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The molecular mobility of propane and propylene has been studied by 2H NMR method. The activation barriers of diffusion determined from the spin relaxation analysis are in a good agreement with the values obtained by Liu et al. (EC3H8 = 38 kJ/mol, EC3H6 = 13.5 kJ/mol). High activation energy of the rotation inside the cavity for propylene compared to propane (8 kJ/mol vs 3.8 kJ/mol) implies stronger interaction of this adsorbate with the walls of the cavity. | condensed matter |
Gluon fusion processes like single and double Higgs production exhibit slow convergence and pose severe computational challenges. We show how the top-quark mass dependence of the virtual amplitudes can be reconstructed with a conformal mapping and Pad\'e approximants based on the expansion in the inverse top-quark mass and the non-analytic terms in the expansion around the top threshold. The method is then applied at two- and three-loop order. | high energy physics phenomenology |
Using the truncated form factor expansion the low-temperature asymptotics for the transverse dynamical structure factor of the magnetically polarized $XX$ chain is studied. Unlike the previous paper we do not use the representation of structure factor in terms of the corresponding magnetic susceptibility. This enables to obtain correct results at small and negative frequencies. | condensed matter |
Boron-doped diamond (BDD) has attracted much attentions in semi-/super-conductor physics and electrochemistry, where the surface structures play crucial roles. Herein, we systematically re-examined the probable surface reconstructions of the bare and H-terminated BDD(100) and (111) surfaces by using density functional theory (DFT). For the optimized structures, we performed STM image simulations based on Tersoff-Hamman scheme and calculations of the projected density of states. We found that: on the BDD(100), the p(2x1) reconstruction has lowest energy and the c(2x2) reconstruction has 0.1673 eV/surface-atom energy higher; On the BDD(111), the ideal (1x1) has lowest energy, the single chain SC-(2x1) and Pandey chain PC-(2x1) have 0.3415 eV/surface-atom and 0.6576 eV/surface-atom higher energy, respectively. The BDD(111) appears to have more reconstructions than the BDD(100) which supports to the idea that the BDD(111) is more electrochemically reactive than the BDD(100). In addition, we study the impact of the Boron dopant on the surface states of the BDD(111) and suggest the Boron-enhanced graphitization on the BDD(111). The results give an insight into the surface stability of the BDD. | condensed matter |
Attosecond Pulse Trains (APT) generated by high-harmonic generation (HHG) of high-intensity near-infrared (IR) laser pulses have proven valuable for studying the electronic dynamics of atomic and molecular species. However, the high intensities required for high-photon-energy, high-flux HHG usually limit the class of adequate laser systems to repetition rates below 10~kHz. Here, APT's generated from the 100 kHz, 160 W, 40 fs laser system (HR1) of the Extreme Light Infrastructure Attosecond Light Pulse Source (ELI-ALPS) are reconstructed using the Reconstruction of Attosecond Beating By Interference of two-photon Transitions (RABBIT) technique. These experiments constitute the first attosecond time-resolved photoelectron spectroscopy measurements performed at 100 kHz repetition rate and the first attosecond experiments performed at ELI-ALPS. These RABBIT measurements were taken with an additional IR field temporally locked to the extreme-ultraviolet APT, resulting in an atypical omega beating. We show that the phase of the 2-omega beating recorded under these conditions is strictly identical to that observed in standard RABBIT measurements within second-order perturbation theory. This work highlights an experimental simplification for future experiments based on attosecond interferometry (or RABBIT), which is particularly useful when lasers with high average powers are used. | physics |
A phenomenological analysis of the scalar glueball and scalar meson spectra is carried out by using the AdS/QCD framework in the bottom-up approach. The resulting spectra are in good agreement for glueballs with lattice QCD results and for mesons with PDG data. We make use of the relation between the mode functions in AdS/QCD and the wave functions in Light-Front $QCD$ to discuss the mixing of glueballs and mesons. The results of our investigation point out that above 2 GeV scalar particles will appear in almost degenerate pairs of unmixed glueball and mesons states leading to an interesting phenomenology whereby gluon dynamics could be well investigated. | high energy physics phenomenology |
There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes. Some of the most common features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and the Auto-correlation Function (ACF). In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment. The experiment is performed using four different tool overhang lengths with varying cutting speed and the depth of cut. We then examine the resulting signals and tag them as either chatter or chatter-free. The tagged signals are then used to train a classifier. The classification methods include the most common algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Gradient Boost (GB). Our results show that features extracted from the Fourier spectrum are the most informative when training a classifier and testing on data from the same cutting configuration yielding accuracy as high as %96. However, the accuracy drops significantly when training and testing on two different configurations with different structural eigenfrequencies. Thus, we conclude that while these traditional features can be highly tuned to a certain process, their transfer learning ability is limited. We also compare our results against two other methods with rising popularity in the literature: Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). The latter two methods, especially EEMD, show better transfer learning capabilities for our dataset. | electrical engineering and systems science |
We present a model atom for C I - C II - C III - C IV using the most up-to-date atomic data and evaluated the non-local thermodynamic equilibrium (NLTE) line formation in classical 1D atmospheric models of O-B-type stars. Our models predict the emission lines of C II 9903~\AA\ and 18535~\AA\ to appear at effective temperature \Teff~$\geq$~17\,500~K, those of C II 6151~\AA\ and 6461~\AA\ to appear at \Teff~$>$~25\,000~K, and those of C III 5695, 6728--44, 9701--17~\AA\ to appear at \Teff~$\geq$~35\,000~K (log~$g$=4.0). Emission occurs in the lines of minority species, where the photoionization-recombination mechanism provides a depopulation of the lower levels to a greater extent than the upper levels. For C II 9903 and 18535~\AA, the upper levels are mainly populated from C III reservoir through the Rydberg states. For C III 5695 and 6728--44~\AA, the lower levels are depopulated due to photon losses in UV transitions at 885, 1308, and 1426--28~\AA\ which become optically thin in the photosphere. We analysed the lines of C I, C II, C III, and C IV for twenty-two O-B-type stars with temperature range between 15\,800 $\leq$~\Teff~$\leq$ 38\,000~K. Abundances from emission lines of C I, C II and C III are in agreement with those from absorption ones for most of the stars. We obtained log~$\epsilon_{\rm C}$=8.36$\pm$0.08 from twenty B-type stars, that is in line with the present-day Cosmic Abundance Standard. The obtained carbon abundances in 15~Mon and HD~42088 from emission and absorption lines are 8.27$\pm$0.11 and 8.31$\pm$0.11, respectively. | astrophysics |
We present the results of experimental studies on the transverse and longitudinal dynamics of a single electron in the IOTA storage ring. IOTA is a flexible machine dedicated to beam physics experiments with electrons and protons. A method was developed to reliably inject and circulate a controlled number of electrons in the ring. A key beam diagnostic system is the set of sensitive high-resolution digital cameras for the detection of synchrotron light emitted by the electrons. With 60--130 electrons in the machine, we measured beam lifetime and derived an absolute calibration of the optical system. At exposure times of 0.5~s, the cameras were sensitive to individual electrons. Camera images were used to reconstruct the time evolution of oscillation amplitudes of a single electron in all 3~degrees of freedom. The evolution of amplitudes directly showed the interplay between synchrotron-radiation damping, quantum excitations, and scattering with the residual gas. From the distribution of measured single-electron oscillation amplitudes, we deduced transverse emittances, momentum spread, damping times, and beam energy. Estimates of residual-gas density and composition were calculated from the measured distributions of vertical scattering angles. Combining scattering and lifetime data, we also provide an estimate of the aperture of the ring. To our knowledge, this is the first time that the dynamics of a single electron is tracked in all three dimensions with digital cameras in a storage ring. | physics |
As a variant of standard convolution, a dilated convolution can control effective receptive fields and handle large scale variance of objects without introducing additional computational costs. To fully explore the potential of dilated convolution, we proposed a new type of dilated convolution (referred to as inception convolution), where the convolution operations have independent dilation patterns among different axes, channels and layers. To develop a practical method for learning complex inception convolution based on the data, a simple but effective search algorithm, referred to as efficient dilation optimization (EDO), is developed. Based on statistical optimization, the EDO method operates in a low-cost manner and is extremely fast when it is applied on large scale datasets. Empirical results validate that our method achieves consistent performance gains for image recognition, object detection, instance segmentation, human detection, and human pose estimation. For instance, by simply replacing the 3x3 standard convolution in the ResNet-50 backbone with inception convolution, we significantly improve the AP of Faster R-CNN from 36.4% to 39.2% on MS COCO. | computer science |
Reducing the impact of seismic activity on the motion of suspended optics is essential for the operation of ground-based gravitational wave detectors. During periods of increased seismic activity, low-frequency ground translation and tilt cause the Advanced LIGO observatories to lose `lock', reducing their duty cycles. This paper applies modern global-optimisation algorithms to aid in the design of the `sensor correction' filter, used in the control of the active platforms. It is shown that a particle swarm algorithm that minimises a cost-function approximating the differential RMS velocity between platforms can produce control filters that perform better across most frequencies in the control bandwidth than those currently installed. These tests were conducted using training data from the LIGO Hanford Observatory seismic instruments and simulations of the HAM-ISI (Horizontal Access Module Internal Seismic Isolation) platforms. These results show that new methods of producing control filters are ready for use at LIGO. The filters were implemented at LIGO's Hanford Observatory, and use the resulting data to refine the cost function. | astrophysics |
This paper proposes a cooperative angle-of-arrival(AoA) estimation, taking advantage of co-processing channel state information (CSI) from a group of access points that receive signals of the same source. Since received signals are sparse, we use Compressive Sensing (CS) to address the AoA estimation problem. We formulate this problem as a penalized l0-norm minimization, reformulate it as an Ising energy problem, and solve it using Markov Chain Monte Carlo (MCMC). Simulation results show that our proposed method outperforms the existing methods in the literature. | electrical engineering and systems science |
In the upcoming process to overcome the limitations of the standard von Neumann architecture, synaptic electronics is gaining a primary role for the development of in-memory computing. In this field, Ge-based compounds have been proposed as switching materials for nonvolatile memory devices and for selectors. By employing the classical molecular dynamics, we study the structural features of both the liquid states at 1500K and the amorphous phase at 300K of Ge-rich and Se-rich chalcogenides binary GexSe1-x systems in the range x 0.4-0.6. The simulations rely on a model of interatomic potentials where ions interact through steric repulsion, as well as Coulomb and charge-dipole interactions given by the large electronic polarizability of Se ions. Our results indicate the formation of temperature-dependent hierarchical structures with short-range local orders and medium-range structures, which vary with the Ge content. Our work demonstrates that nanosecond-long simulations, not accessible via ab initio techniques, are required to obtain a realistic amorphous phase from the melt. Our classical molecular dynamics simulations are able to describe the profound structural differences between the melt and the glassy structures of GeSe chalcogenides. These results open to the understanding of the interplay between chemical composition, atomic structure, and electrical properties in switching materials. | condensed matter |
The drag force exerted on an object intruding into granular media can depend on the object's velocity as well as the depth penetrated. We report on intrusion experiments at constant speed over four orders in magnitude together with systematic molecular dynamics simulations well beyond the quasi-static regime. We find that velocity dependence crosses over to depth dependence at a characteristic time after initial impact. This crossover time scale, which depends on penetration speed, depth, gravity and intruder geometry, challenges current models that assume additive contributions to the drag. | condensed matter |
An image is not just a collection of objects, but rather a graph where each object is related to other objects through spatial and semantic relations. Using relational reasoning modules, such as the non-local module \cite{wang2017non}, can therefore improve object detection. Current schemes apply such dedicated modules either to a specific layer of the bottom-up stream, or between already-detected objects. We show that the relational process can be better modeled in a coarse-to-fine manner and present a novel framework, applying a non-local module sequentially to increasing resolution feature maps along the top-down stream. In this way, information can naturally passed from larger objects to smaller related ones. Applying the module to fine feature maps further allows the information to pass between the small objects themselves, exploiting repetitions of instances of the same class. In practice, due to the expensive memory utilization of the non-local module, it is infeasible to apply the module as currently used to high-resolution feature maps. We redesigned the non local module, improved it in terms of memory and number of operations, allowing it to be placed anywhere along the network. We further incorporated relative spatial information into the module, in a manner that can be incorporated into our efficient implementation. We show the effectiveness of our scheme by improving the results of detecting small objects on COCO by 1-2 AP points over Faster and Mask RCNN and by 1 AP over using non-local module on the bottom-up stream. | computer science |
We study Winter or delta-shell model at finite volume (length), describing a small resonating cavity weakly-coupled to a large one. For generic values of the coupling, a resonance of the usual model corresponds, in the finite-volume case, to a compression of the spectral lines; for specific values of the coupling, a resonance corresponds instead to a degenerate or a quasi-degenerate doublet. A secular term of the form g^3 N occurs in the perturbative expansion of the momenta (or of the energies) of the particle at third order in g, where g is the coupling among the cavities and N is the ratio of the length of the large cavity over the length of the small one. These secular terms, which tend to spoil the convergence of the perturbative series in the large volume case, N >> 1, are resummed to all orders in g by means of standard multi-scale methods. The resulting improved perturbative expansions provide a rather complete analytic description of resonance dynamics at finite volume. | quantum physics |
We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and our goal is to help understand the effectiveness of various implicit and explicit dimensionality-reduction methods that exploit manifold structure. Our first key contribution is to establish a novel nonasymptotic version of the Weyl law from differential geometry. From this we are able to show that certain spaces of smooth functions on a manifold are effectively finite-dimensional, with a complexity that scales according to the manifold dimension rather than any ambient data dimension. Finally, we show that given (potentially noisy) function values taken uniformly at random over a manifold, a kernel regression estimator (derived from the spectral decomposition of the manifold) yields minimax-optimal error bounds that are controlled by the effective dimension. | statistics |
Estimates are obtained for the initial coefficients of a normalized analytic function $f$ in the unit disk $\mathbb{D}$ such that $f$ and the analytic extension of $f^{-1}$ to $\mathbb{D}$ belong to certain subclasses of univalent functions. The bounds obtained improve some existing known bounds. | mathematics |
The International Virtual Observatory Alliance (IVOA) has developed and built, in the last two decades, an ecosystem of distributed resources, interoperable and based upon open shared technological standards. In doing so the IVOA has anticipated, putting into practice for the astrophysical domain, the ideas of FAIR-ness of data and service resources and the Open-ness of sharing scientific results, leveraging on the underlying open standards required to fill the above. In Europe, efforts in supporting and developing the ecosystem proposed by the IVOA specifications has been provided by a continuous set of EU funded projects up to current H2020 ESCAPE ESFRI cluster. In the meantime, in the last years, Europe has realised the importance of promoting the Open Science approach for the research communities and started the European Open Science Cloud (EOSC) project to create a distributed environment for research data, services and communities. In this framework the European VO community, had to face the move from the interoperability scenario in the astrophysics domain into a larger audience perspective that includes a cross-domain FAIR approach. Within the ESCAPE project the CEVO Work Package (Connecting ESFRI to EOSC through the VO) has one task to deal with this integration challenge: a challenge where an existing, mature, distributed e-infrastructure has to be matched to a forming, more general architecture. CEVO started its works in the first months of 2019 and has already worked on the integration of the VO Registry into the EOSC e-infrastructure. This contribution reports on the first year and a half of integration activities, that involve applications, services and resources being aware of the VO scenario and compatible with the EOSC architecture. | astrophysics |
In chemical reaction networks, bistability can only occur far from equilibrium. It is associated with a first-order phase transition where the control parameter is the thermodynamic force. At the bistable point, the entropy production is known to be discontinuous with respect to the thermodynamic force.We show that the fluctuations of the entropy production have an exponential volume-dependence when the system is bistable. At the phase transition, the exponential prefactor is the height of the effective potential barrier between the two fixed-points. Our results obtained for Schl\"ogl's model can be extended to any chemical network. | condensed matter |
This appendix provides a short proof for sample path continuity of the Brownian motion induced by an arbitrary centered Gaussian measure on a separable Banach space, and also some perturbation results for the spectrum of compact self-adjoint operators on a Hilbert space. | mathematics |
Ancient hydrology is recorded by sedimentary rocks on Mars. The most voluminous sedimentary rocks that formed during Mars' Hesperian period are sulfate-rich rocks, explored by the $Opportunity$ rover from 2004-2012 and soon to be investigated by the $Curiosity$ rover at Gale crater. A leading hypothesis for the origin of these sulfates is that the cations were derived from evaporation of deep-sourced groundwater, as part of a global circulation of groundwater. Global groundwater circulation would imply sustained warm Earthlike conditions on Early Mars. Global circulation of groundwater including infiltration of water initially in equilibrium with Mars' CO$_2$ atmosphere implies subsurface formation of carbonate. We find that the CO$_2$ sequestration implied by the global groundwater hypothesis for the origin of sulfate-rich rocks on Mars is 30-5000 bars if the $Opportunity$ data are representative of Hesperian sulfate-rich rocks, which is so large that (even accounting for volcanic outgassing) it would bury the atmosphere. This disfavors the hypothesis that the cations for Mars' Hesperian sulfates were derived from upwelling of deep sourced groundwater. If, instead, Hesperian sulfate-rich rocks are approximated as pure Mg-sulfate (no Fe), then the CO$_2$ sequestration is 0.3-400 bars. The low end of this range is consistent with the hypothesis that the cations for Mars' Hesperian sulfates were derived from upwelling of deep sourced groundwater. In both cases, carbon sequestration by global groundwater circulation actively works to terminate surface habitability, rather than being a passive marker of warm Earthlike conditions. $Curiosity$ will soon be in a position to discriminate between these two hypotheses. Our work links Mars sulfate cation composition, carbon isotopes, and climate change. | astrophysics |
While abstraction is a classic tool of verification to scale it up, it is not used very often for verifying neural networks. However, it can help with the still open task of scaling existing algorithms to state-of-the-art network architectures. We introduce an abstraction framework applicable to fully-connected feed-forward neural networks based on clustering of neurons that behave similarly on some inputs. For the particular case of ReLU, we additionally provide error bounds incurred by the abstraction. We show how the abstraction reduces the size of the network, while preserving its accuracy, and how verification results on the abstract network can be transferred back to the original network. | computer science |
The influence of passivating ligand on electron-phonon relaxation dynamics of the smallest sized gold clusters was studied using ultrafast transient absorption spectroscopy and theoretical modeling. The electron dynamics in Au279, Au329, and Au329 passivated with TBBT, SC2Ph and SC6, respectively, were investigated. Ultrafast transient absorption measurements were also carried out on Au~1400 (SC6) and Au~2000 (SC6) to understand the influence of the size on electron-phonon relaxation with the same passivating ligand. The study has revealed interesting aspects on the role of ligand on electron-phonon relaxation dynamics wherein the aromatic passivating ligands, SC2Ph and TBBT, have shown smaller power dependence and higher plasmon bleach indicating dampened plasmon resonance while the cluster with aliphatic passivating ligand has behaved similarly to regular plasmonic gold nanoparticles. To model the effect of the ligand on the plasmonic properties of the investigated samples , free electron density correction factor of each one was calculated using three-layered Mie theory, and the results show that SC6 inter-acts least with core-gold while TBBT and SC2Ph have a greater effect on the surface electronic conductivity that is attributed to pi-interaction of the ligand with gold. The results also shed light on unusual electron-phonon relaxation and smaller slope observed for Au329 (SC2Ph) that is ascribed to surface gold-pi interaction creating a hybrid state. In contrast, extended pi-interaction is probably the reason for plasmonic nature observed in Au279 (TBBT) even though its size is smaller when compared to Au329. In addition, the results also have shown that the electron-phonon coupling has increased with an increase in the size of the cluster and theoretical modeling has shown higher electron conductivity for larger plasmonic gold clusters. | condensed matter |
Sobolev trace inequalities on nonhomogeneous fractional Sobolev spaces are established. | mathematics |
The scenario approach is a general data-driven algorithm to chance-constrained optimization. It seeks the optimal solution that is feasible to a carefully chosen number of scenarios. A crucial step in the scenario approach is to compute the cardinality of essential sets, which is the smallest subset of scenarios that determine the optimal solution. This paper addresses the challenge of efficiently identifying essential sets. For convex problems, we demonstrate that the sparsest dual solution of the scenario problem could pinpoint the essential set. For non-convex problems, we show that two simple algorithms return the essential set when the scenario problem is non-degenerate. Finally, we illustrate the theoretical results and computational algorithms on security-constrained unit commitment (SCUC) in power systems. In particular, case studies of chance-constrained SCUC are performed in the IEEE 118-bus system. Numerical results suggest that the scenario approach could be an attractive solution to practical power system applications. | electrical engineering and systems science |
Within the one-excitation context of two identical two-level atoms interacting with a common cavity, we examine the dynamics of all bipartite one-to-other entanglements between each qubit and the remaining part of the whole system. We find a new non-analytic "sudden" dynamical behavior of entanglement. Specifically, the sum of the three one-to-other entanglements of the system can be suddenly frozen at its maximal value or can be suddenly thawed from this value in a periodic manner. We calculate the onset timing of sudden freezing and sudden thawing under several different initial conditions. The phenomenon of permanent freezing for entanglement is also found. We also identify a non-trivial upper limit for the sum of three individual entanglements, which exposes the concept of entanglement sharing in a shrunken "volume". Further analyses about freezing and thawing processes reveal quantitative and qualitative laws of entanglement sharing. | quantum physics |
Two-dimensional 3-vector (\textit{d}=2, \textit{n}=3) lattice model with inversion site symmetry and fundamental group of its order-parameter space $\Pi_1 (\mathcal{R})= Z_{2}$, did not exhibit the expected topological transition despite stable defects associated with its uniaxial orientational order. This model is investigated specifically requiring the medium to host distinct classes of defects associated with the three ordering directions, facilitating their simultaneous interactions. The necessary non-Abelian isotropy subgroup of $\mathcal{R}$ is realized by assigning $D_{2}$ site symmetry, resulting in $\Pi_1 (\mathcal{R})= \mathbb{Q }$ (the group of quaternions). With liquid crystals serving as prototype model, a general biquadratic Hamiltonian is chosen to incorporate equally attractive interactions among the three local directors resulting in an orientational order with the desired topology. A Monte Carlo investigation based on the density of states shows that this model exhibits a transition, simultaneously mediated by the three distinct defects with topological charge $1/2$ (disclinations), to a low-temperature critical state characterized by a line of critical points with quasi-long range order of its directors, their power-law exponents vanishing as temperature tends to zero. It is argued that with \textit{n}=3, simultaneous participation of all spin degrees through their homotopically inequivalent defects is necessary to mediate a transition in the two-dimensional system to a topologically ordered state. | condensed matter |
This paper presents an experimental evaluation on the accuracy of indoor localisation. The research was carried out as part of a European Union project targeting the creation of ICT solutions for older adult care. Current expectation is that advances in technology will supplement the human workforce required for older adult care, improve their quality of life and decrease healthcare expenditure. The proposed approach is implemented in the form of a configurable cyber-physical system that enables indoor localization and monitoring of older adults living at home or in residential buildings. Hardware consists of custom developed luminaires with sensing, communication and processing capabilities. They replace the existing lighting infrastructure, do not look out of place and are cost effective. The luminaires record the strength of a Bluetooth signal emitted by a wearable device equipped by the monitored user. The system's software server uses trilateration to calculate the person's location based on known luminaire placement and recorded signal strengths. However, multipath fading caused by the presence of walls, furniture and other objects introduces localisation errors. Our previous experiments showed that room-level accuracy can be achieved using software-based filtering for a stationary subject. Our current objective is to assess system accuracy in the context of a moving subject, and ascertain whether room-level localization is feasible in real time. | computer science |
Structural health monitoring (SHM) systems use the non-destructive testing principle for damage identification. As part of SHM, the propagation of ultrasonic guided waves (UGWs) is tracked and analyzed for the changes in the associated wave pattern. These changes help identify the location of a structural damage, if any. We advance existing research by accounting for uncertainty in the material and geometric properties of a structure. The physics model used in this study comprises of a monolithically coupled system of acoustic and elastic wave equations, known as the wave propagation in fluid-solid and their interface (WpFSI) problem. As the UGWs propagate in the solid, fluid, and their interface, the wave signal displacement measurements are contrasted against the benchmark pattern. For the numerical solution, we develop an efficient algorithm that successfully addresses the inherent complexity of solving the multiphysics problem under uncertainty. We present a procedure that uses Gaussian process regression and convolutional neural network for predicting the UGW propagation in a solid-fluid and their interface under uncertainty. First, a set of training images for different realizations of the uncertain parameters of the inclusion inside the structure is generated using a monolithically-coupled system of acoustic and elastic wave equations. Next, Gaussian processes trained with these images are used for predicting the propagated wave with convolutional neural networks for further enhancement to produce high-quality images of the wave patterns for new realizations of the uncertainty. The results indicate that the proposed approach provides an accurate prediction for the WpFSI problem in the presence of uncertainty. | electrical engineering and systems science |
A decision-maker must consider cofounding bias when attempting to apply machine learning prediction, and, while feature selection is widely recognized as important process in data-analysis, it could cause cofounding bias. A causal Bayesian network is a standard tool for describing causal relationships, and if relationships are known, then adjustment criteria can determine with which features cofounding bias disappears. A standard modification would thus utilize causal discovery algorithms for preventing cofounding bias in feature selection. Causal discovery algorithms, however, essentially rely on the faithfulness assumption, which turn out to be easily violated in practical feature selection settings. In this paper, we propose a meta-algorithm that can remedy existing feature selection algorithms in terms of cofounding bias. Our algorithm is induced from a novel adjustment criterion that requires rather than faithfulness, an assumption which can be induced from another well-known assumption of the causal sufficiency. We further prove that the features added through our modification convert cofounding bias into prediction variance. With the aid of existing robust optimization technologies that regularize risky strategies with high variance, then, we are able to successfully improve the throughput performance of decision-making optimization, as is shown in our experimental results. | statistics |
Low-density spreading non-orthogonal multiple-access (LDS-NOMA) is considered where $K$ single-antenna user-equipments (UEs) communicate with a base-station (BS) over $F$ fading sub-carriers. Each UE $k$ spreads its data symbols over $d_k<F$ sub-carriers. We aim to identify the LDS-code allocations that maximize the ergodic mutual information (EMI). The BS assigns resources solely based on pathlosses. Conducting analysis in the regime where $F$, $K$, and ${d_k,\forall k}$ converge to $+\infty$ at the same rate, we present EMI as a deterministic equivalent plus a residual term. The deterministic equivalent is a function of pathlosses and spreading codes, and the small residual term scales as $\mathcal{O}(\frac{1}{\min(d_k^2)})$. We formulate an optimization problem to get the set of all spreading codes, irrespective of sparsity constraints, which maximize the deterministic EMI. This yields a simple resource allocation rule that facilitates the construction of desired LDS-codes via an efficient partitioning algorithm. The acquired LDS-codes additionally harness the small incremental gain inherent in the residual term, and thus, attain near-optimal values of EMI in the finite regime. While regular LDS-NOMA is found to be asymptotically optimal in symmetric models, an irregular spreading arises in generic asymmetric cases. The spectral efficiency enhancement relative to regular and random spreading is validated numerically. | electrical engineering and systems science |
This paper presents a computationally efficient algorithm for eco-driving over long prediction horizons. The eco-driving problem is formulated as a bi-level program, where the bottom level is solved offline, pre-optimizing gear as a function of longitudinal velocity and acceleration. The top level is solved online, optimizing a nonlinear dynamic program with travel time, kinetic energy and acceleration as state variables. To further reduce computational effort, the travel time is adjoined to the objective by applying necessary Pontryagin Maximum Principle conditions, and the nonlinear program is solved using real-time iteration sequential quadratic programming scheme in a model predictive control framework. Compared to standard cruise control, the energy savings of using the proposed algorithm is up to 15.71%. | electrical engineering and systems science |
In this paper, we propose a new deep learning framework for an automatic myocardial infarction evaluation from clinical information and delayed enhancement-MRI (DE-MRI). The proposed framework addresses two tasks. The first task is automatic detection of myocardial contours, the infarcted area, the no-reflow area, and the left ventricular cavity from a short-axis DE-MRI series. It employs two segmentation neural networks. The first network is used to segment the anatomical structures such as the myocardium and left ventricular cavity. The second network is used to segment the pathological areas such as myocardial infarction, myocardial no-reflow, and normal myocardial region. The segmented myocardium region from the first network is further used to refine the second network's pathological segmentation results. The second task is to automatically classify a given case into normal or pathological from clinical information with or without DE-MRI. A cascaded support vector machine (SVM) is employed to classify a given case from its associated clinical information. The segmented pathological areas from DE-MRI are also used for the classification task. We evaluated our method on the 2020 EMIDEC MICCAI challenge dataset. It yielded an average Dice index of 0.93 and 0.84, respectively, for the left ventricular cavity and the myocardium. The classification from using only clinical information yielded 80% accuracy over five-fold cross-validation. Using the DE-MRI, our method can classify the cases with 93.3% accuracy. These experimental results reveal that the proposed method can automatically evaluate the myocardial infarction. | electrical engineering and systems science |
As a consequence of the approximate spin-valley symmetry in graphene, the ground state of electrons in graphene at charge neutrality is a particular SU(4) quantum-Hall ferromagnet to minimize their exchange energy. If only the Coulomb interaction is taken into account, this ferromagnet can appeal either to the spin degree of freedom or equivalently to the valley pseudo-spin degree of freedom. This freedom in choice is then limited by subleading energy scales that explicitly break the SU(4) symmetry, the simplest of which is given by the Zeeman effect that orients the spin in the direction of the magnetic field. In addition, there are also valley symmetry breaking terms that can arise from short-range interactions or electron-phonon couplings. Here, we build upon the phase diagram, which has been obtained by Kharitonov [Phys. Rev. B \textbf{85}, 155439 (2012)], in order to identify the different skyrmions that are compatible with these types of quantum-Hall ferromagnets. Similarly to the ferromagnets, the skyrmions at charge neutrality are described by the $\text{Gr}(2,4)$ Grassmannian at the center, which allows us to construct the skyrmion spinors. The different skyrmion types are then obtained by minimizing their energy within a variational approach, with respect to the remaining free parameters that are not fixed by the requirement that the skyrmion at large distances from their center must be compatible with the ferromagnetic background. We show that the different skyrmion types have a clear signature in the local, sublattice-resolved, spin magnetization, which is in principle accessible in scanning-tunneling microscopy and spectroscopy. | condensed matter |
Stochastic Gradient Boosting (SGB) is a widely used approach to regularization of boosting models based on decision trees. It was shown that, in many cases, random sampling at each iteration can lead to better generalization performance of the model and can also decrease the learning time. Different sampling approaches were proposed, where probabilities are not uniform, and it is not currently clear which approach is the most effective. In this paper, we formulate the problem of randomization in SGB in terms of optimization of sampling probabilities to maximize the estimation accuracy of split scoring used to train decision trees. This optimization problem has a closed-form nearly optimal solution, and it leads to a new sampling technique, which we call Minimal Variance Sampling (MVS). The method both decreases the number of examples needed for each iteration of boosting and increases the quality of the model significantly as compared to the state-of-the art sampling methods. The superiority of the algorithm was confirmed by introducing MVS as a new default option for subsampling in CatBoost, a gradient boosting library achieving state-of-the-art quality on various machine learning tasks. | statistics |
We devise a new class of criteria to certify the nonclassicality of photon- and phonon-number statistics. Our criteria extend and strengthen the broadly used Klyshko's criteria, which require knowledge of only a finite set of Fock-state probabilities. This makes the criteria well-suited to experimental implementation in realistic conditions. Moreover, we prove the completeness of our method in some scenarios, showing that, when only two or three Fock-state probabilities are known, it detects all finite distributions incompatible with classical states. In particular, we show that our criteria detect a broad class of noisy Fock states as nonclassical, even when Klyshko's do not. The method is directly applicable to trapped-ion, superconducting circuits, and optical and optomechanical experiments with photon-number resolving detectors. This work represents a significant milestone towards a complete characterisation of the nonclassicality accessible from limited knowledge of the Fock-state probabilities. | quantum physics |
Using numerical simulations, we study the non-equilibrium coarsening dynamics of a binary solvent around spherical colloids in the presence of a temperature gradient. The coarsening dynamics following a temperature quench is studied by solving the coupled modified Cahn-Hilliard-Cook equation and the heat diffusion equation, which describe the concentration profile and the temperature field, respectively. For the temperature field we apply a suitable boundary condition. We observe the formation of circular layers of different phases around the colloid whereas away from the colloid patterns of spinodal decomposition persist. Additionally, we investigate the dependence of the pattern formation on the quench temperature. Our simulation mimics an experimental system where the colloid is heated by laser illumination. Note that we look at the cooling of a solvent with an upper critical temperature, whereas the experimental analogue is the laser-heating of a solvent with a lower critical temperature. We also study a two colloid system. Here, we observe that a bridge of one phase forms connecting the two colloids. Also, we study the force acting on the colloids that is generated by the chemical potential gradient. | condensed matter |
To what extent can the Planck satellite observations be interpreted as confirmation of the quantum part of the inflationary paradigm? Has it "seen" the Bunch-Davies state? We compare and contrast the Bunch-Davies interpretation with one using a so-called entangled state in which the fluctuations of a spectator scalar field are entangled with those of the metric perturbations $\zeta$. We first show how a spectator scalar field $\Sigma$, with an expectation value $\sigma(t)$ that evolves in time, will generically generate such a state. We then use this state to compute the power spectrum $P_{\zeta}(k)$ and thence the temperature anisotropies $C_l$ in the Cosmic Microwave Background (CMB). We find interesting differences from the standard calculations using the Bunch-Davies (BD) state. We argue that existing data may already be used to place interesting bounds on this class of deviations from the BD state and that, for some values of the parameters of the state, the power spectra may be consistent with the Planck satellite data. | high energy physics theory |
Motivated by the persistent anomalies in the semileptonic $B$-meson decays, we investigate the competency of LHC data to constrain the $R_{D^{(*)}}$-favoured parameter space in a charge $-1/3$ scalar leptoquark ($\mathcal S_1$) model. We consider some scenarios with one large free coupling to accommodate the $R_{D^{(*)}}$ anomalies. As a result, some of them dominantly yield nonresonant $\tau\tau$ and $\tau\nu$ events at the LHC through the $t$-channel $\mathcal S_1$ exchange. So far, no experiment has searched for leptoquarks using these signatures and the relevant resonant leptoquark searches are yet to put any strong exclusion limit on the parameter space. We recast the latest $\tau\tau$ and $\tau\nu$ resonance search data to obtain new exclusion limits. The nonresonant processes strongly interfere (destructively in our case) with the Standard Model background and play the determining role in setting the exclusion limits. To obtain precise limits, we include non-negligible effects coming from the subdominant (resonant) pair and inclusive single leptoquark productions systematically in our analysis. To deal with large destructive interference, we make use of the transverse mass distributions from the experiments in our statistical analysis. In addition, we also recast the relevant direct search results to obtain the most stringent collider bounds on these scenarios to date. These are independent bounds and are competitive to other known bounds. Finally, we indicate how one can adopt these bounds to a wide class of models with $\mathcal S_1$ that are proposed to accommodate the $R_{D^{(*)}}$ anomalies. | high energy physics phenomenology |
Trend analysis of meteorological parameters (temperature, pressure, and relative humidity) as well as calculated refractivity, equivalent potential temperature (EPT) for a pseudo-adiabatic process, and field strength in Calabar, Southern Nigeria has been analyzed using Mann-Kendall trend test and Sens slope estimator. Data of the meteorological parameters were obtained from the Nigerian Meteorological Agency (NiMet) in Calabar for 14 years (2005 - 2018). Results show that the maximum and average temperature, atmospheric pressure, refractivity, EPT and field strength all exhibited a positive Kendall Z value with 2.52, 0.33, 3.83, 0.77, 0.44 and 3.18 respectively which indicated an increasing trend over time, with only maximum temperature, atmospheric pressure and field strength showing a significant increase at 5% (0.05) level of significance, since their calculated p-values (0.012, 0.0001, and 0.001) were less than 0.05. The relative humidity and minimum ambient temperature showed a decrease in trend over time as they both had a negative Kendall Z values (-0.11 and -1.09 respectively), however, together with the average ambient temperature and refractivity, their trend was not significant 5% level of significance since their calculated p-values were all more than 0.05. Linear regression, correlation and partial differentiation showed that relative humidity has the most effect on the changes in seasonal refractivity and an indirect relationship with field strength variability. The relationship between EPT and refractivity has been discovered to be very strong and positive. Descriptive statistics has been used to portray the seasonal and annual trend of all parameters. | physics |
Segmentation of brain tumors and their subregions remains a challenging task due to their weak features and deformable shapes. In this paper, three patterns (cross-skip, skip-1 and skip-2) of distributed dense connections (DDCs) are proposed to enhance feature reuse and propagation of CNNs by constructing tunnels between key layers of the network. For better detecting and segmenting brain tumors from multi-modal 3D MR images, CNN-based models embedded with DDCs (DDU-Nets) are trained efficiently from pixel to pixel with a limited number of parameters. Postprocessing is then applied to refine the segmentation results by reducing the false-positive samples. The proposed method is evaluated on the BraTS 2019 dataset with results demonstrating the effectiveness of the DDU-Nets while requiring less computational cost. | electrical engineering and systems science |
Extreme weather events have a significant impact on the aging and outdated power distribution infrastructures. These high-impact low-probability (HILP) events often result in extended outages and loss of critical services, thus, severely affecting customers' safety. This calls for the need to ensure resilience in distribution networks by quickly restoring the critical services during a disaster. This paper presents an advanced feeder restoration method to restore critical loads using distributed energy resources (DERs). A resilient restoration approach is proposed that jointly maximizes the amount of restored critical loads and optimizes the restoration times by optimally allocating grid's available DER resources. The restoration problem is modeled as a mixed-integer linear program with the objective of maximizing the resilience to post-restoration failures while simultaneously satisfying grid's critical connectivity and operational constraints and ensuring a radial operation for a given open-loop feeder configuration. Simulations are performed to demonstrate the effectiveness of the proposed approach using IEEE 123-node feeder with 5 DERs supplying 11 critical loads and IEEE 906-bus feeder with 3 DERs supplying 17 critical loads. The impacts of DER availability and fuel reserve on restored networks are assessed and it is shown that the proposed approach is successfully able to restore a maximum number of critical loads using available DERs. | electrical engineering and systems science |
We propose a stochastic prediction-control framework to promote safety in automated driving by directly controlling the joint state probability density functions (PDFs) subject to the vehicle dynamics via trajectory-level state feedback. To illustrate the main ideas, we focus on a multi-lane highway driving scenario although the proposed framework can be adapted to other contexts. The computational pipeline consists of a PDF prediction layer, followed by a PDF control layer. The prediction layer performs moving horizon nonparametric forecasts for the ego and the non-ego vehicles' stochastic states, and thereby derives safe target PDF for the ego. The latter is based on the forecasted collision probabilities, and promotes the probabilistic safety for the ego. The PDF control layer designs a feedback that optimally steers the joint state PDF subject to the controlled ego dynamics while satisfying the endpoint PDF constraints. Our computation for the PDF prediction layer leverages the structure of the controlled Liouville PDE to evolve the joint PDF values, as opposed to empirically approximating the PDFs. Our computation for the PDF control layer leverages the differential flatness structure in vehicle dynamics. We harness recent theoretical and algorithmic advances in optimal mass transport, and the Schr\"{o}dinger bridge. The numerical simulations illustrate the efficacy of the proposed framework. | electrical engineering and systems science |
Design Thinking workshops are used by companies to help generate new ideas for technologies and products by engaging subjects in exercises to understand their users' wants and become more empathetic towards their needs. The "aha moment" experienced during these thought-provoking, step outside the yourself activities occurs when a group of persons iterate over several problems and converge upon a solution that will fit seamlessly everyday life. With the increasing use and cost of Design workshops being offered, it is important that technology be developed that can help identify empathy and its onset in humans. This position paper presents an approach to modeling empathy using Gaussian mixture models and heart rate and skin conductance. This paper also presents an updated approach to Design Thinking that helps to ensure participants are thinking outside of their own race's, culture's, or other affiliations' motives. | computer science |
Autonomous construction of deep neural network (DNNs) is desired for data streams because it potentially offers two advantages: proper model's capacity and quick reaction to drift and shift. While the self-organizing mechanism of DNNs remains an open issue, this task is even more challenging to be developed for standard multi-layer DNNs than that using the different-depth structures, because the addition of a new layer results in information loss of previously trained knowledge. A Neural Network with Dynamically Evolved Capacity (NADINE) is proposed in this paper. NADINE features a fully open structure where its network structure, depth and width, can be automatically evolved from scratch in an online manner and without the use of problem-specific thresholds. NADINE is structured under a standard MLP architecture and the catastrophic forgetting issue during the hidden layer addition phase is resolved using the proposal of soft-forgetting and adaptive memory methods. The advantage of NADINE, namely elastic structure and online learning trait, is numerically validated using nine data stream classification and regression problems where it demonstrates performance improvement over prominent algorithms in all problems. In addition, it is capable of dealing with data stream regression and classification problems equally well. | computer science |
Invariance in duality transformation, the self-dual property, has important applications in electromagnetic engineering. In the present paper, the problem of most general linear and local boundary conditions with self-dual property is studied. Expressing the boundary conditions in terms of a generalized impedance dyadic, the self-dual boundaries fall in two sets depending on symmetry or antisymmetry of the impedance dyadic. Previously known cases are found to appear as special cases of the general theory. Plane-wave reflection from boundaries defined by each of the two cases of self-dual conditions are analyzed and waves matched to the corresponding boundaries are determined. As a numerical example, reflection from a special case, the self-dual EH boundary, is computed for two planes of incidence. | physics |
This work explores Boltzmann's time hypothesis, which associates the perceived direction of "time flow" with the second law of thermodynamics. We discuss mechanisms that can be responsible for the action of the second law, for directional properties of time and, ultimately, for the perception that past events cause future events. Special attention is paid to possibility of testing these mechanisms in experiments. It is argued that CP-violations known in particle physics may offer such an opportunity. A time-symmetric version of Everett's many-worlds interpretation (which is based on a thermodynamic interpretation of quantum collapses) is given in the Appendix. | quantum physics |
Both uncertainty estimation and interpretability are important factors for trustworthy machine learning systems. However, there is little work at the intersection of these two areas. We address this gap by proposing a novel method for interpreting uncertainty estimates from differentiable probabilistic models, like Bayesian Neural Networks (BNNs). Our method, Counterfactual Latent Uncertainty Explanations (CLUE), indicates how to change an input, while keeping it on the data manifold, such that a BNN becomes more confident about the input's prediction. We validate CLUE through 1) a novel framework for evaluating counterfactual explanations of uncertainty, 2) a series of ablation experiments, and 3) a user study. Our experiments show that CLUE outperforms baselines and enables practitioners to better understand which input patterns are responsible for predictive uncertainty. | statistics |
Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks. | computer science |
Although the spectra of heavy quarkonium systems has been successfully explained by certain QCD motivated potential models, their strong decays are difficult to deal with. We perform a microscopic calculation of charmonium strong decays using the same constituent quark model which successfully describes the $c\bar{c}$ meson spectrum. We compare the numerical results with the $^{3}P_{0}$ and the experimental data. Comparison with other predictions from similar models are included. | high energy physics phenomenology |
We examine an intriguing possibility that a single field is responsible for both inflation and dark matter, focussing on the minimal set--up where inflation is driven by a scalar coupling to curvature. We study in detail the reheating process in this framework, which amounts mainly to particle production in a quartic potential, and distinguish thermal and non--thermal dark matter options. In the non--thermal case, the reheating is impeded by backreaction and rescattering, making this possibility unrealistic. On the other hand, thermalized dark matter is viable, yet the unitarity bound forces the inflaton mass into a narrow window close to half the Higgs mass. | high energy physics phenomenology |
Sphaleron is a non-perturbative solution of electroweak gauge theories, which is crucially important for the scenario of electroweak baryogenesis. The sphaleron energy depends on details of the mechanism for the electroweak symmetry breaking. We find that in many of new physics models the deviation of the sphaleron energy from the prediction in the standard model is proportional to that of the triple Higgs boson coupling with opposite signs. This interesting relation would be useful to determine the sphaleron energy by measuring this couplings at future collider experiments. | high energy physics phenomenology |
We show that the non-gravitational sectors of certain 6d and 5d supergravity theories can be decomposed into superconformal field theories (SCFTs) which are coupled together by pairwise identifying and gauging mutual global symmetries. In the case of 6d supergravity, we consider F-theory on compact elliptic Calabi-Yau 3-folds with base $B=T^4/\mathbb{Z}_n\times \mathbb{Z}_m$ and we show in many examples that the non-gravitational field theory sectors can be described as configurations of coupled 6d $(1,0)$ SCFTs. We also conjecture that the effective 2d $(0,4)$ SCFTs living on the self-dual strings of the 6d theories lead to holographically dual descriptions of type IIB string theory on $AdS_3\times S^3\times B$ and moreover that their elliptic genera can be used to compute the degeneracies of 5d spinning BPS black holes along with all-genus topological string amplitudes on the corresponding compact 3-fold. In the case of 5d supergravity, we consider M-theory on compact Calabi-Yau 3-folds and using similar ideas as in the 6d case we show the complete non-gravitational sector of 5d supergravity theories can be decomposed into coupled 5d $\mathcal{N}=1$ SCFTs. Furthermore, using this picture we propose a generalized topological vertex formalism which, excluding some curve classes, seems to capture all-genus topological string amplitudes for the mirror quintic. | high energy physics theory |
Many clinical studies evaluate the benefit of treatment based on both survival and other clinical outcomes. In these clinical studies, there are situations that the clinical outcomes are truncated by death, where subjects die before their clinical outcome is measured. Treating outcomes as "missing" or "censored" due to death can be misleading for treatment effect evaluation. We show that if we use the median in the survivors or in the always-survivors to summarize clinical outcomes, we may conclude a trade-off exists between the probability of survival and good clinical outcomes, even in settings where both the probability of survival and the probability of any good clinical outcome are better for one treatment. Therefore, we advocate not always treating death as a mechanism through which clinical outcomes are missing, but rather as part of the outcome measure. To account for the survival status, we describe the survival-incorporated median as an alternative summary measure for outcomes in the presence of death. The survival-incorporated median is the threshold such that 50% of the population is alive with an outcome above that threshold. We use conceptual examples to show that the survival-incorporated median provides a simple and useful summary measure to inform clinical practice. | statistics |
We model clusters of axions with spherically symmetric momentum but arbitrary spatial distributions and study the directional profile of photos produced in their evolution through spontaneous and stimulated decay of axions via the process $a \rightarrow \gamma + \gamma$. Several specific examples are presented. | high energy physics phenomenology |
Determining the lack of association between an outcome variable and a number of different explanatory variables is frequently necessary in order to disregard a proposed model. This paper proposes a non-inferiority test for the coefficient of determination (or squared multiple correlation coefficient), R-squared, in a linear regression analysis with random predictors. The test is derived from inverting a one-sided confidence interval based on a scaled central F distribution. | statistics |
Session identification is a common strategy used to develop metrics for web analytics and behavioral analyses of user-facing systems. Past work has argued that session identification strategies based on an inactivity threshold is inherently arbitrary or advocated that thresholds be set at about 30 minutes. In this work, we demonstrate a strong regularity in the temporal rhythms of user initiated events across several different domains of online activity (incl. video gaming, search, page views and volunteer contributions). We describe a methodology for identifying clusters of user activity and argue that regularity with which these activity clusters appear implies a good rule-of-thumb inactivity threshold of about 1 hour. We conclude with implications that these temporal rhythms may have for system design based on our observations and theories of goal-directed human activity. | computer science |
Interfacing single emitters and photonic nanostructures enables modifying their emission properties, such as enhancing individual decay rates or controlling the emission direction. To achieve full control, the single emitter must be positioned in the nanostructures deterministically. Here, we use spectroscopy to gain spectral and spatial information about individual quantum dots in order to position each emitter in a pre-determined location in a unit cell of a photonic-crystal waveguide. Depending on the spatial and spectral positioning within the structured nanophotonic mode, we observe that the quantum dot emission can either be suppressed or enhanced. These results demonstrate the capacity of photonic-crystal waveguides to control the emission of single photons and that the ability to position quantum dots will be crucial to the creation of complex multi-emitter quantum photonic circuits. | quantum physics |
Eight-dimensional non-geometric heterotic strings with gauge algebra $\mathfrak{e}_8\mathfrak{e}_7$ were constructed by Malmendier and Morrison as heterotic duals of F-theory on K3 surfaces with $\Lambda^{1,1}\oplus E_8\oplus E_7$ lattice polarization. Clingher, Malmendier and Shaska extended these constructions to eight-dimensional non-geometric heterotic strings with gauge algebra $\mathfrak{e}_7\mathfrak{e}_7$ as heterotic duals of F-theory on $\Lambda^{1,1}\oplus E_7\oplus E_7$ lattice polarized K3 surfaces. In this study, we analyze the points in the moduli of non-geometric heterotic strings with gauge algebra $\mathfrak{e}_7\mathfrak{e}_7$, at which the non-Abelian gauge groups on the F-theory side are maximally enhanced. The gauge groups on the heterotic side do not allow for the perturbative interpretation at these points. We show that these theories can be described as deformations of the stable degenerations, as a result of coincident 7-branes on the F-theory side. From the heterotic viewpoint, this effect corresponds to the insertion of 5-branes. These effects can be used to understand nonperturbative aspects of nongeometric heterotic strings. Additionally, we build a family of elliptic Calabi-Yau 3-folds by fibering elliptic K3 surfaces, which belong to the F-theory side of the moduli of non-geometric heterotic strings with gauge algebra $\mathfrak{e}_7\mathfrak{e}_7$, over $\mathbb{P}^1$. We find that highly enhanced gauge symmetries arise on F-theory on the built elliptic Calabi-Yau 3-folds. | high energy physics theory |
The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled trials, instead observational data and appropriate study design must be used. There are major challenges with observational studies, one of which is confounding that can lead to biased estimates of the causal effects. Controlling for confounding is commonly performed by simple adjustment for measured confounders; although, often this is not enough. Recent advances in the field of causal inference have dealt with confounding by building on classical standardisation methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where different estimators were developed to overcome the limitations of the previous one. Furthermore, we also briefly introduce the potential outcomes framework, illustrate the use of different methods using an illustration from the health care setting, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to apply in their own observational study. The code can be accessed at https://github.com/migariane/TutorialCausalInferenceEstimators | statistics |
Controlled branching processes are stochastic growth population models in which the number of individuals with reproductive capacity in each generation is controlled by a random control function. The purpose of this work is to examine the Approximate Bayesian Computation (ABC) methods and to propose appropriate summary statistics for them in the context of these processes. This methodology enables to approximate the posterior distribution of the parameters of interest satisfactorily without explicit likelihood calculations and under a minimal set of assumptions. In particular, the tolerance rejection algorithm, the sequential Monte Carlo ABC algorithm, and a post-sampling correction method based on local-linear regression are provided. The accuracy of the proposed methods are illustrated and compared with a "likelihood free" Markov chain Monte Carlo technique by the way of a simulated example developed with the statistical software R. | statistics |
This paper aims to revisit and expand upon previous work on the "hot hand" phenomenon in basketball, specifically in the NBA. Using larger, modern data sets, we test streakiness of shooting patterns and the presence of hot hand behavior in free throw shooting, while going further by examining league-wide hot hand trends and the changes in individual player behavior. Additionally, we perform simulations in order to assess their power. While we find no evidence of the hot hand in game-play and only weak evidence in free throw trials, we find that some NBA players exhibit behavioral changes based on the outcome of their previous shot. | statistics |
We present a theory of neutrino oscillations in a dense medium which goes beyond the effective matter potential used in the description of the MSW effect. We show how the purity of the neutrino state is degraded by neutrino interactions with the environment and how neutrino--matter interactions can be a source of decoherence. We present new oscillation formulae for neutrinos interacting with leptons and carry out a numerical analysis which exhibits deviations from the MSW formulae for propagation in dense objects, such as white dwarfs, with energies of order of $1 GeV$. In particular, we show that at high density and/or high neutrino energy, the vanishing transition probabilities derived for MSW effect, are non zero when the scattering is taken into account. Moreover, we analyze CP and CPT symmetry violations due to the scattering term and to the related decoherence effect. | high energy physics phenomenology |
A model for tokamak discharge through deep learning has been done on a superconducting long-pulse tokamak (EAST). This model can use the control signals (i.e. Neutral Beam Injection (NBI), Ion Cyclotron Resonance Heating (ICRH), etc) to model normal discharge without the need for doing real experiments. By using the data-driven methodology, we exploit the temporal sequence of control signals for a large set of EAST discharges to develop a deep learning model for modeling discharge diagnostic signals, such as electron density $n_{e}$, store energy $W_{mhd}$ and loop voltage $V_{loop}$. Comparing the similar methodology, we use Machine Learning techniques to develop the data-driven model for discharge modeling rather than disruption prediction. Up to 95% similarity was achieved for $W_{mhd}$. The first try showed promising results for modeling of tokamak discharge by using the data-driven methodology. The data-driven methodology provides an alternative to physical-driven modeling for tokamak discharge modeling. | physics |
We study the design of dynamic assignment control in networks with a fixed number of circulating resources (supply units). Each time a demand arises, the controller has (limited) flexibility in choosing the node from which to assign a supply unit. If no supply units are available at any compatible node, the demand is lost. If the demand is served, this causes to the supply unit to relocate to the "destination" of the demand. We study how to minimize the proportion of lost requests in steady state (or over a finite horizon) via a large deviations analysis. We propose a family of simple state-dependent policies called Scaled MaxWeight (SMW) policies that dynamically manage the distribution of supply in the network. We prove that under a complete resource pooling condition (analogous to the condition in Hall's marriage theorem), any SMW policy leads to exponential decay of demand-loss probability as the number of supply units scales to infinity. Further, there is an SMW policy that achieves the $\textbf{optimal}$ loss exponent among all assignment policies, and we analytically specify this policy in terms of the demand arrival rates for all origin-destination pairs. The optimal SMW policy maintains high supply levels adjacent to structurally under-supplied nodes. We discuss two applications: (i) Shared transportation platforms (like ride-hailing and bikesharing): We incorporate travel delays in our model and show that SMW policies for assignment control continue to have exponentially small loss. Simulations of ride-hailing based on the NYC taxi dataset demonstrate excellent performance. (ii) Service provider selection in scrip systems (like for babysitting or for kidney exchange): With only cosmetic modifications to the setup, our results translate fully to a model of scrip systems and lead to strong performance guarantees for a "Scaled Minimum Scrip" service provider selection rule. | mathematics |
We show that the observed quark/lepton mass hierarchy can be realized dynamically on an interval extra dimension with point interactions. In our model, the positions of the point interactions play a crucial role to control the quark/lepton mass hierarchy and are determined by the minimization of the Casimir energy. By use of the exact extra-dimensional coordinate-dependent vacuum expectation value of a gauge singlet scalar, we find that there is a parameter set, where the positions of the point interactions are stabilized and fixed, which can reproduce the experimental values of the quark masses precisely enough, while the charged lepton part is less relevant. We also show that possible mixings among the charged leptons will improve the situation significantly. | high energy physics phenomenology |
Built environment features (BEFs) refer to aspects of the human constructed environment, which may in turn support or restrict health related behaviors and thus impact health. In this paper we are interested in understanding whether the spatial distribution and quantity of fast food restaurants (FFRs) influence the risk of obesity in schoolchildren. To achieve this goal, we propose a two-stage Bayesian hierarchical modeling framework. In the first stage, examining the position of FFRs relative to that of some reference locations - in our case, schools - we model the distances of FFRs from these reference locations as realizations of Inhomogenous Poisson processes (IPP). With the goal of identifying representative spatial patterns of exposure to FFRs, we model the intensity functions of the IPPs using a Bayesian non-parametric viewpoint and specifying a Nested Dirichlet Process prior. The second stage model relates exposure patterns to obesity, offering two different approaches to accommodate uncertainty in the exposure patterns estimated in the first stage: in the first approach the odds of obesity at the school level is regressed on cluster indicators, each representing a major pattern of exposure to FFRs. In the second, we employ Bayesian Kernel Machine regression to relate the odds of obesity to the multivariate vector reporting the degree of similarity of a given school to all other schools. Our analysis on the influence of patterns of FFR occurrence on obesity among Californian schoolchildren has indicated that, in 2010, among schools that are consistently assigned to a cluster, there is a lower odds of obesity amongst 9th graders who attend schools with most distant FFR occurrences in a 1-mile radius as compared to others. | statistics |
We present a comprehensive study on oscillation of high-energy neutrinos from two different environments: blue supergiant progenitors that may harbor low-power gamma-ray burst (GRB) jets and neutron star merger ejecta that would be associated with short gamma-ray bursts. We incorporate the radiation constraint that gives a necessary condition for nonthermal neutrino production, and account for the time evolution of the jet, which allows us to treat neutrino oscillation in matter more accurately. For massive star progenitors, neutrino injection inside the star can lead to nonadiabatic oscillation patterns in the 1 TeV - 10 TeV and is also visible in the flavor ratio. For neutron star merger ejecta, we find a similar behavior in the 100 GeV - 10 TeV region and the oscillation may result in a $\nu_e$ excess around 1 TeV. These features, which enable us to probe the progenitors of long and short GRBs, could be seen by future neutrino detectors with precise flavor ratio measurements. We also discuss potential contributions to the diffuse neutrino flux measured by IceCube, and find parameter sets allowing choked low-power GRB jets to account for the neutrino flux in the 10 TeV - 100 TeV range without violating the existing constraints. | astrophysics |
Extremely low density planets ('super-puffs') are a small but intriguing subset of the transiting planet population. With masses in the super-Earth range ($1-10$ M$_{\oplus}$) and radii akin to those of giant planets ($>4$ R$_{\oplus}$), their large envelopes may have been accreted beyond the water snow line and many appear to be susceptible to catastrophic mass loss. Both the presence of water and the importance of mass loss can be explored using transmission spectroscopy. Here, we present new HST WFC3 spectroscopy and updated Kepler transit depth measurements for the super-puff Kepler-79d. We do not detect any molecular absorption features in the $1.1-1.7$ $\mu$m WFC3 bandpass and the combination of Kepler and WFC3 data are consistent with a flat line model, indicating the presence of aerosols in the atmosphere. We compare the shape of Kepler-79d's transmission spectrum to predictions from a microphysical haze model that incorporates an outward particle flux due to ongoing mass loss. We find that photochemical hazes offer an attractive explanation for the observed properties of super-puffs like Kepler-79d, as they simultaneously render the near-infrared spectrum featureless and reduce the inferred envelope mass loss rate by moving the measured radius (optical depth unity surface during transit) to lower pressures. We revisit the broader question of mass loss rates for super-puffs and find that the age estimates and mass loss rates for the majority of super-puffs can be reconciled if hazes move the photosphere from the typically assumed pressure of $\sim 10$ mbar to $\sim 10 \; \mu$bar. | astrophysics |
We introduce a generalization structure of the su(1,1) algebra which depends on a function of one generator of the algebra, f(H). Following the same ideas developed to the generalized Heisenberg algebra (GHA) and to the generalized su(2), we show that a symmetry is present in the sequence of eigenvalues of one generator of the algebra. Then, we construct the Barut-Girardello coherent states associated with the generalized su(1,1) algebra for a particle in a P\"oschl-Teller potential. Furthermore, we compare the time evolution of the uncertainty relation of the constructed coherent states with that of GHA coherent states. The generalized su(1,1) coherent states are very localized. | high energy physics theory |
We analyse the feasibility of low-scale leptogenesis where the inverse seesaw (ISS) and linear seesaw (LSS) terms are not simultaneously present. In order to generate the necessary mass splittings, we adopt a Minimal Lepton Flavour Violation (MLFV) hypothesis where a sterile neutrino mass degeneracy is broken by flavour effects. We find that resonant leptogenesis is feasible in both scenarios. However, because of a flavour alignment issue, MLFV-ISS leptogenesis succeeds only with a highly tuned choice of Majorana masses. For MLFV-LSS, on the other hand, a large portion of parameter space is able to generate sufficient asymmetry. In both scenarios we find that the lightest neutrino mass must be of order $10^{-2}\text{ eV}$ or below for successful leptogenesis. We briefly explore implications for low-energy flavour violation experiments, in particular $\mu \rightarrow e\,\gamma$. We find that the future MEG-II experiment, while sensitive to MLFV in our setup, will not be sensitive to the specific regions required for resonant leptogenesis. | high energy physics phenomenology |
When are two germs of analytic systems conjugate or orbitally equivalent under an analytic change of coordinates in the neighborhood of a singular point? A way to answer is to use normal forms. But there are large classes of dynamical systems for which the change of coordinates to a normal form diverges. In this paper we discuss the case of singularities for which the normalizing transformation is $k$-summable, thus allowing to provide moduli spaces. We explain the common geometric features of these singularities, and show that the study of their unfoldings allows understanding both the singularities themselves, and the geometric obstructions to convergence of the normalizing transformations. We also present some moduli spaces for generic $k$-parameter families unfolding such singularities. | mathematics |
We consider the finite volume mean values of current operators in integrable spin chains with local interactions, and provide an alternative derivation of the exact result found recently by the author and two collaborators. We use a certain type of long range deformation of the local spin chains, which was discovered and explored earlier in the context of the AdS/CFT correspondence. This method is immediately applicable also to higher rank models: as a concrete example we derive the current mean values in the SU(3)-symmetric fundamental model, solvable by the nested Bethe Ansatz. The exact results take the same form as in the Heisenberg spin chains: they involve the one-particle eigenvalues of the conserved charges and the inverse of the Gaudin matrix. | condensed matter |
Supernova theory suggests that black holes of a stellar origin cannot attain masses in the range of 50-135 solar masses in isolation. We argue here that this mass gap is filled in by black holes that grow by gas accretion in dense stellar clusters, such as protoglobular clusters. The accretion proceeds rapidly, during the first 10 megayears of the cluster life, before the remnant gas is depleted. We predict that binaries of black holes within the mass gap can be observed by LIGO. | astrophysics |
We investigate the use of Answer Set Programming to solve variations of gossip problems, by modeling them as epistemic planning problems. | computer science |
We consider an asymptotically free vectorial gauge theory, with gauge group $G$ and $N_f$ fermions in a representation $R$ of $G$, having an infrared fixed point of the renormalization group. We calculate scheme-independent series expansions for the anomalous dimensions of higher-spin bilinear fermion operators at this infrared fixed point up to $O(\Delta_f^3)$, where $\Delta_f$ is an $N_f$-dependent expansion variable. Our general results are evaluated for several special cases, including the case $G={\rm SU}(N_c)$ with $R$ equal to the fundamental and adjoint representations. | high energy physics phenomenology |
[Abridged] Asteroid mining is not necessarily a distant prospect. Hayabusa2 and OSIRIS-REx have recently rendezvoused with near-Earth asteroids and will return samples to Earth. While there is significant science motivation for these missions, there are also resource interests. Space agencies and commercial entities are particularly interested in ices and water-bearing minerals that could be used to produce rocket fuel in space. The internationally coordinated roadmaps of major space agencies depend on utilizing the natural resources of such celestial bodies. Several companies have already created plans for intercepting and extracting water and minerals from near-Earth objects, as even a small asteroid could have high economic worth. However, the low surface gravity of asteroids could make the release of mining waste and the subsequent formation of debris streams a consequence of asteroid mining. Strategies to contain material during extraction could still eventually require the purposeful jettison of waste to avoid managing unwanted mass. Using simulations, we explore the formation of mining debris streams by integrating particles released from four select asteroids. Radiation effects are included, and a range of debris sizes are explored. The simulation results are used to investigate the timescales for debris stream formation, the sizes of the streams, and the meteoroid fluxes compared with sporadic meteoroids. We find that for prodigious mining activities resulting in the loss of a few percent of the asteroid's mass or more, it is possible to produce streams that exceed the sporadic flux during stream crossing for some meteoroid sizes. The result of these simulations are intended to highlight potential unintended consequences that could result from NewSpace activity, which could help to inform efforts to develop international space resource guidelines. | astrophysics |
We perform a comprehensive study of the homogeneous finite modular group $A'_5$ which is the double covering of $A_5$. The integral weight and level 5 modular forms have been constructed up to weight 6 and they are decomposed into the irreducible representations of $A'_5$. Then we perform a systematical analysis of the $A'_5$ modular models for lepton masses and mixing. The phenomenologically viable models with minimal number of free parameters and the results of fit are presented. We find out 8 models with 9 real free parameters which can accommodate the experimental data of lepton sector. After including generalized CP symmetry, 4 viable models with 7 free parameters are found out. We apply $A'_5$ modular symmetry to the quark sector, and a quark-lepton unification model is given. The framework of modular invariance is extended to include the rational weight modular forms of level 5. The ring of modular forms at level 5 can be generated by two algebraically independent weight $1/5$ modular forms denoted by $F_1(\tau)$ and $F_2(\tau)$. We give the expressions of the rational weight modular forms of level 5 up to weight $3$ and arrange them into the irreducible multiplets of finite metaplectic group $\widetilde{\Gamma}_5\cong A'_5\times Z_5$. A neutrino mass model with $\widetilde{\Gamma}_5$ modular symmetry is presented, and the phenomenological predictions of the model are analyzed numerically. | high energy physics phenomenology |
We present a detailed spectroscopic investigation of a thermal $^{87}$Rb atomic vapour in a magnetic field of 1.5~T in the Voigt geometry. We fit experimental spectra for all Stokes parameters with our theoretical model \textit{ElecSus} and find very good quantitative agreement, with RMS errors of $\sim 1.5$\% in all cases. We extract the magnetic field strength and the angle between the polarisation of the light and the magnetic field from the atomic signal, and we measure the birefringence effects of the cell windows on the optical rotation signals. This allows us to carry out precise measurements at a high field strength and arbitrary geometries, allowing further development of possible areas of application for atomic magnetometers. | physics |
We embed Nelson's stochastic quantization in the Schwartz-Meyer second order geometry framework. The result is a non-perturbative theory of quantum mechanics on (pseudo)-Riemannian manifolds. Within this approach, we derive stochastic differential equations for massive spin-0 test particles charged under scalar potentials, vector potentials and gravity. Furthermore, we derive the associated Schr\"odinger equation. The resulting equations show that massive scalar particles must be conformally coupled to gravity in a theory of quantum gravity. We conclude with a discussion of some prospects of the stochastic framework. | high energy physics theory |
We present a novel class of nonlinear controllers that interpolates among differently behaving linear controllers as a case study for recently proposed Linear and Nonlinear System Level Synthesis framework. The structure of the nonlinear controller allows for simultaneously satisfying performance and safety objectives defined for small- and large-disturbance regimes. The proposed controller is distributed, handles delays, sparse actuation, and localizes disturbances. We show our nonlinear controller always outperforms its linear counterpart for constrained LQR problems. We further demonstrate the anti-windup property of an augmented control strategy based on the proposed controller for saturated systems via simulation. | electrical engineering and systems science |
Travel demand management measures/policies are important to sustain positive changes among individuals' travel behaviour. An integrated agent-based microsimulation platform provides a rich framework for examining such interventions to assess their impacts using indicators about demand as well as supply side. This paper presents an approach, where individual schedules, derived from a lighter version of an activity-based model, are fed into a MATSIM simulation framework. Simulations are performed for two European cities i.e. Hasselt (Belgium), Bologna (Italy). After calibrating the modelling framework against aggregate traffic counts for the base case, the impacts of a few traffic management policies (restricting car access, increase in bus frequency) are examined. The results indicate that restricting car access is more effective in terms of reducing traffic from the network and also shifting car drivers/passengers to other modes of travel. The enhancement of bus infrastructure in relation to increase in frequency caused shifting of bicyclist towards public transport, which is an undesirable result of the policy if the objective is to improve sustainability and environment. In future research, the framework will be enhanced to integrate emission and air dispersion models to ascertain effects on air quality as a result of such interventions. | physics |
We portray the evolution of the Covid-19 epidemic during the crisis of March-April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This also leads to an algorithm, allowing one to detect epidemic resurgences. We rely on a transport PDE epidemiological model, and we use methods from Perron-Frobenius theory and tropical geometry. | statistics |
In this work, we discuss two component fermionic FIMP dark matter (DM) in a popular $B-L$ extension of the standard model (SM) with inverse seesaw mechanism. Due to the introduced $\mathbb{Z}_{2}$ discrete symmetry, a keV SM gauge singlet fermion is stable and can be a warm DM candidate. Also, this $\mathbb{Z}_{2}$ symmetry helps the lightest right-handed neutrino, with mass of order GeV, to be a long-lived or stable particle by choosing a corresponding Yukawa coupling to be very small. Firstly, in the absence of a GeV DM component (i.e., without tuning its corresponding Yukawa coupling), we consider only a keV DM as a single component DM produced by the freeze-in mechanism. Secondly, we study a two component FIMP DM scenario and emphasize that the correct ballpark DM relic density bound can be achieved for a wide parameter space. | high energy physics phenomenology |
We prove that if $X$ is a complex projective K3 surface and $g>0$, then there exist infinitely many families of curves of geometric genus $g$ on $X$ with maximal, i.e., $g$-dimensional, variation in moduli. In particular every K3 surface contains a curve of geometric genus 1 which moves in a non-isotrivial family. This implies a conjecture of Huybrechts on constant cycle curves and gives an algebro-geometric proof of a theorem of Kobayashi that a K3 surface has no global symmetric differential forms. | mathematics |
We scrutinize the novel chiral transport phenomenon driven by spacetime torsion, namely the chiral torsional effect (CTE). We calculate the torsion-induced chiral currents with finite temperature, density and curvature in the most general torsional gravity theory. The conclusion complements the previous study on the CTE by including curvature and substantiates the relation between the CTE and the Nieh-Yan anomaly. We also analyze the response of chiral torsional current to an external electromagnetic field. The resulting topological current is analogous to that in the axion electrodynamics. | high energy physics theory |
Coudert et al. (SODA'18) proved that under the Strong Exponential-Time Hypothesis, for any $\epsilon >0$, there is no ${\cal O}(2^{o(k)}n^{2-\epsilon})$-time algorithm for computing the diameter within the $n$-vertex cubic graphs of clique-width at most $k$. We present an algorithm which given an $n$-vertex $m$-edge graph $G$ and a $k$-expression, computes all the eccentricities in ${\cal O}(2^{{\cal O}(k)}(n+m)^{1+o(1)})$ time, thus matching their conditional lower bound. It can be modified in order to compute the Wiener index and the median set of $G$ within the same amount of time. On our way, we get a distance-labeling scheme for $n$-vertex $m$-edge graphs of clique-width at most $k$, using ${\cal O}(k\log^2{n})$ bits per vertex and constructible in ${\cal O}(k(n+m)\log{n})$ time from a given $k$-expression. Doing so, we match the label size obtained by Courcelle and Vanicat (DAM 2016), while we considerably improve the dependency on $k$ in their scheme. As a corollary, we get an ${\cal O}(kn^2\log{n})$-time algorithm for computing All-Pairs Shortest-Paths on $n$-vertex graphs of clique-width at most $k$. This partially answers an open question of Kratsch and Nelles (STACS'20). | computer science |
This is an up-to-date introduction to and overview of the Minimum Description Length (MDL) Principle, a theory of inductive inference that can be applied to general problems in statistics, machine learning and pattern recognition. While MDL was originally based on data compression ideas, this introduction can be read without any knowledge thereof. It takes into account all major developments since 2007, the last time an extensive overview was written. These include new methods for model selection and averaging and hypothesis testing, as well as the first completely general definition of {\em MDL estimators}. Incorporating these developments, MDL can be seen as a powerful extension of both penalized likelihood and Bayesian approaches, in which penalization functions and prior distributions are replaced by more general luckiness functions, average-case methodology is replaced by a more robust worst-case approach, and in which methods classically viewed as highly distinct, such as AIC vs BIC and cross-validation vs Bayes can, to a large extent, be viewed from a unified perspective. | statistics |
We demonstrate single-site-resolved fluorescence imaging of ultracold $^{87}\mathrm{Rb}$ atoms in a triangular optical lattice by employing Raman sideband cooling. Combining a Raman transition at the D1 line and a photon scattering through an optical pumping of the D2 line, we obtain images with low background noise. The Bayesian optimisation of 11 experimental parameters for fluorescence imaging with Raman sideband cooling enables us to achieve single-atom detection with a high fidelity of $(96.3 \pm 1.3)$%. Single-atom and single-site resolved detection in a triangular optical lattice paves the way for the direct observation of spin correlations or entanglement in geometrically frustrated systems. | condensed matter |
Water maser emitting planetary nebulae (H$_2$O-PNe) are believed to be among the youngest PNe. We present new optical narrow- and broad-band images, intermediate- and high-resolution long-slit spectra, and archival optical images of the H$_2$O-PN IRAS 18061--2505. It appears a pinched-waist bipolar PN consisting of knotty lobes with some point-symmetric regions, a bow-shock near the tip of each lobe, and a very compact inner nebula where five components are identified in the spectra by their kinematic and emission properties. The water masers most probably reside in an oxygen-rich ring tracing the equatorial region of the bipolar lobes. These two structures probably result from common envelope evolution plus several bipolar and non-bipolar collimated outflows that have distorted the lobes. The bow-shocks could be related to a previous phase to that of common envelope. The inner nebula may be attributed to a late or very late thermal pulse that occurred before 1951.6 when it was not detectable in the POSSI-Blue image. Chemical abundances and other properties favour a 3--4 M$_{\odot}$ progenitor, although if the common envelope phase accelerated the evolution of the central star, masses <1.5 M$_{\odot}$ cannot be discarded. The age of the bipolar lobes is incompatible with the existence of water masers in IRAS 18061--2505, which may have been lately reactivated through shocks in the oxygen-rich ring, that are generated by the thermal pulse, implying that this PN is not extremely young. We discuss H$_2$O-PNe and possibly related objects in the light of our results for IRAS 18061--2505. | astrophysics |
Random graph matching refers to recovering the underlying vertex correspondence between two random graphs with correlated edges; a prominent example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs $G(n,\frac{d}{n})$. This can be viewed as an average-case and noisy version of the graph isomorphism problem. Under this model, the maximum likelihood estimator is equivalent to solving the intractable quadratic assignment problem. This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which perfectly recovers the true vertex correspondence with high probability, provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges. For dense graphs and sparse graphs, this can be improved to $\delta = O( \log^{-2/3}(n) )$ and $\delta = O( \log^{-2}(d) )$ respectively, both in polynomial time. The methodology is based on appropriately chosen distance statistics of the degree profiles (empirical distribution of the degrees of neighbors). Before this work, the best known result achieves $\delta=O(1)$ and $n^{o(1)} \leq d \leq n^c$ for some constant $c$ with an $n^{O(\log n)}$-time algorithm \cite{barak2018nearly} and $\delta=\tilde O((d/n)^4)$ and $d = \tilde{\Omega}(n^{4/5})$ with a polynomial-time algorithm \cite{dai2018performance}. | statistics |
ZX-calculus is a strict mathematical formalism for graphical quantum computing which is based on the field of complex numbers. In this paper, we extend its power by generalising ZX-calculus to such an extent that it is universal both in an arbitrary commutative ring and in an arbitrary commutative semiring. Furthermore, we follow the framework of arXiv:2007.13739 to prove respectively that the proposed ZX-calculus over an arbitrary commutative ring (semiring) is complete for matrices over the same ring (semiring), via a normal form inspired from matrix elementary operations such as row addition and row multiplication. This work could lead to various applications including doing elementary number theory in string diagrams. | quantum physics |
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is available. We propose hybrid control barrier functions for hybrid systems as a means to synthesize safe control inputs. Based on this notion, we present an optimization-based framework to learn such hybrid control barrier functions from data. Importantly, we identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned hybrid control barrier functions, and hence the safety of the system. We illustrate our findings in two simulations studies, including a compass gait walker. | electrical engineering and systems science |
In Sanskrit, small words (morphemes) are combined to form compound words through a process known as Sandhi. Sandhi splitting is the process of splitting a given compound word into its constituent morphemes. Although rules governing word splitting exists in the language, it is highly challenging to identify the location of the splits in a compound word. Though existing Sandhi splitting systems incorporate these pre-defined splitting rules, they have a low accuracy as the same compound word might be broken down in multiple ways to provide syntactically correct splits. In this research, we propose a novel deep learning architecture called Double Decoder RNN (DD-RNN), which (i) predicts the location of the split(s) with 95% accuracy, and (ii) predicts the constituent words (learning the Sandhi splitting rules) with 79.5% accuracy, outperforming the state-of-art by 20%. Additionally, we show the generalization capability of our deep learning model, by showing competitive results in the problem of Chinese word segmentation, as well. | computer science |
Example-guided image synthesis has been recently attempted to synthesize an image from a semantic label map and an exemplary image. In the task, the additional exemplary image serves to provide style guidance that controls the appearance of the synthesized output. Despite the controllability advantage, the previous models are designed on datasets with specific and roughly aligned objects. In this paper, we tackle a more challenging and general task, where the exemplar is an arbitrary scene image that is semantically unaligned to the given label map. To this end, we first propose a new Masked Spatial-Channel Attention (MSCA) module which models the correspondence between two unstructured scenes via cross-attention. Next, we propose an end-to-end network for joint global and local feature alignment and synthesis. In addition, we propose a novel patch-based self-supervision scheme to enable training. Experiments on the large-scale CCOO-stuff dataset show significant improvements over existing methods. Moreover, our approach provides interpretability and can be readily extended to other tasks including style and spatial interpolation or extrapolation, as well as other content manipulation. | computer science |
Semantic knowledge graphs are large-scale triple-oriented databases for knowledge representation and reasoning. Implicit knowledge can be inferred by modeling and reconstructing the tensor representations generated from knowledge graphs. However, as the sizes of knowledge graphs continue to grow, classical modeling becomes increasingly computational resource intensive. This paper investigates how quantum resources can be capitalized to accelerate the modeling of knowledge graphs. In particular, we propose the first quantum machine learning algorithm for making inference on tensorized data, e.g., on knowledge graphs. Since most tensor problems are NP-hard, it is challenging to devise quantum algorithms to support that task. We simplify the problem by making a plausible assumption that the tensor representation of a knowledge graph can be approximated by its low-rank tensor singular value decomposition, which is verified by our experiments. The proposed sampling-based quantum algorithm achieves exponential speedup with a runtime that is polylogarithmic in the dimension of knowledge graph tensor. | quantum physics |
Multi-beam antenna technologies have provided lots of promising solutions to many current challenges faced in wireless mesh networks. The antenna can establish several beamformings simultaneously and initiate concurrent transmissions or receptions using multiple beams, thereby increasing the overall throughput of the network transmission. Multi-beam antenna has the ability to increase the spatial reuse, extend the transmission range, improve the transmission reliability, as well as save the power consumption. Traditional Medium Access Control (MAC) protocols for wireless network largely relied on the IEEE 802.11 Distributed Coordination Function(DCF) mechanism, however, IEEE 802.11 DCF cannot take the advantages of these unique capabilities provided by multi-beam antennas. This paper surveys the MAC protocols for wireless mesh networks with multi-beam antennas. The paper first discusses some basic information in designing multi-beam antenna system and MAC protocols, and then presents the main challenges for the MAC protocols in wireless mesh networks compared with the traditional MAC protocols. A qualitative comparison of the existing MAC protocols is provided to highlight their novel features, which provides a reference for designing the new MAC protocols. To provide some insights on future research, several open issues of MAC protocols are discussed for wireless mesh networks using multi-beam antennas. | computer science |
Classical Cepheid and RR Lyrae variables are radially pulsating stars that trace young and old-age stellar populations, respectively. These classical pulsating stars are the most sensitive probes for the precision stellar astrophysics and the extragalactic distance measurements. Despite their extensive use as standard candles thanks to their well-defined Period-Luminosity relations, distance measurements based on these objects suffer from their absolute primary calibrations, metallicity effects, and other systematic uncertainties. Here, I present a review of classical Cepheid, RR Lyrae, and Type II Cepheid variables starting with a historical introduction and describing their basic evolutionary and pulsational properties. I will focus on recent theoretical and observational efforts to establish absolute scale for these standard candles at multiple wavelengths. The application of these classical pulsating stars to high-precision cosmic distance scale will be discussed along with observational systematics. I will summarize with an outlook for further improvements in our understanding of these classical pulsators in the upcoming era of extremely large telescopes. | astrophysics |
We place theoretical constraints on the leading deviations to four-fermion standard model interactions. Invoking S-matrix analyticity, partial wave unitarity, and UV improvement of the amplitude, we develop new dispersion relations that imply a positivity condition on dimension-six fermionic operators. For the SMEFT, positivity charts the boundary of the consistent theory, including a pattern of inequalities between interactions. These relations form a bridge between new physics searches: discovery of flavor-violating $\tau$ decay at Belle II would imply flavor-conserving new physics below 25 TeV. | high energy physics phenomenology |
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