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We study the perturbative corrections to the weak coupling limit type Gorini-Kossakowski-Sudarshan-Lindblad equation for the reduced density matrix of an open system. For the spin-boson model in the rotating wave approximation at zero temperature we show that the perturbative part of the density matrix satisfies the time-independent Gorini-Kossakowski-Sudarshan-Lindblad equation for arbitrary order of the perturbation theory if all the moments of the reservoir correlation function are finite. But the initial condition for perturbative part of the density matrix does not only differ from that for the whole density matrix, but also fails to be a density matrix under certain resonance conditions. | quantum physics |
Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture, and then investigates the effectiveness of three different DNN-model structures. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility. | electrical engineering and systems science |
In this work, we introduce a new methodology to construct a network of epicenters that avoids problems found in well-established methodologies when they are applied to global catalogs of earthquakes located in shallow zones. The new methodology involves essentially the introduction of a time window which works as a temporal filter. Our approach is more generic and for small regions the results coincide with previous findings. The network constructed with that model has small-world properties and the distribution of node connectivity follows a non-traditional function, namely a q-exponential, where scale-free properties are present. The vertices with larger connectivity in the network correspond to the areas with very intense seismic activities in the period considered. These new results strengthen the hypothesis of long spatial and temporal correlations between earthquakes. | physics |
Mechanical modes are a potentially useful resource for quantum information applications, such as quantum-level wavelength transducers, due to their ability to interact with electromagnetic radiation across the spectrum. A significant challenge for wavelength transducers is thermomechanical noise in the mechanical mode, which pollutes the transduced signal with thermal states. In this paper, we eliminate thermomechanical noise in the GHz-frequency mechanical breathing mode of a piezoelectric optomechanical crystal using cryogenic cooling in a dilution refrigerator. We optically measure an average thermal occupancy of the mechanical mode of only $0.7\pm0.4 ~ \mathrm{phonons}$, providing a path towards low-noise microwave-to-optical conversion in the quantum regime. | quantum physics |
Correlation Networks (CNs) inherently suffer from redundant information in their network topology. Bayesian Networks (BNs), on the other hand, include only non-redundant information (from a probabilistic perspective) resulting in a sparse topology from which generalizable physical features can be extracted. We advocate the use of BNs to construct data-driven complex networks as they can be regarded as the probabilistic backbone of the underlying complex system. Results are illustrated at the hand of a global climate dataset. | physics |
We present a novel approach to construct a color tagger, i.e. an observable that is able to discriminate the decay of a color-singlet into two jets from a two-jet background in a different color configuration. We do this by explicitly taking the ratio of the corresponding leading-order matrix elements in the soft limit, thus obtaining an observable that is provably optimal within our approximation. We call this observable the "jet color ring" and we compare its performance to other color-sensitive observables such as jet pull and dipolarity. We also assess the performance of the jet color ring in simulations by applying it to the case of the hadronic decays of a boosted Higgs boson and of an electroweak boson. | high energy physics phenomenology |
The reactor antineutrinos are used for the precise measurement of oscillation parameters in the 3-neutrino model, and also used to investigate active-sterile neutrino mixing sensitivity in the 3$+$1 neutrino framework. In the present work, we study the feasibility of sterile neutrino search with the Indian Scintillator Matrix for Reactor Anti-Neutrino (ISMRAN) experimental set-up using electron antineutrinos ($\overline{\nu}_e$) produced from reactor as a source. The so-called 3$+$1 scenario is considered for active-sterile neutrino mixing, which leads to projected exclusion curves in the sterile neutrino mass and mixing angle plane. The analysis is performed considering both the reactor and detector related parameters. It is found that, the ISMRAN set-up can observe the active-sterile neutrino mixing sensitivity for $\sin^{2}2\theta_{14} \geq$ 0.064 and $\Delta m^{2}_{41}$ = 1.0 eV$^2$ at 90$\%$ confidence level for an exposure of 1 ton-year by using neutrinos produced from the DHRUVA reactor with thermal power of 100 MW$_{th}$. It is also observed that, there is a significant improvement of the active-sterile neutrino mixing parameter $\sin^{2}2\theta_{14}$ to $\sim$ 0.03 at the same $\Delta m^{2}_{41}$ by putting the ISMRAN detector set-up at a distance of 20 m from the compact proto-type fast breeder reactor (PFBR) facility with thermal power of 1250 MW$_{th}$. | high energy physics phenomenology |
We prove that, in any flavor transition, neutrino oscillation CP violating asymmetries in matter have two disentangled components: (a) a CPT-odd T-invariant term, non-vanishing iff there are interactions with matter; (b) a T-odd CPT-invariant term, non-vanishing iff there is genuine CP violation. As function of the baseline, these two terms are distinct L-even and L-odd observables, respectively. In the experimental region of terrestrial accelerator neutrinos, we calculate their approximate expressions from which we prove that, at medium baselines, the CPT-odd component is small and nearly $\delta$-independent, so it can be subtracted from the experimental CP asymmetry as a theoretical background, provided the hierarchy is known. At long baselines, on the other hand, we find that (i) a Hierarchy-odd term in the CPT-odd component dominates the CP asymmetry for energies above the first oscillation node, and (ii) the CPT-odd term vanishes, independent of the CP phase $\delta$, at E = 0.92 GeV(L/1300 km) near the second oscillation maximum, where the T-odd term is almost maximal and proportional to $\sin\delta$. A measurement of the CP asymmetry in these energy regions would thus provide separate information on (i) the neutrino mass ordering, and (ii) direct evidence of genuine CP violation in the lepton sector. | high energy physics phenomenology |
M5 branes on a D-type ALE singularity display various phenomena that introduce additional massless degrees of freedom. The M5 branes are known to fractionate on a D-type singularity. Whenever two fractional M5 branes coincide, tensionless strings arise. Therefore, these systems do not admit a low-energy Lagrangian description. Focusing on the 6-dimensional N=(1,0) world-volume theories on the M5 branes, the vacuum moduli space has two branches were either the scalar fields in the tensor multiplet or the scalars in the hypermultiplets acquire a non-trivial vacuum expectation value. As suggested in previous work, the Higgs branch may change drastically whenever a BPS-string becomes tensionless. Recently, magnetic quivers have been introduced with the aim to capture all Higgs branches over any point of the tensor branch. In this paper, the formalism is extended to Type IIA brane configurations involving O6 planes. Since the 6d N=(1,0) theories are composed of orthosymplectic gauge groups, the derivation rules for the magnetic quiver in the presence of O6 planes have to be conjectured. This is achieved by analysing the 6d theories for a single M5 brane on a D-type singularity and deriving the magnetic quivers for the finite and infinite gauge coupling Higgs branch from a brane configuration. The validity of the proposed derivation rules is underpinned by deriving the associated Hasse diagram. For multiple M5 branes, the approach of this paper provides magnetic quivers for all Higgs branches over any point of the tensor branch. In particular, an interesting infinite gauge coupling transition is found that is related to the SO(8) non-Higgsable cluster. | high energy physics theory |
Dementia-related agitation causes high stress for dementia caregivers (CG) and to persons with dementia (PWD). Current clinical research suggests that dementia agitation can be affected or triggered by the ambient environment and other contextual factors. In this study, we evaluate this hypothesis through an analysis of ambient environmental data collected with a remote sensing system deployed in the homes of PWDs and their CGs. Furthermore, we determine if the occurrence of dementia-related agitation can be predicted from ambient environmental data, creating the potential for agitation to be prevented via the environmental alteration. These collected data are used to learn the environmental patterns using a predictive model approach. The agitation labels, used in model training, are provided by the CGs living with the PWDs. The results of the agitation prediction model evaluation suggest that ambient environment can be used as predictors for upcoming dementia-related agitation. We also observed that environmental triggers for agitation are PWD-specific. Future opportunities and techniques used to understand triggers for dementia agitation are also discussed. | electrical engineering and systems science |
An end-to-end learning approach is proposed for the joint design of transmitted waveform and detector in a radar system. Detector and transmitted waveform are trained alternately: For a fixed transmitted waveform, the detector is trained using supervised learning so as to approximate the Neyman-Pearson detector; and for a fixed detector, the transmitted waveform is trained using reinforcement learning based on feedback from the receiver. No prior knowledge is assumed about the target and clutter models. Both transmitter and receiver are implemented as feedforward neural networks. Numerical results show that the proposed end-to-end learning approach is able to obtain a more robust radar performance in clutter and colored noise of arbitrary probability density functions as compared to conventional methods, and to successfully adapt the transmitted waveform to environmental conditions. | electrical engineering and systems science |
We study dynamical chiral symmetry breaking for quarks in the fundamental representation of $SU(N_c)$ for $N_f$ number of light quark flavors. We also investigate the phase diagram of quantum chromodynamics at finite temperature $T$ and/or in the presence of a constant external magnetic field $eB$. The unified formalism for this analysis is provided by a symmetry-preserving Schwinger-Dyson equations treatment of a vector$\times$vector contact interaction model which encodes several well-established features of quantum chromodynamics to mimic the latter as closely as possible. Deconfinement and chiral symmetry restoration are triggered above a critical value of $N_f$ at $T=0=eB$. On the other hand, increasing temperature itself screens strong interactions, thus ensuring that a smaller value of $N_f$ is sufficient to restore chiral symmetry at higher temperatures. We also observe the well-known phenomenon of magnetic catalysis for a strong enough magnetic field. However, we note that if the effective coupling strength of the model decreases as a function of magnetic field, it can trigger inverse magnetic catalysis in a certain window of this functional dependence. Our model allows for the simultaneous onset of dynamical chiral symmetry breaking and confinement for each case. Qualitative as well as quantitative predictions of our simple but effective model are in reasonably satisfactory agreement with lattice results and other reliable and refined predictions based upon intricate continuum studies of quantum chromodynamics. | high energy physics phenomenology |
We propose to search for a boosted dark matter (DM) particle from astrophysical sources using an emulsion detector in deep underground facilities. We further propose using high-$Z$ material such as the lead for a larger DM-nucleus coherent scattering cross section above a threshold. The boosted DM will scatter into an excited DM. While the nuclear recoil energy is not detected, the decay products from the excited DM can be recorded by the proposed detector. Backgrounds such as the high energy solar neutrinos can be controlled by the reconstructed track topology. The proposed detector has the potential to find a boosted DM in the sub-GeV mass region, and in certain parameter space, an observation can be made while there is no sensitivity in other large-volume detectors such as Super-K. | high energy physics phenomenology |
We study the collective decay of two-level emitters coupled to a nonlinear waveguide, for example, a nanophotonic lattice or a superconducting resonator array with strong photon-photon interactions. Under these conditions a new decay channel into bound photon pairs emerges, through which spatial correlations between emitters are established by regular interference as well as interactions between the photons. We derive an effective Markovian theory to model the resulting decay dynamics of an arbitrary distribution of emitters and identify collective effects beyond the usual phenomena of super- and subradiance. Specifically, in the limit of many close-by emitters, we find that the system undergoes a super-correlated decay process where either all the emitters are in the excited state or in the ground state, but not in any of the intermediate states. The predicted effects can be probed in state-of-the-art waveguide QED experiments and provide a striking example of how the dynamics of open quantum systems can be modified by many-body effects in a non-harmonic environment. | quantum physics |
New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being "altruistic" i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance. | electrical engineering and systems science |
Optimally operating an integrated electricity-gas system (IEGS) is significant for the energy sector. However, the IEGS operation model's nonconvexity makes it challenging to solve the optimal dispatch problem in the IEGS. This letter proposes an improved Benders decomposition (IBD) algorithm catering to a commonly used steady-state dispatch model of the IEGS. This IBD algorithm leverages a refined decomposition structure where the subproblems become linear and ready to be solved in parallel. We analytically compare our IBD algorithm with an existing Benders decomposition algorithm and a typical piecewise linearization method. Case studies have substantiated the higher computational efficiency of our IBD algorithm. | mathematics |
This paper studies the non-negative solutions of the Keller-Segel model with a nonlocal nonlinear source in a bounded domain. The competition between the aggregation and the nonlocal reaction term is highlighted: when the growth factor is stronger than the dampening effect, with the help of the nonlocal term, the model admits a classical solution which is uniformly bounded. Moreover, when the growth factor is of the same order compared to the dampening effect, the nonlocal nonlinear exponents can prevent the chemotactic collapse. Global existence of classical solutions is shown for an appropriate range of the exponents as well as convergence to the constant equilibrium state. | mathematics |
This research is concerned with the development of a realistic model for e-procurement adoption by organisations and groups observing the Rules of Islamic Sharia (RIS). This model is intended to be based on the behavioural control, subjective norms, and the recognition of the benefits and risks of e procurement adoption. The developed model,(E-PAM), combined and extended two existing models previously used for information technology adoption. Central to the design of the E-PAM is the principle that a realistic model should consider all relevant psychological, social, cultural, demography, and religious factors. . | computer science |
Insulating honeycomb ferromagnet CrI$_3$ has recently attracted considerable attention due to its potential use for dissipationless spintronics applications. Recently, topological spin excitations have been observed experimentally in bulk CrI$_3$ by L. Chen, et al. [Phys. Rev. X ${\bf 8}$, 041028 (2018)] using inelastic neutron scattering. This suggest that bulk CrI$_3$ has strong spin-orbit coupling and its spin Hamiltonian should include a next-nearest neighbour Dzyaloshinskii-Moriya (DM) interaction. Inspired by this experiment, we study non-equilibrium emergent photon-dressed topological spin and thermal Hall transports in laser-irradiated CrI$_3$ with and without the DM interaction. We show that the spin excitations can be manipulated into different topological phases with different Chern numbers. Most importantly, we show that the emergent photon-dressed spin and thermal Hall response can be switched to different signs. Hence, the generated magnon spin photocurrents can be manipulated by the laser field, which is of great interest in ultrafast spin current generation and could pave the way for future applications of CrI$_3$ to topological opto-spintronics and opto-magnonics. | condensed matter |
In the littlest Higgs model with T-parity(LHT), we study the single production of vector-like top partner with the subsequent decay $T_{+}\to Wb$ in the leptonic channel at the $ep$ colliders. Focus on the LHeC ($ \sqrt{s} $ = 1.98 TeV) and FCC-eh ($ \sqrt{s} $ = 5.29 TeV), we investigate the observability of the single top partner production with the unpolarized and polarized electron beams, respectively. As a result, the statistical significance can be enhanced by the polarized electron beams. Under the current constraints, the search for $T_{+}$ in the $Wb$ channel at the LHeC cannot provide a stronger limit on the top partner mass. By contrast, the search for the $T_{+}$ in this channel at the FCC-eh with polarized $ e^- $ beams can exclude the top partner mass up to 1350 GeV, 1500 GeV and 1565 GeV with integrated luminosities of 100 fb$^{-1}$, 1000 fb$^{-1}$ and 3000 fb$^{-1}$ at the 2$\sigma$ level, which is an improvement with respect to the current indirect searches and the LHC direct searches. Furthermore, we also give an extrapolation to the high-luminosity LHC with $\sqrt{s}=14$ TeV and $ L=3000~\rm{fb}^{-1} $. Our results show that the LHT model is still a natural solution to the shortcomings of the electroweak and scalar sector although it has been constrained severely. | high energy physics phenomenology |
We present a new model of electron transport in warm and hot dense plasmas which combines the quantum Landau-Fokker-Planck equation with the concept of mean-force scattering. We obtain electrical and thermal conductivities across several orders of magnitude in temperature, from warm dense matter conditions to hot, nondegenerate plasma conditions, including the challenging crossover regime between the two. The small-angle approximation characteristic of Fokker-Planck collision theories is mitigated to good effect by the construction of accurate effective Coulomb logarithms based on mean-force scattering, which allows the theory to remain accurate even at low temperatures, as compared with high-fidelity quantum simulation results. Electron-electron collisions are treated on equal footing as electron-ion collisions. Their accurate treatment is found to be essential for hydrogen, and is expected to be important to other low-Z elements. We find that electron-electron scattering remains influential to the value of the thermal conductivity down to temperatures somewhat below the Fermi energy. The accuracy of the theory seems to falter only for the behavior of the thermal conductivity at very low temperatures due to a subtle interplay between the Pauli exclusion principle and the small-angle approximation as they pertain to electron-electron scattering. Even there, the model is in fair agreement with ab initio simulations. | physics |
Modeling of the NICER X-ray waveform of the pulsar PSR J0030+0451, aimed to constrain the neutron star mass and radius, has inferred surface hot-spots (the magnetic polar caps) that imply significantly non-dipolar magnetic fields. To this end, we investigate magnetic field configurations that comprise offset dipole plus quadrupole components using static vacuum field and force-free global magnetosphere models. Taking into account the compactness and observer angle values provided by Miller et al. (2019) and Riley et al. (2019), we compute geodesics from the observer plane to the polar caps to compute the resulting X-ray light curve. We explore, through Markov chain Monte Carlo techniques, the detailed magnetic field configurations that can reproduce the observed X-ray light curve and have discovered degeneracies, i.e., diverse field configurations, which can provide sufficient descriptions to the NICER X-ray waveforms. Having obtained the force-free field structures, we then compute the corresponding synchronous gamma-ray light curves following Kalapotharakos et al. (2014) these we compare to those obtained by Fermi-LAT, to provide models consistent with both the X-ray and the gamma-ray data, thereby restricting further the multipole field parameters. An essential aspect of this approach is the proper computation of the relative phase between the synchronous X- and gamma-ray light curves. We conclude with a discussion of the broader implications of our study. | astrophysics |
Deep learning has proved itself to be a powerful tool to develop data-driven signal processing algorithms for challenging engineering problems. By learning the key features and characteristics of the input signals, instead of requiring a human to first identify and model them, learned algorithms can beat many man-made algorithms. In particular, deep neural networks are capable of learning the complicated features in nature-made signals, such as photos and audio recordings, and use them for classification and decision making. The situation is rather different in communication systems, where the information signals are man-made, the propagation channels are relatively easy to model, and we know how to operate close to the Shannon capacity limits. Does this mean that there is no role for deep learning in the development of future communication systems? | computer science |
Quantum entanglement is the quintessence of quantum information processing mostly limited to the microscopic regime governed by Heisenberg uncertainty principle. For practical applications, however, macroscopic entanglement gives great benefits in both photon loss and sensitivity. Recently, a novel method of macroscopic entanglement generation has been proposed and demonstrated in a coupled interferometric system using classical laser light, where superposition between binary bases in each interferometric system plays a key role. Here, the function of path superposition applied to independent bipartite classical systems is analyzed to unveil secrets of quantum features and to convert a classical system into a quantum system without violating quantum mechanics. | quantum physics |
Constant modulus sequence having lower side-lobe levels in its auto-correlation function plays an important role in the applications like SONAR, RADAR and digital communication systems. In this paper, we consider the problem of minimizing the Integrated Sidelobe Level (ISL) metric, to design a complex unimodular sequence of any length. The underlying optimization problem is solved iteratively using the Block Majorization-Minimization(MM) technique, which ensures that the resultant algorithm to be monotonic. We also show a computationally efficient way to implement the algorithm using Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) operations. Numerical experiments were conducted to compare the proposed algorithm with the state-of-the art algorithms and was found that the proposed algorithm performs better in terms of computational complexity and speed of convergence. | electrical engineering and systems science |
The possibility to achieve charge-to-spin conversion via Rashba spin-orbit effects provide stimulating opportunities toward the development of nanoscale spintronics. Here we use first-principles calculations to study the electronic and spintronic properties of Tl$_2$O/PtS$_2$ heterostructure, for which we have confirmed the dynamical stability by its positive phonon frequencies. An unexpectedly high binding energy of -0.38 eV per unit cell depicts strong interlayer interactions between Tl$_2$O and PtS$_2$. Interestingly, we discover Rashba spin-splitting's (with large $\alpha_R$ value) in the valence band of Tl$_2$O stemming from interfacial spin-orbit effects caused by PtS$_2$. The role of van der Waals binding on the orbital rearrangements has been studied using electron localization function and atomic orbital projections, which explains in detail the electronic dispersion near the Fermi level. Moreover, we explain the distinct band structure alignment in momentum space but separation in real space of Tl$_2$O/PtS$_2$ heterostructure. Since 2D Tl$_2$O still awaits experimental confirmation, we calculate, for the first time, the Raman spectra of pristine Tl$_2$O and the Tl$_2$O/PtS$_2$ heterostructure and discuss peak positions corresponding to vibrational modes of the atoms. These findings offer a promising avenue to explore spin physics for potential spintronics applications via 2D heterostructures. | condensed matter |
In the standard model the running quartic coupling becomes negative during its renormalization group flow, which destabilizes the vacuum. We consider U(1) extensions of the standard model, with an extra complex scalar field and a Majorana-type neutrino Yukawa coupling. These additional couplings affect the renormalization group flow of the quartic couplings. We compute the beta-functions of the extended model at one-loop order in perturbation theory and study how the parameter space of the new scalar couplings can be constrained by the requirement of stable vacuum and perturbativity up to the Planck scale. | high energy physics phenomenology |
This paper is devoted to the uniqueness of inverse acoustic scattering problems with the modulus of near-field data. By utilizing the superpositions of point sources as the incident waves, we rigorously prove that the phaseless near-fields collected on an admissible surface can uniquely determine the location and shape of the obstacle as well as its boundary condition and the refractive index of a medium inclusion, respectively. We also establish the uniqueness in determining a locally rough surface from the phaseless near-field data due to superpositions of point sources. These are novel uniqueness results in inverse scattering with phaseless near-field data. | mathematics |
Angle-resolved photoemission spectroscopy (ARPES) is one of the most powerful experimental techniques in condensed matter physics. Synchrotron ARPES, which uses photons with high flux and continuously tunable energy, has become particularly important. However, an excellent synchrotron ARPES system must have features such as a small beam spot, super-high energy resolution, and a user-friendly operation interface. A synchrotron beamline and an endstation (BL03U) were designed and constructed at the Shanghai Synchrotron Radiation Facility. The beam spot size at the sample position is 7.5 (V) $\mu$m $\times$ 67 (H) $\mu$m, and the fundamental photon range is 7-165 eV; the ARPES system enables photoemission with an energy resolution of 2.67 [email protected] eV. In addition, the ARPES system of this endstation is equipped with a six-axis cryogenic sample manipulator (the lowest temperature is 7 K) and is integrated with an oxide molecular beam epitaxy system and a scanning tunneling microscope, which can provide an advanced platform for in-situ characterization of the fine electronic structure of condensed matter. | physics |
In this paper, two interesting eigenvalue comparison theorems for the first non-zero Steklov eigenvalue of the Laplacian have been established for manifolds with radial sectional curvature bounded from above. Besides, sharper bounds for the first non-zero eigenvalue of the Wentzell eigenvalue problem of the weighted Laplacian, which can be seen as a natural generalization of the classical Steklov eigenvalue problem, have been obtained. | mathematics |
The mass accretion rate is the fundamental parameter to understand the process of mass assembly that results in the formation of a low-mass star. This parameter has been largely studied in Classical TTauri stars in star-forming regions with ages of 1-10Myr. However, little is known about the accretion properties of young stellar objects (YSOs) in younger regions and early stages of star formation, such as in the Class0/I phases. We present new NIR spectra of 17 ClassI/Flat and 35 ClassII sources located in the young (<1Myr) NGC1333 cluster, acquired with the KMOS instrument at the VLT. Our goal is to study whether the mass accretion rate evolves with age, as suggested by the widely adopted viscous evolution model, by comparing the properties of the NGC1333 members with samples of older regions. We measured the stellar parameters and accretion rates of our sample, finding a correlation between accretion and stellar luminosity, and between mass accretion rate and stellar mass. Both correlations are compatible within the errors with the older Lupus star-forming region, while only the latter is consistent with results from ChamaeleonI. The ClassI sample shows larger accretion luminosities with respect to the ClassII stars of the same cloud. However, the derived accretion rates are not sufficiently high to build up the inferred stellar masses, assuming steady accretion during the ClassI lifetime. This suggests that the sources are not in their main accretion phase and that most of their mass has already been accumulated during a previous stage and/or that the accretion is an episodic phenomenon. We show that some of the targets originally classified as Class I through Spitzer photometry are in fact evolved or low accreting objects. This evidence can have implications for the estimated protostellar phase lifetimes. Further observations are needed to determine if this is a general result. | astrophysics |
Multiple Sclerosis (MS) and microvascular leukoencephalopathy are two distinct neurological conditions, the first caused by focal autoimmune inflammation in the central nervous system, the second caused by chronic white matter damage from atherosclerotic microvascular disease. Both conditions lead to signal anomalies on Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images, which can be distinguished by an expert neuroradiologist, but which can look very similar to the untrained eye as well as in the early stage of both diseases. In this paper, we attempt to train a 3-dimensional deep neural network to learn the specific features of both diseases in an unsupervised manner. For this manner, in a first step we train a generative neural network to create artificial MR images of both conditions with approximate explicit density, using a mixed dataset of multiple sclerosis, leukoencephalopathy and healthy patients containing in total 5404 volumes of 3096 patients. In a second step, we distinguish features between the different diseases in the latent space of this network, and use them to classify new data. | electrical engineering and systems science |
We provide a general framework to model the growth of networks consisting of different coupled layers. Our aim is to estimate the impact of one such layer on the dynamics of the others. As an application, we study a scientometric network, where one layer consists of publications as nodes and citations as links, whereas the second layer represents the authors. This allows to address the question how characteristics of authors, such as their number of publications or number of previous co-authors, impacts the citation dynamics of a new publication. To test different hypotheses about this impact, our model combines citation constituents and social constituents in different ways. We then evaluate their performance in reproducing the citation dynamics in nine different physics journals. For this, we develop a general method for statistical parameter estimation and model selection that is applicable to growing multi-layer networks. It takes both the parameter errors and the model complexity into account and is computationally efficient and scalable to large networks. | physics |
High-precision space-based photometry obtained by the \emph{Kepler} and \emph{TESS} missions has revealed evidence of rotational modulation associated with main sequence (MS) A and late-B type stars. Generally, such variability in these objects is attributed to inhomogeneous surface structures (e.g. chemical spots), which are typically linked to strong magnetic fields ($B\gtrsim100\,{\rm G}$) visible at the surface. It has been reported that $\approx44$~per~cent of all A-type stars observed during the \emph{Kepler} mission exhibit rotationally modulated light curves. This is surprising considering that $\lesssim10$~per~cent of all MS A-type stars are known to be strongly magnetic (i.e. they are Ap/Bp stars). We present a spectroscopic monitoring survey of 44 A and late-B type stars reported to exhibit rotational modulation in their \emph{Kepler} light curves. The primary goal of this survey is to test the hypothesis that the variability is rotational modulation by comparing each star's rotational broadening ($v\sin{i}$) with the equatorial velocities ($v_{\rm eq}$) inferred from the photometric periods. We searched for chemical peculiarities and binary companions in order to provide insight into the origin of the apparent rotational modulation. We find that 14 stars in our sample have $v\sin{i}>v_{\rm eq}$ and/or have low-mass companions that may contribute to or be responsible for the observed variability. Our results suggest that more than $10$~per~cent of all MS A and late-B type stars may exhibit inhomogeneous surface structures; however, the incidence rate is likely $\lesssim30$~per~cent. | astrophysics |
Glasses are nonequilibrium solids with properties highly dependent on their method of preparation. It is established that in vapor-deposited molecular glasses structural organization and properties could be readily tuned with deposition rate and substrate temperature. In contrast, it has not yet been demonstrated whether the atomic packing of strong network forming glasses such as GeO2 could be modified at short and medium range (< 2 nm) through vapor deposition at elevated temperatures. In this work, we show that the structure of vapor-deposited amorphous GeO2 (a-GeO2), characterized by GeO4 tetrahedra connected in rings of various sizes, evolves into a more ordered configuration containing an increased population of 6-membered rings at elevated temperatures. It is also demonstrated that deposition near the glass transition temperature (Tg) is more efficient than post-growth annealing in modifying the atomic organization at medium range. The improvement in medium range order correlates with the reduction of the room temperature internal friction which decreases by as much as 44% when a-GeO2 is deposited at 0.83 Tg. In combination, these results show a strong correlation between medium range order and internal friction as predicted by theory. | condensed matter |
We present a complete analysis of the problem of convection-diffusion in low Re, 2-dimensional flows with distributions of singularities, such as those found in open-space microfluidics and in groundwater flows. Using Boussinesq transformations and solving the problem in streamline coordinates, we obtain concentration profiles in flows with complex arrangements of sources and sinks for both high and low Pe. These yield the complete analytical concentration profile at every point in applications that previously relied on material surface tracking, local lump models or numerical analysis such as microfluidic probes, groundwater heat pumps, or diffusive flows in porous media. Using conformal transforms, we generate families of symmetrical solutions from simple ones, and provide a general methodology that can be used to analyze any arrangement of source and sinks. The solutions obtained that contain the explicit dependence on the various parameters of the problems, such as Pe, the spacing of the apertures and their relative injection and aspiration rates. In particular, we show that the high Pe models can model problems with Pe as low as 1 with a maximum error committed of under $10\%$, and that this error decreases approximately as $Pe^{-1.5}$. | physics |
This paper discusses an optimization method called Modified Bee Colony algorithm (MBC) based on a particular intelligent behavior of honeybee swarms. The algorithm was checked in a few benchmarks like Shekel, Rozenbroke, Himmelblau and Rastrigin functions, then was applied to parameter identification for reactive flow problems in periodic porous media. The simulation results show that the performance and efficiency of MBC algorithm are comparable to the other parameter identification methods and strategies, at the same time it is able to better capture local minima for the considered class of problems. The proposed identification approach is applicable for different geometries (random and periodic) and for a range of process parameters. In this paper the potential of the approach is demonstrated in identifying parameters of Langmuir isotherm for low Peclet and high Damkoler numbers reactive flow in a 2D periodic porous media with circular inclusions. Finite element approximation in space and implicit time discretization are exploited. | computer science |
The work discussed and presented in this paper focuses on the history matching of reservoirs by integrating 4D seismic data into the inversion process using machine learning techniques. A new integrated scheme for the reconstruction of petrophysical properties with a modified Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in a synthetic reservoir is proposed. The permeability field inside the reservoir is parametrised with an unsupervised learning approach, namely K-means with Singular Value Decomposition (K-SVD). This is combined with the Orthogonal Matching Pursuit (OMP) technique which is very typical for sparsity promoting regularisation schemes. Moreover, seismic attributes, in particular, acoustic impedance, are parametrised with the Discrete Cosine Transform (DCT). This novel combination of techniques from machine learning, sparsity regularisation, seismic imaging and history matching aims to address the ill-posedness of the inversion of historical production data efficiently using ES-MDA. In the numerical experiments provided, I demonstrate that these sparse representations of the petrophysical properties and the seismic attributes enables to obtain better production data matches to the true production data and to quantify the propagating waterfront better compared to more traditional methods that do not use comparable parametrisation techniques. | electrical engineering and systems science |
We describe here a new algorithm to model the water contents of the atmosphere from GNSS slant wet delays relative to a single receiver. We first make the assumption that the water vapor contents are mainly governed by a scale height (exponential law), and secondly that the departures from this decaying exponential can be mapped as a set of low degree 3D Zernike functions (w.r.t. space) and Tchebyshev polynomials (w.r.t. time.) We give an example of inversion with data acquired over a one day time span at the Geodesy Observatory of Tahiti. | physics |
Hierarchically structured materials, which possess distinct features on different length scales, are ubiquitous in nature and engineering. In many cases, one structural level may be ordered while another structural level may be disordered. Here, we investigate the impact of structural disorder on the mechanical properties of hierarchical filamentous structures. Through simulations of networks with two hierarchical levels, we show that disorder does not change how stiffness scales with the mean coordination number - the average number of bonds per node - on large and small length scales. However, we find that network rigidity and stiffness depend strongly on the presence or absence of disorder on the small length scale, but not on the large length scale. In fact, the amount of material necessary for a fully connected, network ordered on the small scale is insufficient to create even a marginally rigid network with small-scale disorder. We trace these phenomena back to a difference in the maximum mean coordination number on the small scale. While single length scale ordered and disordered networks have similar mean coordination numbers in the interior and on surfaces, we find that disorder strongly impacts the structure of surfaces, resulting in a larger fraction of surface nodes on the small scale. While this effect increases in strength as large scale bonds become narrower, it persists even for bonds that are wider than they are long (i.e., aspect ratios < 1). | condensed matter |
We give another alternative proof to the Kawamata semiampleness theorem for the log canonical divisors on klt varieties which are nef and abundant. After the first version of this article was posted to the e-print Arxiv, Prof. Fujino notified the author that the quick and essential proof ([Fujino. On Kawamata's theorem.(EMS 2011), Rem 2.7]) is already known. The author would like to thank him. More precisely, Prof. Fujino already gave the quick and essential proof ([Fujino. On Kawamata's thm.(EMS 2011), Rem 2.7], [Fujino. Finite generation of the lc ring in dim 4. (Kyoto J. Math. 50 (2010)), Rem 3.15]) from the finite generation thm (Birkar-Cascini-Hacon-McKernan [BCHM]) of the lc rings for klt pairs and from the fact (cf. Mourougane-Russo [MoRu, C.R.A.S. Math. 325 (1997)]) that a nef and abundant $\mathbf{Q}$-divisor $D$ is semiample if its graded ring is finitely generated:"For a nef and abundant lc divisor which is klt, the lc ring is finitely generated, thus it is semiample." [BCHM] first proved that the minimal model program runs for big klt lc divisors and next implied the finite generation of the lc rings for klt lc divisors which are not necessarily big from the Fujino-Mori lc bdle formula ([FM, J. Differential Geom., 56 (2000)]). Mourougane-Russo [MoRu] implies the semiampleness of a nef and abundant $\mathbf{Q}$-divisor whose graded ring is finitely generated, using the Kawamata numerically trivial fibrations ([Kawamata. Pluricanonical systems. Invent. Math. 79 (1985)]). Consequently the author withdraw the article. | mathematics |
In this short paper, we will review the proposal of a correspondence between the doubled geometry of Double Field Theory and the higher geometry of bundle gerbes. Double Field Theory is T-duality covariant formulation of the supergravity limit of String Theory, which generalises Kaluza-Klein theory by unifying metric and Kalb-Ramond field on a doubled-dimensional space. In light of the proposed correspondence, this doubled geometry is interpreted as an atlas description of the higher geometry of bundle gerbes. In this sense, Double Field Theory can be interpreted as a field theory living on the total space of the bundle gerbe, just like Kaluza-Klein theory is set on the total space of a principal bundle. This correspondence provides a higher geometric interpretation for para-Hermitian geometry which opens the door to its generalisation to the other Extended Field Theories. This review is based on, but not limited to, my talk at the workshop Generalized Geometry and Applications at Universit\"at Hamburg on 3rd of March 2020. | high energy physics theory |
Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated method for connecting scientific literature to synthesis insights. Starting from natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for arbitrary materials. We show the potential of this technique by predicting precursors for two perovskite materials, using only training data published over a decade prior to their first reported syntheses. We demonstrate that the model learns representations of materials corresponding to synthesis-related properties, and that the model's behavior complements existing thermodynamic knowledge. Finally, we apply the model to perform synthesizability screening for proposed novel perovskite compounds. | condensed matter |
Two-dimensional (2D) transition metal dichalcogenides MX2 (M = Mo, W, X = S, Se, Te) attracts enormous research interests in recent years. Its 2H phase possesses an indirect to direct bandgap transition in 2D limit, and thus shows great application potentials in optoelectronic devices [1]. The 1T' crystalline phase transition can drive the monolayer MX2 to be a 2D topological insulator. Here we realized the molecular beam epitaxial (MBE) growth of both the 1T' and 2H phase monolayer WSe2 on bilayer graphene (BLG) substrate. The crystalline structures of these two phases were characterized using scanning tunneling microscopy. The monolayer 1T'-WSe2 was found to be metastable, and can transform into 2H phase under post-annealing procedure. The phase transition temperature of 1T'-WSe2 grown on BLG is lower than that of 1T' phase grown on 2H-WSe2 layers. This thermo-driven crystalline phase transition makes the monolayer WSe2 to be an ideal platform for the controlling of topological phase transitions in 2D materials family. | condensed matter |
A dominating set of a graph $G$ is a set $D\subseteq V_G$ such that every vertex in $V_G-D$ is adjacent to at least one vertex in $D$, and the domination number $\gamma(G)$ of $G$ is the minimum cardinality of a dominating set of $G$. In this paper we provide a new characterization of bipartite graphs whose domination number is equal to the cardinality of its smaller partite set. Our characterization is based upon a new graph operation. | mathematics |
In this paper, we address a variant of the marketing mix optimization (MMO) problem which is commonly encountered in many industries, e.g., retail and consumer packaged goods (CPG) industries. This problem requires the spend for each marketing activity, if adjusted, be changed by a non-negligible degree (minimum change) and also the total number of activities with spend change be limited (maximum number of changes). With these two additional practical requirements, the original resource allocation problem is formulated as a mixed integer nonlinear program (MINLP). Given the size of a realistic problem in the industrial setting, the state-of-the-art integer programming solvers may not be able to solve the problem to optimality in a straightforward way within a reasonable amount of time. Hence, we propose a systematic reformulation to ease the computational burden. Computational tests show significant improvements in the solution process. | mathematics |
This note is devoted to the analysis of T-duality of Dp-brane when we perform T-duality along directions that are transverse to world-volume of Dp-brane. | high energy physics theory |
This paper concerns the nonautonomous reaction-diffusion equation \[ u_t=u_{xx}+ug(t,x-ct,u), \quad t>0,x\in\mathbb{R}, \] where $c\in\mathbb{R}$ is the shifting speed, and the time periodic nonlinearity $ug(t,\xi,u)$ is asymptotically of KPP type as $\xi \to-\infty$ and is negative as $\xi\to+\infty$. Under a subhomogeneity condition, we show that there is $c^*>0$ such that a unique forced time periodic wave exists if and only $|c|< c^*$ and it attracts other solutions in a certain sense according to the tail behavior of initial values. In the case where $|c|\ge c^*$, the propagation dynamics resembles that of the limiting system as $\xi\to\pm \infty$, depending on the shifting direction. | mathematics |
As shown in recent research, deep neural networks can perfectly fit randomly labeled data, but with very poor accuracy on held out data. This phenomenon indicates that loss functions such as cross-entropy are not a reliable indicator of generalization. This leads to the crucial question of how generalization gap should be predicted from the training data and network parameters. In this paper, we propose such a measure, and conduct extensive empirical studies on how well it can predict the generalization gap. Our measure is based on the concept of margin distribution, which are the distances of training points to the decision boundary. We find that it is necessary to use margin distributions at multiple layers of a deep network. On the CIFAR-10 and the CIFAR-100 datasets, our proposed measure correlates very strongly with the generalization gap. In addition, we find the following other factors to be of importance: normalizing margin values for scale independence, using characterizations of margin distribution rather than just the margin (closest distance to decision boundary), and working in log space instead of linear space (effectively using a product of margins rather than a sum). Our measure can be easily applied to feedforward deep networks with any architecture and may point towards new training loss functions that could enable better generalization. | statistics |
Frequency domain (FD) multi-user detection (MUD) has been shown to be an effective means of approaching the theoretical per-user capacity for single carrier modulation (SCM) schemes in massive MIMO scenarios with highly dispersive channels. When a cyclic prefix is added to the SCM waveform, the circulant structures of the resulting convolutional channel matrix allows for relatively simple expressions for the FD detection. In this paper, we develop a computationally efficient minimum mean squared error (MMSE) FD-MUD technique in a time-division duplexing (TDD) massive MIMO setup and show how processing steps can be shared between uplink and downlink. | electrical engineering and systems science |
In this work, we have explored via first principles study of mechanical properties including Vickers hardness and mechanical anisotropy, electronic charge density distribution, Fermi surface, thermodynamic and optical properties of the recently predicted thermodynamically stable MAX phase boride Hf3PB4 for the first time. The calculated lattice constants of the optimized cell are consistent with those found by the predicted data available. Mechanical properties such as C44, B, G, Y, Hmacro and Hmicro of Hf3PB4 boride are compared with those of existing MAX phases. None of the MAX compounds synthesized so far has higher Hmacro and/or Hmicro than that of the predicted Hf3PB4 nanolaminate. Calculations of stiffness constants (Cij) indicate that Hf3PB4 is mechanically stable. The extraordinarily high values of elastic moduli and hardness parameters are explained with the use of density of states (DOS) and charge density mapping (CDM). The high stiffness of Hf3PB4 arises because of the additional B atoms which results in the strong B B covalent bonds in the crystal. The band structure and DOS calculations are used to confirm the metallic properties with dominant contribution from the Hf-5d states to the electronic states around the Fermi level. The technologically important thermal parameters such Debye temperature, minimum thermal conductivity, Gruneisen parameter and melting temperature of Hf3PB4 are calculated. It has been found that the estimated melting temperature of Hf3PB4 is also the highest among all the MAX phase nanolaminates. The important optical constants are calculated and analyzed in detail and their relevance to possible applications in the optoelectronic sectors is discussed. Our study reveals that Hf3PB4 has the potential to be the hardest known MAX phase based on the values of C44, Hmacro and Hmicro. | condensed matter |
We perform a numerical study of higher order saturation corrections to the dilute-dense approximation for multi-particle production in high-energy hadronic collisions in the framework of the Color Glass Condensate. We compare semi-analytical results obtained by performing a leading order expansion in the dilute field of the projectile with numerical simulations of the full Classical Yang-Mills dynamics for a number of phenomenologically relevant observables. By varying the saturation momentum of the target and the projectile, we establish the regime of validity of the dilute-dense approximation and assess the magnitude and basic features of higher order saturation corrections. In particular, we find that dilute-dense approximation faithfully reproduces dense-dense results if restricted to the range of its validity. | high energy physics phenomenology |
We construct integrable deformations of the $\lambda$-type for asymmetrically gauged WZW models. This is achieved by a modification of the Sfetsos gauging procedure to account for a possible automorphism that is allowed in $G/G$ models. We verify classical integrability, derive the one-loop beta function for the deformation parameter and give the construction of integrable D-brane configurations in these models. As an application, we detail the case of the $\lambda$-deformation of the cigar geometry corresponding to the axial gauged $SL(2,R)/U(1)$ theory at large $k$. Here we also exhibit a range of both A-type and B-type integrability preserving D-brane configurations. | high energy physics theory |
The quantum dynamics of isolated systems under quench condition exhibits a variety of interesting features depending on the integrable/chaotic nature of system. We study the exact dynamics of trivially integrable system of harmonic chains under a multiple quench protocol. Out of time ordered correlator of two Hermitian operators at large time displays scrambling in the thermodynamic limit. In this limit, the entanglement entropy and the central component of momentum distribution both saturate to a steady state value. We also show that reduced density matrix assumes the diagonal form long after multiple quenches for large system size. These exact results involving infinite dimensional Hilbert space are indicative of local thermal behaviour for a trivially integrable harmonic chain. | quantum physics |
We describe our solution approach for Pommerman TeamRadio, a competition environment associated with NeurIPS 2019. The defining feature of our algorithm is achieving sample efficiency within a restrictive computational budget while beating the previous years learning agents. The proposed algorithm (i) uses imitation learning to seed the policy, (ii) explicitly defines the communication protocol between the two teammates, (iii) shapes the reward to provide a richer feedback signal to each agent during training and (iv) uses masking for catastrophic bad actions. We describe extensive tests against baselines, including those from the 2019 competition leaderboard, and also a specific investigation of the learned policy and the effect of each modification on performance. We show that the proposed approach is able to achieve competitive performance within half a million games of training, significantly faster than other studies in the literature. | computer science |
Electric fields may decay by quantum tunneling: as calculated by Schwinger, an electron-positron pair may be summoned from the vacuum. In this paper I calculate the pair-production rate at nonzero temperatures. I find that at high temperatures the decay rate is dominated by a new instanton that involves both thermal fluctuation and quantum tunneling; this decay is exponentially faster than the rate in the literature. I also calculate the decay rate when the electric field wraps a compact circle (at zero temperature). The same new instanton also governs this rate: I find that for small circles decay is dominated by a process that drops the electric field by one unit, but does not produce charged particles. | high energy physics theory |
Quantum walks are accepted as a generic model for quantum transport. The character of the transport crucially depends on the properties of the walk like its geometry and the driving coin. We demonstrate that increasing transport distance between source and target or adding redundant branches to the actual graph may surprisingly result in a significant enhancement of transport efficiency. We explain analytically the observed non-classical effects using the concept of trapped states for several intriguing geometries including the ladder graph, the Cayley tree and its modifications. | quantum physics |
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how a novel type of adaptive spiking recurrent neural network (SRNN) is able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks. To achieve this, we model standard and adaptive multiple-timescale spiking neurons as self-recurrent neural units, and leverage surrogate gradients and auto-differentiation in the PyTorch Deep Learning framework to efficiently implement backpropagation-through-time, including learning of the important spiking neuron parameters to adapt our spiking neurons to the tasks. | computer science |
We present results from analytic solutions to the running coupling, full next-to-leading order, and collinearly improved next-to-leading order Balitsky-Kovchegov equations in the saturation region with the smallest dipole size QCD running coupling prescription. The analytic results of the $S$-matrix of the latter two equations show that the $\exp(-\mathcal{O}(Y^{3/2}))$ rapidity dependence of the solutions are replaced by $\exp(-\mathcal{O}(Y))$ dependence once the running coupling prescription is switched from parent dipole to the smallest dipole prescription, which indicate that the $S$-matrix has a strong dependence on the choice of running coupling prescription. We compute the numerical solutions of these Balitsky-Kovchegov equations with the smallest and parent dipole running coupling prescriptions, the numerical results confirm the analytic outcomes. The rare fluctuations of the $S$-matrix on top of next-to-leading order corrections are also studied under the smallest dipole running coupling prescription in the center of mass frame. It shows that the rare fluctuations are strongly suppressed and less important in the smallest dipole running coupling prescription case as compared to the parent dipole running coupling prescription case. | high energy physics phenomenology |
In a minimal flow, the hitting time is the exponent of the power law, as r goes to zero, for the time needed by orbits to become r-dense. We show that on the so-called Ornithorynque origami the hitting time of the flow in an irrational slope equals the diophantine type of the slope. We give a general criterion for such equality. In general, for genus at least two, hitting time is strictly bigger than diophantine type. | mathematics |
Pseudo-healthy synthesis is the task of creating a subject-specific `healthy' image from a pathological one. Such images can be helpful in tasks such as anomaly detection and understanding changes induced by pathology and disease. In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy. We disentangle what appears to be healthy and where disease is as a segmentation map, which are then recombined by a network to reconstruct the input disease image. We train our models adversarially using either paired or unpaired settings, where we pair disease images and maps when available. We quantitatively and subjectively, with a human study, evaluate the quality of pseudo-healthy images using several criteria. We show in a series of experiments, performed on ISLES, BraTS and Cam-CAN datasets, that our method is better than several baselines and methods from the literature. We also show that due to better training processes we could recover deformations, on surrounding tissue, caused by disease. Our implementation is publicly available at \url{https://tobeprovided.upon.acceptance} | electrical engineering and systems science |
CMB full-sky temperature data show a hemispherical asymmetry in power nearly aligned with the Ecliptic. In real space, this anomaly can be quantified by the temperature variance in the northern and southern Ecliptic hemispheres, with the north displaying an anomalously low variance while the south appears consistent with expectations from the best-fitting theory, LCDM. While this is a well-established result in temperature, the low signal-to-noise ratio in current polarization data prevents a similar comparison. Even though temperature and polarization are correlated, polarization realizations constrained by temperature data show that the lack of variance is not expected to be present in polarization data. Therefore, a natural way of testing whether the temperature result is a fluke is to measure the variance of CMB polarization components. In anticipation of future CMB experiments that will allow for high-precision large-scale polarization measurements, we study how variance of polarization depends on LCDM parameters' uncertainties by forecasting polarization maps with Planck's MCMC chains. We find that, unlike temperature variance, polarization variance is noticeably sensitive to present uncertainties in cosmological parameters. This comes mainly from the current poor constraints on the reionization optical depth, tau, and the fact that tau drives variance at low multipoles. In this work we show how the variance of polarization maps generically depends on the cosmological parameters. We demonstrate how the improvement in the tau measurement seen between Planck's two latest data releases results in a tighter constraint on polarization variance expectations. Finally, we consider even smaller uncertainties on tau and how more precise measurements of tau can drive the expectation for polarization variance in a hemisphere close to that of the cosmic-variance-limited distribution. | astrophysics |
Over 50 years ago, Lov\'{a}sz proved that two graphs are isomorphic if and only if they admit the same number of homomorphisms from any graph [Acta Math. Hungar. 18 (1967), pp. 321--328]. In this work we prove that two graphs are quantum isomorphic (in the commuting operator framework) if and only if they admit the same number of homomorphisms from any planar graph. As there exist pairs of non-isomorphic graphs that are quantum isomorphic, this implies that homomorphism counts from planar graphs do not determine a graph up to isomorphism. Another immediate consequence is that determining whether there exists some planar graph that has a different number of homomorphisms to two given graphs is an undecidable problem, since quantum isomorphism is known to be undecidable. Our characterization of quantum isomorphism is proven via a combinatorial characterization of the intertwiner spaces of the quantum automorphism group of a graph based on counting homomorphisms from planar graphs. This result inspires the definition of "graph categories" which are analogous to, and a generalization of, partition categories that are the basis of the definition of easy quantum groups. Thus we introduce a new class of "graph-theoretic quantum groups" whose intertwiner spaces are spanned by maps associated to (bi-labeled) graphs. Finally, we use our result on quantum isomorphism to prove an interesting reformulation of the Four Color Theorem: that any planar graph is 4-colorable if and only if it has a homomorphism to a specific Cayley graph on the symmetric group $S_4$ which contains a complete subgraph on four vertices but is not 4-colorable. | quantum physics |
We examine ac driven skyrmions interacting with the interface between two different obstacle array structures. We consider drive amplitudes at which skyrmions in a bulk obstacle lattice undergo only localized motion and show that when an obstacle lattice interface is introduced, directed skyrmion transport can occur along the interface. The skyrmions can be guided by a straight interface and can also turn corners to follow the interface. For a square obstacle lattice embedded in a square pinning array with a larger lattice constant, we find that skyrmions can undergo transport in all four primary symmetry directions under the same fixed ac drive. We map where localized or translating motion occurs as a function of the ac driving parameters. Our results suggest a new method for controlling skyrmion motion based on transport along obstacle lattice interfaces. | condensed matter |
The longitudinal process that leads to university student drop out in STEM subjects can be described by referring to a) inter-individual differences (e.g., cognitive abilities) as well as b) intra-individual changes (e.g., affective states), c) (unobserved) heterogeneity of trajectories, and d) time-dependent variables. Large dynamic latent variable model frameworks for intensive longitudinal data (ILD) have been proposed which are (partially) capable of simultaneously separating the complex data structures (e.g., DLCA; Asparouhov, Hamaker, & Muth\'en, 2017; DSEM; Asparouhov, Hamaker, & Muth\'en, 2018; NDLC-SEM, Kelava & Brandt, 2019). From a methodological perspective, forecasting in dynamic frameworks allowing for real-time inferences on latent or observed variables based on ongoing data collection has not been an extensive research topic. From a practical perspective, there has been no empirical study on student drop out in math that integrates ILD, dynamic frameworks, and forecasting of critical states of the individuals allowing for real-time interventions. In this paper, we show how Bayesian forecasting of multivariate intra-individual variables and time-dependent class membership of individuals (affective states) can be performed in these dynamic frameworks. To illustrate our approach, we use an empirical example where we apply forecasting methodology to ILD from a large university student drop out study in math with multivariate observations collected over 50 measurement occasions from multiple students (N = 122). More specifically, we forecast emotions and behavior related to drop out. This allows us to model (i) just-in-time interventions, (ii) detection of heterogeneity in trajectories, and (iii) prediction of emerging dynamic states (e.g. critical stress levels or pre-decisional states). | statistics |
A code $C$ is called propelinear if there is a subgroup of $Aut(C)$ of order $|C|$ acting transitively on the codewords of $C$. In the paper new propelinear perfect binary codes of any admissible length more than $7$ are obtained by a particular case of the Solov'eva concatenation construction--1981 and the regular subgroups of the general affine group of the vector space over $GF(2)$. | mathematics |
We consider geothermal inverse problems and uncertainty quantification from a Bayesian perspective. Our main goal is to make standard, `out-of-the-box' Markov chain Monte Carlo (MCMC) sampling more feasible for complex simulation models by using suitable approximations. To do this, we first show how to pose both the inverse and prediction problems in a hierarchical Bayesian framework. We then show how to incorporate so-called posterior-informed model approximation error into this hierarchical framework, using a modified form of the Bayesian approximation error (BAE) approach. This enables the use of a `coarse', approximate model in place of a finer, more expensive model, while accounting for the additional uncertainty and potential bias that this can introduce. Our method requires only simple probability modelling, a relatively small number of fine model simulations, and only modifies the target posterior -- any standard MCMC sampling algorithm can be used to sample the new posterior. These corrections can also be used in methods that are not based on MCMC sampling. We show that our approach can achieve significant computational speed-ups on two geothermal test problems. We also demonstrate the dangers of naively using coarse, approximate models in place of finer models, without accounting for the induced approximation errors. The naive approach tends to give overly confident and biased posteriors while incorporating BAE into our hierarchical framework corrects for this while maintaining computational efficiency and ease-of-use. | statistics |
In this paper, we study the problem of inverse electromagnetic scattering to recover multilayer human tissue profiles using ultrawideband radar systems. We pose the recovery problem as a blind deconvolution problem, in which we simultaneously estimate both the transmitted pulse and the underlying dielectric and geometric properties of the one-dimensional tissue profile. We propose comprehensive Bayesian Markov Chain Monte Carlo methods, where the sampler parameters are adaptively updated to maintain desired acceptance ratios. We present the recovery performance of the proposed algorithms on simulated synthetic measurements. We also derive theoretical bounds for the estimation of dielectric properties and provide minimum achievable mean-square-errors for unbiased estimators. | electrical engineering and systems science |
We address the problem of power allocation and secondary user (SU) selection in the downlink from a secondary base station (SBS) equipped with a large number of antennas in an underlay cognitive radio network. A new optimization framework is proposed in order to select the maximum number of SUs and compute power allocations in order to satisfy instantaneous rate or QoS requirements of SUs. The optimization framework also aims to restrict the interference to primary users (PUs) below a predefined threshold using available imperfect CSI at the SBS. In order to obtain a feasible solution for power allocation and user selection, we propose a low-complexity algorithm called DeleteSU-with-Maximum-Power-allocation (DMP). Theoretical analysis is provided to compute the interference to PUs and the number of SUs exceeding the required rate. The analysis and simulations show that the proposed DMP algorithm outperforms the stateof-the art selection algorithm in terms of serving more users with minimum rate constraints, and it approaches the optimal solution if the number of antennas is an order of magnitude greater than the number of users. | electrical engineering and systems science |
Open containers, i.e., containers without covers, are an important and ubiquitous class of objects in human life. In this letter, we propose a novel method for robots to "imagine" the open containability affordance of a previously unseen object via physical simulations. We implement our imagination method on a UR5 manipulator. The robot autonomously scans the object with an RGB-D camera. The scanned 3D model is used for open containability imagination which quantifies the open containability affordance by physically simulating dropping particles onto the object and counting how many particles are retained in it. This quantification is used for open-container vs. non-open-container binary classification (hereafter referred to as open container classification). If the object is classified as an open container, the robot further imagines pouring into the object, again using physical simulations, to obtain the pouring position and orientation for real robot autonomous pouring. We evaluate our method on open container classification and autonomous pouring of granular material on a dataset containing 130 previously unseen objects with 57 object categories. Although our proposed method uses only 11 objects for simulation calibration (training), its open container classification aligns well with human judgements. In addition, our method endows the robot with the capability to autonomously pour into the 55 containers in the dataset with a very high success rate. We also compare to a deep learning method. Results show that our method achieves the same performance as the deep learning method on open container classification and outperforms it on autonomous pouring. Moreover, our method is fully explainable. | computer science |
The mass and distance functions of free-floating planets (FFPs) would give major insights into the formation and evolution of planetary systems, including any systematic differences between those in the disk and bulge. We show that the only way to measure the mass and distance of individual FFPs over a broad range of distances is to observe them simultaneously from two observatories separated by $D\sim {\cal O}(0.01\,AU)$ (to measure their microlens parallax $\pi_{\rm E}$) and to focus on the finite-source point-lens (FSPL) events (which yield the Einstein radius $\theta_{\rm E}$). By combining the existing KMTNet 3-telescope observatory with a 0.3m $4\,{\rm deg}^2$ telescope at L2, of order 130 such measurements could be made over four years, down to about $M\sim 6\,M_\oplus$ for bulge FFPs and $M\sim 0.7\,M_\oplus$ for disk FFPs. The same experiment would return masses and distances for many bound planetary systems. A more ambitious experiment, with two 0.5m satellites (one at L2 and the other nearer Earth) and similar camera layout but in the infrared, could measure masses and distances of sub-Moon mass objects, and thereby probe (and distinguish between) genuine sub-Moon FFPs and sub-Moon ``dwarf planets'' in exo-Kuiper Belts and exo-Oort Clouds. | astrophysics |
CP4 3HDM is a unique three-Higgs-doublet model equipped with a higher-order CP symmetry in the scalar and Yukawa sector. Based on a single assumption (the minimal model with a CP-symmetry of order 4 and no accidental symmetry), it leads to a remarkable correlation between its scalar and Yukawa sectors, which echoes in its phenomenology. A recent scan of the parameter space of CP4 3HDM under the assumption of scalar alignment identified a few dozens of points which passed many flavour constraints. In the present work we show, however, that almost all of these points are now ruled out by the recent LHC searches of $t \to H^+ b$ with subsequent hadronic decays of $H^+$. Apart from a few points with charged Higgses heavier than the top quark, only one point survives all the checks, the model with an exotic, non-2HDM-like generation pattern of $H^+$ couplings with quarks. One can expect many more points with exotic $H^+$ couplings to quarks if the scalar alignment assumption is relaxed. | high energy physics phenomenology |
Many real-world mission-critical applications require continual online learning from noisy data and real-time decision making with a defined confidence level. Probabilistic models and stochastic neural networks can explicitly handle uncertainty in data and allow adaptive learning-on-the-fly, but their implementation in a low-power substrate remains a challenge. Here, we introduce a novel hardware fabric that implements a new class of stochastic NN called Neural-Sampling-Machine that exploits stochasticity in synaptic connections for approximate Bayesian inference. Harnessing the inherent non-linearities and stochasticity occurring at the atomic level in emerging materials and devices allows us to capture the synaptic stochasticity occurring at the molecular level in biological synapses. We experimentally demonstrate in-silico hybrid stochastic synapse by pairing a ferroelectric field-effect transistor -based analog weight cell with a two-terminal stochastic selector element. Such a stochastic synapse can be integrated within the well-established crossbar array architecture for compute-in-memory. We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference. We perform network-level simulations to highlight the salient automatic weight normalization feature introduced by the stochastic synapses of the NSM that paves the way for continual online learning without any offline Batch Normalization. We also showcase the Bayesian inferencing capability introduced by the stochastic synapse during inference mode, thus accounting for uncertainty in data. We report 98.25%accuracy on standard image classification task as well as estimation of data uncertainty in rotated samples. | condensed matter |
Approximate solutions of the Dirac equation are found for ultrarelativistic particles moving in a periodic potential, which depends only on one coordinate, transverse to the largest component of the momentum of the incoming particle. As an example we employ these solutions to calculate the radiation emission of positrons and electrons trapped in the planar potential found between the (110) planes in Silicon. This allows us to compare with the semi-classical method of Baier, Katkov and Strakhovenko, which includes the effect of spin and photon recoil, but neglects the quantization of the transverse motion. For high-energy electrons, the high-energy part of the angularly integrated photon energy spectrum calculated with the found wave functions differs from the corresponding one calculated with the semi-classical method. However, for lower particle energies it is found that the angularly integrated emission energy spectra obtained via the semi-classical method is in fairly good agreement with the full quantum calculation except that the positions of the harmonic peaks in photon energy and the photon emission angles are shifted. | high energy physics phenomenology |
The parameter estimation of unnormalized models is a challenging problem. The maximum likelihood estimation (MLE) is computationally infeasible for these models since normalizing constants are not explicitly calculated. Although some consistent estimators have been proposed earlier, the problem of statistical efficiency remains. In this study, we propose a unified, statistically efficient estimation framework for unnormalized models and several efficient estimators, whose asymptotic variance is the same as the MLE. The computational cost of these estimators is also reasonable and they can be employed whether the sample space is discrete or continuous. The loss functions of the proposed estimators are derived by combining the following two methods: (1) density-ratio matching using Bregman divergence, and (2) plugging-in nonparametric estimators. We also analyze the properties of the proposed estimators when the unnormalized models are misspecified. The experimental results demonstrate the advantages of our method over existing approaches. | statistics |
This paper extends the univariate Theory of Connections, introduced in (Mortari,2017), to the multivariate case on rectangular domains with detailed attention to the bivariate case. In particular, it generalizes the bivariate Coons surface, introduced by (Coons,1984), by providing analytical expressions, called "constrained expressions," representing all possible surfaces with assigned boundary constraints in terms of functions and arbitrary-order derivatives. In two dimensions, these expressions, which contain a freely chosen function, g(x,y), satisfy all constraints no matter what the g(x,y) is. The boundary constraints considered in this article are Dirichlet, Neumann, and any combinations of them. Although the focus of this article is on two-dimensional spaces, the final section introduces the "Tensor Theory of Connections," validated by mathematical proof. This represents the multivariate extension of the Theory of Connections subject to arbitrary-order derivative constraints in rectangular domains. The main task of this paper is to provide an analytical procedure to obtain constrained expressions in any space that can be used to transform constrained problems into unconstrained problems. This theory is proposed mainly to better solve PDEs and stochastic differential equations. | mathematics |
We consider inclusion of interactions between the higher derivative extended Chern-Simons and charged scalar field. We demonstrate that the order $N$ extended Chern-Simons and order $2n$ charged scalar admit the $(N+n)$-parameter series of interaction vertices. The interactions are in general non-Lagrangian, but they preserve a certain conserved second-rank tensor, whose parameters are determined by the coupling. The $00$-component of this tensor can be bounded even if the canonical energy of the model is unbounded before the inclusion of interaction. If the $00$-component of conserved tensor is bounded, the theory is stable. | high energy physics theory |
Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector is regarded as a state-of-the-art MIMO detector because of its exceptional performance. However, we find that the EP MIMO detector cannot guarantee to achieve the optimal performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection for the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). In order to obtain the optimal initial variance and damping factors, we adopt a deep learning scheme, in which we unfold the iterative processing of MEPD to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios. | electrical engineering and systems science |
We determine, up to multiplicative constants, the number of integers $n\le x$ that have no prime factor $\le w$ and a divisor in $(y,2y]$. Our estimate is uniform in $x,y,w$. We apply this to determine the order of the number of distinct integers in the $N\times N$ multiplication table which are free of prime factors $\le w$, and the number of distinct fractions of the form $\frac{a_1a_2}{b_1b_2}$ with $1\le a_1 \le b_1\le N$ and $1\le a_2\le b_2 \le N$. | mathematics |
We calculate the homological blocks for Seifert manifolds from the exact expression for the $G=SU(N)$ Witten-Reshetikhin-Turaev invariants of Seifert manifolds obtained by Lawrence, Rozansky, and Mari\~no. For the $G=SU(2)$ case, it is possible to express them in terms of the false theta functions and their derivatives. For $G=SU(N)$, we calculate them as a series expansion and also discuss some properties of the contributions from the abelian flat connections to the Witten-Reshetikhin-Turaev invariants for general $N$. We also provide an expected form of the $S$-matrix for general cases and the structure of the Witten-Reshetikhin-Turaev invariants in terms of the homological blocks. | high energy physics theory |
Stress in dense granular materials and other athermal particle aggregates is transmitted through a visually striking subnetwork of interparticle contacts, the filamentary segments of which are referred to as force chains. The emergence of such preferred subnetwork in structurally disordered media with constituents interacting primarily by physical contact is not fully understood. In this work, we study locally correlated transport in Random Geometric Graphs (RGGs), and show the spontaneous emergence of preferred subnetwork. Our findings reveal that, despite structural disorder, system spanning localization of fluxes transmitted through a spatial network can emerge from short ranged correlations. The spatial and statistical features of the subnetwork are surprisingly similar to the strong force network in simulated grain assemblies, and provides insights on the structure and spatial scale of significance of the force chains. | condensed matter |
Fundamental quantum electrodynamical (QED) processes such as spontaneous emission and electron-photon scattering encompass a wealth of phenomena that form one of the cornerstones of modern science and technology. Conventionally, calculations in QED and in other field theories assume that incoming particles are single-momentum states. The possibility that coherent superposition states, i.e. "shaped wavepackets", will alter the result of fundamental scattering processes is thereby neglected, and is instead assumed to sum to an incoherent (statistical) distribution in the incoming momentum. Here, we show that free-electron wave-shaping can be used to engineer quantum interferences that alter the results of scattering processes in QED. Specifically, the interference of two or more pathways in a QED process (such as photon emission) enables precise control over the rate of that process. As an example, we apply our concept to Bremsstrahlung, a ubiquitous phenomenon that occurs, for instance, in X-ray sources for state-of-the-art medical imaging, security scanning, materials analysis, and astrophysics. We show that free electron wave-shaping can be used to tailor both the spatial and the spectral distribution of emitted photons, enhancing their directionality and monochromaticity, and adding more degrees of freedom that make emission processes like Bremsstrahlung more versatile. The ability to tailor the spatiotemporal attributes of photon emission via quantum interference provides a new degree of freedom in shaping radiation across the entire electromagnetic spectrum. More broadly, the ability to tailor general QED processes through the shaping of free electrons opens up new avenues of control in processes ranging from optical excitation processes (e.g., plasmon and phonon emission) in electron microscopy to free electron lasing in the quantum regime. | quantum physics |
Content-based image retrieval (CBIR) has become one of the most important research directions in the domain of digital data management. In this paper, a new feature extraction schema including the norm of low frequency components in wavelet transformation and color features in RGB and HSV domains are proposed as representative feature vector for images in database followed by appropriate similarity measure for each feature type. In CBIR systems, retrieving results are so sensitive to image features. We address this problem with selection of most relevant features among complete feature set by ant colony optimization (ACO)-based feature selection which minimize the number of features as well as maximize F-measure in CBIR system. To evaluate the performance of our proposed CBIR system, it has been compared with three older proposed systems. Results show that the precision and recall of our proposed system are higher than older ones for the majority of image categories in Corel database. | electrical engineering and systems science |
Contrastive learning has delivered impressive results in many audio-visual representation learning scenarios. However, existing approaches optimize for learning either \textit{global} representations useful for tasks such as classification, or \textit{local} representations useful for tasks such as audio-visual source localization and separation. While they produce satisfactory results in their intended downstream scenarios, they often fail to generalize to tasks that they were not originally designed for. In this work, we propose a versatile self-supervised approach to learn audio-visual representations that generalize to both the tasks which require global semantic information (e.g., classification) and the tasks that require fine-grained spatio-temporal information (e.g. localization). We achieve this by optimizing two cross-modal contrastive objectives that together encourage our model to learn discriminative global-local visual information given audio signals. To show that our approach learns generalizable video representations, we evaluate it on various downstream scenarios including action/sound classification, lip reading, deepfake detection, and sound source localization. | computer science |
A spin-U(1)-symmetry protected momentum-dependent integer-$Z$-valued topological invariant is proposed to time-reversal-invariant (TRI) superconductivity (SC) whose nonzero value will lead to exactly flat surface band(s). The theory is applied to the weakly spin-orbit coupled quasi-1D A$_2$Cr$_3$As$_3$ (A=Na, K, Rb, Cs) superconductors family with highest $T_c$ up to 8.6 K with $p_z$-wave pairing in the $S_z=0$ channel. It's found that up to the leading atomic spin-orbit-coupling (SOC), the whole (001) surface Brillouin zone is covered with exactly-flat surface bands, with some regime hosting three flat bands and the remaining part hosting two. Such exactly-flat surface bands will lead to very sharp zero-bias conductance peak in the scanning tunneling microscopic spectrum. When a tiny subleading spin-flipping SOC is considered, the surface bands will only be slightly split. The application of this theory can be generalized to other unconventional superconductors with weak SOC, particularly to those with mirror-reflection symmetry. | condensed matter |
The lasso is a popular method to induce shrinkage and sparsity in the solution vector (coefficients) of regression problems, particularly when there are many predictors relative to the number of observations. Solving the lasso in this high-dimensional setting can, however, be computationally demanding. Fortunately, this demand can be alleviated via the use of screening rules that discard predictors prior to fitting the model, leading to a reduced problem to be solved. In this paper, we present a new screening strategy: look-ahead screening. Our method uses safe screening rules to find a range of penalty values for which a given predictor cannot enter the model, thereby screening predictors along the remainder of the path. In experiments we show that these look-ahead screening rules improve the performance of existing screening strategies. | statistics |
Northern line-of-sight extinction within Gale Crater during the 2018 global dust storm was monitored daily using MSL's Navcam. Additional observations with Mastcam (north) and Navcam (all directions) were obtained at a lower cadence. Using feature identification and geo-referencing, extinction was estimated in all possible directions. Peak extinction of $>1.1$ km$^{-1}$ was measured between sols 2086 and 2090, an order of magnitude higher than previous maxima. Northern and western directions show an initial decrease, followed by a secondary peak in extinction, not seen in column opacity measurements. Due to foreground topography, eastern direction results are provided only as limits, and southern results were indeterminable. Mastcam red and green filter results agree well, but blue filter results show higher extinctions, likely due to low signal-to-noise. Morning results are systematically higher than afternoon results, potentially indicative of atmospheric mixing. | astrophysics |
In this paper, we focus on a theory-practice gap for Adam and its variants (AMSgrad, AdamNC, etc.). In practice, these algorithms are used with a constant first-order moment parameter $\beta_{1}$ (typically between $0.9$ and $0.99$). In theory, regret guarantees for online convex optimization require a rapidly decaying $\beta_{1}\to0$ schedule. We show that this is an artifact of the standard analysis and propose a novel framework that allows us to derive optimal, data-dependent regret bounds with a constant $\beta_{1}$, without further assumptions. We also demonstrate the flexibility of our analysis on a wide range of different algorithms and settings. | statistics |
A number of direct detection experiments are searching for electron excitations created by scattering of sub-GeV dark matter. We present an alternate formulation of dark matter-electron scattering in terms of the dielectric response of a material. For dark matter which couples to electrons, this approach automatically accounts for in-medium screening effects, which were not included in previous rate calculations for semiconductor targets. We show that the screening effects appear for both scalar and vector mediators. The result is a non-negligible reduction of reach for direct detection experiments which use dielectric materials as targets. We also explore different determinations of the dielectric response, including first-principles density functional theory (DFT) calculations and a data-driven analytic approximation using a Mermin oscillator model. | high energy physics phenomenology |
We investigate the properties of near-conformal dynamics in a sector of large charge when approaching the lower boundary of the conformal window from the chirally broken phase. To elucidate our approach we use the time-honored example of the phenomenologically relevant SU(2) color theory featuring $N_f$ Dirac fermions transforming in the fundamental representation of the gauge group. In the chirally broken phase we employ the effective pion Lagrangian featuring also a pseudo-dilaton to capture a possible smooth conformal-to-non-conformal phase transition. We charge the baryon symmetry of the Lagrangian and study its impact on the ground state and spectrum of the theory as well as the would-be conformal dimensions of the lowest large-charge operator. We moreover study the effects of and dependence on the fermion mass term. | high energy physics theory |
We discuss parallels between students and teachers in the process of pedagogical reform. Reform aims for students to develop their own process for becoming independent learners, and to gain personal ownership. Likewise teachers can develop their own personally owned reform process if they have the encouragement and freedom to take individual initiative. We argue that tools, such as text materials, technology, etc., are merely objects that should be kept in perspective as secondary within an overarching ever ongoing process. And we discuss how melding teacher freedom with collaboration can foster far-reaching change. | mathematics |
Let $X$ be a CR manifold with transversal, proper CR $G$-action. We show that $X/G$ is a complex space such that the quotient map is a CR map. Moreover the quotient is universal, i.e. every invariant CR map into a complex manifold factorises uniquely over a holomorphic map on $X/G$. We then use this result and complex geometry to proof an embedding theorem for (non-compact) strongly pseudoconvex CR manifolds with transversal $G \rtimes S^1$-action. The methods of the proof are applied to obtain a projective embedding theorem for compact CR manifolds. | mathematics |
Model order reduction is the approximation of dynamical systems into equivalent systems with smaller order. Model reduction has been studied extensively for different types of systems. In this paper, we present two methods for multi input multi output linear systems. These methods are based on solvents, also called block poles. These methods are particularly suitable if the given system is in matrix transfer function form. The first method eliminates solvents one by one whereas, the second method can eliminate multiple solvents at the same time. The two presented methods are implemented in MATLAB in order to provide a systematic method for the model order reduction of MIMO linear systems. | electrical engineering and systems science |
The tremendous diversity of zeolite frameworks makes ab initio simulations of their structure, stability, reactivity and, properties virtually impossible. To enable large-scale reactive simulations of zeolites with ab initio quality, we trained neural network potentials (NNP) with the SchNet architecture on a structurally diverse DFT database. This database was iteratively extended by active learning to cover the configuration space from low-density zeolites to high-pressure silica polymorphs including low-energy equilibrium configurations and high-energy transition states. The resulting reactive NNPs model equilibrium structures, vibrational properties, and phase transitions at high temperatures such as thermal zeolite collapse in excellent agreement with both DFT and experiments. The novel NNPs allowed revision of a zeolite database containing more than 330 thousand hypothetical zeolites previously generated employing analytical force fields. NNP structure optimizations revealed more than 20 thousand additional hypothetical frameworks in the thermodynamically accessible range of zeolite synthesis. Additionally, the obtained zeolite database provides vital input for future machine learning studies on the structure, stability, reactivity and properties of hypothetical and existing zeolites. | condensed matter |
This work investigates alternate pre-emphasis filters used as part of the loss function during neural network training for nonlinear audio processing. In our previous work, the error-to-signal ratio loss function was used during network training, with a first-order highpass pre-emphasis filter applied to both the target signal and neural network output. This work considers more perceptually relevant pre-emphasis filters, which include lowpass filtering at high frequencies. We conducted listening tests to determine whether they offer an improvement to the quality of a neural network model of a guitar tube amplifier. Listening test results indicate that the use of an A-weighting pre-emphasis filter offers the best improvement among the tested filters. The proposed perceptual loss function improves the sound quality of neural network models in audio processing without affecting the computational cost. | electrical engineering and systems science |
We compute the spectrum of extremal nonBPS black holes in four dimensions by studying supergravity on their AdS$_2\times S^2$ near horizon geometry. We find that the spectrum exhibits significant simplifications even though supersymmetry is completely broken. We interpret our results in the framework of nAdS$_2$/nCFT$_1$ correspondence and by comparing with dimensional reduction from AdS$_3$/CFT$_2$ duality. As an additional test we compute quantum corrections to extremal black hole entropy on the nonBPS branch and recover results previously determined using very different methods. | high energy physics theory |
We analyze the global symmetries of ${\cal N}=4$ supersymmetric mechanics involving $4n$-dimensional Quaternion-K\"ahler (QK) $1D$ sigma models on projective spaces $\mathbb{H}{\rm H}^n$ and $\mathbb{H}{\rm P}^n$ as the bosonic core. All Noether charges associated with global worldline symmetries are shown to vanish as a result of equations of motion, which implies that we deal with a severely constrained hamiltonian system. The complete hamiltonian analysis of the bosonic sector is performed. | high energy physics theory |
Many of the control policies that were put into place during the Covid-19 pandemic had a common goal: to flatten the curve of the number of infected people so that its peak remains under a critical threshold. This letter considers the challenge of engineering a strategy that enforces such a goal using control theory. We introduce a simple formulation of the optimal flattening problem, and provide a closed form solution.This is augmented through nonlinear closed loop tracking of the nominal solution, with the aim of ensuring close-to-optimal performance under uncertain conditions. A key contribution ofthis paper is to provide validation of the method with extensive and realistic simulations in a Covid-19 scenario, with particular focus on the case of Codogno - a small city in Northern Italy that has been among the most harshly hit by the pandemic. | electrical engineering and systems science |
We propose the Positive Resampler to solve the problem associated with event samples from state-of-the-art predictions for scattering processes at hadron colliders typically involving a sizeable number of events contributing with negative weight. The proposed method guarantees positive weights for all physical distributions, and a correct description of all observables. A desirable side product of the method is the possibility to reduce the size of event samples produced by General Purpose Event Generators, thus lowering the resource demands for subsequent computing-intensive event processing steps. We demonstrate the viability and efficiency of our approach by considering its application to a next-to-leading order + parton shower merged prediction for the production of a $W$ boson in association with multiple jets. | high energy physics phenomenology |
Using the expressions for generalized ADT current and potential in a self consistent manner, we derive the asymptotic symmetry algebra on AdS$_3$ and the near horizon extremal BTZ spacetimes. The structure of symmetry algebra among the conserved charges for asymptotic killing vectors matches exactly with the known results thus establishing the algebraic equivalence between the well known existing formalisms for obtaining the conserved charges and the generalized ADT charges. | high energy physics theory |
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