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Magnetic topological insulators (MTIs) offer a combination of topologically nontrivial characteristics and magnetic order and show promise in terms of potentially interesting physical phenomena such as the quantum anomalous Hall (QAH) effect and topological axion insulating states. However, the understanding of their properties and potential applications have been limited due to a lack of suitable candidates for MTIs. Here, we grow two-dimensional single crystals of Mn(SbxBi(1-x))2Te4 bulk and exfoliate them into thin flakes in order to search for intrinsic MTIs. We perform angle-resolved photoemission spectroscopy, low-temperature transport measurements, and first-principles calculations to investigate the band structure, transport properties, and magnetism of this family of materials, as well as the evolution of their topological properties. We find that there exists an optimized MTI zone in the Mn(SbxBi(1-x))2Te4 phase diagram, which could possibly host a high-temperature QAH phase, offering a promising avenue for new device applications.
|
condensed matter
|
Facial expression recognition is a major problem in the domain of artificial intelligence. One of the best ways to solve this problem is the use of convolutional neural networks (CNNs). However, a large amount of data is required to train properly these networks but most of the datasets available for facial expression recognition are relatively small. A common way to circumvent the lack of data is to use CNNs trained on large datasets of different domains and fine-tuning the layers of such networks to the target domain. However, the fine-tuning process does not preserve the memory integrity as CNNs have the tendency to forget patterns they have learned. In this paper, we evaluate different strategies of fine-tuning a CNN with the aim of assessing the memory integrity of such strategies in a cross-dataset scenario. A CNN pre-trained on a source dataset is used as the baseline and four adaptation strategies have been evaluated: fine-tuning its fully connected layers; fine-tuning its last convolutional layer and its fully connected layers; retraining the CNN on a target dataset; and the fusion of the source and target datasets and retraining the CNN. Experimental results on four datasets have shown that the fusion of the source and the target datasets provides the best trade-off between accuracy and memory integrity.
|
computer science
|
5G and future cellular networks intend to incorporate low earth orbit (LEO) satellite communication systems (SatCom) to solve the coverage and availability problems that cannot be addressed by satellite-based or ground-based infrastructure alone. This integration of terrestrial and non terrestrial networks poses many technical challenges which need to be identified and addressed. To this aim, we design and simulate the downlink of a LEO SatCom compatible with 5G NR, with a special focus on the design of the beamforming codebook at the satellite side. The performance of this approach is evaluated for the link between a LEO satellite and a mobile terminal in the Ku band, assuming a realistic channel model and commercial antenna array designs, both at the satellite and the terminal. Simulation results provide insights on open research challenges related to analog codebook design and hybrid beamforming strategies, requirements of the antenna terminals to provide a given SNR, or required beam reconfiguration capabilities among others.
|
electrical engineering and systems science
|
We propose a mechanism borrowed from string theory which yields a non-singular transition from a phase of Ekpyrotic contraction to the expanding phase of Standard Big Bang cosmology. The same mechanism converts the initial vacuum spectrum of cosmological fluctuations before the bounce into a scale-invariant one, and also changes the spectrum of gravitational waves into an almost scale-invariant one. The scalar and tensor tilts are predicted to be the same, in contrast to the predictions from the "String Gas Cosmology" scenario. The amplitude of the gravitational wave spectrum depends on the ratio of the string scale to the Planck scale and may be in reach of upcoming experiments.
|
high energy physics theory
|
This paper follows on from a previous one in which it was shown that it is possible, within a de Broglie-Bohm style ontology for quantum mechanics, to incorporate action and reaction between the particle and its guiding field while remaining consistent with the usual experimental predictions. Whereas the previous paper focussed on the Dirac equation, the present work addresses the Schrodinger case and demonstrates that the same two-way interaction can be achieved. The aim in each case is to increase the physical plausibility of such models. The transition to include the reaction of the particle back on the field, and hence energy and momentum conservation, is attained by employing standard Lagrangian techniques. In formulating this description an interesting bonus emerges in that the hitherto unrelated concept of a gauge transformation is found to arise naturally as an essential part of the formalism. In particular, the phase S of the gauge transformation is seen to be the action function describing the hidden motion of the particle.
|
quantum physics
|
To explain the observed muon anomaly and simultaneously evade bounds from lepton flavor violation in the same model parameter space is a long cherished dream. In view of a generalized Two Higgs Doublet Model, with a Yukawa structure as a perturbation of Type-X, we are able to get substantial parameter space satisfying this criteria. We are focusing on a region with "{\bf wrong-sign}" lepton-Yukawa coupling which gives rise to an interesting phenomenological consequences. We found that in the "wrong-sign" region, it is possible to probe the low-mass pseudoscalar in flavor-violating decay mode with considerably better significance compared to the "right-sign" region. Performing a simple cut-based analysis we show that at 14 TeV run of the LHC with $300 fb^{-1}$ integrated luminosity, part of the model parameter space can be probed with significance $\geq 5\sigma$ which further improves with Artificial Neural Network analysis.
|
high energy physics phenomenology
|
The Miles theory of wave amplification by wind is extended to the case of finite depth h and a shear flow with (constant) vorticity {\Omega}. Vorticity is characterised through the non-dimensional parameter {\nu} = {\Omega} U_1 /g, where g the gravitational acceleration, U_1 a characteristic wind velocity and k the wavenumber. The notion of 'wave age' is generalised to account for the effect of vorticity. Several widely used growth rates are derived analytically from the dispersion relation of the wind/water interface, and their dependence on both water depth and vorticity is derived and discussed. Vorticity is seen to shift the maximum wave age, similar to what was previously known to be the effect of water depth. At the same time, a novel effect arises and the growth coefficients, at identical wave age and depth, are shown to experience a net increase or decrease according to the shear gradient in the water flow.
|
physics
|
The four-flavor hard-wall holographic QCD is studied to evaluate the couplings of $(D^{_{-(*-)}}, \bar{D}^{_0}, a_1^{_-})$, $(D^{_{-(*-)}}, \bar{D}^{_0}, b_1^{_-})$, $(D_{s}^{_{-(*-)}},\bar{D}^{_0}, K_{1A}^{_-})$, $(D_s^{_{-(*-)}}, \bar{D}^{_0}, K_{1B}^{_{-(*-)}})$, $(D_s^{_{+(*+)}}, D^{_+}, K_{1A}^{_0})$, $(D_s^{_{+(*+)}}, D^{_+}, K_{1B}^{_0})$, $(D^{_{-(*-)}}, \bar{D}^{_{0(*0)}}, \rho^{_-})$, $(D_s^{_{-(*-)}}, \bar{D}^{_{0(*0)}}, K^{_{*-}})$, $(D^{_{0(*0)}}, \bar{D}^{_{0(*0)}}, \psi)$, $(D_1^{_-}, \bar{D}_1^{_0}, \pi^{_-})$, $(D_{s1}^{_-}, \bar{D}_1^{_0}, K^{_{-}})$, $(D_1^{_0}, \bar{D}_1^{_0}, \eta_c)$, $(\psi, D^{_{0(*0)}}, D^{_+}, \pi^{_-})$, $(\psi, D^{_{0(*0)}}, \bar{D}^{_0}, \pi^{_0})$, $(\psi, D_{s}^{_{+(*+)}}, D^{_-}, K^{_0})$, $(\psi, D^{_{0(*0)}}, D^{_+}, a_1^{_-})$, $(\psi, D^{_{0(*0)}}, D^{_+}, b_1^{_-})$, $(\psi, D_s^{_{+(*+)}}, D^{_-}, K_{1B}^{_0})$ and $(\psi, D_s^{_{+(*+)}}, D^{_-}, K_{1B}^{_0})$ vertices. Moreover, the values of the masses of $D^{_{0(*0)}}$, $D_s^{_{-(*-)}}$, $\omega$, $\psi$, $D_1^{_0}$, $D_1^{_{-}}$, $K^0$, $\eta_{c}$, $D_{s1}^{_-}$ and $\chi_{_{c1}}$ as well as the decay constant of $\pi^-$, $D^{_{-(*-)}}$, $K^-$, $\rho^-$, $D_1^{_-}$ , $a_1^-$ and $D_s^{_{-(*-)}}$ are estimated in this study. A comparison is also made between our results and the experimental values of the masses and decay constants. Our results for strong couplings are also compared with the 3PSR and LCSR predictions.
|
high energy physics phenomenology
|
Using the dipole picture for electron-nucleus deep inelastic scattering at small Bjorken $x$, we study the effects of gluon saturation in the nuclear target on the cross-section for SIDIS (single inclusive hadron, or jet, production). We argue that the sensitivity of this process to gluon saturation can be enhanced by tagging on a hadron (or jet) which carries a large fraction $z \simeq 1$ of the longitudinal momentum of the virtual photon. This opens the possibility to study gluon saturation in relatively hard processes, where the virtuality $Q^2$ is (much) larger than the target saturation momentum $Q_s^2$, but such that $z(1-z)Q^2\lesssim Q_s^2$. Working in the limit $z(1-z)Q^2\ll Q_s^2$, we predict new phenomena which would signal saturation in the SIDIS cross-section. For sufficiently low transverse momenta $k_\perp\ll Q_s$ of the produced particle, the dominant contribution comes from elastic scattering in the black disk limit, which exposes the unintegrated quark distribution in the virtual photon. For larger momenta $k_\perp\gtrsim Q_s$, inelastic collisions take the leading role. They explore gluon saturation via multiple scattering, leading to a Gaussian distribution in $k_\perp$ centred around $Q_s$. When $z(1-z)Q^2\ll Q^2$, this results in a Cronin peak in the nuclear modification factor (the $R_{pA}$ ratio) at moderate values of $x$. With decreasing $x$, this peak is washed out by the high-energy evolution and replaced by nuclear suppression ($R_{pA}<1$) up to large momenta $k_\perp\gg Q_s$. Still for $z(1-z)Q^2\ll Q_s^2$, we also compute SIDIS cross-sections integrated over $k_\perp$. We find that both elastic and inelastic scattering are controlled by the black disk limit, so they yield similar contributions, of zeroth order in the QCD coupling.
|
high energy physics phenomenology
|
Machine learning models have traditionally been developed under the assumption that the training and test distributions match exactly. However, recent success in few-shot learning and related problems are encouraging signs that these models can be adapted to more realistic settings where train and test distributions differ. Unfortunately, there is severely limited theoretical support for these algorithms and little is known about the difficulty of these problems. In this work, we provide novel information-theoretic lower-bounds on minimax rates of convergence for algorithms that are trained on data from multiple sources and tested on novel data. Our bounds depend intuitively on the information shared between sources of data, and characterize the difficulty of learning in this setting for arbitrary algorithms. We demonstrate these bounds on a hierarchical Bayesian model of meta-learning, computing both upper and lower bounds on parameter estimation via maximum-a-posteriori inference.
|
statistics
|
We describe a technique for making one-dimensional ohmic contacts to individual graphene layers encapsulated in hexagonal boron nitride (h-BN) using CF4 and O2 plasmas. The high etch selectivity of h-BN against graphene (>1000) is achieved by increasing the plasma pressure, which enables etching of h-BN, while graphene acts as an etch stop to protect underlying h-BN. A low-pressure O2 plasma anisotropically etches graphene in the vertical direction, which exposes graphene edges at h-BN sidewalls. Despite the O2 plasma bombardment, the lower h-BN layer functions as an insulating layer. Thus, this method allows us to pattern metal electrodes on h-BN over a second graphene layer. Subsequent electron-beam lithography and evaporation fabricate metal contacts at the graphene edges that are active down to cryogenic temperatures. This fabrication method is demonstrated by the preparation of a graphene Hall bar with a graphite back-gate and double bilayer-graphene Hall bar devices. The high flexibility of the device geometries enabled by this method creates access to a variety of experiments on electrostatically coupled graphene layers.
|
condensed matter
|
Topology is quickly becoming a cornerstone in our understanding of electronic systems. Like their electronic counterparts, bosonic systems can exhibit a topological band structure, but in real materials it is difficult to ascertain their topological nature, as their ground state is a simple condensate or the vacuum, and one has to rely instead on excited states, for example a characteristic thermal Hall response. Here we propose driving a topological magnon insulator with an electromagnetic field and show that this causes edge mode instabilities and a large non-equilibrium steady-state magnon edge current. Building on this, we discuss several experimental signatures that unambiguously establish the presence of topological magnon edge modes. Furthermore, our amplification mechanism can be employed to power a topological travelling-wave magnon amplifier and topological magnon laser, with applications in magnon spintronics. This work thus represents a step toward functional topological magnetic materials.
|
condensed matter
|
Wireless connectivity is instrumental in enabling scalable federated learning (FL), yet wireless channels bring challenges for model training, in which channel randomness perturbs each worker's model update while multiple workers' updates incur significant interference under limited bandwidth. To address these challenges, in this work we formulate a novel constrained optimization problem, and propose an FL framework harnessing wireless channel perturbations and interference for improving privacy, bandwidth-efficiency, and scalability. The resultant algorithm is coined analog federated ADMM (A-FADMM) based on analog transmissions and the alternating direction method of multipliers (ADMM). In A-FADMM, all workers upload their model updates to the parameter server (PS) using a single channel via analog transmissions, during which all models are perturbed and aggregated over-the-air. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper including the honest-but-curious PS, thereby preserving data privacy against model inversion attacks. We formally prove the convergence and privacy guarantees of A-FADMM for convex functions under time-varying channels, and numerically show the effectiveness of A-FADMM under noisy channels and stochastic non-convex functions, in terms of convergence speed and scalability, as well as communication bandwidth and energy efficiency.
|
computer science
|
The neutralino sector of the semi-constrained next-to-minimal supersymmetric standard model is explored under recent experimental constraints, with special attention to dark matter (DM) limits. The effects of the upper and lower bounds of dark matter relic density and recent direct detection constraints on spin-independent and -dependent cross-sections are thoroughly analyzed. Particularly, we show which regions of the parameter space are ruled out due to the different dark matter constraints and the corresponding model-specific parameters: $\lambda, \kappa, A_{\lambda}$, and $A_{\kappa}$. We analyze all annihilation and co-annihilation processes (with heavier neutralinos and charginos) that contribute to the dark matter relic density. The mass components of the dark matter candidate, the lightest neutralino $\tilde{\chi}_1^0$, are studied, and the decays of heavy neutralinos and charginos, especially $\tilde{\chi}_2^0$ and $\tilde{\chi}_1^+$, into the lightest neutralino are examined. We impose semi-universal boundary conditions at the Grand Unified Theory scale and require a moderate range of $\tan{\beta} \lesssim 10$. We find that the allowed parameter space is associated with a heavy mass spectrum in general and that the lightest neutralino is mostly Higgsino with a mass range that resides mostly between 1000 and 1500 GeV. However, smaller mass values can be achieved if the DM candidate is bino-like or singlino-like.
|
high energy physics phenomenology
|
Integrating atomic quantum memories based on color centers in diamond with on-chip photonic devices would enable entanglement distribution over long distances. However, efforts towards integration have been challenging because color centers can be highly sensitive to their environment, and their properties degrade in nanofabricated structures. Here, we describe a heterogeneously integrated, on-chip, III-V diamond platform designed for neutral silicon vacancy (SiV0) centers in diamond that circumvents the need for etching the diamond substrate. Through evanescent coupling to SiV0 centers near the surface of diamond, the platform will enable Purcell enhancement of SiV0 emission and efficient frequency conversion to the telecommunication C-band. The proposed structures can be realized with readily available fabrication techniques.
|
quantum physics
|
The Antarctic Impulsive Transient Antenna (ANITA) experiment has observed two air shower events with energy $\sim 500~{\rm PeV}$ emerging from the Earth with exit angles $\sim 30^\circ$ above the horizon. As was immediately noted by the ANITA Collaboration, these events (in principle) could originate in the atmospheric decay of an upgoing $\tau$-lepton produced through a charged current interaction of a $\nu_\tau$ inside the Earth. However, the relatively steep arrival angles of these perplexing events create tension with the standard model (SM) neutrino-nucleon interaction cross section. Deepening the conundrum, the IceCube neutrino telescope and the Pierre Auger Observatory with substantially larger exposures to cosmic $\nu_\tau$'s in this energy range have not observed any events. This lack of observation implies that the messenger particle (MP) giving rise to ANITA events must produce an air shower event rate at least a factor of 40 larger than that produced by a flux of $\tau$-neutrinos to avoid conflicts with the upper limits reported by the IceCube and the Pierre Auger collaborations. In addition, the sensitivity of ANITA to MP-induced events must be comparable to or larger than those of IceCube and Auger to avoid conflict with the non-observation of any signal at these facilities. Beyond SM interpretations of ANITA events can be classified according to whether the MPs: (i) live inside the Earth, (ii) originate in neutrino-nucleon collisions inside the Earth, (iii) come from cosmological distances. In this communication we investigate the positive and negative facets of these three classes of models.
|
high energy physics phenomenology
|
Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary size quasi-1D bosonic system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.
|
quantum physics
|
In this paper, we focus on a variant of the Berth Allocation Problem (BAP), which aims at assigning berthing times and positions to vessels in container terminals. The problem, known as the multi-port berth allocation problem (MPBAP) extends the BAP to cover multiple ports where vessel traveling speeds are optimized between ports, thus exploiting the potentials of a collaboration between carriers and terminal operators. Exploiting a graph representation of the problem, we reformulate an existing mixed-integer problem formulation into a generalized set partitioning problem where each variable refers to a sequence of feasible berths in the ports visited by the vessel. Integrating column generation and cut separation in a branch-and-cut-and-price procedure, the method is able to outperform commercial solvers in a set of benchmark instances and adapts better to larger instances. In addition, we apply methods of cooperative game theory for distributing efficiently the savings of a potential collaboration and show that both carriers and terminal operators would benefit from such collaboration.
|
mathematics
|
We consider one-particle reducible (1PR) contributions to QED and scalar QED processes in external fields, at one-loop and two-loop order. We investigate three cases in detail: constant crossed fields, constant magnetic fields, and plane waves. We find that 1PR tadpole contributions in plane waves and constant crossed fields are non-zero, but contribute only divergences to be renormalised away. In constant magnetic fields, on the other hand, tadpole contributions give physical corrections to processes at one-loop and beyond. Our calculations are exact in the external fields and we give strong and weak field expansions in the magnetic case.
|
high energy physics theory
|
The possibilities and conditions of effective interaction, in particular acceleration, of charged particles by the field of plane electromagnetic wave in the presence of an external constant mag-netic field are considered. It is shown that the well-known conditions of cyclotron resonances require generalization. New conditions for the resonant interaction of charged particles are for-mulated, which contain not only the strength of the external magnetic field (as the well-known conditions of cyclotron resonances) but also the field strength of the wave. strengths. It is shown that new resonance conditions open up new possibilities for effective particle acceleration.
|
physics
|
Meta-optics has achieved major breakthroughs in the past decade; however, conventional forward design faces challenges as functionality complexity and device size scale up. Inverse design aims at optimizing meta-optics design but has been currently limited by expensive brute-force numerical solvers to small devices, which are also difficult to realize experimentally. Here, we present a general inverse design framework for aperiodic large-scale complex meta-optics in three dimensions, which alleviates computational cost for both simulation and optimization via a fast-approximate solver and an adjoint method, respectively. Our framework naturally accounts for fabrication constraints via a surrogate model. In experiments, we demonstrate, for the first time, aberration-corrected metalenses working in the visible with high numerical aperture, poly-chromatic focusing, and large diameter up to centimeter scale. Such large-scale meta-optics opens a new paradigm for applications, and we demonstrate its potential for future virtual-reality platforms by using a meta-eyepiece and a laser back-illuminated micro-Liquid Crystal Display.
|
physics
|
We study the possibility of future $e^{+}e^{-}$ colliders to disentangle different new physics contributions to the production of heavy sterile Majorana neutrinos in the lepton number violating channel $e^{+}e^{-}\rightarrow l^{+} l^{+}+ 4 jets$, with $l=e, \mu, \tau$. This is done investigating the final anti-tau polarization trails and initial beam polarization dependence of the signal on effective operators with distinct Dirac-Lorentz structure contributing to the Majorana neutrino production and decay, which parameterize new physics from a higher energy scale. We find both analyses could well disentangle possible vectorial and scalar operators contributions.
|
high energy physics phenomenology
|
In this paper we use a close connection between the coupled wire construction (CWC) of Abelian quantum Hall states and the theory of composite bosons to extract the Laughlin wave function and the hydrodynamic effective theory in the bulk, including the Wen-Zee topological action, directly from the CWC. We show how rotational invariance can be recovered by fine-tuning the interactions. A simple recipe is also given to construct general Abelian quantum Hall states desceibed by the multi-component Wen-Zee action.
|
condensed matter
|
Nitrogen vacancy (NV) centers, optically active atomic defects in diamond, have been widely applied to emerging quantum sensing, imaging, and network efforts, showing unprecedented field sensitivity and nanoscale spatial resolution. Many of these advantages derive from their excellent quantum-coherence, controllable entanglement, and high fidelity of operations, enabling opportunities to outperform the classical counterpart. Exploiting this cutting-edge quantum metrology, we report noninvasive measurement of intrinsic spin fluctuations of magnetic insulator thin films with a spontaneous out-of-plane magnetization. The measured field dependence of NV relaxation rates is well correlated to the variation of magnon density and band structure of the magnetic samples, which are challenging to access by the conventional magnetometry methods. Our results highlight the significant opportunities offered by NV centers in diagnosing the noise environment of functional magnetic elements, providing valuable information to design next-generation, high-density, and scalable spintronic devices.
|
condensed matter
|
Inspired by the event accumulation around 2.6 GeV in the $\eta^\prime\pi^+\pi^-$ invariant mass spectrum of $J/\psi\to \gamma \eta^\prime\pi^+\pi^-$, which was reported by the BESIII Collaboration, we carry out the study of the mass spectrum and decay behavior of four radial excitations in the pseudoscalar meson family, which include $\eta^{(\prime)}(6S)$ and $\eta^{(\prime)}(7S)$. Combining with these analysis, we present the calculation of the reactions induced by a pion or kaon on the proton target which are relevant to these four discussed states. According to this information, we give concrete experimental suggestion of searching for them, which will become a new task for future experiments.
|
high energy physics phenomenology
|
We consider the Boolean model with random radii based on Cox point processes. Under a condition of stabilization for the random environment, we establish existence and non-existence of subcritical regimes for the size of the cluster at the origin in terms of volume, diameter and number of points. Further, we prove uniqueness of the infinite cluster for sufficiently connected environments.
|
mathematics
|
Although deep learning research and applications have grown rapidly over the past decade, it has shown limitation in healthcare applications and its reachability to people in remote areas. One of the challenges of incorporating deep learning in medical data classification or prediction is the shortage of annotated training data in the healthcare industry. Medical data sharing privacy issues and limited patient population size can be stated as some of the reasons for training data insufficiency in healthcare. Methods to exploit deep learning applications in healthcare have been proposed and implemented in this dissertation. Traditional diagnosis of diabetic retinopathy requires trained ophthalmologists and expensive imaging equipment to reach healthcare centres in order to provide facilities for treatment of preventable blindness. Diabetic people residing in remote areas with shortage of healthcare services and ophthalmologists usually fail to get periodical diagnosis of diabetic retinopathy thereby facing the probability of vision loss or impairment. Deep learning and mobile application development have been integrated in this dissertation to provide an easy to use point-of-care smartphone based diagnosis of diabetic retinopathy. In order to solve the challenge of shortage of healthcare centres and trained ophthalmologists, the standalone diagnostic service was built so as to be operated by a non-expert without an internet connection. This approach could be transferred to other areas of medical image classification.
|
electrical engineering and systems science
|
Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable of capturing nonlinear relationships. The standard strategy is to first map the data into a high dimensional feature space using kernels prior to a projection onto a low dimensional space. While KDR methods can be easily solved by keeping the most dominant eigenvectors of the kernel matrix, its features are no longer easy to interpret. Alternatively, Interpretable KDR (IKDR) is different in that it projects onto a subspace \textit{before} the kernel feature mapping, therefore, the projection matrix can indicate how the original features linearly combine to form the new features. Unfortunately, the IKDR objective requires a non-convex manifold optimization that is difficult to solve and can no longer be solved by eigendecomposition. Recently, an efficient iterative spectral (eigendecomposition) method (ISM) has been proposed for this objective in the context of alternative clustering. However, ISM only provides theoretical guarantees for the Gaussian kernel. This greatly constrains ISM's usage since any kernel method using ISM is now limited to a single kernel. This work extends the theoretical guarantees of ISM to an entire family of kernels, thereby empowering ISM to solve any kernel method of the same objective. In identifying this family, we prove that each kernel within the family has a surrogate $\Phi$ matrix and the optimal projection is formed by its most dominant eigenvectors. With this extension, we establish how a wide range of IKDR applications across different learning paradigms can be solved by ISM. To support reproducible results, the source code is made publicly available on \url{https://github.com/chieh-neu/ISM_supervised_DR}.
|
statistics
|
Probability of reflection $R(E)$ off a finite attractive scattering potential at zero or low energies is ordinarily supposed to be 1. However, a fully attractive potential presents a paradoxical result that $R(0)=0$ or $R(0)<1$, when an effective parameter $q$ of the potential admits special discrete values. Here, we report another class of finite potentials which are well-barrier (attractive-repulsive) type and which can be made to possess much less reflection at zero and low energies for a band of low values of $q$. These well-barrier potentials have only two real turning points for $E \in(V_{min}, V_{max})$, excepting $E=0$. We present two exactly solvable and two numerically solved models to confirm this phenomenon.
|
quantum physics
|
We consider a setup consisting of two coupled sheets of bilayer graphene in the regime of strong spin-orbit interaction, where electrostatic confinement is used to create an array of effective quantum wires. We show that for suitable interwire couplings the system supports a topological insulator phase exhibiting Kramers partners of gapless helical edge states, while the additional presence of a small in-plane magnetic field and weak proximity-induced superconductivity leads to the emergence of zero-energy Majorana corner states at all four corners of a rectangular sample, indicating the transition to a second-order topological superconducting phase. The presence of strong electron-electron interactions is shown to promote the above phases to their exotic fractional counterparts. In particular, we find that the system supports a fractional topological insulator phase exhibiting fractionally charged gapless edge states and a fractional second-order topological superconducting phase exhibiting zero-energy $\mathbb{Z}_{2m}$ parafermion corner states, where $m$ is an odd integer determined by the position of the chemical potential.
|
condensed matter
|
The aim of this work is to improve the modelling of ion populations in higher density, lower temperature astrophysical plasmas, of the type commonly found in lower solar and stellar atmospheres. Ion population models for these regions frequently employ the coronal approximation, which assumes conditions more suitable to the upper solar atmosphere, where high temperatures and lower densities prevail. Using the coronal approximation for modelling the solar transition region gives theoretical lines intensities for the Li-like and Na-like isoelectronic sequences which can be factors of 2-5 times lower than observed. The works of Burgess & Summers (1969) and Nussbaumer & Storey (1975) showed the important part ions in excited levels play when included in the modelling. Their models, however, used approximations for the atomic rates to determine the ion balance. Presented here is the first stage in updating these earlier models of carbon by using rates from up-to-date atomic calculations and more recent photo-ionising radiances for the quiet Sun. Where such atomic rates are not readily available, in the case of electron-impact direct ionisation and excitation--auto-ionisation, new calculations have been made and compared to theoretical and experimental studies. The effects each atomic process has on the ion populations as density changes is demonstrated, and final results from the modelling are compared to the earlier works. Lastly, the new results for ion populations are used to predict line intensities for the solar transition region in the quiet Sun, and these are compared with predictions from coronal-approximation modelling and with observations. Significant improvements in the predicted line intensities are seen in comparison to those obtained from zero-density modelling of carbon.
|
astrophysics
|
In this paper, an analytical predictive model of interface charge traps in symmetric long channel double-gate junctionless transistors is proposed based on a charge-based model. Interface charge traps arising from the exposure to chemicals, high-energy ionizing radiation or aging mechanism could degrade the charge-voltage characteristics. The model is predictive in a range of temperature from 77K to 400K. The validity of the approach is confirmed by extensive comparisons with numerical TCAD simulations in all regions of operation from deep depletion to accumulation and linear to saturation.
|
physics
|
A novel scheme is proposed for generating a polarized positron beam via multiphoton Breit-Wheeler process during the collision of a 10 GeV, pC seeding electron beam with the other 1 GeV, nC driving electron beam. The driving beam provides the strong self-generated field, and a suitable transverse deviation distance between two beams enables the field experienced by the seeding beam to be unipolar, which is crucial for realizing the positron polarization. We employ the particle simulation with a Monte-Carlo method to calculate the spin- and polarization-resolved photon emission and electron-positron pair production in the local constant field approximation. Our simulation results show that a highly polarized positron beam with polarization above $40\%$ can be generated in several femtoseconds, which is robust with respect to parameters of two electron beams. Based on an analysis of the influence of $\gamma$-photon polarization on the polarized pair production, we find that a polarized seeding beam of the proper initial polarization can further improve the positron polarization to $60\%$.
|
physics
|
Deciphering the associations between network connectivity and nodal attributes is one of the core problems in network science. The dependency structure and high-dimensionality of networks pose unique challenges to traditional dependency tests in terms of theoretical guarantees and empirical performance. We propose an approach to test network dependence via diffusion maps and distance-based correlations. We prove that the new method yields a consistent test statistic under mild distributional assumptions on the graph structure, and demonstrate that it is able to efficiently identify the most informative graph embedding with respect to the diffusion time. The methodology is illustrated on both simulated and real data.
|
statistics
|
The intelligent reflecting surface (IRS) is an emerging technique to extend the wireless coverage. In this letter, the performance of hybrid automatic repeat request (hybrid-ARQ) for an IRS-assisted system is analyzed. More specifically, the outage performance of the IRS-aided system using hybrid-ARQ protocol with chase combining is studied. Asymptotic analysis also shows that the outage performance improves better than linearly by increasing number of reflectors of the IRS-aided system. The results also verify the potential of combining the ARQ scheme in the link layer of the IRS-aided system and demonstrate that very small change of path loss condition can impact the performance largely.
|
computer science
|
In this paper, we exploit the effective way to leverage contextual information to improve the speech dereverberation performance in real-world reverberant environments. We propose a temporal-contextual attention approach on the deep neural network (DNN) for environment-aware speech dereverberation, which can adaptively attend to the contextual information. More specifically, a FullBand based Temporal Attention approach (FTA) is proposed, which models the correlations between the fullband information of the context frames. In addition, considering the difference between the attenuation of high frequency bands and low frequency bands (high frequency bands attenuate faster than low frequency bands) in the room impulse response (RIR), we also propose a SubBand based Temporal Attention approach (STA). In order to guide the network to be more aware of the reverberant environments, we jointly optimize the dereverberation network and the reverberation time (RT60) estimator in a multi-task manner. Our experimental results indicate that the proposed method outperforms our previously proposed reverberation-time-aware DNN and the learned attention weights are fully physical consistent. We also report a preliminary yet promising dereverberation and recognition experiment on real test data.
|
electrical engineering and systems science
|
A quantum state transformation can be generally approximated by single- and two-qubit gates. This, however, does not hold with noisy intermediate-scale quantum technologies due to the errors appearing in the gate operations, where errors of two-qubit gates such as controlled-NOT and SWAP operations are dominated. In this work, we present a cost efficient single-copy certification for a realization of a two-qubit gate in the presence of depolarization noise, where it is aimed to identify if the realization is noise-free, or not. It is shown that entangled resources such as entangled states and a joint measurement are not necessary for the purpose, i.e., a noise-free two-qubit gate is not needed to certify an implementation of a two-qubit gate. A proof-of-principle demonstration is presented with photonic qubits.
|
quantum physics
|
Let $X$ be a variety defined over a local field $K$ of mixed characteristic $(0,p)$ with a totally degenerate reduction in the sense of Raskind and Xarles. Generalizing earlier work of Raskind and Xarles and relying on some conjectures we define a map, which we call the toric regulator, from the various motivic cohomology groups of $X$ to certain $p$-adically uniformized tori over $K$. This construction captures the part of the \'etale regulators on $X$ that land in the Galois cohomology of the submodules of cohomology which are extensions of $\mathbb{Z}_l$ by $\mathbb{Z}_l(1)$, simultaneously for all $l$. We also discuss the relation with the log-syntomic regulator and study a number of examples. In particular, for $K_2$ of a Mumford curve we find a relation with the rigid analytic regulator of \'Pal and for $K_1$ of the product of Mumford curves we conjecture a formula for the toric regulator.
|
mathematics
|
In this paper we investigate equilibria of continuous differential equation models of network dynamics. The motivation comes from gene regulatory networks where each directed edge represents either down- or up-regulation, and is modeled by a sigmoidal nonlinear function. We show that the existence and stability of equilibria of a sigmoidal system is determined by a combinatorial analysis of the limiting switching system with piece-wise constant non-linearities. In addition, we describe a local decomposition of a switching system into a product of simpler cyclic feedback systems, where the cycles in each decomposition correspond to a particular subset of network loops.
|
mathematics
|
Many microscopy applications are limited by the total amount of usable light and are consequently challenged by the resulting levels of noise in the acquired images. This problem is often addressed via (supervised) deep learning based denoising. Recently, by making assumptions about the noise statistics, self-supervised methods have emerged. Such methods are trained directly on the images that are to be denoised and do not require additional paired training data. While achieving remarkable results, self-supervised methods can produce high-frequency artifacts and achieve inferior results compared to supervised approaches. Here we present a novel way to improve the quality of self-supervised denoising. Considering that light microscopy images are usually diffraction-limited, we propose to include this knowledge in the denoising process. We assume the clean image to be the result of a convolution with a point spread function (PSF) and explicitly include this operation at the end of our neural network. As a consequence, we are able to eliminate high-frequency artifacts and achieve self-supervised results that are very close to the ones achieved with traditional supervised methods.
|
electrical engineering and systems science
|
Identifying important nodes is one of the central tasks in network science, which is crucial for analyzing the structure of a network and understanding the dynamical processes on a network. Most real-world systems are time-varying and can be well represented as temporal networks. Motivated by the classic gravity model in physics, we propose a temporal gravity model to identify influential nodes in temporal networks. Two critical elements in the gravity model are the masses of the objects and the distance between two objects. In the temporal gravity model, we treat nodes as the objects, basic node properties, such as static and temporal properties, as the nodes' masses. We define temporal distances, i.e., fastest arrival distance and temporal shortest distance, as the distance between two nodes in our model. We utilize our model as well as the baseline centrality methods on important nodes identification. Experimental results on ten real-world datasets show that the temporal gravity model outperforms the baseline methods in quantifying node structural influence. Moreover, when we use the temporal shortest distance as the distance between two nodes, our model is robust and performs the best in quantifying node spreading influence compared to the baseline methods.
|
physics
|
The current COVID-19 pandemic is now getting contained, albeit at the cost of morethan2.3million human lives. A critical phase in any pandemic is the early detection of cases to develop preventive treatments and strategies. In the case of COVID-19,several studies have indicated that chest radiography images of the infected patients show characteristic abnormalities. However, at the onset of a given pandemic, such asCOVID-19, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because the time spent in collecting large amounts of data from infected persons could lead to the loss of human lives and delays in preventive interventions. Therefore, we formulate the problem of identifying early cases in a pandemic as an anomaly detection problem, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest (COVID-19 in our case). To solve this problem, we present several unsupervised deep learning approaches, including convolutional and adversarially trained autoencoder. We tested two settings on a publicly available dataset (COVIDx)by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-19 as an anomaly. Afterperforming3-fold cross validation, we obtain a ROC-AUC of0.765. These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays
|
electrical engineering and systems science
|
We explore nonlocality of three-qubit pure symmetric states shared between Alice, Bob and Charlie using the Clauser-Horne-Shimony-Holt (CHSH) inequality. We make use of the elegant parametrization in the canonical form of these states, proposed by Meill and Meyer (Phys. Rev. A {\bf 96}, 062310 (2017)) based on Majorana geometric representation. The reduced two-qubit states, extracted from an arbitrary pure entangled symmetric three-qubit state do not violate the CHSH inequality and hence they are CHSH-local. However, when Alice and Bob perform a CHSH test, after conditioning over measurement results of Charlie, nonlocality of the state is revealed. We have also shown that two different families of three-qubit pure symmetric states, consisting of two and three distinct spinors (qubits) respectively, can be distinguished based on the strength of violation in the conditional CHSH nonlocality test. Furthermore, we identify {\em six} of the 46 classes of tight Bell inequalities in the three-party, two-setting, two-outcome i.e., (3,2,2) scenario (Phys. Rev. A 94, 062121 (2016)). Among the two inequivalent families of three-qubit pure symmetric states, only the states belonging to three distinct spinor class show maximum violations of these six tight Bell inequalities.
|
quantum physics
|
Blindness in diabetic patients caused by retinopathy (characterized by an increase in the diameter and new branches of the blood vessels inside the retina) is a grave concern. Many efforts have been made for the early detection of the disease using various image processing techniques on retinal images. However, most of the methods are plagued with the false detection of the blood vessel pixels. Given that, here, we propose a modified matched filter with the first derivative of Gaussian. The method uses the top-hat transform and contrast limited histogram equalization. Further, we segment the modified multiscale matched filter response by using a binary threshold obtained from the first derivative of Gaussian. The method was assessed on a publicly available database (DRIVE database). As anticipated, the proposed method provides a higher accuracy compared to the literature. Moreover, a lesser false detection from the existing matched filters and its variants have been observed.
|
electrical engineering and systems science
|
We report the growth of mm-sized Pmnb-Li2FeSiO4 single crystals by means of the optical floating-zone method at high argon pressure and describe the conditions required for a stable growth process. The crystal structure is determined and refined by single-crystal X-ray diffraction. The lattice constants amount to a = 6.27837(3) A, b = 10.62901(6) A and c = 5.03099(3) A at 100 K. In addition, we present high-resolution neutron powder diffraction data that suggest that the slight Li-Fe site exchange seems to be intrinsic to this material. High quality of the crystal is confirmed by very sharp anomalies in the static magnetic susceptibility and in the specific heat associated with the onset of long-range antiferromagnetic order at TN = 17.0(5) K and pronounced magnetic anisotropy for the three crystallographic axes. Furthermore, magnetic susceptibility excludes the presence of sizable amounts of magnetic impurity phases.
|
condensed matter
|
The integrated massless vertex operator in an $AdS_5\times S^5$ background in the pure spinor formalism is constructed in terms of superfields.
|
high energy physics theory
|
We perform the large-$N$ expansion in the Schwinger-Keldysh formulation of non-equilibrium quantum systems with matrix degrees of freedom, and study universal features of the anticipated dual string theory. We find a rich refinement of the topological genus expansion: In the original formulation, the future time instant where the forward and backward branches of the Schwinger-Keldysh time contour meet is associated with its own worldsheet genus expansion. After the Keldysh rotation, the worldsheets decompose into a classical and quantum part.
|
high energy physics theory
|
Multidimensional NMR spectroscopy is one of the basic tools for determining the structure of biomolecules. Unfortunately, the resolution of the spectra is often limited by inter-nuclear couplings. This limitation cannot be overcome by common ways of increasing resolution, i.e. non-uniform sampling (NUS) followed by compressed sensing (CS) reconstruction. In this paper, we show how to enrich CS processing with virtual decoupling leading to an increase in resolution, sensitivity, and overall quality of NUS reconstruction. A mathematical description of the decoupling by deconvolution approach explains the effects of noise, modulation of the sampling schedule, and reveals relation with the underlying assumption of the CS. The gain in resolution and sensitivity is demonstrated for the basic experiment used for protein backbone assignment 3D HNCA applied to two large protein systems: intrinsically disordered 441-residue Tau and a 509-residue globular bacteriophytochrome fragment.
|
physics
|
We study the estimation of a single parameter characterizing families of unitary transformations acting on two systems. We consider the situation with the presence of bottleneck, i.e. only one of the systems can be measured to gather information. The estimation capabilities are related to unitaries' generators. In particular, we establish continuity of quantum Fisher information with respect to generators. Furthermore, we find conditions on the generators to achieve the same maximum quantum Fisher information we would have in the absence of bottleneck. We also discuss the usefulness of initial entanglement across the two systems as well as across multiple estimation instances.
|
quantum physics
|
The ocean wave distribution in a specific region of space and time is described by its sea state. Knowledge about the sea states a ship encounters on a journey can be used to assess various parameters of risk and wear associated with the journey. Two important characteristics of the sea state are the significant wave height and mean wave period. We propose a joint spatial model of these two quantities on the north Atlantic ocean. The model describes the distribution of the logarithm of the two quantities as a bivariate Gaussian random field. This random field is modeled as a solution to a system of coupled stochastic partial differential equations. The bivariate random field can model a wide variety of non-stationary anisotropy and allows for arbitrary, and different, differentiability for the two marginal fields. The parameters of the model are estimated on data of the north Atlantic using a stepwise maximum likelihood method. The fitted model is used to derive the distribution of accumulated fatigue damage for a ship sailing a transatlantic route. Also, a method for estimating the risk of capsizing due to broaching-to, based on the joint distribution of the two sea state characteristics, is investigated. The risks are calculated for a transatlantic route between America and Europe using both data and the fitted model. The results show that the model compares well with observed data. Also, it shows that the bivariate model is needed and cannot simply be approximated by a model of significant wave height alone.
|
statistics
|
We give a general description of the interplay that can occur between local and global anomalies, in terms of (co)bordism. Mathematically, such an interplay is encoded in the non-canonical splitting of short exact sequences known to classify invertible field theories. We study various examples of the phenomenon in 2, 4, and 6 dimensions. We also describe how this understanding of anomaly interplay provides a rigorous bordism-based version of an old method for calculating global anomalies (starting from local anomalies in a related theory) due to Elitzur and Nair.
|
high energy physics theory
|
In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.
|
computer science
|
Person re-identification (re-ID), is a challenging task due to the high variance within identity samples and imaging conditions. Although recent advances in deep learning have achieved remarkable accuracy in settled scenes, i.e., source domain, few works can generalize well on the unseen target domain. One popular solution is assigning unlabeled target images with pseudo labels by clustering, and then retraining the model. However, clustering methods tend to introduce noisy labels and discard low confidence samples as outliers, which may hinder the retraining process and thus limit the generalization ability. In this study, we argue that by explicitly adding a sample filtering procedure after the clustering, the mined examples can be much more efficiently used. To this end, we design an asymmetric co-teaching framework, which resists noisy labels by cooperating two models to select data with possibly clean labels for each other. Meanwhile, one of the models receives samples as pure as possible, while the other takes in samples as diverse as possible. This procedure encourages that the selected training samples can be both clean and miscellaneous, and that the two models can promote each other iteratively. Extensive experiments show that the proposed framework can consistently benefit most clustering-based methods, and boost the state-of-the-art adaptation accuracy. Our code is available at https://github.com/FlyingRoastDuck/ACT_AAAI20.
|
computer science
|
How to estimate heterogeneity, e.g. the effect of some variable differing across observations, is a key question in political science. Methods for doing so make simplifying assumptions about the underlying nature of the heterogeneity to draw reliable inferences. This paper allows a common way of simplifying complex phenomenon (placing observations with similar effects into discrete groups) to be integrated into regression analysis. The framework allows researchers to (i) use their prior knowledge to guide which groups are permissible and (ii) appropriately quantify uncertainty. The paper does this by extending work on "structured sparsity" from a traditional penalized likelihood approach to a Bayesian one by deriving new theoretical results and inferential techniques. It shows that this method outperforms state-of-the-art methods for estimating heterogeneous effects when the underlying heterogeneity is grouped and more effectively identifies groups of observations with different effects in observational data.
|
statistics
|
We propose a new framework to solve online optimization and learning problems in unknown and uncertain dynamical environments. This framework enables us to simultaneously learn the uncertain dynamical environment while making online decisions in a quantifiably robust manner. The main technical approach relies on the theory of distributional robust optimization that leverages adaptive probabilistic ambiguity sets. However, as defined, the ambiguity set usually leads to online intractable problems, and the first part of our work is directed to find reformulations in the form of online convex problems for two sub-classes of objective functions. To solve the resulting problems in the proposed framework, we further introduce an online version of the Nesterov accelerated-gradient algorithm. We determine how the proposed solution system achieves a probabilistic regret bound under certain conditions. Two applications illustrate the applicability of the proposed framework.
|
electrical engineering and systems science
|
In itinerant systems, electron-electron interactions may lead to the formation of local magnetic moments and their effective exchange coupling, which in turn gives rise to long-range magnetic order. Therefore, when moment formation is weakened, such as in the single-band Hubbard model on a square lattice with the on-site repulsion being randomly switched off on a fraction $x$ of sites, magnetic order is suppressed beyond some critical $x_c$, which was found to lie below the classical percolation threshold, $x_c^\text{(perc,sq)}$. Here we study dilute magnetism in flat band systems, namely in the Hubbard model on a `Lieb' lattice. Interestingly, we show that magnetic order persists to $x$ almost twice as large as the classical percolation threshold for the lattice, thus emphasizing the central role of electron itinerancy to the magnetic response. The analysis of the orbital-resolved order parameters reveals that the contribution of the four-fold coordinated `d' sites to magnetism is dramatically affected by dilution, while the localized `p' states of the flat band provide the dominant contribution to long-range correlations. We also examine the transport properties, which suggest the existence of an insulator-to-metal transition in the same range of the critical magnetic dilution.
|
condensed matter
|
We consider a Metropolis--Hastings method with proposal $\mathcal{N}(x, hG(x)^{-1})$, where $x$ is the current state, and study its ergodicity properties. We show that suitable choices of $G(x)$ can change these compared to the Random Walk Metropolis case $\mathcal{N}(x, h\Sigma)$, either for better or worse. We find that if the proposal variance is allowed to grow unboundedly in the tails of the distribution then geometric ergodicity can be established when the target distribution for the algorithm has tails that are heavier than exponential, but that the growth rate must be carefully controlled to prevent the rejection rate approaching unity. We also illustrate that a judicious choice of $G(x)$ can result in a geometrically ergodic chain when probability concentrates on an ever narrower ridge in the tails, something that is not true for the Random Walk Metropolis.
|
statistics
|
We compute the cohomological invariants of $\mathcal{H}_g$, the moduli stack of smooth hyperelliptic curves, for every odd $g$.
|
mathematics
|
Recently Sahoo and Sen obtained a series of remarkable results concerning sub-leading soft photon and graviton theorems in four dimensions. Even though the S- matrix is infrared divergent, they have shown that the sub-leading soft theorems are well defined and exact statements in QED and perturbative Quantum Gravity. However unlike the well studied Cachazo-Strominger soft theorems in tree-level amplitudes, the new sub-leading soft expansion is at the order ln {\omega} (where {\omega} is the soft frequency) and the corresponding soft factors structurally show completely different properties then their tree-level counterparts. Whence it is natural to ask if these theorems are associated to asymptotic symmetries of the S-matrix. We consider this question in the context of sub-leading soft photon theorem in scalar QED and show that there are indeed an infinity of conservation laws whose Ward identities are equivalent to the loop-corrected soft photon theorem. This shows that in the case of four dimensional QED, the leading and sub-leading soft photon theorems are equivalent to Ward identities of (asymptotic) charges.
|
high energy physics theory
|
Entity linkage (EL) is a critical problem in data cleaning and integration. In the past several decades, EL has typically been done by rule-based systems or traditional machine learning models with hand-curated features, both of which heavily depend on manual human inputs. With the ever-increasing growth of new data, deep learning (DL) based approaches have been proposed to alleviate the high cost of EL associated with the traditional models. Existing exploration of DL models for EL strictly follows the well-known twin-network architecture. However, we argue that the twin-network architecture is sub-optimal to EL, leading to inherent drawbacks of existing models. In order to address the drawbacks, we propose a novel and generic contrastive DL framework for EL. The proposed framework is able to capture both syntactic and semantic matching signals and pays attention to subtle but critical differences. Based on the framework, we develop a contrastive DL approach for EL, called CorDEL, with three powerful variants. We evaluate CorDEL with extensive experiments conducted on both public benchmark datasets and a real-world dataset. CorDEL outperforms previous state-of-the-art models by 5.2% on public benchmark datasets. Moreover, CorDEL yields a 2.4% improvement over the current best DL model on the real-world dataset, while reducing the number of training parameters by 97.6%.
|
computer science
|
In this research, some of the issues that arise from the scalarization of the multi-objective optimization problem in the Advantage Actor Critic (A2C) reinforcement learning algorithm are investigated. The paper shows how a naive scalarization can lead to gradients overlapping. Furthermore, the possibility that the entropy regularization term can be a source of uncontrolled noise is discussed. With respect to the above issues, a technique to avoid gradient overlapping is proposed, while keeping the same loss formulation. Moreover, a method to avoid the uncontrolled noise, by sampling the actions from distributions with a desired minimum entropy, is investigated. A comprehensive pilot experiment is carried out to show how the proposed methods considerably speeds up the training. The proposed approach can be applied to any Advantage-based Reinforcement Learning algorithm.
|
computer science
|
We report constraints on the nucleon-dark matter particle cross section using the internal luminosity of observed white dwarf stars in the globular cluster Messier 4. Our results cover the parameter space corresponding to relatively light dark matter particles, in the mass range $0.1~GeV-5~GeV$, which is known to be very difficult to be probed by direct dark matter searches. The additional luminosity coming from self-annihilations of dark matter particles captured inside the stars must not exceed the observed luminosity. Imposing that condition, we obtain for the spin independent cross section of light dark matter particles on baryons $\sigma_{N\chi}$ the upper bound: $\sigma_{N\chi} < 4 \times 10^{-41}{\rm cm^2}$.
|
high energy physics phenomenology
|
We analyse the spectroscopic and photometric variability of the Oe star HD 60848 over the last twenty five years. The spectra reveal recurrent, but irregular cycles of increased circumstellar emission lines. These cycles are highly asymmetric displaying a slow increase over about 6 years, followed by a fast decay within about 6 months. Our analysis focuses on the most recent cycle (2013 - 2020). The equivalent width and velocity separation of the emission peaks indicate variations of the outer disk radius by a factor ~ 2.2, although the variability appears more complex than expected from first principle relations for optically thin Keplerian disks. We observe a time delay between the variations of the strengths of He I 5876 on the one hand and H-alpha and H-beta on the other hand. We interpret this behaviour in a two-step disk growth scenario, where the disk first expands radially before its density increases. A difference in behaviour is also seen between H-alpha and the H I Paschen lines, with the latter displaying a more symmetric cycle, similar to the photometric variability. The rather fast decays of the H-alpha emission observed in 2001, 2009 and 2018 - 2019 suggest that the strong radiation field and early spectral type of the star lead to a faster dissipation of the disk than in later-type Be stars, as theoretically expected. We discuss X-ray observations of the star both during a high and a low-emission state. The X-ray spectrum is soft at both epochs, and the X-ray fluxes are only marginally different and remain consistent with the canonical Lx/Lbol relation of O-type stars. These results indicate that the circumstellar decretion disk of HD 60848 has essentially no impact on the star's X-ray emission, and that the latter most likely arises inside the stellar wind.
|
astrophysics
|
User representation learning is vital to capture diverse user preferences, while it is also challenging as user intents are latent and scattered among complex and different modalities of user-generated data, thus, not directly measurable. Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data. Both users and topics are embedded to the same space to encode users' social connections and text content, to facilitate joint modeling of different modalities, via a probabilistic generative framework. We evaluated the proposed solution on large collections of Yelp reviews and StackOverflow discussion posts, with their associated network structures. The proposed model outperformed several state-of-the-art topic modeling based user models with better predictive power in unseen documents, and state-of-the-art network embedding based user models with improved link prediction quality in unseen nodes. The learnt user representations are also proved to be useful in content recommendation, e.g., expert finding in StackOverflow.
|
computer science
|
We define a latent structure model (LSM) random graph as a random dot product graph (RDPG) in which the latent position distribution incorporates both probabilistic and geometric constraints, delineated by a family of underlying distributions on some fixed Euclidean space, and a structural support submanifold from which the latent positions for the graph are drawn. For a one-dimensional latent structure model with known structural support, we show how spectral estimates of the latent positions of an RDPG can be used for efficient estimation of the paramaters of the LSM. We describe how to estimate or learn the structural support in cases where it is unknown, with an illustrative focus on graphs with latent positions along the Hardy-Weinberg curve. Finally, we use the latent structure model formulation to test bilateral homology in the Drosophila connectome.
|
statistics
|
The realization of multimode optomechanical interactions in the single-photon strong-coupling regime is a desired task in cavity optomechanics, but it remains a challenge in realistic physical systems. In this work, we propose a reliable scheme to simulate a three-mode optomechanical system working in the single-photon strong-coupling regime based on the Fredkin-type interaction. This is achieved by utilizing two strong drivings to the two exchangly-coupled modes in the Fredkin-type coupling involving one optical mode and two mechanical-like modes. As an application of this enhanced three-mode nonlinear optomechanical coupling, we show how to generate entangled-cat states of the mechanical-like modes using the conditional displacement mechanism. The quantum coherence effects in the generated states are investigated by calculating two-mode joint Wigner function and quantum entanglement. The influence of the dissipation effect on the state generation is considered in the open-system case.
|
quantum physics
|
We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited. As a complement to the growing literature on spectral design of covariance kernels, the core of our proposal is to model power spectral densities through a rectangular function, which results in a kernel based on the sinc function with straightforward extensions to non-centred (around zero frequency) and frequency-varying cases. In addition to its use in regression, the relationship between the sinc kernel and the classic theory is illuminated, in particular, the Shannon-Nyquist theorem is interpreted as posterior reconstruction under the proposed kernel. Additionally, we show that the sinc kernel is instrumental in two fundamental signal processing applications: first, in stereo amplitude modulation, where the non-centred sinc kernel arises naturally. Second, for band-pass filtering, where the proposed kernel allows for a Bayesian treatment that is robust to observation noise and missing data. The developed theory is complemented with illustrative graphic examples and validated experimentally using real-world data.
|
statistics
|
Quantum electrodynamics (QED) of electrons confined in a plane and that yet can undergo interactions mediated by an unconstrained photon has been described by the so-called {\it pseudo-QED} (PQED), the (2+1)-dimensional version of the equivalent dimensionally reduced original QED. In this work, we show that PQED with a nonlocal Chern-Simons term is dual to the Chern-Simons Higgs model at the quantum level. We apply the path-integral formalism in the dualization of the Chern-Simons Higgs model to first describe the interaction between quantum vortex particle excitations in the dual model. This interaction is explicitly shown to be in the form of a Bessel-like type of potential in the static limit. This result {\it per se} opens exciting possibilities for investigating topological states of matter generated by interactions, since the main difference between our new model and the PQED is the presence of a nonlocal Chern-Simons action. Indeed, the dual transformation yields an unexpected square root of the d'Alembertian operator, namely, $(\sqrt{-\Box})^{-1}$ multiplied by the well-known Chern-Simons action. Despite the nonlocality, the resulting model is still gauge invariant and preserves the unitarity, as we explicitly prove. {}Finally, when coupling the resulting model to Dirac fermions, we then show that pairs of bounded electrons are expected to appear, with a typical distance between the particles being inversely proportional to the topologically generated mass for the gauge field in the dual model.
|
high energy physics theory
|
We reconsider the two-point string scattering amplitudes of massless Neveu-Schwarz--Neveu-Schwarz states of Type IIB orientifold superstring theory on the disk and projective plane in ten dimensions and analyse its $\alpha'$ expansion. We also discuss the unoriented Type IIB theory on $T^6/\mathbb{Z}_2\times\mathbb{Z}_2$ where two-point string scattering amplitudes of the complex K\"ahler moduli and complex structures of the untwisted sector are computed on the disk and projective plane. New results are obtained together with known ones. Finally, we compare string scattering amplitudes results at $\alpha'^2$-order with the (curvature)$^2$ terms in the low energy effective action of D-branes and $\Omega$-planes in both cases.
|
high energy physics theory
|
A particle detection system that exploits the scintillation light produced by ionizing particles in liquid argon (LAr) has been assembled at CERN. The system is based on 10 large-area photomultiplier tubes (PMT) immersed in a 1500-liter dewar filled with liquid argon and equipped with an extendible feed-through and mechanical support for an alpha source (241Am). The position of the source can be changed with respect to the PMT plane in vertical and horizontal directions. Arrays of silicon photomultiplier (SiPM) photodetectors, integrated in the source support, are used for the data acquisition trigger and to define the t0 of the light generation. PMT and SiPM signals can be recorded at different distances and different positions allowing the measurement of some of the LAr scintillation light properties. The system was studied and characterized in detail, and physics results on LAr scintillation properties are expected soon.
|
physics
|
Stress is known as one of the major factors threatening human health. A large number of studies have been performed in order to either assess or relieve stress by analyzing the brain and heart-related signals. In this study, signals produced by functional Near-Infrared Spectroscopy (fNIRS) of the brain recorded from 10 healthy volunteers are employed to assess the stress induced by the Montreal Imaging Stress Task by means of a deep learning system. The proposed deep learning system consists of two main parts: First, the one-dimensional convolutional neural network is employed to build informative feature maps. Then, a stack of deep fully connected layers is used to predict the stress existence probability. Experiment results showed that the trained fNIRS model performs stress classification by achieving 88.52 -+ 0.77% accuracy. Employment of the proposed deep learning system trained on the fNIRS measurements leads to higher stress classification accuracy than the existing methods proposed in fNIRS studies in which the same experimental procedure has been employed. The proposed method suggests better stability with lower variation in prediction. Furthermore, its low computational cost opens up the possibility to be applied in real-time stress assessment.
|
electrical engineering and systems science
|
We present a fully local treatment of the double slit experiment in the formalism of quantum field theory. Our exposition is predominantly pedagogical in nature and exemplifies the fact that there is an entirely local description of the quantum double slit interference that does not suffer from any supposed paradoxes usually related to the wave-particle duality. The wave-particle duality indeed vanishes in favour of the field picture in which particles should not be regarded as the primary elements of reality and only represent excitations of some specific field configurations. Our treatment is general and can be applied to any other phenomenon involving quantum interference of any bosonic or fermionic field, both spatially and temporally. For completeness, we present the full treatment of single qubit interference in the same spirit.
|
quantum physics
|
Objectives: To evaluate the relationship between population size and number of crimes in cities across twelve countries and assess the impact of per capita measurements on crime analyses, depending on offense type. Methods: We use data on burglaries and thefts at the city level and evaluate the relationship between crime numbers and population size using probabilistic scaling analysis. We estimate the growth exponent of each offense type and use Kendall rank correlation to assess the impact of a linear growth assumption (i.e., per-capita analysis) on cities rankings. Result: In nine out of eleven countries, theft increases superlinearly with population size; in two of them, it increases linearly. In eight out of ten countries, burglary increases linearly with population size; in two of them, it increases superlinearly. In nonlinear scenarios, using per capita rates to rank cities produces substantially different rankings from rankings adjusted for population size. Conclusions: Comparing cities using per capita crime rates (e.g., crime per 100,000 people per year) assumes that crime increases linearly with population size. Our findings indicate, however, that this assumption is unfounded, implying that one should be cautious when using per capita rankings. When crime increases nonlinearly with population, per capita rates do not remove population effects. The contrasting crime growth of burglary and theft also suggests that different crime dynamics at the local level lead to different macro-level features in cities.
|
physics
|
Sensing capability is one of the most highlighted new feature of future 6G wireless networks. This paper addresses the sensing potential of Large Intelligent Surfaces (LIS) in an exemplary Industry 4.0 scenario. Besides the attention received by LIS in terms of communication aspects, it can offer a high-resolution rendering of the propagation environment. This is because, in an indoor setting, it can be placed in proximity to the sensed phenomena, while the high resolution is offered by densely spaced tiny antennas deployed over a large area. By treating an LIS as a radio image of the environment relying on the received signal power, we develop techniques to sense the environment, by leveraging the tools of image processing and machine learning. Once a holographic image is obtained, a Denoising Autoencoder (DAE) network can be used for constructing a super-resolution image leading to sensing advantages not available in traditional sensing systems. Also, we derive a statistical test based on the Generalized Likelihood Ratio (GLRT) as a benchmark for the machine learning solution. We test these methods for a scenario where we need to detect whether an industrial robot deviates from a predefined route. The results show that the LIS-based sensing offers high precision and has a high application potential in indoor industrial environments.
|
electrical engineering and systems science
|
Using the approach based on conformal symmetry we calculate the two-loop coefficient function for the vector flavor-nonsinglet contribution to deeply-virtual Compton scattering (DVCS). The analytic expression for the coefficient function in momentum fraction space is presented in the $\overline{\text{MS}}$ scheme. The corresponding next-to-next-to-leading order correction to the Compton form factor $\mathcal{H}$ for a simple model of the generalized parton distribution appears to be rather large: a factor two smaller than the next-to-leading order correction, approximately $\sim 10$\% of the tree level result in the bulk of the kinematic range, for $Q^2=4$~GeV$^2$.
|
high energy physics phenomenology
|
Metasurfaces have provided a novel and promising platform for the realization of compact and large-scale optical devices. The conventional metasurface design approach assumes periodic boundary conditions for each element, which is inaccurate in most cases since the near-field coupling effects between elements will change when surrounded by non-identical structures. In this paper, we propose a deep learning approach to predict the actual electromagnetic (EM) responses of each target meta-atom placed in a large array with near-field coupling effects taken into account. The predicting neural network takes the physical specifications of the target meta-atom and its neighbors as input, and calculates its phase and amplitude in milliseconds. This approach can be applied to explain metasurfaces' performance deterioration caused by mutual coupling and further used to optimize their efficiencies once combined with optimization algorithms. To demonstrate the efficacy of this methodology, we obtain large improvements in efficiency for a beam deflector and a metalens over the conventional design approach. Moreover, we show the correlations between a metasurface's performance and its design errors caused by mutual coupling are not bound to certain specifications (materials, shapes, etc.). As such, we envision that this approach can be readily applied to explore the mutual coupling effects and improve the performance of various metasurface designs.
|
physics
|
A large number of cameras embedded on smart-phones, drones or inside cars have a direct access to external motion sensing from gyroscopes and accelerometers. On these power-limited devices, video compression must be of low-complexity. For this reason, we propose a "Sensor-Aided Block Matching Algorithm" which exploits the presence of a motion sensor synchronized with a camera to reduce the complexity of the motion estimation process in an inter-frame video codec. Our solution extends the work previously done on rotational motion estimation to an original estimation of the translational motion through a depth map. The proposed algorithm provides a complexity reduction factor of approximately 2.5 compared to optimized block-matching motion compensated inter-frame video codecs while maintaining high image quality and providing as by-product a depth map of the scene.
|
electrical engineering and systems science
|
Laboratory (laser and Z-pinch) opacity measurements of well-characterized plasmas provide data to assist inertial confinement fusion, astrophysics and atomic-physics research. In order to test the atomic-physics codes devoted to the calculation of radiative properties of hot plasmas, such experiments must fulfill a number of requirements. In this work, we discuss some sources of uncertainty in absorption-spectroscopy experiments, concerning areal mass, background emission, intensity of the backlighter and self-emission of the plasma. We also study the impact of spatial non-uniformities of the sample.
|
physics
|
The two-pole structure refers to the fact that particular single states in the spectrum as listed in the PDG tables are often two states. The story began with the $\Lambda(1405)$, when in 2001, using unitarized chiral perturbation theory, it was observed that there are two poles in the complex plane, one close to the $\bar{K}p$ and the other close to the $\pi\Sigma$ threshold. This was later understood combining the SU(3) limit and group-theoretical arguments. Different unitarization approaches that all lead to the two-pole structure have been considered in the mean time, showing some spread in the pole positions. This fact is now part of the PDG book, though it is not yet listed in the summary tables. Here, I will discuss the open ends and critically review approaches that can not deal with this issue. In the meson sector some excited charm mesons are good candidates for such a two-pole structure. Next, I consider in detail the $D_0^*(2300)$, that is another candidate for this scenario. Combining lattice QCD with chiral unitary approaches in the finite volume, the precise data of the Hadron Spectrum Collaboration for coupled-channel $D\pi$, $D\eta$, $D_s\bar{K}$ scattering in the isospin $I=1/2$ channel indeed reveal its two-pole structure. Further states in the heavy meson sector with $I=1/2$ exhibiting this phenomenon are predicted, especially in the beauty meson sector. I also discuss the relation of these two-pole structures and the possible molecular nature of the states under consideration.
|
high energy physics phenomenology
|
We study aspects of two-dimensional nonlinear sigma models with Wess-Zumino term corresponding to a nonclosed 3-form, which may arise upon dimensional reduction in the target space. Our goal in this paper is twofold. In a first part, we investigate the conditions for consistent gauging of sigma models in the presence of a nonclosed 3-form. In the Abelian case, we find that the target of the gauged theory has the structure of a contact Courant algebroid, twisted by a 3-form and two 2-forms. Gauge invariance constrains the theory to (small) Dirac structures of the contact Courant algebroid. In the non-Abelian case, we draw a similar parallel between the gauged sigma model and certain transitive Courant algebroids and their corresponding Dirac structures. In the second part of the paper, we study two-dimensional sigma models related to Jacobi structures. The latter generalise Poisson and contact geometry in the presence of an additional vector field. We demonstrate that one can construct a sigma model whose gauge symmetry is controlled by a Jacobi structure, and moreover we twist the model by a 3-form. This construction is then the analogue of WZW-Poisson structures for Jacobi manifolds.
|
high energy physics theory
|
Given the piecewise approach to modeling intermolecular interactions for force fields, they can be difficult to parameterize since they are fit to data like total energies that only indirectly connect to their separable functional forms. Furthermore, by neglecting certain types of molecular interactions such as charge penetration and charge transfer, most classical force fields must rely on, but do not always demonstrate, how cancellation of errors occurs among the remaining molecular interactions accounted for such as exchange repulsion, electrostatics, and polarization. In this work we present the first generation of the (many-body) MB-UCB force field that explicitly accounts for the decomposed molecular interactions commensurate with a variational energy decomposition analysis, including charge transfer, with force field design choices that reduce the computational expense of the MB-UCB potential while remaining accurate. We optimize parameters using only single water molecule and water cluster data up through pentamers, with no fitting to condensed phase data, and we demonstrate that high accuracy is maintained when the force field is subsequently validated against conformational energies of larger water cluster data sets, radial distribution functions of the liquid phase, and the temperature dependence of thermodynamic and transport water properties. We conclude that MB-UCB is comparable in performance to MB-Pol, but is less expensive and more transferable by eliminating the need to represent short-ranged interactions through large parameter fits to high order polynomials.
|
physics
|
We present for the first time a master formula for $\varepsilon'/\varepsilon$, the ratio probing direct CP violation in $K \to \pi\pi$ decays, valid in any theory beyond the Standard Model (BSM). The formula makes use of hadronic matrix elements of BSM operators calculated recently in the Dual QCD approach and the ones of the SM operators from lattice QCD. We emphasize the large impact of several scalar and tensor BSM operators in the context of the emerging $\varepsilon'/\varepsilon$ anomaly. We have implemented the results in the open source code flavio.
|
high energy physics phenomenology
|
We formulate the theory of nonlinear viscoelastic hydrodynamics of anisotropic crystals in terms of dynamical Goldstone scalars of spontaneously broken translational symmetries, under the assumption of homogeneous lattices and absence of plastic deformations. We reformulate classical elasticity effective field theory using surface calculus in which the Goldstone scalars naturally define the position of higher-dimensional crystal cores, covering both elastic and smectic crystal phases. We systematically incorporate all dissipative effects in viscoelastic hydrodynamics at first order in a long-wavelength expansion and study the resulting rheology equations. In the process, we find the necessary conditions for equilibrium states of viscoelastic materials. In the linear regime and for isotropic crystals, the theory includes the description of Kelvin-Voigt materials. Furthermore, we provide an entirely equivalent description of viscoelastic hydrodynamics as a novel theory of higher-form superfluids in arbitrary dimensions where the Goldstone scalars of partially broken generalised global symmetries play an essential role. An exact map between the two formulations of viscoelastic hydrodynamics is given. Finally, we study holographic models dual to both these formulations and map them one-to-one via a careful analysis of boundary conditions. We propose a new simple holographic model of viscoelastic hydrodynamics by adopting an alternative quantisation for the scalar fields.
|
high energy physics theory
|
In this work, a seismocardiogram (SCG) based breathing-state measuring method is proposed for m-health applications. The aim of the proposed framework is to assess the human respiratory system by identifying degree-of-breathings, such as breathlessness, normal breathing, and long and labored breathing. For this, it is needed to measure cardiac-induced chest-wall vibrations, reflected in the SCG signal. Orthogonal subspace projection is employed to extract the SCG cycles with the help of a concurrent ECG signal. Subsequently, fifteen statistically significant morphological-features are extracted from each of the SCG cycles. These features can efficiently characterize physiological changes due to varying respiratory rates. Stacked autoencoder (SAE) based architecture is employed for the identification of different respiratory-effort levels. The performance of the proposed method is evaluated and compared with other standard classifiers for 1147 analyzed SCG-beats. The proposed method gives an overall average accuracy of 91.45% in recognizing three different breathing states. The quantitative analysis of the performance results clearly shows the effectiveness of the proposed framework. It may be employed in various healthcare applications, such as pre-screening medical sensors and IoT based remote health-monitoring systems.
|
electrical engineering and systems science
|
We derive a formula for the torus partition sum of the symmetric product of $T\bar T$ deformed CFT's, using previous work on long strings in (deformed) $AdS_3$, and universality. The result is given by an integral transform of the partition function for the block of the symmetric product, summed over its Hecke transforms, and is manifestly modular invariant. The spectrum is interpretable as a gas of multiply wound long strings with a particular orientation.
|
high energy physics theory
|
We study the notion of efficiency for cooperative games on simplicial complexes. In such games, the grand coalition $[n]$ may be forbidden, and, thus, it is a non-trivial problem to study the total number of payoff $v_{\Delta}$ of a cooperative game $(\Delta, v)$. We address this question in the more general setting, by characterizing the individual values that satisfy the general efficient requirement $v_{\Delta}^{gen}$ for a generic efficiency assignment. The traditional and the probabilistic efficiency are treated as a special case of this general efficiency. Finally, we introduce a new notion of efficiency arising from the combinatorial and topological property of the simplicial complex $\Delta$. The efficiency in this scenario is called simplicial and we characterize the individual values fulfilling this constraint.
|
mathematics
|
We consider a computational problem where the goal is to approximate the maximum eigenvalue of a two-local Hamiltonian that describes Heisenberg interactions between qubits located at the vertices of a graph. Previous work has shed light on this problem's approximability by product states. For any instance of this problem the maximum energy attained by a product state is lower bounded by the Max Cut of the graph and upper bounded by the standard Goemans-Williamson semidefinite programming relaxation of it. Gharibian and Parekh described an efficient classical approximation algorithm for this problem which outputs a product state with energy at least 0.498 times the maximum eigenvalue in the worst case, and observe that there exist instances where the best product state has energy 1/2 of optimal. We investigate approximation algorithms with performance exceeding this limitation which are based on optimizing over tensor products of few-qubit states and shallow quantum circuits. We provide an efficient classical algorithm which achieves an approximation ratio of at least 0.53 in the worst case. We also show that for any instance defined by a 3- or 4-regular graph, there is an efficiently computable shallow quantum circuit that prepares a state with energy larger than the best product state (larger even than its semidefinite programming relaxation).
|
quantum physics
|
Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation. Yet these neural networks are very sensitive to architecture and hyperparameter settings. Optimizing these settings by grid or random search is computationally expensive because it requires many training runs. In this paper, we incorporate architecture search into a single training run through auto-sizing, which uses regularization to delete neurons in a network over the course of training. On very low-resource language pairs, we show that auto-sizing can improve BLEU scores by up to 3.9 points while removing one-third of the parameters from the model.
|
computer science
|
Moir\'{e} superlattices in twisted bilayer graphene and transition-metal dichalcogenides have emerged as a powerful tool for engineering novel band structures and quantum phases of two-dimensional quantum materials. Here we investigate Moir\'{e} physics emerging from twisting two independent hexagonal optical lattices of atomic (pseudo-)spin states (instead of bilayers), which exhibits remarkably different physics from twisted bilayer graphene. We employ a momentum-space tight-binding calculation that includes all range real-space tunnelings, and show that all twist angles $\theta \lesssim 6^{\circ }$ can become magic that support gapped flat bands. Due to greatly enhanced density of states near the flat bands, the system can be driven to superfluid by weak attractive interaction. Strikingly, the superfluid phase corresponds to a Larkin-Ovchinnikov state with finite momentum pairing, resulting from the interplay between flat bands and inter-spin interactions in the unique single-layer spin-twisted lattice. Our work may pave the way for exploring novel quantum phases and twistronics in cold atomic systems.
|
condensed matter
|
In this paper we look for AdS solutions to generalised gravity theories in the bulk in various spacetime dimensions. The bulk gravity action includes the action of a non-minimally coupled scalar field with gravity, and a higher-derivative action of gravity. The usual Einstein-Hilbert gravity is induced when the scalar acquires a non-zero vacuum expectation value. The equation of motion in the bulk shows scenarios where AdS geometry emerges on-shell. We further obtain the action of the fluctuation fields on the background at quadratic and cubic orders.
|
high energy physics theory
|
A measurement setup made of millimeter-wave and ultra wideband transceivers mounted on both a customized UAV and a ground station for full 3D wireless propagation analysis is described in this work. The developed system represents a flexible solution for the characterization of wireless channels and especially of urban propagation, as the drone might be easily located almost anywhere from ground level to the buildings rooftop and beyond. The double directional properties of the channel can be achieved by rotating directive antennas at the link ends. Other possible applications in urban contexts include above ground level propagation, outdoor-to-indoor penetration, line-of-sight to non-line-of-sight transition, scattering from buildings and air-to-ground channel characterization for UAV-assisted wireless communications.
|
electrical engineering and systems science
|
Time crystals are quantum systems which are able to reveal condensed matter behavior in the time domain. It is known that crystalization in time can be observed in a periodically driven many-body system when interactions between particles force a system to evolve with a period which is an integer multiple of a driving period. This phenomenon is dubbed discrete time crystal formation. Here, we consider ultra-cold atoms bouncing on an oscillating atom mirror and show that the system can spontaneously form a discrete time crystal where the ratio of a period of its motion and a driving period is a rational number. This kind of discrete time crystals requires higher order resonant driving which is analyzed here with the help of an original approach.
|
condensed matter
|
Ants are six-legged insects that can carry loads ten times heavier than their body weight. Since having six-legs, they are intrinsically stable. They are powerful and can carry heavy loads. For these reasons, in this paper a new parallel kinematic structure is proposed for a six-legged ant robot. The mechanical structure is designed and optimized in Solidworks. The mechanism has six legs and only two DC motors actuate the six legs so from mechanical point of view the design is an optimal one. The robot is lightweight and semiautonomous due to using wireless modules. This feature makes this robot to be suitable to be used in social robotics and rescue robotics applications. The transmitter program is implemented in supervisor computer using LabVIEW and a microcontroller is used as the main controller. The electronic board is designed and tested in Proteus Professional and the PCB board is implemented in Altium Designer. Microcontroller programming is done in Code Vision.
|
computer science
|
Predicting rupture risk and deciding on optimal treatment plan for intracranial aneurysms (IAs) is possible by quantification of their size and shape. For this purpose the IA has to be isolated from 3D angiogram. State-of-the-art methods perform IA isolation by encoding neurosurgeon's intuition about former non-dilated vessel anatomy through principled approaches like fitting a cutting plane to vasculature surface, using Gaussian curvature and vessel centerline distance constraints, by deformable contours or graph cuts guided by the curvature or restricted by Voronoi surface decomposition and similar. However, the large variability of IAs and their parent vasculature configurations often leads to failure or non-intuitive isolation. Manual corrections are thus required, but suffer from poor reproducibility. In this paper, we aim to increase the accuracy, robustness and reproducibility of IA isolation through two stage deep learning based segmentation of vascular surface. The surface was represented by local patches in form of point clouds, which were fed into first stage multilayer neural network (MNN) to obtain descriptors invariant to point ordering, rotation and scale. Binary classifier as second stage MNN was used to isolate surface belonging to the IA. Method validation was based on 57 DSA, 28 CTA and 5 MRA images, where cross-validation showed high segmentation sensitivity of 0.985, a substantial improvement over 0.830 obtained for the state-of-the-art method on the same datasets. Visual analysis of IA isolation and its high accuracy and reliability consistent across CTA and DSA scans confirmed the clinical applicability of proposed method.
|
electrical engineering and systems science
|
In this paper, we study the stabilization problem of quantum spin-1/2 systems under continuous-time measurements. In the case without feedback, we show exponential stabilization around the excited and ground state by providing a lower bound of the convergence rate. Based on stochastic Lyapunov techniques, we propose a parametrized measurement-based feedback which ensures exponential convergence toward the excited state. Moreover, we give a lower bound of the convergence rate for this case. Then, we discuss the effect of each parameter appeared in the control law in the convergence rate. Finally, we illustrate the efficiency of such feedback law through simulations.
|
quantum physics
|
In this work, the intermeson interactions of double-beauty $\bar{B}\bar{B}$, $\bar{B}\bar{B}^\ast$, and $\bar{B}^\ast\bar{B}^\ast$ systems have been studied with heavy meson chiral effective field theory. The effective potentials are calculated with Weinberg's scheme up to one-loop level. At the leading order, four body contact interactions and one pion exchange contributions are considered. In addition to two pion exchange diagrams, we include the one-loop chiral corrections to contact terms and one pion exchange diagrams at the next-to-leading order. The behaviours of effective potentials both in momentum space and coordinate space are investigated and discussed extensively. We notice the contact terms play important roles in determining the characteristics of the total potentials. Only the potentials in $I(J^P)=0(1^+)$ $\bar{B}\bar{B}^\ast$ and $\bar{B}^\ast\bar{B}^\ast$ systems are attractive, and the corresponding binding energies in these two channels are solved to be $\Delta E_{\bar{B}\bar{B}^\ast}\simeq -12.6^{+9.2}_{-12.9}$ MeV and $\Delta E_{\bar{B}^\ast\bar{B}^\ast}\simeq -23.8^{+16.3}_{-21.5}$ MeV, respectively. The masses of $0(1^+)$ $\bar{B}\bar{B}^\ast$ and $\bar{B}^\ast\bar{B}^\ast$ states lie above the threshold of their electromagnetic decay modes $\bar{B}\bar{B}\gamma$ and $\bar{B}\bar{B}\gamma\gamma$, and thus they can be reconstructable via electromagnetic interactions. Our calculation not only provides some useful information to explore exotic doubly-bottomed molecular states for future experiments, but also is helpful for the extrapolations of Lattice QCD simulations.
|
high energy physics phenomenology
|
The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence or change of disease-specific OCT-based biomarkers. Therefore, the monitoring frequency has a significant influence on the success of AMD therapy. However, the monitoring frequency of current treatment schemes is not individually adapted to the patient and therefore often insufficient. While a higher monitoring frequency would have a positive effect on the success of treatment, in practice it can only be achieved with a home monitoring solution. One of the key requirements of a home monitoring OCT system is a computer-aided diagnosis to automatically detect and quantify pathological changes using specific OCT-based biomarkers. In this paper, for the first time, retinal scans of a novel self-examination low-cost full-field OCT (SELF-OCT) are segmented using a deep learning-based approach. A convolutional neural network (CNN) is utilized to segment the total retina as well as pigment epithelial detachments (PED). It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging. In addition, a convolutional denoising autoencoder (CDAE) refines the CNN prediction, which has previously learned retinal shape information. It is shown that the CDAE refinement can correct segmentation errors caused by artifacts in the OCT image.
|
electrical engineering and systems science
|
Isospin asymmetry is the well-known property of dense quark matter, which exists in the compact stars and is produced in heavy ion collisions. On the other hand, the chiral imbalance between left- and right-handed quarks is another highly anticipated phenomenon that could occur in the dense quark matter. To investigate quark matter under these conditions, we take into account baryon -- $\mu_B$, isospin -- $\mu_I$ and chiral isospin -- $\mu_{I5}$ chemical potentials and study QCD phase portrait using NJL$_4$ model generalized to two massive quarks that could condense into the pion condensation. We have shown that the chiral isospin chemical potential $\mu_{I5}$ generates pion condensation in isospin asymmetric quark matter. Also, we have investigated discrete symmetry (duality) between chiral and pion condensates in the case of massless quarks, which stay relatively instructive even if the quarks have bare mass. To describe hot dense quark matter, in addition to the above-mentioned chemical potentials, we introduce non-zero temperatures into consideration.
|
high energy physics phenomenology
|
We demonstrate that categories of continuous actions of topological monoids on discrete spaces are Grothendieck toposes. We exhibit properties of these toposes, giving a solution to the corresponding Morita-equivalence problem. We characterize these toposes in terms of their canonical points. We identify natural classes of representatives with good topological properties, `powder monoids' and then `complete monoids', for the Morita-equivalence classes of topological monoids. Finally, we show that the construction of these toposes can be made (2-)functorial by considering geometric morphisms induced by continuous semigroup homomorphisms.
|
mathematics
|
Suspended graphene samples are observed to be gently rippled rather than being flat. In [M. Friedrich, U. Stefanelli. Graphene ground states, arXiv:1802.05049], we have checked that this nonplanarity can be rigorously described within the classical molecular-mechanical frame of configurational-energy minimization. There, we have identified all ground-state configurations with graphene topology with respect to classes of next-to-nearest neighbor interaction energies and classified their fine nonflat geometries. In this second paper on graphene nonflatness, we refine the analysis further and prove the emergence of wave patterning. Moving within the frame of [M. Friedrich, U. Stefanelli. Graphene ground states, arXiv:1802.05049], rippling formation in graphene is reduced to a two-dimensional problem for one-dimensional chains. Specifically, we show that almost minimizers of the configurational energy develop waves with specific wavelength, independently of the size of the sample. This corresponds remarkably to experiments and simulations.
|
condensed matter
|
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