cssi/SciSciGPT-SciSciNet huggingface dataset:
Coupled catastrophes: sudden shifts cascade and hop among interdependent systems

An important challenge in several disciplines is to understand how sudden changes can propagate among coupled systems. Examples include the synchronization of business cycles, population collapse in patchy ecosystems, markets shifting to a new technology platform, collapses in prices and in confidence in financial markets, and protests erupting in multiple countries. A number of mathematical models of these phenomena have multiple equilibria separated by saddle-node bifurcations. We study this behaviour in its normal form as fast-slow ordinary differential equations. In our model, a system consists of multiple subsystems, such as countries in the global economy or patches of an ecosystem. Each subsystem is described by a scalar quantity, such as economic output or population, that undergoes sudden changes via saddle-node bifurcations. The subsystems are coupled via their scalar quantity (e.g. trade couples economic output; diffusion couples populations); that coupling moves the locations of their bifurcations. The model demonstrates two ways in which sudden changes can propagate: they can cascade (one causing the next), or they can hop over subsystems. The latter is absent from classic models of cascades. For an application, we study the Arab Spring protests. After connecting the model to sociological theories that have bistability, we use socioeconomic data to estimate relative proximities to tipping points and Facebook data to estimate couplings among countries. We find that although protests tend to spread locally, they also seem to 'hop' over countries, like in the stylized model; this result highlights a new class of temporal motifs in longitudinal network datasets.
Merging neutron star binaries: equation of state and electrodynamics

Merging neutron star (NS) binaries may be detected by ground-based gravitational wave (GW) interferometers (e.g. LIGO/VIRGO) within this decade and may also generate electromagnetic radiation detectable by wide-field, fast imaging telescopes that are coming online. The GWs can provide new constraint on the NS equation of state (including mass-radius relation and the related nuclear symmetry energy). This paper reviews various hydrodynamical and electrodynamical processes in coalescing NS binaries, with focus on the pre-merger phase.
Energy dissipation and transport in nanoscale devices

Understanding energy dissipation and transport in nanoscale structures is of great importance for the design of energy-efficient circuits and energy-conversion systems. This is also a rich domain for fundamental discoveries at the intersection of electron, lattice (phonon), and optical (photon) interactions. This review presents recent progress in understanding and manipulation of energy dissipation and transport in nanoscale solid-state structures. First, the landscape of power usage from nanoscale transistors (∼10−8 W) to massive data centers (∼109 W) is surveyed. Then, focus is given to energy dissipation in nanoscale circuits, silicon transistors, carbon nanostructures, and semiconductor nanowires. Concepts of steady-state and transient thermal transport are also reviewed in the context of nanoscale devices with sub-nanosecond switching times. Finally, recent directions regarding energy transport are reviewed, including electrical and thermal conductivity of nanostructures, thermal rectification, and the role of ubiquitous material interfaces. Open image in new window
Imputation of clinical covariates in time series

Missing data is a common problem in longitudinal datasets which include multiple instances of the same individual observed at different points in time. We introduce a new approach, MedImpute, for imputing missing clinical covariates in multivariate panel data. This approach integrates patient specific information into an optimization formulation that can be adjusted for different imputation algorithms. We present the formulation for a K-nearest neighbors model and derive a corresponding scalable first-order method med.knn. Our algorithm provides imputations for datasets with both continuous and categorical features and observations occurring at arbitrary points in time. In computational experiments on three real-world clinical datasets, we test its performance on imputation and downstream predictive tasks, varying the percentage of missing data, the number of observations per patient, and the mechanism of missing data. The proposed method improves upon both the imputation accuracy and downstream predictive performance relative to the best of the benchmark imputation methods considered. We show that this edge is consistently present both in longitudinal and electronic health records datasets as well as in binary classification and regression settings. On computational experiments on synthetic data, we test the scalability of this algorithm on large datasets, and we show that an efficient method for hyperparameter tuning scales to datasets with 10,000’s of observations and 100’s of covariates while maintaining high imputation accuracy.
Measuring the Impact of Spectre and Meltdown

The Spectre and Meltdown flaws in modern microprocessors represent a new class of attacks that have been difficult to mitigate. The mitigations that have been proposed have known performance impacts. The reported magnitude of these impacts varies depending on the industry sector and expected workload characteristics. In this paper, we measure the performance impact on several workloads relevant to HPC systems. We show that the impact can be significant on both synthetic and realistic workloads. We also show that the performance penalties are difficult to avoid even in dedicated systems where security is a lesser concern.
Grand unification, axion, and inflation in Intermediate Scale Supersymmetry

A class of supersymmetric grand unified theories is introduced that has a single scale below the cutoff, that of the supersymmetry breaking masses $$ \tilde{m} $$ . For a wide range of the dimensionless parameters, agreement with the observed mass of the Higgs boson determines $$ \tilde{m} $$ ~ 109−1013 GeV, yielding Intermediate Scale Supersymmetry. We show that within this framework it is possible for seesaw neutrino masses, axions, and inflation to be described by the scale m, offering the possibility of a unified origin of disparate phenomena. Neutrino masses allowing for thermal leptogenesis can be obtained, and the axion decay constant lies naturally in the range f a ~ 109−1011 GeV, consistent with a recent observational suggestion of high scale inflation. A minimal SU(5) model is presented that illustrates these features. In this model, the only states at the grand unified scale are those of the heavy gauge supermultiplet. The grand unified partners of the Higgs doublets have a mass of order m, leading to the dominant proton decay mode p → $$ \overline{v} $$ K +, which may be probed in upcoming experiments. Dark matter may be winos, with mass environmentally selected to the TeV scale, and/or axions. Gauge coupling unification is found to be successful, especially if the wino is at the TeV scale.
Covariance Matrix Estimation With Non Uniform and Data Dependent Missing Observations

In this paper we study covariance estimation with missing data. We consider missing data mechanisms that can be independent of the data, or have a time varying dependency. Additionally, observed variables may have arbitrary (non uniform) and dependent observation probabilities. For each mechanism, we construct an unbiased estimator and obtain bounds for the expected value of their estimation error in operator norm. Our bounds are equivalent, up to constant and logarithmic factors, to state of the art bounds for complete and uniform missing observations. Furthermore, for the more general non uniform and dependent cases, the proposed bounds are new or improve upon previous results. Our error estimates depend on quantities we call scaled effective rank , which generalize the effective rank to account for missing observations. All the estimators studied in this work have the same asymptotic convergence rate (up to logarithmic factors).
Minimal Twin Surfaces

We report some minimal surfaces that can be seen as copies of a triply periodic minimal surface (TPMS) related by reflections in parallel mirrors. We call them minimal twin surfaces for the resembl...
Solitary zonal structures in subcritical drift waves: a minimum model

Solitary zonal structures have recently been identified in gyrokinetic simulations of subcritical drift-wave (DW) turbulence with background shear flows. However, the nature of these structures has not been fully understood yet. Here, we show that similar structures can be obtained within a reduced model, which complements the modified Hasegawa-Mima equation with a generic primary instability and a background shear flow. We also find that these structures can be qualitatively reproduced in the modified Hasegawa-Wakatani equation, which subsumes the reduced model as a limit. In particular, we illustrate that in both cases, the solitary zonal structures approximately satisfy the same ''equation of state'', which is a local relation connecting the DW envelope with the zonal-flow velocity. Due to this generality, our reduced model can be considered as a minimum model for solitary zonal structures in subcritical DWs.
Antiferromagnetic Spin Wave Field-Effect Transistor

In a collinear antiferromagnet with easy-axis anisotropy, symmetry dictates that the spin wave modes must be doubly degenerate. Theses two modes, distinguished by their opposite polarization and available only in antiferromagnets, give rise to a novel degree of freedom to encode and process information. We show that the spin wave polarization can be manipulated by an electric field induced Dzyaloshinskii-Moriya interaction and magnetic anisotropy. We propose a prototype spin wave field-effect transistor which realizes a gate-tunable magnonic analog of the Faraday effect, and demonstrate its application in THz signal modulation. Our findings open up the exciting possibility of digital data processing utilizing antiferromagnetic spin waves and enable the direct projection of optical computing concepts onto the mesoscopic scale.
Comment on 4D Lorentz invariance violations in the brane-world

The brane-world scenario offers the possibility for signals to travel outside our visible universe and reenter it. We find the condition for a signal emitted from the brane to return to the brane. We study the propagation of such signals and show that, as seen by a 4D observer, these signals arrive earlier than light traveling along the brane. We also study the horizon problem and find that, while the bulk signals can travel far enough to homogenize the visible universe, it is unlikely that they have a significant effect since they are redshifted in the gravitational field of the bulk black hole.
Supernova remnant evolution in wind bubbles: A closer look at Kes 27

Abstract Massive Stars (>8M ⊙ ) lose mass in the form of strong winds. These winds accumulate around the star, forming wind-blown bubbles. When the star explodes as a supernova (SN), the resulting shock wave expands within this wind-blown bubble, rather than the interstellar medium. The properties of the resulting remnant, its dynamics and kinematics, the morphology, and the resulting evolution, are shaped by the structure and properties of the wind-blown bubble. In this article we focus on Kes 27, a supernova remnant (SNR) that has been proposed by [1] to be evolving in a wind-blown bubble, explore its properties, and investigate whether the X-Ray properties could be ascribed to evolution of a SNR in a wind-blown bubble. Our initial model does not support the scenario proposed by [1] , due to the fact that the reflected shock is expanding into much lower densities.
A dynamic process interpretation of the sparse ERGM reference model

Exponential family random graph models (ERGMs) can be understood in terms of a set of structural biases that act on an underlying reference distribution. This distribution determines many aspects o...
Optical design of diffraction-limited x-ray telescopes

Astronomical imaging with micro-arcsecond (µas) angular resolution could enable breakthrough scientific discoveries. Previously proposed µas x-ray imager designs have been interferometers with limited effective collecting area. Here we describe x-ray telescopes achieving diffraction-limited performance over a wide energy band with large effective area, employing a nested-shell architecture with grazing-incidence mirrors, while matching the optical path lengths between all shells. We present two compact nested-shell Wolter Type 2 grazing-incidence telescope designs for diffraction-limited x-ray imaging: a micro-arcsecond telescope design with 14 µas angular resolution and 2.9m2 of effective area at 5 keV photon energy (λ=0.25nm), and a smaller milli-arcsecond telescope design with 525 µas resolution and 645cm2 effective area at 1 keV (λ=1.24nm). We describe how to match the optical path lengths between all shells in a compact mirror assembly and investigate chromatic and off-axis aberrations. Chromatic aberration results from total external reflection off of mirror surfaces, and we greatly mitigate its effects by slightly adjusting the path lengths in each mirror shell. The mirror surface height error and alignment requirements for diffraction-limited performance are challenging but arguably achievable in the coming decades. Because the focal ratio for a diffraction-limited x-ray telescope is extremely large (f/D∼105), the only important off-axis aberration is curvature of field, so a 1 arc sec field of view is feasible with a flat detector. The detector must fly in formation with the mirror assembly, but relative positioning tolerances are on the order of 1 m over a distance of some tens to hundreds of kilometers. Although there are many challenges to achieving diffraction-limited x-ray imaging, we did not find any fundamental barriers.
Swarming, swirling and stasis in sequestered bristle-bots

The collective ability of organisms to move coherently in space and time is ubiquitous in any group of autonomous agents that can move and sense each other and the environment. Here, we investigate...
GLOBAL MODELING OF NEBULAE WITH PARTICLE GROWTH, DRIFT, AND EVAPORATION FRONTS. I. METHODOLOGY AND TYPICAL RESULTS

We model particle growth in a turbulent, viscously evolving protoplanetary nebula, incorporating sticking, bouncing, fragmentation, and mass transfer at high speeds. We treat small particles using a moments method and large particles using a traditional histogram binning, including a probability distribution function of collisional velocities. The fragmentation strength of the particles depends on their composition (icy aggregates are stronger than silicate aggregates). The particle opacity, which controls the nebula thermal structure, evolves as particles grow and mass redistributes. While growing, particles drift radially due to nebula headwind drag. Particles of different compositions evaporate at "evaporation fronts" (EFs) where the midplane temperature exceeds their respective evaporation temperatures. We track the vapor and solid phases of each component, accounting for advection and radial and vertical diffusion. We present characteristic results in evolutions lasting $2 \times 10^5$ years. In general, (a) mass is transferred from the outer to inner nebula in significant amounts, creating radial concentrations of solids at EFs, (b) particle sizes are limited by a combination of fragmentation, bouncing, and drift, (c) "lucky" large particles never represent a significant amount of mass, and (d) restricted radial zones just outside each EF become compositionally enriched in the associated volatiles. We point out implications for mm-submm SEDs and inference of nebula mass, radial banding, the role of opacity on new mechanisms for generating turbulence, enrichment of meteorites in heavy oxygen isotopes, variable and nonsolar redox conditions, primary accretion of silicate and icy planetesimals, and the makeup of Jupiter's core.
CONE-Align: Consistent Network Alignment with Proximity-Preserving Node Embedding

Network alignment, the process of finding correspondences between nodes in different graphs, has many scientific and industrial applications. Existing unsupervised network alignment methods find suboptimal alignments that break up node neighborhoods, i.e. do not preserve matched neighborhood consistency. To improve this, we propose CONE-Align, which models intra-network proximity with node embeddings and uses them to match nodes across networks after aligning the embedding subspaces. Experiments on diverse, challenging datasets show that CONE-Align is robust and obtains 19.25% greater accuracy on average than the best-performing state-of-the-art graph alignment algorithm in highly noisy settings.
The Coupling of Topology and Inflation in Noncommutative Cosmology

We show that, in a model of modified gravity based on the spectral action functional, there is a nontrivial coupling between cosmic topology and inflation, in the sense that the shape of the possible slow-roll inflation potentials obtained in the model from the nonperturbative form of the spectral action is sensitive not only to the geometry (flat or positively curved) of the universe, but also to the different possible non-simply connected topologies. We show this by explicitly computing the nonperturbative spectral action for some candidate flat cosmic topologies given by Bieberbach manifolds and showing that the resulting inflation potential differs from that of the flat torus by a multiplicative factor, similarly to what happens in the case of the spectral action of the spherical forms in relation to the case of the 3-sphere. We then show that, while the slow-roll parameters differ between the spherical and flat manifolds but do not distinguish different topologies within each class, the power spectra detect the different scalings of the slow-roll potential and therefore distinguish between the various topologies, both in the spherical and in the flat case.
A group version of stable regularity

We prove that, given e > 0 and k ≥ 1, there is an integer n such that the following holds. Suppose G is a finite group and A ⊆ G is k-stable. Then there is a normal subgroup H ≤ G of index at most n, and a set Y ⊆ G, which is a union of cosets of H, such that |A △ Y| ≤e|H|. It follows that, for any coset C of H, either |C ∩ A|≤ e|H| or |C \ A| ≤ e |H|. This qualitatively generalises recent work of Terry and Wolf on vector spaces over .
MODELING THE FLYBY ANOMALIES WITH DARK MATTER SCATTERING

We continue our exploration of whether the flyby anomalies can be explained by scattering of spacecraft nucleons from dark matter gravitationally bound to the earth. We formulate and analyze a simple model in which inelastic and elastic scatterers populate shells generated by the precession of circular orbits with normals tilted with respect to the earth's axis. Good fits to the data published by Anderson et al. are obtained.
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks

Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e.g., 99.99% of examples are negatives). In contrast, many recent datasets heuristically choose examples to ensure label balance. We show that these heuristics lead to trained models that generalize poorly: State-of-the art models trained on QQP and WikiQA each have only 2.4% average precision when evaluated on realistically imbalanced test data. We instead collect training data with active learning, using a BERT-based embedding model to efficiently retrieve uncertain points from a very large pool of unlabeled utterance pairs. By creating balanced training data with more informative negative examples, active learning greatly improves average precision to 32.5% on QQP and 20.1% on WikiQA.
Neutrino Energies in a Neutrinosphere

The energies of neutrinos in a neutrinosphere, the dense matter created after the gravitational collapse of a massive star are estimated. Cubic equations for energy eigenvalues of neutrinos are used with the effective masses found by taking the neutrinos at rest in neutrinosphere matter. Large differences in the effective mass of some neutrino species in a neutrinosphere compared to vacuum are found.
Moduli identification methods in Type II compactifications

Recent work on four dimensional effective descriptions of the heterotic string has identified the moduli of such systems as being given by kernels of maps between ordinary Dolbeault cohomology groups. The maps involved are defined by the supergravity data of the background solutions. Such structure is seen both in the case of Calabi-Yau compactifications with non-trivial constraints on moduli arising from the gauge bundle and in the case of some non-Kahler compactifications of the theory. This description of the moduli has allowed the explicit computation of the moduli stabilization effects of a wide range of non-trivial gauge bundles on Calabi-Yau three-folds. In this paper we examine to what extent the ideas and techniques used in this work can be extended to the case of flux compactifications of Type IIB string theory. Certain simplifications arise in the Type IIB case in comparison to the heterotic situation. However, complications also arise due to the richer supergravity data of the theory inducing a more involved map structure. We illustrate our discussion with several concrete examples of compactification of Type IIB string theory on conformal CICY three-folds with flux.
Conditional plausibility measures and Bayesian networks

A general notion of algebraic conditional plausibility measures is defined. Probability measures, ranking functions, possibility measures, and (under the appropriate definitions) sets of probability measures can all be viewed as defining algebraic conditional plausibility measures. It is shown that algebraic conditional plausibility measures can be represented using Bayesian networks.
The Morphologies and Kinematics of Supernova Remnants

We review the major advances in understanding the morphologies and kinematics of supernova remnants (SNRs). Simulations of SN explosions have improved dramatically over the last few years, and SNRs can be used to test models through comparison of predictions with SNRs’ observed large-scale compositional and morphological properties as well as the three-dimensional kinematics of ejecta material. In particular, Cassiopeia A—the youngest known core-collapse SNR in the Milky Way—offers an up-close view of the complexity of these explosive events that cannot be resolved in distant, extragalactic sources. We summarize the progress in tying SNRs to their progenitors’ explosions through imaging and spectroscopic observations, and we discuss exciting future prospects for SNR studies, such as X-ray microcalorimeters.
Discovery of the Putative Pulsar and Wind Nebula Associated with the TeV Gamma-Ray Source HESS J1813–178

We present a Chandra X-ray observation of G12.82-0.02, a shell-like radio supernova remnant coincident with the TeV gamma-ray source HESS J1813-178. We resolve the X-ray emission from the colocated Advanced Satellite for Cosmology and Astrophysics (ASCA) source into a point source surrounded by structured diffuse emission that fills the interior of the radio shell. The morphology of the diffuse emission strongly resembles that of a pulsar wind nebula. The spectrum of the compact source is well characterized by a power law with index Γ ≈ 1.3, typical of young and energetic rotation-powered pulsars. For a distance of 4.5 kpc, consistent with the X-ray absorption and an association with the nearby star formation region W33, the 2-10 keV X-ray luminosities of the putative pulsar and nebula are LPSR = 3.2 × 1033 ergs s-1 and LPWN = 1.4 × 1034 ergs s-1, respectively. Both the flux ratio of LPWN/LPSR = 4.3 and the total luminosity of this system predict a pulsar spin-down power of Ė > 1037 ergs s-1, placing it among the 10 most energetic young pulsars in the Galaxy. A deep search for radio pulsations using the Parkes telescope sets an upper limit of ≈0.07 mJy at 1.4 GHz for periods 50 ms. We discuss the energetics of this source and consider briefly the proximity of bright H II regions to this and several other High Energy Stereoscopic System (H.E.S.S.) sources, which may produce their TeV emission via inverse Compton scattering.
Barren Plateaus in Quantum Neural Network Training Landscapes

Many experimental proposals for noisy intermediate scale quantum devices involve training a parameterized quantum circuit with a classical optimization loop. Such hybrid quantum-classical algorithms are popular for applications in quantum simulation, optimization, and machine learning. Due to its simplicity and hardware efficiency, random circuits are often proposed as initial guesses for exploring the space of quantum states. We show that the exponential dimension of Hilbert space and the gradient estimation complexity make this choice unsuitable for hybrid quantum-classical algorithms run on more than a few qubits. Specifically, we show that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. We argue that this is related to the 2-design characteristic of random circuits, and that solutions to this problem must be studied. Gradient-based hybrid quantum-classical algorithms are often initialised with random, unstructured guesses. Here, the authors show that this approach will fail in the long run, due to the exponentially-small probability of finding a large enough gradient along any direction.
BAYESIAN TECHNIQUES FOR COMPARING TIME-DEPENDENT GRMHD SIMULATIONS TO VARIABLE EVENT HORIZON TELESCOPE OBSERVATIONS

NSF [AST-1207752, AST-1440254, AST 1108753, AST 1312034, 1228509]; NASA/NSF TCAN award [NNX14AB48G]; NFS GRFP [DGE 1144085]
A study on tuning parameter selection for the high-dimensional lasso

High-dimensional predictive models, those with more measurements than observations, require regularization to be well defined, perform well empirically, and possess theoretical guarantees. The amou...
A High-Energy Study of the Geminga Pulsar

We present the results of deep X-ray and γ-ray observations of the Geminga pulsar obtained in the final years of the ASCA and CGRO missions and an upper limit from RXTE. A phase-connected ephemeris from the γ-rays is derived that spans the years 1973-2000 after allowing for a minor glitch in frequency of Δf/f = 6.2 × 10-10 in late 1996. ASCA observations of the hard X-ray pulse profile in 1994 and 1999 confirm this glitch. An improved characterization of the hard X-ray pulse profile and spectrum from the long ASCA observation of 1999 confirms that there is a nonthermal X-ray component that is distinct from the γ-ray spectrum as measured by EGRET. It can be parameterized as a power law of photon index Γ = 1.72 ± 0.10 with a flux of 2.62 × 10-13 ergs cm-2 s-1 in the 0.7-5 keV band and pulsed fraction 0.54 ± 0.05, similar to (but more precise than) values measured previously. An extrapolation of this spectrum into the energy band observed by the RXTE PCA is consistent with the nondetection of pulsed emission from Geminga with that instrument. These results are interpreted in the context of outer-gap models, and motivations for future X-ray observations of Geminga are given.
Measuring Transit Signal Recovery in the Kepler Pipeline. III. Completeness of the Q1–Q17 DR24 Planet Candidate Catalogue with Important Caveats for Occurrence Rate Calculations

With each new version of the Kepler pipeline and resulting planet candidate catalog, an updated measurement of the underlying planet population can only be recovered with a corresponding measurement of the Kepler pipeline detection efficiency. Here we present measurements of the sensitivity of the pipeline (version 9.2) used to generate the Q1–Q17 DR24 planet candidate catalog. We measure this by injecting simulated transiting planets into the pixel-level data of 159,013 targets across the entire Kepler focal plane, and examining the recovery rate. Unlike previous versions of the Kepler pipeline, we find a strong period dependence in the measured detection efficiency, with longer (>40 day) periods having a significantly lower detectability than shorter periods, introduced in part by an incorrectly implemented veto. Consequently, the sensitivity of the 9.2 pipeline cannot be cast as a simple one-dimensional function of the signal strength of the candidate planet signal, as was possible for previous versions of the pipeline. We report on the implications for occurrence rate calculations based on the Q1–Q17 DR24 planet candidate catalog, and offer important caveats and recommendations for performing such calculations. As before, we make available the entire table of injected planet parameters and whether they were recovered by the pipeline, enabling readers to derive the pipeline detection sensitivity in the planet and/or stellar parameter space of their choice.
On realisations of W algebras

Abstract It has been known for some time that W algebras can be realised in terms of an energy-momentum tensor together with additional free scalar fields. Some recent results have shown that more general realisations are also possible. In this paper, we consider a wide class of realisations that may be obtained from the Miura transformation, related to the existence of canonical subalgebras of the Lie algebras on which the W algebras are based. We give explicit formulae for all realisations of this kind, and discuss their applications in W-string theory.
Probing superfast quarks in nuclei through dijet production at the LHC

We investigate dijet production from proton-nucleus collisions at the Large Hadron Collider (LHC) as a means for observing superfast quarks in nuclei with Bjorken $$x>1$$ . Kinematically, superfast quarks can be identified through directly measurable jet kinematics. Dynamically, their description requires understanding several elusive properties of nuclear QCD, such as nuclear forces at very short distances, as well as medium modification of parton distributions in nuclei. In the present work, we develop a model for nuclear parton distributions at large x in which the nuclear dynamics at short distance scales are described by two- and three-nucleon short range correlations (SRCs). Nuclear modifications are accounted for using the color screening model, and an improved description of the EMC effect is reached by using a structure function parametrization that includes higher-twist contributions. We apply QCD evolution at the leading order to obtain nuclear parton distributions in the kinematic regime of the LHC, and based on the obtained distributions calculate the cross section for dijet production. We find that the rates of the dijet production in pA collisions at kinematics accessible by ATLAS and CMS are sufficient not only to observe superfast quarks but also to get information about the practically unexplored three-nucleon SRCs in nuclei. Additionally, the LHC can extend our knowledge of the EMC effect to large $$Q^2$$ where higher-twist effects are negligible.
High-Fidelity Measurement of Qubits Encoded in Multilevel Superconducting Circuits

By using redundant measurements and multiphoton states, a new approach to encoding and reading quantum bits drastically reduces errors that typically lead to a loss of information.
Counterfactual Predictions under Runtime Confounding.

Algorithms are commonly used to predict outcomes under a particular decision or intervention, such as predicting whether an offender will succeed on parole if placed under minimal supervision. Generally, to learn such counterfactual prediction models from observational data on historical decisions and corresponding outcomes, one must measure all factors that jointly affect the outcomes and the decision taken. Motivated by decision support applications, we study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data, but it is either undesirable or impermissible to use some such factors in the prediction model. We refer to this setting as runtime confounding. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting. Our theoretical analysis and experimental results suggest that our method often outperforms competing approaches. We also present a validation procedure for evaluating the performance of counterfactual prediction methods.
Gamma-Ray Bursts as a Probe of the Very High Redshift Universe

There is increasingly strong evidence that gamma-ray bursts (GRBs) are associated with star-forming galaxies and occur near or in the star-forming regions of these galaxies. These associations provide indirect evidence that at least the long GRBs detected by BeppoSAX are a result of the collapse of massive stars. The recent evidence that the light curves and the spectra of the afterglows of GRB 970228 and GRB 980326 appear to contain a supernova component, in addition to a relativistic shock-wave component, provides more direct clues that this is the case. We show that, if many GRBs are indeed produced by the collapse of massive stars, GRBs and their afterglows provide a powerful probe of the very high redshift (z 5) universe. We first establish that GRBs and their afterglows are both detectable out to very high redshifts. We then show that one expects GRBs to occur out to at least z ? 10, and possibly to z ? 15-20, redshifts that are far larger than those expected for the most distant quasars. This implies that there are large numbers of GRBs with peak photon number fluxes below the detection thresholds of BATSE and HETE 2, and even below the detection threshold of Swift. The mere detection of very high redshift GRBs would give us our first information about the earliest generations of stars. We show that GRBs and their afterglows can be used as beacons to locate core-collapse supernovae at redshifts z 1 and to study the properties of these supernovae. We describe the expected properties of the absorption-line systems and the Ly? forest in the spectra of GRB afterglows and discuss various strategies for determining the redshifts of very high redshift GRBs. We then show how the absorption-line systems and the Ly? forest visible in the spectra of GRB afterglows can be used to trace the evolution of metallicity in the universe and to probe the large-scale structure of the universe at very high redshifts. Finally, we show how measurement of the Ly? break in the spectra of GRB afterglows can be used to constrain, or possibly measure, the epoch at which reionization of the universe occurred by using the Gunn-Peterson test.
On Low-Complexity Maximum-Likelihood Decoding of Convolutional Codes

This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods often use the sum log likelihood (SLL) of a Markov path as a bound to disprove the optimality of other Markov path sets and to consequently avoid exhaustive path search. In this letter, it is shown that SLL-based optimality tests are inefficient if one fixes the coding memory and takes the codeword length to infinity. Alternatively, optimality of a source symbol at a given time index can be testified using bounds derived from log likelihoods of the neighboring symbols. It is demonstrated that such neighboring log likelihood (NLL)-based optimality tests, whose efficiency does not depend on the codeword length, can bring significant complexity reduction. The results are generalized to ML sequence detection in a class of discrete-time hidden Markov systems.
Coulomb gap and variable range hopping in a pinned Wigner crystal

It is shown that pinning of the electron Wigner crystal by a small concentration of charged impurities creates the finite density of charged localized states near the Fermi level. In the case of residual impurities in the spacer this density of states is related to nonlinear screening of a close acceptor by a Wigner crystal vacancy. On the other hand, intentional doping by a remote layer of donors is a source of a long range potential, which generates dislocations in Wigner crystal. Dislocations in turn create charged localized states near the Fermi level. In both cases Coulomb interaction of localized states leads to the soft Coulomb gap and ES variable range hopping at low enough temperatures. (© 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
On Inverse Probability Weighting for Nonmonotone Missing at Random Data.

ABSTRACTThe development of coherent missing data models to account for nonmonotone missing at random (MAR) data by inverse probability weighting (IPW) remains to date largely unresolved. As a consequence, IPW has essentially been restricted for use only in monotone MAR settings. We propose a class of models for nonmonotone missing data mechanisms that spans the MAR model, while allowing the underlying full data law to remain unrestricted. For parametric specifications within the proposed class, we introduce an unconstrained maximum likelihood estimator for estimating the missing data probabilities which is easily implemented using existing software. To circumvent potential convergence issues with this procedure, we also introduce a constrained Bayesian approach to estimate the missing data process which is guaranteed to yield inferences that respect all model restrictions. The efficiency of standard IPW estimation is improved by incorporating information from incomplete cases through an augmented estimati...
Frequency-encoded photonic qubits for scalable quantum information processing

Among the objectives for large-scale quantum computation is the quantum interconnect: a device that uses photons to interface qubits that otherwise could not interact. However, the current approaches require photons indistinguishable in frequency—a major challenge for systems experiencing different local environments or of different physical compositions altogether. Here, we develop an entirely new platform that actually exploits such frequency mismatch for processing quantum information. Labeled “spectral linear optical quantum computation” (spectral LOQC), our protocol offers favorable linear scaling of optical resources and enjoys an unprecedented degree of parallelism, as an arbitrary N-qubit quantum gate may be performed in parallel on multiple N-qubit sets in the same linear optical device. Not only does spectral LOQC offer new potential for optical interconnects, but it also brings the ubiquitous technology of high-speed fiber optics to bear on photonic quantum information, making wavelength-configurable and robust optical quantum systems within reach.
Modeling delay in genetic networks: From delay birth-death processes to delay stochastic differential equations

Delay is an important and ubiquitous aspect of many biochemical processes. For example, delay plays a central role in the dynamics of genetic regulatory networks as it stems from the sequential assembly of first mRNA and then protein. Genetic regulatory networks are therefore frequently modeled as stochastic birth-death processes with delay. Here, we examine the relationship between delay birth-death processes and their appropriate approximating delay chemical Langevin equations. We prove a quantitative bound on the error between the pathwise realizations of these two processes. Our results hold for both fixed delay and distributed delay. Simulations demonstrate that the delay chemical Langevin approximation is accurate even at moderate system sizes. It captures dynamical features such as the oscillatory behavior in negative feedback circuits, cross-correlations between nodes in a network, and spatial and temporal information in two commonly studied motifs of metastability in biochemical systems. Overall, these results provide a foundation for using delay stochastic differential equations to approximate the dynamics of birth-death processes with delay.
On Vaughan Pratt's crossword problem

Vaughan Pratt has introduced objects consisting of pairs $(A,W)$ where $A$ is a set and $W$ a set of subsets of $A,$ such that (i) $W$ contains $\emptyset$ and $A,$ (ii) if $C$ is a subset of $A\times A$ such that for every $a\in A,$ both $\{b\mid (a,b)\in C\}$ and $\{b\mid (b,a)\in C\}$ are members of $W$ (a crossword with all rows and columns in $W),$ then $\{b\mid (b,b)\in C\}$ (the diagonal word ) also belongs to $W,$ and (iii) for all distinct $a,b\in A,$ the set $W$ has an element which contains $a$ but not $b.$ He has asked whether for every $A,$ the only such $W$ is the set of all subsets of $A.$ We answer that question in the negative. We also obtain several positive results, in particular, a positive answer to the above question if $W$ is closed under complementation. We obtain partial results on whether there can exist counterexamples to Pratt's question with $W$ countable.
S-duality in gauge theories as a canonical transformation

Abstract We show that S-duality in four dimensional non-supersymmetric abelian gauge theories can be formulated as a canonical transformation in the phase space of the theory. This transformation is the usual interchange between electric and magnetic degrees of freedom. It is shown that in phase space the modular anomaly emerges as the result of integrating out the momenta degrees of freedom. The generalization to d dimensional abelian gauge theories of p-forms is also considered. In the case of non-abelian gauge theories a careful analysis of the constraints implied by the canonical transformation shows that it does not relate Yang-Mills theories with inverted couplings. In fact the dual theory is shown to be of Freedman-Townsend's type, also with tilde ν = − 1 τ , τ = θ 2π + 4πi g 2 .
Sensitivity and insensitivity of galaxy cluster surveys to new physics

We study the implications and limitations of galaxy cluster surveys for constraining models of particle physics and gravity beyond the Standard Model. Flux limited cluster counts probe the history of large scale structure formation in the universe, and as a result provide useful constraints on cosmological parameters. As a result of uncertainties in some aspects of cluster dynamics, cluster surveys are currently more useful for analyzing physics that would affect the formation of structure than physics that would modify the appearance of clusters. As an example we consider the ΛCDM (Λ cold dark matter) cosmology and dimming mechanisms, such as photon–axion mixing.
On Intermediate Subfactors of Goodman-de la Harpe-Jones Subfactors

In this paper we present a conjecture on intermediate subfactors which is a generalization of Wall’s conjecture from the theory of finite groups. Motivated by this conjecture, we determine all intermediate subfactors of Goodman-Harpe-Jones subfactors, and as a result we verify that Goodman-Harpe-Jones subfactors verify our conjecture. Our result also gives a negative answer to a question motivated by a conjecture of Aschbacher-Guralnick.
Time reversal symmetry of generalized quantum measurements with past and future boundary conditions

We expand the time reversal symmetry arguments of quantum mechanics, originally proposed by Wigner in the context of unitary dynamics, to contain situations including generalized measurements for monitored quantum systems. We propose a scheme to derive the time-reversed measurement operators by considering the Schrodinger picture dynamics of a qubit coupled to a measuring device, and show that the time-reversed measurement operators form a Positive Operator Valued Measure (POVM) set. We present three particular examples to illustrate time reversal of measurement operators: (1) the Gaussian spin measurement, (2) a dichotomous POVM for spin, and (3) the measurement of qubit fluorescence. We then propose a general rule to unravel any rank two qubit measurement, and show that the backward dynamics obeys the retrodicted equations of the forward dynamics starting from the time reversed final state. We demonstrate the time reversal invariance of dynamical equations using the example of qubit fluorescence. We also generalize the discussion of a statistical arrow of time for continuous quantum measurements introduced by Dressel et al. (Phys Rev Lett 119:220507, 2017): we show that the backward probabilities can be computed from a process similar to retrodiction from the time-reversed final state, and extend the definition of an arrow of time to ensembles prepared with pre- and post-selections, where we obtain a non-vanishing arrow of time in general. We discuss sufficient conditions for when time’s arrow vanishes and show our method also captures the contributions to time’s arrow due to natural physical processes like relaxation of an atom to its ground state. As a special case, we recover the time reversibility of the weak value as its complex conjugate using our method, and discuss how our conclusions differ from the time-symmetry argument of Aharonov–Bergmann–Lebowitz rule.
Bit threads and holographic entanglement of purification

Generalizing the bit thread formalism, we use convex duality to derive dual flow programs to the bipartite and multipartite holographic entanglement of purification proposals and then prove several inequalities using these constructions. In the multipartite case we find the flows exhibit novel behavior which allows for a constrained flux on the boundary of the homology region. We show this flux can be made distinct from bi-partite terms and reflects the truly multipartite portion of the holographic entanglement of purification.
3D Phase Retrieval at Nano-Scale via Accelerated Wirtinger Flow

Imaging 3D nano-structures at very high resolution is crucial in a variety of scientific fields. However, due to fundamental limitations of light propagation we can only measure the object indirectly via 2D intensity measurements of the 3D specimen through highly nonlinear projection mappings where a variety of information (including phase) is lost. Reconstruction therefore involves inverting highly non-linear and seemingly noninvertible mappings. In this paper, we introduce a novel technique where the 3D object is directly reconstructed from an accurate non-linear propagation model. Furthermore, we characterize the ambiguities of this model and leverage a priori knowledge to mitigate their effect and also significantly reduce the required number of measurements and hence the acquisition time. We demonstrate the performance of our algorithm via numerical experiments aimed at nano-scale reconstruction of 3D integrated circuits. Moreover, we provide rigorous theoretical guarantees for convergence to stationarity.
Sparse Signal Models for Data Augmentation in Deep Learning ATR

Automatic Target Recognition (ATR) algorithms classify a given Synthetic Aperture Radar (SAR) image into one of the known target classes using a set of training images. Recently, learning methods have shown to achieve state-of-the-art classification accuracy if abundant training data is available sampled uniformly over the classes and their poses. In this paper, we consider the problem of improving the generalization performance of learning methods in SAR-ATR when training data is limited. We propose a data augmentation approach using sparse signal models that capitalizes on commonly observed phenomenology of wide-angle synthetic aperture radar (SAR) imagery. Specifically, we exploit the sparsity of the scattering centers in the spatial domain as well as the limited persistence of the scattering coefficients in the azimuthal domain to solve the ill-posed problem of over-parametrized model fitting. Using this fitted model, we synthesize new images at poses not available in training set to augment the training data used by CNN. We validate the performance of the proposed model based data augmentation strategy on subsampled versions of the MSTAR dataset. The experimental results show that for the training data starved region, the proposed method provides a significant gain in the generalization performance of the resulting ATR algorithm.