum physics
|
This paper is devoted to providing a unifying approach to the study of the uniqueness of unconditional bases, up to equivalence and permutation, of infinite direct sums of quasi-Banach spaces. Our new approach to this type of problem permits us to show that a wide class of vector-valued sequence spaces have a unique unconditional basis up to a permutation. In particular, solving a problem from [F. Albiac and C. Ler\'anoz, Uniqueness of unconditional bases in nonlocally convex $\ell_1$-products, J. Math. Anal. Appl. 374 (2011), no. 2, 394--401] we show that if $X$ is quasi-Banach space with a strongly absolute unconditional basis then the infinite direct sum $\ell_{1}(X)$ has a unique unconditional basis up to a permutation, even without knowing whether $X$ has a unique unconditional basis or not. Applications to the uniqueness of unconditional structure of infinite direct sums of non-locally convex Orlicz and Lorentz sequence spaces, among other classical spaces, are also obtained as a by-product of our work.
|
mathematics
|
In seesaw mechanism, if right handed (RH) neutrino masses are generated dynamically by a gauged $U(1)$ symmetry breaking, a stochastic gravitational wave background (SGWB) sourced by a cosmic string network could be a potential probe of leptogenesis. We show that the leptogenesis mechanism that facilitates the dominant production of lepton asymmetry via the quantum effects of right-handed neutrinos in gravitational background, can be probed by GW detectors as well as next-generation neutrinoless double beta decay ($0\nu\beta\beta$) experiments in a complementary way. We infer that for a successful leptogenesis, an exclusion limit on $f-\Omega_{\rm GW}h^2$ plane would correspond to an exclusion on the $|m_{\beta\beta}|-m_1$ plane as well. We consider a normal light neutrino mass ordering and discuss how recent NANOGrav pulsar timing data (if interpreted as GW signal) e.g., at 95$\%$ CL, would correlate with the potential discovery or null signal in $0\nu\beta\beta$ decay experiments.
|
high energy physics phenomenology
|
In this paper, we address the problem of adaptive learning for autoregressive moving average (ARMA) model in the quaternion domain. By transforming the original learning problem into a full information optimization task without explicit noise terms, and then solving the optimization problem using the gradient descent and the Newton analogues, we obtain two online learning algorithms for the quaternion ARMA. Furthermore, regret bound analysis accounting for the specific properties of quaternion algebra is presented, which proves that the performance of the online algorithms asymptotically approaches that of the best quaternion ARMA model in hindsight.
|
statistics
|
We consider the implications of an ultra-light fermionic dark matter candidate that carries baryon number. This naturally arises if dark matter has a small charge under standard model baryon number whilst having an asymmetry equal and opposite to that in the visible universe. A prototypical model is a theory of dark baryons charged under a non-Abelian gauge group, i.e., a dark Quantum Chromo-Dynamics (QCD). For sub-eV dark baryon masses, the inner region of dark matter halos is naturally at 'nuclear density', allowing for the formation of exotic states of matter, akin to neutron stars. The Tremaine-Gunn lower bound on the mass of fermionic dark matter, i.e., the dark baryons, is violated by the strong short-range self-interactions, cooling via emission of light dark pions, and the Cooper pairing of dark quarks that occurs at densities that are high relative to the (ultra-low) dark QCD scale. We develop the astrophysics of these STrongly-interacting Ultra-light Millicharged Particles (STUMPs) utilizing the equation of state of dense quark matter, and find halo cores consistent with observations of dwarf galaxies. These cores are prevented from core-collapse by pressure of the 'neutron star', which suggests ultra-light dark QCD as a resolution to core-cusp problem of collisionless cold dark matter. The model is distinguished from ultra-light bosonic dark matter through through direct detection and collider signatures, as well as by phenomena associated with superconductivity, such as Andreev reflection and superconducting vortices.
|
astrophysics
|
In the era of multimedia and Internet, people are eager to obtain information from offline to online. Quick Response (QR) codes and digital watermarks help us access information quickly. However, QR codes look ugly and invisible watermarks can be easily broken in physical photographs. Therefore, this paper proposes a novel method to embed hyperlinks into natural images, making the hyperlinks invisible for human eyes but detectable for mobile devices. Our method is an end-to-end neural network with an encoder to hide information and a decoder to recover information. From original images to physical photographs, camera imaging process will introduce a series of distortion such as noise, blur, and light. To train a robust decoder against the physical distortion from the real world, a distortion network based on 3D rendering is inserted between the encoder and the decoder to simulate the camera imaging process. Besides, in order to maintain the visual attraction of the image with hyperlinks, we propose a loss function based on just noticeable difference (JND) to supervise the training of encoder. Experimental results show that our approach outperforms the previous method in both simulated and real situations.
|
electrical engineering and systems science
|
We argue for an exponential bound characterizing the chaotic properties of modular Hamiltonian flow of QFT subsystems. In holographic theories, maximal modular chaos is reflected in the local Poincare symmetry about a Ryu-Takayanagi surface. Generators of null deformations of the bulk extremal surface map to modular scrambling modes -positive CFT operators saturating the bound- and their algebra probes the bulk Riemann curvature, clarifying the modular Berry curvature proposal of arXiv:1903.04493.
|
high energy physics theory
|
We consider RG interfaces for boundary RG flows in two-dimensional QFTs. Such interfaces are particular boundary condition changing operators linking the UV and IR conformal boundary conditions. We refer to them as RG operators. In this paper we study their general properties putting forward a number of conjectures. We conjecture that an RG operator is always a conformal primary such that the OPE of this operator with its conjugate must contain the perturbing UV operator when taken in one order and the leading irrelevant operator (when it exists) along which the flow enters the IR fixed point, when taken in the other order. We support our conjectures by perturbative calculations for flows between nearby fixed points, by a non-perturbative variational method inspired by the variational method proposed by J.~Cardy for massive RG flows, and by numerical results obtained using boundary TCSA. The variational method has a merit of its own as it can be used as a first approximation in charting the global structure of the space of boundary RG flows. We also discuss the role of the RG operators in the transport of states and local operators. Some of our considerations can be generalised to two-dimensional bulk flows, clarifying some conceptual issues related to the RG interface put forward by D.~Gaiotto for bulk $\phi_{1,3}$ flows.
|
high energy physics theory
|
We show that the cop number of toroidal graphs is at most 3. This resolves a conjecture by Schroeder from 2001 which is implicit in a question by Andreae from 1986.
|
mathematics
|
With the advent of robot-assisted surgery, the role of data-driven approaches to integrate statistics and machine learning is growing rapidly with prominent interests in objective surgical skill assessment. However, most existing work requires translating robot motion kinematics into intermediate features or gesture segments that are expensive to extract, lack efficiency, and require significant domain-specific knowledge. We propose an analytical deep learning framework for skill assessment in surgical training. A deep convolutional neural network is implemented to map multivariate time series data of the motion kinematics to individual skill levels. We perform experiments on the public minimally invasive surgical robotic dataset, JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS). Our proposed learning model achieved a competitive accuracy of 92.5%, 95.4%, and 91.3%, in the standard training tasks: Suturing, Needle-passing, and Knot-tying, respectively. Without the need of engineered features or carefully-tuned gesture segmentation, our model can successfully decode skill information from raw motion profiles via end-to-end learning. Meanwhile, the proposed model is able to reliably interpret skills within 1-3 second window, without needing an observation of entire training trial. This study highlights the potentials of deep architectures for an proficient online skill assessment in modern surgical training.
|
computer science
|
There are intensive efforts searching for new phenomena in many present and future scientific experiments such as LHC at CERN, CLIC, ILC and many others. These new signals are usually rare and frequently contaminated by many different background events. Starting from the concept of profile likelihood we obtain what can be called a profile $\chi^2$-function for counting experiments which has no background parameters to be fitted. Signal and background statistical fluctuations are automatically taking in account even when the content of some bins are zero. This paper analyzes the profile $\chi^2$-function for fitting binned data in counting experiment when signal and background events obey Poisson statistics. The background events are estimated previously, either by Monte Carlo events, ``idle" run events or any other reasonable way. The here studied method applies only when the background and signal are completely independent events, i.e, they are non-coherent events. The profile $\chi^2$-function has shown to have a fast convergence, with fewer events, to the ``true'' values for counting experiments as shown in MC toy tests. It works properly even when the bin contents are low and also when the signal to background ratio is small. Other interesting points are also presented and discussed. One of them is that the background parameter does not need to be estimated with very high precision even when there are few signal events during a fitting procedure. An application to Higgs boson discovery is discussed using previously published ATLAS/LHC experiment data.
|
high energy physics phenomenology
|
We propose a novel modeling framework to study the effect of covariates of various types on the conditional distribution of the response. The methodology accommodates flexible model structure, allows for joint estimation of the quantiles at all levels, and involves a computationally efficient estimation algorithm. Extensive numerical investigation confirms good performance of the proposed method. The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate).
|
statistics
|
Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to \textit{explain} black box models, rather than creating models that are \textit{interpretable} in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
|
statistics
|
This paper explores the impacts of decarbonisation of heat on demand and subsequently on the generation capacity required to secure against system adequacy standards. Gas demand is explored as a proxy variable for modelling the electrification of heating demand in existing housing stock, with a focus on impacts on timescales of capacity markets (up to four years ahead). The work considers the systemic changes that electrification of heating could introduce, including biases that could be introduced if legacy modelling approaches continue to prevail. Covariates from gas and electrical regression models are combined to form a novel, time-collapsed system model, with demand-weather sensitivities determined using lasso-regularized linear regression. It is shown, using a GB case study with one million domestic heat pump installations per year, that the sensitivity of electrical system demand to temperature (and subsequently sensitivities to cold/warm winter seasons) could increase by 50% following four years of heat demand electrification. A central estimate of 1.75 kW additional peak demand per heat pump is estimated, with variability across three published heat demand profiles leading to a range of more than 14 GW in the most extreme cases. It is shown that the legacy approach of scaling historic demand, as compared to the explicit modelling of heat, could lead to over-procurement of 0.79 GW due to bias in estimates of additional capacity to secure. Failure to address this issue could lead to {\pounds}100m overspend on capacity over ten years.
|
statistics
|
In this paper, a modified Gerchberg Saxton algorithm for generating improved robust binary hologram is presented.
|
electrical engineering and systems science
|
Combining the recent developments of the observations of $\Omega$ sates we calculate the $\Omega$ spectrum up to the $N=2$ shell within a nonrelativistic constituent quark potential model. Furthermore, the strong and radiative decay properties for the $\Omega$ resonances within the $N=2$ shell are evaluated by using the masses and wave functions obtained from the potential model. It is found that the newly observed $\Omega(2012)$ resonance is most likely to be the spin-parity $J^P=3/2^-$ $1P$-wave state $\Omega(1^{2}P_{3/2^{-}})$, it also has a large potential to be observed in the $\Omega(1672)\gamma$ channel. Our calculation shows that the 1$P$-, 1$D$-, and 2$S$-wave $\Omega$ baryons have a relatively narrow decay width of less than 50 MeV. Based on the obtained decay properties and mass spectrum, we further suggest optimum channels and mass regions to find the missing $\Omega$ resonances via the strong and/or radiative decay processes.
|
high energy physics phenomenology
|
In this paper, we apply the Logistic PCA (LPCA) as a dimensionality reduction tool for visualizing patterns and characterizing the relevance of mathematics abilities from a given population measured by a large-scale assessment. We establish an equivalence of parameters between LPCA, Inner Product Representation (IPR) and the two paramenter logistic model (2PL) from the Item Response Theory (IRT). This equivalence provides three complemetary ways of looking at data that assists professionals in education to perform in-context interpretations. Particularly, we analyse the data collected from SPAECE, a large-scale assessment in Mathematics that has been applied yearly in the public educational system of the state of Cear\'a, Brazil. As the main result, we show that the the poor performance of examinees in the end of middle school is primarily caused by their disabilities in number sense.
|
statistics
|
Quantum coherence is a prime resource in quantum computing and quantum communication. Quantum coherence of an arbitrary qubit state can be created at a remote location using maximally entangled state, local operation and classical communication. However, if there is a noisy channel acting on one side of the shared resource, then, it is not possible to create perfect quantum coherence remotely. Here, we present a method for the creation of quantum coherence at a remote location via the use of entangled state and indefinite causal order. We show this specifically for the superposition of two completely depolarizing channels, two partially depolarizing channels and one completely depolarizing channel along with a unitary operator. We find that when the indefinite causal order of channels act on one-half of the entangled pair, then the shared state looses entanglement, but can retain non-zero quantum discord. This finding may have some interesting applications on its own where discord can be consumed as a resource. Our results suggest that the indefinite causal order along with a tiny amount of quantum discord can act as a resource in creating non-zero quantum coherence in the absence of entanglement.
|
quantum physics
|
We develop a very general version of the hyperbola method which extends the known method by Blomer and Br\"udern for products of projective spaces to a very large class of toric varieties. We use it to count Campana points of bounded log-anticanonical height on many split toric $\mathbb{Q}$-varieties with torus invariant boundary. We apply the strong duality principle in linear programming to show the compatibility of our results with the conjectured asymptotic.
|
mathematics
|
We study gravitational perturbations of electrically charged black holes in (3+1)-dimensional Einstein-Born-Infeld gravity with a positive cosmological constant. For the axial perturbations, we obtain a set of decoupled Schrodinger-type equations, whose formal expressions, in terms of metric functions, are the same as those without cosmological constant, corresponding to the Regge-Wheeler equation in the proper limit. We compute the quasi-normal modes (QNMs) of the decoupled perturbations using the Schutz-Iyer-Will's WKB method. We discuss the stability of the charged black holes by investigating the dependence of quasi-normal frequencies on the parameters of the theory, correcting some errors in the literature. It is found that all the axial perturbations are stable for the cases where the WKB method applies. There are cases where the conventional WKB method does not apply, like the three-turning-points problem, so that a more generalized formalism is necessary for studying their QNMs and stabilities. We find that, for the degenerate horizons with the "point-like" horizons at the origin, the QNMs are quite long-lived, close to the quasi-resonance modes, in addition to the "frozen" QNMs for the Nariai-type horizons and the usual (short-lived) QNMs for the extremal black hole horizons. This is a genuine effect of the branch which does not have the general relativity limit. We also study the exact solution near the (charged) Nariai limit and find good agreements even far beyond the limit for the imaginary frequency parts.
|
high energy physics theory
|
Using an effective field theory approach for higher-spin fields, we derive the interactions of colour singlet and electrically neutral particles with a spin higher than unity, concentrating on the spin-3/2, spin-2, spin-5/2 and spin-3 cases. We compute the decay rates and production cross sections in the main channels for spin-3/2 and spin-2 states at both electron-positron and hadron colliders, and identify the most promising novel experimental signatures for discovering such particles at the LHC. The discussion is qualitatively extended to the spin-5/2 and spin-3 cases. Higher-spin particles exhibit a rich phenomenology and have signatures that often resemble the ones of supersymmetric and extra-dimensional theories. To enable further studies of higher-spin particles at collider and beyond, we collect the relevant Feynman rules and other technical details.
|
high energy physics phenomenology
|
Recently a certain conceptual puzzle in the AdS/CFT correspondence, concerning the growth of quantum circuit complexity and the wormhole volume, has been identified by Bouland-Fefferman-Vazirani and Susskind. In this note, we propose a resolution of the puzzle and save the quantum Extended Church-Turing thesis by arguing that there is no computational shortcut in measuring the volume due to gravitational backreaction from bulk observers. A certain strengthening of the firewall puzzle from the computational complexity perspective, as well as its potential resolution, is also presented.
|
high energy physics theory
|
Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities, it is common for polyglots to code-mix when conversing with each other. Inspired by this phenomenon, we present two strong black-box adversarial attacks (one word-level, one phrase-level) for multilingual models that push their ability to handle code-mixed sentences to the limit. The former uses bilingual dictionaries to propose perturbations and translations of the clean example for sense disambiguation. The latter directly aligns the clean example with its translations before extracting phrases as perturbations. Our phrase-level attack has a success rate of 89.75% against XLM-R-large, bringing its average accuracy of 79.85 down to 8.18 on XNLI. Finally, we propose an efficient adversarial training scheme that trains in the same number of steps as the original model and show that it improves model accuracy.
|
computer science
|
We ask the question of classical integrability for certain (classes of) supergravity vacua that contain an AdS$_3$ factor arising in massive IIA and IIB theories and realizing various and different amounts of supersymmetry. Our approach is based on a well-established method of analytic non-integrability for Hamiltonian systems. To detect a non-integrable sector we consider a non-trivially wrapped string soliton and study its fluctuations. We answer in the negative for each and every one of the supergravity solutions. That is, of course, modulo very specific limits where the metrics reduce to the AdS$_3 \times$ S$^3 \times \tilde{S}^3 \times$ S$^1$ and AdS$_3 \times$ S$^3 \times$ T$^4$ solutions which are known to be integrable.
|
high energy physics theory
|
We calculate $\bar{B}_s^0\to \phi$ translation form factors $V$, $A_0$, $A_1$, $A_2$ based on QCD sum rule and study the nonleptonic two-body decay of $\bar{B}_s^0\to J/\psi \phi$ with the form factors obtained. We calculate the time-integrated branching ratio of $\bar{B}_s^0\to J/\psi \phi$ decay. The results for both the total branching ratio and the cases for the final vectors in longitudinal and transverse polarizations are consistent with experimental data.
|
high energy physics phenomenology
|
When constructing models to summarize clinical data to be used for simulations, it is good practice to evaluate the models for their capacity to reproduce the data. This can be done by means of Visual Predictive Checks (VPC), which consist of (1) several reproductions of the original study by simulation from the model under evaluation, (2) calculating estimates of interest for each simulated study and (3) comparing the distribution of those estimates with the estimate from the original study. This procedure is a generic method that is straightforward to apply, in general. Here we consider the application of the method to time to event data and consider the special case when a time-varying covariate is not known or cannot be approximated after event time. In this case, simulations cannot be conducted beyond the end of the follow-up time (event or censoring time) in the original study. Thus, the simulations must be censored at the end of the follow-up time. Since this censoring is not random, the standard KM estimates from the simulated studies and the resulting VPC will be biased. We propose to use inverse probability of censoring weighting (IPoC) method to correct the KM estimator for the simulated studies and obtain unbiased VPCs. For analyzing the Cantos study, the IPoC weighting as described here proved valuable and enabled the generation of VPCs to qualify PKPD models for simulations. Here, we use a generated data set, which allows illustration of the different situations and evaluation against the known truth.
|
statistics
|
Optimal Transport (OT) defines geometrically meaningful "Wasserstein" distances, used in machine learning applications to compare probability distributions. However, a key bottleneck is the design of a "ground" cost which should be adapted to the task under study. In most cases, supervised metric learning is not accessible, and one usually resorts to some ad-hoc approach. Unsupervised metric learning is thus a fundamental problem to enable data-driven applications of Optimal Transport. In this paper, we propose for the first time a canonical answer by computing the ground cost as a positive eigenvector of the function mapping a cost to the pairwise OT distances between the inputs. This map is homogeneous and monotone, thus framing unsupervised metric learning as a non-linear Perron-Frobenius problem. We provide criteria to ensure the existence and uniqueness of this eigenvector. In addition, we introduce a scalable computational method using entropic regularization, which - in the large regularization limit - operates a principal component analysis dimensionality reduction. We showcase this method on synthetic examples and datasets. Finally, we apply it in the context of biology to the analysis of a high-throughput single-cell RNA sequencing (scRNAseq) dataset, to improve cell clustering and infer the relationships between genes in an unsupervised way.
|
statistics
|
We perform high-resolution spectroscopy of the $3$d$~^2$D$_{3/2} - 3$d$~^2$D$_{5/2}$ interval in all stable even isotopes of $^A$Ca$^+$ (A = 40, 42, 44, 46 and 48) with an accuracy of $\sim$ 20 Hz using direct frequency-comb Raman spectroscopy. Combining these data with isotope shift measurements of the 4s$~^2$S$_{1/2} \leftrightarrow 3$d$~^2$D$_{5/2}$ transition, we carry out a King plot analysis with unprecedented sensitivity to coupling between electrons and neutrons by bosons beyond the Standard Model. Furthermore, we estimate the sensitivity to such bosons from equivalent spectroscopy in Ba$^+$ and Yb$^+$. Finally, the data yield isotope shifts of the 4s$~^2$S$_{1/2} \leftrightarrow 3$d$~^2$D$_{3/2}$ transition at 10 part-per-billion through combination with recent data of Knollmann et al (2019).
|
physics
|
It is currently understood that a particle detector registers the same response when immersed in a thermal bath in flat spacetime as it would for Gibbons-Hawking radiation in the presence of a cosmological horizon. While a pair of sufficiently separated Unruh-deWitt detectors can differentiate these two spacetimes via the amount of entanglement they can extract, we show contrariwise that a single such detector can perform this task. Utilizing a recent framework allowing us to describe the detector in a quantum-controlled superposition of two different trajectories, we show that a detector in a superposition of inertial paths at fixed co-moving distance in a thermal bath registers a different response compared with an analogous scenario in an expanding de Sitter universe. The detector's response to the background quantum field elicits novel information about the curvature and causal structure of the background spacetime, demonstrating its capability as a probe of these global properties.
|
quantum physics
|
Quantum metrology is the use of genuinely quantum properties such as entanglement as a resource to outperform classical sensing strategies. Typically, entanglement is created by implementing gate operations or inducing many-body interactions. However, existing sensing schemes with these approaches require accurate control of the probe system such as switching on/off the interaction among qubits, which can be challenging for practical applications. Here, we propose an entanglement-enhanced sensing scheme with an always-on nearest-neighbor interaction between qubits. We adopt the transverse field Ising chain as the probe system, and make use of the so-called "quantum domino" dynamics for the generation of the entangled states. In addition to the advantage that our scheme can be implemented without controlling the interactions, it only requires initialization of the system, projective measurements on a single qubit, and control of the uniform magnetic fields. We can achieve an improved sensitivity beyond the standard quantum limit even under the effect of realistic decoherence.
|
quantum physics
|
In this note, we explicitly compute the vacuum expectation value of the commutator of scalar fields in a d-dimensional conformal field theory on the cylinder. We find from explicit calculations that we need smearing not only in space but also in time to have finite commutators except for those of free scalar operators. Thus the equal time commutators of the scalar fields are not well-defined for a non-free conformal field theory, even if which is defined from the Lagrangian. We also have the commutator for a conformal field theory on Minkowski space, instead of the cylinder, by taking the small distance limit.
|
high energy physics theory
|
Verification and validation of automated driving functions impose large challenges. Currently, scenario-based approaches are investigated in research and industry, aiming at a reduction of testing efforts by specifying safety relevant scenarios. To define those scenarios and operate in a complex real-world design domain, a structured description of the environment is needed. Within the PEGASUS research project, the 6-Layer Model (6LM) was introduced for the description of highway scenarios. This paper refines the 6LM and extends it to urban traffic and environment. As defined in PEGASUS, the 6LM provides the possibility to categorize the environment and, therefore, functions as a structured basis for subsequent scenario description. The model enables a structured description and categorization of the general environment, without incorporating any knowledge or anticipating any functions of actors. Beyond that, there is a variety of other applications of the 6LM, which are elaborated in this paper. The 6LM includes a description of the road network and traffic guidance objects, roadside structures, temporary modifications of the former, dynamic objects, environmental conditions and digital information. The work at hand specifies each layer by categorizing its items. Guidelines are formulated and explanatory examples are given to standardize the application of the model for an objective environment description. In contrast to previous publications, the model and its design are described in far more detail. Finally, the holistic description of the 6LM presented includes remarks on possible future work when expanding the concept to machine perception aspects.
|
computer science
|
In this work, we have studied the effects from increasing the strength of the applied electric field on the charge transport of hydrogenated graphitic fibers. Resistivity measurements were carried out for direct currents in the nA - mA range and for temperatures from 1.9 K to 300 K. The high-temperature non-ohmic voltage-current dependence is well described by the nonlinear random resistor network model applied to systems that are disordered at all scales. The temperature-dependent resistivity shows linear, step-like transitions from insulating to metallic states as well as plateau features. As more current is being sourced, the fiber becomes more conductive and thus the current density goes up. The most interesting features is observed in high electric fields. As the fiber is cooled, the resistivity first decreases linearly with the temperature and then enters a plateau region at a temperature T ? 260 ? 280 K that is field-independent. These observations on a system made out of carbon, hydrogen, nitrogen, and oxygen atoms suggest possible electric-field induced superconductivity with a high critical temperature that was predicted from studying the role of chirality on the origin of life [1].
|
condensed matter
|
Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices to perform multiple kernel learning. Apart from the huge consumption of manpower, computation and memory resources, most of these models seek point estimation of their parameters, and are prone to overfitting to small training data. This paper presents an adaptive kernel nonlinear max-margin multi-view learning model under the Bayesian framework. Specifically, we regularize the posterior of an efficient multi-view latent variable model by explicitly mapping the latent representations extracted from multiple data views to a random Fourier feature space where max-margin classification constraints are imposed. Assuming these random features are drawn from Dirichlet process Gaussian mixtures, we can adaptively learn shift-invariant kernels from data according to Bochners theorem. For inference, we employ the data augmentation idea for hinge loss, and design an efficient gradient-based MCMC sampler in the augmented space. Having no need to compute the Gram matrix, our algorithm scales linearly with the size of training set. Extensive experiments on real-world datasets demonstrate that our method has superior performance.
|
computer science
|
Motivated by low-altitude cusp observations of small-scale (~ 1 km) field-aligned currents (SSFACs) interpreted as ionospheric Alfv\'en resonator modes, we investigated the effects of Alfv\'en wave energy deposition on thermospheric upwelling and the formation of air density enhancements in and near the cusp. Such density enhancements were commonly observed near 400 km altitude by the CHAMP satellite. They are not predicted by empirical thermosphere models, and they are well-correlated with the observed SSFACs. A parameterized model for the altitude dependence of the Alfv\'en wave electric field, constrained by CHAMP data, has been developed and embedded in the Joule heating module of the National Center for Atmospheric Research (NCAR) Coupled Magnetosphere-Ionosphere-Thermosphere (CMIT) model. The CMIT model was then used to simulate the geospace response to an interplanetary stream interaction region (SIR) that swept past Earth on 26-27 March 2003. CMIT diagnostics for the thermospheric mass density at 400 km altitude show: 1) CMIT without Alfv\'enic Joule heating usually underestimates CHAMP's orbit-average density; inclusion of Alfv\'enic heating modestly improves CMIT's orbit-average prediction of the density (by a few %), especially during the more active periods of the SIR event. 2) The improvement in CMIT's instantaneous density prediction with Alfv\'enic heating included is more significant (up to 15%) in the vicinity of the cusp heating region, a feature that the MSIS empirical thermosphere model misses for this event. Thermospheric density changes of 20-30% caused by the cusp-region Alfv\'enic heating sporadically populate the polar region through the action of corotation and neutral winds.
|
physics
|
I examine the molecular dynamics of ice growth from water vapor, focusing on how the attachment kinetics can be augmented by edge-dependent surface diffusion. Although there are significant uncertainties in developing an accurate physical model of this process, it is possible to make some reasonable estimates of surface diffusion rates and admolecule density enhancements, derived from our basic understanding of ice-crystal growth processes. A quantitative model suggests that edge-dependent surface diffusion could substantially enhance terrace nucleation on narrow faceted surfaces, especially at the onset of surface premelting. This result supports our hypothesized mechanism for structure-dependent attachment kinetics, which readily explains the changes in snow crystal growth morphology with temperature depicted in the well-known Nakaya diagram. Many of the model features described here may be amenable to further quantitative investigation using existing computational models of the molecular structure and dynamics of the ice surface.
|
condensed matter
|
Few solar system asteroids and comets are found in high eccentricity orbits ($e > 0.9$) but in the primordial planetesimal disks and in exoplanet systems around dying stars such objects are believed to be common. For 2006 HY51, the main belt asteroid with the highest known eccentricity 0.9684, we investigate the probable rotational states today using our computer-efficient chaotic process simulation method. Starting with random initial conditions, we find that this asteroid is inevitably captured into stable spin-orbit resonances typically within tens to a hundred Myr. The resonances are confirmed by direct integration of the equation of motion in the vicinity of end-points. Most resonances are located at high spin values above 960 times the mean motion (such as 964:1 or 4169:4), corresponding to rotation periods of a few days. We discover three types of resonance in the high-eccentricity regime: 1) regular circulation with weakly librating aphelion velocities and integer-number spin-orbit commensurabilities; 2) switching resonances of higher order with orientation alternating between aligned (0 or $\pi$) and sidewise ($\pi/2$) angles at aphelia and perihelia; 3) jumping resonances with aphelion spin alternating between two quantum states in the absence of spin-orbit commensurability. The islands of equilibrium are numerous at high spin rates but small in parameter space area, so that it takes millions of orbits of chaotic wandering to accidentally entrap in one of them. We discuss the implications of this discovery for the origins and destiny of high-eccentricity objects and the prospects of extending this analysis to the full 3D treatment.
|
astrophysics
|
Terminal ductal lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses' Health Study (NHS). A first set of 92 WSIs was annotated for TDLUs, acini and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73$\pm$0.09, and segmented TDLUs and adipose tissue with Dice scores of 0.86$\pm$0.11 and 0.86$\pm$0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 95% CI [0.51, 0.83], 0.81, 95% CI [0.67, 0.90], and 0.73, 95% CI [0.54, 0.85], respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of >0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80, 95% CI [0.63, 0.90] for number of TDLUs per tissue area, 0.57, 95% CI [0.19, 0.77] for median TDLU span, and 0.80, 95% CI [0.62, 0.89] for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status.
|
electrical engineering and systems science
|
In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in the resource-constrained environments. In this paper, we propose to incorporate multi-armed bandit approaches into sequential change-point detection to develop an efficient bandit change-point detection algorithm. Our proposed algorithm, termed Thompson-Sampling-Shiryaev-Roberts-Pollak (TSSRP), consists of two policies per time step: the adaptive sampling policy applies the Thompson Sampling algorithm to balance between exploration for acquiring long-term knowledge and exploitation for immediate reward gain, and the statistical decision policy fuses the local Shiryaev-Roberts-Pollak statistics to determine whether to raise a global alarm by sum shrinkage techniques. Extensive numerical simulations and case studies demonstrate the statistical and computational efficiency of our proposed TSSRP algorithm.
|
statistics
|
In this paper we construct a number of cubic interaction vertices for massless bosonic and fermionic higher spin fields in flat four dimensional space. First of all, we construct these cubic vertices in AdS_4 space using a so-called Fradkin-Vasiliev approach, which works only for the non-zero cosmological constant. Then we consider a flat limit taking care on all the higher derivative terms which FV-approach generates. We restrict ourselves with the four dimensions because this allows us to use the frame-like multispinor formalism which greatly simplifies all calculations and provides a description for bosons and fermions on equal footing.
|
high energy physics theory
|
We revisit the consistency of torus partition functions in (1+1)$d$ fermionic conformal field theories, combining traditional ingredients of modular invariance/covariance with a modernized understanding of bosonization/fermionization dualities. Various lessons can be learned by simply examining the oft-ignored Ramond sector. For several extremal/kinky modular functions in the bootstrap literature, we can either rule out or identify the underlying theory. We also revisit the ${\cal N} = 1$ Maloney-Witten partition function by calculating the spectrum in the Ramond sector, and further extending it to include the modular sum of seed Ramond characters. Finally, we perform the full ${\cal N} = 1$ RNS modular bootstrap and obtain new universal results on the existence of relevant deformations preserving different amounts of supersymmetry.
|
high energy physics theory
|
For the permittivity tensor of photoelastic anisotropic crystals we obtain the exact non-linear dependence on the Cauchy stress tensor. We obtain the same result for its square root whose principal components, the crystal principal refractive index, are the starting point for any photoelastic analysis of transparent crystals. From these exact results then we obtain, in a total general manner, the linearized expressions to within higher-order terms in the stress tensor for both the permittivity tensor and its square root. We finish by showing some relavant examples of both non-linear and linearized relations for optically isotropic, uniaxial and biaxial crystals.
|
physics
|
Code comment has been an important part of computer programs, greatly facilitating the understanding and maintenance of source code. However, high-quality code comments are often unavailable in smart contracts, the increasingly popular programs that run on the blockchain. In this paper, we propose a Multi-Modal Transformer-based (MMTrans) code summarization approach for smart contracts. Specifically, the MMTrans learns the representation of source code from the two heterogeneous modalities of the Abstract Syntax Tree (AST), i.e., Structure-based Traversal (SBT) sequences and graphs. The SBT sequence provides the global semantic information of AST, while the graph convolution focuses on the local details. The MMTrans uses two encoders to extract both global and local semantic information from the two modalities respectively, and then uses a joint decoder to generate code comments. Both the encoders and the decoder employ the multi-head attention structure of the Transformer to enhance the ability to capture the long-range dependencies between code tokens. We build a dataset with over 300K <method, comment> pairs of smart contracts, and evaluate the MMTrans on it. The experimental results demonstrate that the MMTrans outperforms the state-of-the-art baselines in terms of four evaluation metrics by a substantial margin, and can generate higher quality comments.
|
computer science
|
We show that, for the M\"obius function $\mu(n)$, we have $$ \sum_{x < n\leq x+x^{\theta}}\mu(n)=o(x^{\theta}) $$ for any $\theta>0.55$. This improves on a result of Ramachandra from 1976, which is valid for $\theta>7/12$. Ramachandra's result corresponded to Huxley's $7/12$ exponent for the prime number theorem in short intervals. The main new idea leading to the improvement is using Ramar\'e's identity to extract a small prime factor from the $n$-sum. The proof method also allows us to improve on an estimate of Zhan for the exponential sum of the M\"obius function as well as some results on multiplicative functions and almost primes in short intervals.
|
mathematics
|
We study a flavor model that the quark sector has the $S_3$ modular symmetry,while the lepton sector has the $A_4$ modular symmetry. Our model leads to characteristic quark mass matrices which are consistent with experimental data of quark masses, mixing angles and the CP violating phase. The lepton sector is also consistent with the experimental data of neutrino oscillations. We also study baryon and lepton number violations in our flavor model.
|
high energy physics phenomenology
|
Tip-enhanced Raman spectroscopy (TERS) has reached nanometer spatial resolution for measurements performed at ambient conditions and sub-nanometer resolution at ultra high vacuum. Super-resolution (beyond the tip apex diameter) TERS has been obtained, mostly in the gap mode configuration, where a conductive substrate localizes the electric fields. Here we present experimental and theoretical TERS to explore the field distribution responsible for spectral enhancement. We use gold tips of $40\pm 10 \ \text{nm}$ apex diameter to measure TERS on graphene, a spatially delocalized two-dimensional sample, sitting on different substrates: (i) glass, (ii) a thin layer of gold and (iii) a surface covered with $12\ \text{nm}$ diameter gold spheres, for which $6\ \text{nm}$ resolution is achieved at ambient conditions. The super-resolution is due to the field configuration resulting from the coupled tip-sample-substrate system, exhibiting a non-trivial spatial surface distribution. The field distribution and the symmetry selection rules are different for non-gap vs. gap mode configurations. This influences the overall enhancement which depends on the Raman mode symmetry and substrate structure.
|
physics
|
A comprehensive study is given to short gamma-ray bursts (sGRBs) in the third Swift/BAT GRB Catalog from December 2004 to July 2019. We examine in details the temporal properties of the three components in the prompt gamma-ray emission phase, including precursors, main peaks and extended emissions (EE). We investigate the similarity of the main peaks between one-component and two-component sGRBs. It is found that there is no substantial difference among their main peaks. Importantly, comparisons are made between in the single-peaked sGRBs and the double-peaked sGRBs. It is found that our results of main peaks in Swift/BAT sGRBs are essentially consistent with those in CGRO/BATSE ones recently found in our paper I. Interestingly, we suspect, besides the newly-found MODE I/II evolution forms of pulses in BATSE sGRBs in paper I, that there would have more evolution modes of pulses across differently adjacent energy channels in view of the Swift/BAT observations. We further inspect the correlation of the main peaks with either the precursors or the EEs. We find that the main peaks tend to last longer than the precursors but shorter than the EEs. In particular, we verify the power-law correlations related with peak fluxes of the three components, strongly suggesting that they are produced from the similar central engine activities. Especially, we compare the temporal properties of GRB 170817A with other sGRBs with EE and find no obvious differences between them.
|
astrophysics
|
Pulses applied to an inhomogeneously broadened set of harmonic oscillators, previously prepared in squeezed states, can lead to a recovery of coherence, manifesting itself as echoes, similar to those exhibited by an ensemble of spins when excited by properly designed electromagnetic pulses. Such echoes, of classical or quantum nature, are expected to arise in the squeezing of linear systems of various sorts and, in particular, light and vibrational modes.
|
quantum physics
|
A class of high-order canonical symplectic structure-preserving geometric algorithms are developed for high-quality simulations of the quantized Dirac-Maxwell theory based strong-field quantum electrodynamics (SFQED) and relativistic quantum plasmas (RQP) phenomena. The Lagrangian density of an interacting bispinor-gauge fields theory is constructed in a conjugate real fields form. The canonical symplectic form and canonical equations of this field theory are obtained by the general Hamilton's principle on cotangent bundle. Based on discrete exterior calculus, the gauge field components are discreted to form a cochain complex, and the bispinor components are naturally discreted on a staggered dual lattice as combinations of differential forms. With pull-back and push-forward gauge covariant derivatives, the discrete action is gauge invariant. A well-defined discrete canonical Poisson bracket generates a semi-discrete lattice canonical field theory (LCFT), which admits the canonical symplectic form, unitary property, gauge symmetry and discrete Poincar\'e subgroup. The Hamiltonian splitting method, Cayley transformation and symmetric composition technique are introduced to construct a class of high-order numerical schemes. These schemes involve two degenerate fermion flavors and are locally unconditional stable, which also preserve the geometric structures. Equipped with statistically quantization-equivalent ensemble models of the Dirac vacuum and non-trivial plasma backgrounds, the schemes are expected to have excellent performance in secular simulations of relativistic quantum effects. The algorithms are verified in detail by numerical energy spectra. Real-time LCFT simulations are successfully implemented for the nonlinear Schwinger mechanism induced $e$-$e^+$ pairs creation and vacuum Kerr effect, which open a new door toward high-quality simulations in SFQED and RQP.
|
quantum physics
|
We study synthesis problems with constraints in partially observable Markov decision processes (POMDPs), where the objective is to compute a strategy for an agent that is guaranteed to satisfy certain safety and performance specifications. Verification and strategy synthesis for POMDPs are, however, computationally intractable in general. We alleviate this difficulty by focusing on planning applications and exploiting typical structural properties of such scenarios; for instance, we assume that the agent has the ability to observe its own position inside an environment. We propose an abstraction refinement framework which turns such a POMDP model into a (fully observable) probabilistic two-player game (PG). For the obtained PGs, efficient verification and synthesis tools allow to determine strategies with optimal safety and performance measures, which approximate optimal schedulers on the POMDP. If the approximation is too coarse to satisfy the given specifications, an refinement scheme improves the computed strategies. As a running example, we use planning problems where an agent moves inside an environment with randomly moving obstacles and restricted observability. We demonstrate that the proposed method advances the state of the art by solving problems several orders-of-magnitude larger than those that can be handled by existing POMDP solvers. Furthermore, this method gives guarantees on safety constraints, which is not supported by the majority of the existing solvers.
|
computer science
|
Let $G$ be a graph of minimum degree at least $k$ and let $G_p$ be the random subgraph of $G$ obtained by keeping each edge independently with probability $p$. We are interested in the size of the largest complete minor that $G_p$ contains when $p = \frac{1+\varepsilon}{k}$ with $\varepsilon >0$. We show that with high probability $G_p$ contains a complete minor of order $\tilde{\Omega}(\sqrt{k})$, where the $\sim$ hides a polylogarithmic factor. Furthermore, in the case where the order of $G$ is also bounded above by a constant multiple of $k$, we show that this polylogarithmic term can be removed, giving a tight bound.
|
mathematics
|
orthoDr is a package in R that solves dimension reduction problems using orthogonality constrained optimization approach. The package serves as a unified framework for many regression and survival analysis dimension reduction models that utilize semiparametric estimating equations. The main computational machinery of orthoDr is a first-order algorithm developed by \cite{wen2013feasible} for optimization within the Stiefel manifold. We implement the algorithm through Rcpp and OpenMP for fast computation. In addition, we developed a general-purpose solver for such constrained problems with user-specified objective functions, which works as a drop-in version of optim(). The package also serves as a platform for future methodology developments along this line of work.
|
statistics
|
Computer vision algorithms are being implemented across a breadth of industries to enable technological innovations. In this paper, we study the problem of computer vision based customer tracking in retail industry. To this end, we introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket. In addition, we describe an illustrative example of the use of this dataset for tracking participants based on a head tracking model in an effort to minimize errors due to occlusion. Furthermore, we propose a model for recognizing customers and staff based on their movement patterns. The model is evaluated using a real-world dataset collected in a supermarket over a 24-hour period that achieves 98% accuracy during training and 93% accuracy during evaluation.
|
computer science
|
The continuous flow of gas and dark matter across scales in the cosmic web can generate correlated dynamical properties of haloes and filaments (and the magnetic fields they contain). With this work, we study the halo spin properties and orientation with respect to filaments, and the morphology of the magnetic field around these objects, for haloes with masses in the range 1e8-1e14 Msun and filaments up to 8 Mpc long. Furthermore, we study how these properties vary in presence, or lack thereof, of different (astro)physical processes and with different magnetic initial conditions. We perform cosmological magnetohydrodynamical simulations with the Eulerian code Enzo and we develop a simple and robust algorithm to study the filamentary connectivity of haloes in three dimensions. We investigate the morphological and magnetic properties and focus on the alignment of the magnetic field along filaments: our analysis suggests that the degree of this alignment is partially dependent on the physical processes involved, as well as on magnetic initial conditions. We discuss the contribution of this effect on a potential attempt to detect the magnetic field surrounding these objects: we find that it introduces a bias in the estimation of the magnetic field from Faraday rotation measure techniques. Specifically, given the strong tendency we find for extragalactic magnetic fields to align with the filaments axis, the value of the magnetic field can be underestimated by a factor 3, because this effect contributes to making the line-of-sight magnetic field (for filaments in the plane of the sky) much smaller than the total one.
|
astrophysics
|
Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the potential to aid the clinical workflow. In this abstract, we share the first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset. We envision that these results will serve as a benchmark for researchers who might work with this dataset in future.
|
electrical engineering and systems science
|
The energy conversion efficiency of far-from-equilibrium systems is generally limited by irreversible thermodynamic fluxes that make contact with different heat baths. For complex systems, the states of the maximum efficiency and the minimum entropy production are usually not equivalent. Here we show that the proper adjustments of the interaction between the energy and matter currents offer some important criteria for the performance characterizations of thermal agents, regardless of the system types and transition protocols. The universal thermodynamic coupling rule plays a critical role in irreversible processes. A double quantum dot system is applied to demonstrate that the performances of heat engines or refrigrators can be enhanced by suitably adjusting the coupling strength between thermodynamic fluxes.
|
quantum physics
|
In this proposal, a cost-effective energy harvesting and management system have been proposed. The regular power keeps around 200 Watt while the peak power can reach 300 Watt. The cost of this system satisfies the requirements and budget for residents in the rural area and live off-grid. It could be a potential solution to the global energy crisis, particularly the billions of people living in severe energy poverty. Also, it is an important renewable alternative to conventional fossil fuel electricity generation not only the cost of manufacturing is low and high efficiency, but also it is safe and eco-friendly.
|
electrical engineering and systems science
|
This note proposes a new proof and new perspectives on the so-called Elliptical Potential Lemma. This result is important in online learning, especially for linear stochastic bandits. The original proof of the result, however short and elegant, does not give much flexibility on the type of potentials considered and we believe that this new interpretation can be of interest for future research in this field.
|
statistics
|
Origins of giant planets on tight orbits, so called hot Jupiters, are a long-lasting question in the planetary formation and evolution theory. The answer seems to be hidden in architectures of those systems that remain only partially understood. Using multi-sector time-series photometry from the Transiting Exoplanet Survey Satellite, we searched for additional planets in the KELT-18, KELT-23, KELT-24, Qatar-8, WASP-62, WASP-100, WASP-119, and WASP-126 planetary systems using both the transit technique and transit timing method. Our homogenous analysis has eliminated the presence of transiting companions down to the terrestrial-size regime in the KELT-23 and WASP-62 systems, and down to mini-Neptunes or Neptunes in the remaining ones. Transit timing analysis has revealed no sign of either long-term trends or periodic perturbations for all the studied hot Jupiters, including the WASP-126 b for which deviations from a Keplerian model were claimed in the literature. The loneliness of the planets of the sample speaks in favour of the high-eccentricity migration mechanism that probably brought them to their tight orbits observed nowadays. As a byproduct of our study, the transit light curve parameters were redetermined with a substantial improvement of the precision for 6 systems. For KELT-24 b, a joint analysis allowed us to place a tighter constraint on its orbital eccentricity.
|
astrophysics
|
We analyze the temporal distribution of sunspot groups for even and odd cycles in the range SC12-SC24. It seems that cycle 24 is a characteristic even cycle, although with low amplitude. The number of large sunspot groups for cycle 24 is relatively smaller than for the average of both even and odd cycles SC12-SC23, and there is a deep decline of the large groups in the middle of the cycle. Temporal evolution of the sunspot groups of the even cycles is non-synchronous such that the northern hemisphere distribution of groups maximizes earlier that the southern hemisphere groups. This leads to a double-peak structure for the average even cycle. On the other hand, the distributions of the sunspot groups of odd cycles maximize simultaneously. We show that this double-peak structure intensifies the Gnevyshev gap (GG) for the even cycles, but is not its primary cause. On the contrary, we show that the GG exists for even and odd cycles, and separately on both hemispheres. We resample all cycles to have equal number of 3945 days and study the difference in the evolution of average total group area and average group area of the even and odd cycles separately. The analysis shows that there is a decline in both total area and average area in the even cycles 1445 days (about four years) after the beginning of the cycle, which is at least 99 % significant for both total and average area. The odd cycles do not have such a clear decline.
|
astrophysics
|
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