cssi/SciSciGPT-SciSciNet huggingface dataset:
Audio Event Detection using Weakly Labeled Data

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised techniques employing strongly labeled data. However, the labels available for majority of multimedia data are generally weak and do not provide sufficient detail for such methods to be employed. In this paper we propose a framework for learning acoustic event detectors using only weakly labeled data. We first show that audio event detection using weak labels can be formulated as an Multiple Instance Learning problem. We then suggest two frameworks for solving multiple-instance learning, one based on support vector machines, and the other on neural networks. The proposed methods can help in removing the time consuming and expensive process of manually annotating data to facilitate fully supervised learning. Moreover, it can not only detect events in a recording but can also provide temporal locations of events in the recording. This helps in obtaining a complete description of the recording and is notable since temporal information was never known in the first place in weakly labeled data.
Opportunistic scheduling for full-duplex uplink-downlink networks

We study opportunistic scheduling and the sum capacity of cellular networks with a full-duplex multi-antenna base station and a large number of single-antenna half-duplex users. Simultaneous uplink and downlink over the same band results in uplink-to-downlink interference, degrading performance. We present a simple opportunistic joint uplink-downlink scheduling algorithm that exploits multiuser diversity and treats interference as noise. We show that in homogeneous networks, our algorithm achieves the same sum capacity as what would have been achieved if there was no uplink-to-downlink interference, asymptotically in the number of users. The algorithm does not require interference CSI at the base station or uplink users. It is also shown that for a simple class of heterogeneous networks without sufficient channel diversity, it is not possible to achieve the corresponding interference-free system capacity. We discuss the potential for using device-to-device side-channels to overcome this limitation in heterogeneous networks.
Implicit particle filtering for models with partial noise, and an application to geomagnetic data assimilation

Abstract. Implicit particle filtering is a sequential Monte Carlo method for data assimilation, designed to keep the number of particles manageable by focussing attention on regions of large probability. These regions are found by minimizing, for each particle, a scalar function F of the state variables. Some previous implementations of the implicit filter rely on finding the Hessians of these functions. The calculation of the Hessians can be cumbersome if the state dimension is large or if the underlying physics are such that derivatives of F are difficult to calculate, as happens in many geophysical applications, in particular in models with partial noise, i.e. with a singular state covariance matrix. Examples of models with partial noise include models where uncertain dynamic equations are supplemented by conservation laws with zero uncertainty, or with higher order (in time) stochastic partial differential equations (PDE) or with PDEs driven by spatially smooth noise processes. We make the implicit particle filter applicable to such situations by combining gradient descent minimization with random maps and show that the filter is efficient, accurate and reliable because it operates in a subspace of the state space. As an example, we consider a system of nonlinear stochastic PDEs that is of importance in geomagnetic data assimilation.
Electromagnetic polarizabilities and charge radii of the nucleons in the diquark-model

Abstract The diquark model is used tolculate the electromagnetic polarizabilities and charge radii of the nucleons for three different potentials. Making the scalar diquark lower in mass introduces a mixing angle θ between the |56> and |70> states, which allows an improvement in value of all 6 properties. Generalizing the Gamov-Teller matrix and the magnetic moment operator to the diquark model gives constraints on this mixing. We obtain for the Richardson potential θ = 23.2°, α p = 7.9 −0.9 +1.0 × 10 −4 fm 3 , α n = 7.7 −0.6 +0.3 × 10 −4 fm 3 , β p = 5.4 −0.4 +1.6 × 10 −4 fm 3 , β n = 6.7 −0.7 +1.3 × 10 −4 fm 3 , 〈 r 2 〉 p = 0.37 −0.03 +0.02 fm 2 , 〈 r 2 〉 n = −0.07 −0.02 +0.03 fm 2 . Additional poin cloud contributions could improve on all six results.
Improved performance of semiconductor laser tracking frequency gauge

We describe new results from the semiconductor-laser tracking frequency gauge, an instrument that can perform sub-picometer distance measurements and has applications in gravity research and in space-based astronomical instruments proposed for the study of light from extrasolar planets. Compared with previous results, we have improved incremental distance accuracy by a factor of two, to 0.9 pm in 80 s averaging time, and absolute distance accuracy by a factor of 20, to 0.17 μm in 1000 s. After an interruption of operation of a tracking frequency gauge used to control a distance, it is now possible, using a nonresonant measurement interferometer, to restore the distance to picometer accuracy by combining absolute and incremental distance measurements.
Evidence against an association between gamma-ray bursts and Type I supernovae

We use a rigorous method, based on Bayesian inference, for calculating the odds favoring the hypothesis that any particular class of astronomical transients pro- duce gamma-ray bursts over the hypothesis that they do not. We then apply this method to a sample of 83 Type Ia supernovae and a sample of 20 Type Ib-Ic supernovae. We nd overwhelming odds against the hypothesis that all Type Ia supernovae produce gamma-ray bursts, whether at low redshift (10 9 :1) or high-redshift (10 12 :1), and very large odds (6000:1) against the hypothesis that all Type Ib, Ib/c, and Ic supernovae produce observable gamma-ray bursts. We nd large odds (34:1) against the hypothesis that a fraction of Type Ia supernovae produce observable gamma-ray bursts, and moderate odds (6:1) against the hypothesis that a fraction of Type Ib-Ic supernovae pro- duce observable bursts.
An empirical investigation towards efficient multi-domain language model pre-training

Pre-training large language models has become a standard in the natural language processing community. Such models are pre-trained on generic data (e.g. BookCorpus and English Wikipedia) and often fine-tuned on tasks in the same domain. However, in order to achieve state-of-the-art performance on out of domain tasks such as clinical named entity recognition and relation extraction, additional in domain pre-training is required. In practice, staged multi-domain pre-training presents performance deterioration in the form of catastrophic forgetting (CF) when evaluated on a generic benchmark such as GLUE. In this paper we conduct an empirical investigation into known methods to mitigate CF. We find that elastic weight consolidation provides best overall scores yielding only a 0.33% drop in performance across seven generic tasks while remaining competitive in bio-medical tasks. Furthermore, we explore gradient and latent clustering based data selection techniques to improve coverage when using elastic weight consolidation and experience replay methods.
Cyclic Cocycles on Twisted Convolution Algebras

We give a construction of cyclic cocycles on convolution algebras twisted by gerbes over discrete translation groupoids. For proper etale groupoids, Tu and Xu (Adv Math 207(2):455–483, 2006) provide a map between the periodic cyclic cohomology of a gerbe-twisted convolution algebra and twisted cohomology groups which is similar to the construction of Mathai and Stevenson (Adv Math 200(2):303–335, 2006). When the groupoid is not proper, we cannot construct an invariant connection on the gerbe; therefore to study this algebra, we instead develop simplicial techniques to construct a simplicial curvature 3-form representing the class of the gerbe. Then by using a JLO formula we define a morphism from a simplicial complex twisted by this simplicial curvature 3-form to the mixed bicomplex computing the periodic cyclic cohomology of the twisted convolution algebras.
Floquet dynamics of classical and quantum cavity fields

Abstract We show that the time-dependence of electromagnetic field in a periodically modulated cavity can be effectively analyzed using a Floquet map. The map relates the field states separated by one period of the drive; iterative application of the map allows to determine field configuration after arbitrary number of drive periods. For resonant and near-resonant drives, the map has stable and unstable fixed points, which are the loci of infinite energy concentration in the long time limit. The Floquet map method can be applied both to classical and quantum massless field problems, including the dynamical Casimir effect. The stroboscopic time evolution implemented by the map can be interpreted in terms of the wave propagation in a curved space, with the fixed points of the map corresponding to the black hole and white hole horizons. More practically, the map can be used to design protocols for signal compression/decompression, cooling, and sensing.
SPECTRUM OF KINETIC-ALFVÉN TURBULENCE

A numerical study of strong kinetic-Alfven turbulence at scales smaller than the ion gyroscale is presented, and a phenomenological model is proposed that argues that magnetic and density fluctuations are concentrated mostly in two-dimensional structures, which leads to their Fourier energy spectra E(k ){proportional_to}k {sup -8/3} , where k is the wavevector component normal to the strong background magnetic field. The results may provide an explanation for recent observations of magnetic and density fluctuations in the solar wind at sub-proton scales.
Onset of cavitation in the quark-gluon plasma

We study the onset of bubble formation (cavitation) in the quark-gluon plasma as a result of the reduction of the effective pressure from bulk-viscous corrections. By calculating velocity gradients in typical models for quark-gluon plasma evolution in heavy-ion collisions, we obtain results for the critical bulk viscosity above which cavitation occurs. Since present experimental data for heavy-ion collision seems inconsistent with the presence of bubbles above the phase transition temperature of QCD, our results may be interpreted as an upper limit of the bulk viscosity in nature. Our results indicate that bubble formation is consistent with the expectation of hadronisation in low-temperature QCD.
Parameters in Categorified Quantum Groups

In this note we give explicit isomorphisms of 2-categories between various versions of the categorified quantum group associated to a simply-laced Kac-Moody algebra. These isomorphisms are convenient when working with the categorified quantum group. They make it possible to translate results from the $\mathfrak {g}\mathfrak {l}_{n}$ variant of the 2-category to the $\mathfrak {s}\mathfrak {l}_{n}$ variant and transfer results between various conventions in the literature. We also extend isomorphisms of finite type KLR algebras for different choices of parameters to the level of 2-categories.
Generalized Langevin Equations for Systems with Local Interactions

We present a new method to approximate the Mori–Zwanzig (MZ) memory integral in generalized Langevin equations describing the evolution of smooth observables in high-dimensional nonlinear systems with local interactions. Building upon the Faber operator series we recently developed for the orthogonal dynamics propagator, and an exact combinatorial algorithm that allows us to compute memory kernels from first principles, we demonstrate that the proposed method is effective in computing auto-correlation functions, intermediate scattering functions and other important statistical properties of the observable. We also develop a new stochastic process representation of the MZ fluctuation term for systems in statistical equilibrium. Numerical applications are presented for the Fermi–Pasta–Ulam model, and for random wave propagation in homegeneous media.
ALTERNATIVE CONFORMAL QUANTUM MECHANICS

We investigate a one-dimensional quantum mechanical model, which is invariant under translations and dilations but does not respect the conventional conformal invariance. We describe the possibility of modifying the conventional conformal transformation such that a scale invariant theory is also invariant under this new conformal transformation.
Gauging permutation symmetries as a route to non-Abelian fractons

We discuss the procedure for gauging on-site $\mathbb{Z}_2$ global symmetries of three-dimensional lattice Hamiltonians that permute quasi-particles and provide general arguments demonstrating the non-Abelian character of the resultant gauged theories. We then apply this general procedure to lattice models of several well known fracton phases: two copies of the X-Cube model, two copies of Haah's cubic code, and the checkerboard model. Where the former two models possess an on-site $\mathbb{Z}_2$ layer exchange symmetry, that of the latter is generated by the Hadamard gate. For each of these models, upon gauging, we find non-Abelian subdimensional excitations, including non-Abelian fractons, as well as non-Abelian looplike excitations and Abelian fully mobile pointlike excitations. By showing that the looplike excitations braid non-trivially with the subdimensional excitations, we thus discover a novel gapped quantum order in 3D, which we term a "panoptic" fracton order. This points to the existence of parent states in 3D from which both topological quantum field theories and fracton states may descend via quasi-particle condensation. The gauged cubic code model represents the first example of a gapped 3D phase supporting (inextricably) non-Abelian fractons that are created at the corners of fractal operators.
The Impact of a Major Security Event on an Open Source Project: The Case of OpenSSL

Context: The Heartbleed vulnerability brought OpenSSL to international attention in 2014. The almost moribund project was a key security component in public web servers and over a billion mobile devices. This vulnerability led to new investments in OpenSSL. Objective: The goal of this study is to determine how the Heart-bleed vulnerability changed the software evolution of OpenSSL. We study changes in vulnerabilities, code quality, project activity, and software engineering practices. Method: We use a mixed methods approach, collecting multiple types of quantitative data and qualitative data from web sites and an interview with a developer who worked on post-Heartbleed changes. We use regression discontinuity analysis to determine changes in levels and slopes of code and project activity metrics resulting from Heartbleed. Results: The OpenSSL project made tremendous improvements to code quality and security after Heartbleed. By the end of 2016, the number of commits per month had tripled, 91 vulnerabilities were found and fixed, code complexity decreased significantly, and OpenSSL obtained a CII best practices badge, certifying its use of good open source development practices. Conclusions: The OpenSSL project provides a model of how an open source project can adapt and improve after a security event. The evolution of OpenSSL shows that the number of known vulnerabilities is not a useful indicator of project security. A small number of vulnerabilities may simply indicate that a project does not expend much effort to finding vulnerabilities. This study suggests that project activity and CII badge best practices may be better indicators of code quality and security than vulnerability counts.
Theoretical modeling expressions for networked enzymatic signal processing steps as logic gates optimized by filtering

We describe modeling approaches to a ‘network’ of connected enzyme-catalyzed reactions, with added biochemical processes that introduce biochemical filtering steps into the functioning of such a biocatalytic cascade. Theoretical expressions are derived that allow simple, few-parameter modeling of processes concatenated in such cascades, both with and without filtering. The modeling approach captures and explains features identified in earlier studies of enzymatic processes considered as potential ‘network components’ for multi-step information/signal processing systems.
High-Resolution Transmission Spectra of Earth through Geological Time

The next generation of ground- and space-based telescopes will be able to observe rocky Earth-like planets in the near future, transiting their host star. We explore how the transmission spectrum of Earth changed through its geological history. These transmission spectra provide a template for how to characterize an Earth-like exoplanet - from a young prebiotic world to a modern Earth. They also allow us to explore at what point in its evolution a distant observer could identify life on our Pale Blue Dot and other worlds like it. We chose atmosphere models representative of five geological epochs of Earth's history, corresponding to a prebiotic high CO2-world 3.9 billion years ago (Ga), an anoxic world around 3.5 Ga, and 3 epochs through the rise of oxygen from 0.2 percent to present atmospheric levels of 21 percent. Our transmission spectra show atmospheric spectral features, which would show a remote observer that Earth had a biosphere since about 2 billion years ago. These high-resolution transmission spectral database of Earth through geological time from the VIS to the IR is available online and can be used as a tool to optimize our observation strategy, train retrieval methods, and interpret upcoming observations with JWST, the Extremely Large Telescopes and future mission concepts like Origins, HabEx, and LUOVIR.
The volume and Chern-Simons invariant of a representation

We give an efficient simplicial formula for the volume and Chern-Simons invariant of a boundary-parabolic PSL(2,C)-representation of a tame 3-manifold. If the representation is the geometric representation of a hyperbolic 3-manifold, our formula computes the volume and Chern-Simons invariant directly from an ideal triangulation with no use of additional combinatorial topology. In particular, the Chern-Simons invariant is computed just as easily as the volume
Everything we'd like to do with LSST data, but we don't know (yet) how.

The Large Synoptic Survey Telescope (LSST), the next-generation optical imaging survey sited at Cerro Pachon in Chile, will provide an unprecedented database of astronomical measurements. The LSST design, with an 8.4m (6.7m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq. deg. of sky to be covered twice per night, every three to four nights on average, with typical 5-sigma depth for point sources of r=24.5 (AB). With over 800 observations in ugrizy bands over a 10-year period, these data will enable a deep stack reaching r=27.5 (about 5 magnitudes deeper than SDSS) and faint time-domain astronomy. The measured properties of newly discovered and known astrometric and photometric transients will be publicly reported within 60 sec after observation. The vast database of about 30 trillion observations of 40 billion objects will be mined for the unexpected and used for precision experiments in astrophysics. In addition to a brief introduction to LSST, we discuss a number of astro-statistical challenges that need to be overcome to extract maximum information and science results from LSST dataset.
Collective flow without hydrodynamics: simulation results for relativistic ion collisions

Flow signatures in experimental data from relativistic ion collisions are usually interpreted as a fingerprint of the presence of a hydrodynamic phase during the evolution of these systems. In this work, flow signatures arising from event-by-event viscous hydrodynamics are compared to those arising from event-by-event non-interacting particle dynamics (free streaming), both followed by a late-stage hadronic cascade, in \(d + \mathrm{Au}\,\), \(^3\mathrm{He} + \mathrm{Au}\,\)at \(\sqrt{s}=200\) GeV and \(p + \mathrm{Pb}\,\)collisions at \(\sqrt{s}=5\) TeV, respectively. For comparison, also \(\mathrm{Pb} + \mathrm{Pb}\,\)collisions at \(\sqrt{s}=2.76\) TeV are simulated. It is found that non-hydrodynamic evolution can give rise to radial flow equal to or larger than hydrodynamics with \(\eta /s=0.08\) in all simulated collision systems. In light-on-heavy-ion collisions, free streaming gives rise to triangular and quadrupolar flow comparable to or larger than that from hydrodynamics, but it generally leads to considerably smaller elliptic flow. As expected, free streaming leads to considerably less elliptic, triangular and quadrupolar flow than hydrodynamics in nucleus–nucleus collisions, such as event-by-event \(\mathrm{Pb} + \mathrm{Pb}\,\)collisions at \(\sqrt{s}=2.76\) TeV.
Synthetic gauge fields and homodyne transmission in Jaynes?Cummings lattices

Many-body physics is traditionally concerned with systems of interacting massive particles. Recent studies of effective interactions between photons, induced in the circuit quantum electrodynamics (QED) architecture by coupling the microwave field to superconducting qubits, have paved the way for photon-based many-body physics. We derive the magnitude and intrinsic signs of photon hopping amplitudes in such circuit QED arrays. For a finite, ring-shaped Jaynes–Cummings lattice exposed to a synthetic gauge field, we show that degeneracies in the single-excitation spectrum emerge, which can give rise to strong correlations for the interacting system with multiple excitations. We calculate the homodyne transmission for such a device, explain the generalization of vacuum Rabi splittings known for the single-site Jaynes–Cummings model and identify fingerprints of interactions beyond the linear response regime.
Lessons from the Onset of a Common Envelope Episode: the Remarkable M31 2015 Luminous Red Nova Outburst

This paper investigates the recent stellar merger transient M31LRN 2015 in the Andromeda galaxy. We analyze published optical photometry and spectroscopy along with a Hubble Space Telescope detection of the color and magnitude of the pre-outburst source. The transient outburst is consistent with dynamically driven ejecta at the onset of a common envelope episode, which eventually leads to the complete merger of a binary system. The light curve appears to contain two components: first $\sim10^{-2} M_\odot$ of fast ejecta driven by shocks at the onset of common envelope, and later, $\sim0.3 M_\odot$ of further ejecta as the secondary becomes more deeply engulfed within the primary. Just prior to merger, we find that the primary star is a $3-5.5 M_\odot$ sub-giant branch primary star with radius of $30-40R_\odot$. Its position in the color-magnitude diagram shows that it is growing in radius, consistent with a picture where it engulfs its companion. By matching the properties of the primary star to the transient outburst, we show that the optical transient lasts less than ten orbits of the original binary, which had a pre-merger period of $\sim 10$ days. We consider the possible orbital dynamics leading up to the merger, and argue that if the system merged due to the Darwin tidal instability it implies a lower mass main sequence companion of $0.1-0.6M_\odot$. This analysis represents a promising step toward a more detailed understanding of flows in common envelope episodes through direct observational constraints.
Geometric phase magnetometry using a solid-state spin.

A key challenge of magnetometry lies in the simultaneous optimization of magnetic field sensitivity and maximum field range. In interferometry-based magnetometry, a quantum two-level system acquires a dynamic phase in response to an applied magnetic field. However, due to the 2π periodicity of the phase, increasing the coherent interrogation time to improve sensitivity reduces field range. Here we introduce a route towards both large magnetic field range and high sensitivity via measurements of the geometric phase acquired by a quantum two-level system. We experimentally demonstrate geometric-phase magnetometry using the electronic spin associated with the nitrogen vacancy (NV) color center in diamond. Our approach enables unwrapping of the 2π phase ambiguity, enhancing field range by 400 times. We also find additional sensitivity improvement in the nonadiabatic regime, and study how geometric-phase decoherence depends on adiabaticity. Our results show that the geometric phase can be a versatile tool for quantum sensing applications. When performing interferometry-based magnetometry, there is generally a trade-off between sensitivity and range. Here, instead, the authors demonstrate a geometric-phase-based protocol which allows a 400-fold enhancement in static magnetic field range with a single NV-centre without reducing sensitivity.
Noncommutative Inverse Scattering Method for the Kontsevich System

We formulate an analog of Inverse Scattering Method for integrable systems on noncommutative associative algebras. In particular, we define Hamilton flows, Casimir elements and noncommutative analog of the Lax matrix. The noncommutative Lax element generates infinite family of commuting Hamilton flows on an associative algebra. The proposed approach to integrable systems on associative algebras satisfies certain universal property, in particular, it incorporates both classical and quantum integrable systems as well as provides a basis for further generalization. We motivate our definition by explicit construction of noncommutative analog of Lax matrix for a system of differential equations on associative algebra recently proposed by Kontsevich. First, we present these equations in the Hamilton form by defining a bracket of Loday type on the group algebra of the free group with two generators. To make the definition more constructive, we utilize (with certain generalizations) the Van den Bergh approach to Loday brackets via double Poisson brackets. We show that there exists an infinite family of commuting flows generated by the noncommutative Lax element.
The stubborn roots of metabolic cycles

Efforts to catalogue the structure of metabolic networks have generated highly detailed, genome-scale atlases of biochemical reactions in the cell. Unfortunately, these atlases fall short of capturing the kinetic details of metabolic reactions, instead offering only topological information from which to make predictions. As a result, studies frequently consider the extent to which the topological structure of a metabolic network determines its dynamic behaviour, irrespective of kinetic details. Here, we study a class of metabolic networks known as non-autocatalytic metabolic cycles, and analytically prove an open conjecture regarding the stability of their steady states. Importantly, our results are invariant to the choice of kinetic parameters, rate laws, equilibrium fluxes and metabolite concentrations. Unexpectedly, our proof exposes an elementary but apparently open problem of locating the roots of a sum of two polynomials S = P + Q, when the roots of the summand polynomials P and Q are known. We derive two new results named the Stubborn Roots Theorems, which provide sufficient conditions under which the roots of S remain qualitatively identical to the roots of P. Our study illustrates how complementary feedback, from classical fields such as dynamical systems to biology and vice versa, can expose fundamental and potentially overlooked questions.
COMPLEXITY RESULTS FOR CR MAPPINGS BETWEEN SPHERES

Using elementary number theory, we prove several results about the complexity of CR mappings between spheres. It is known that CR mappings between spheres, invariant under finite groups, lead to sharp bounds for degree estimates on real polynomials constant on a hyperplane. We show here that there are infinitely many degrees for which the uniqueness of sharp examples fails. The proof uses a Pell equation. We then sharpen our results and obtain various congruences guaranteeing nonuniqueness. We also show that a gap phenomenon for proper mappings between balls does not occur beyond a certain target dimension. This proof uses the solution of the postage stamp problem.
A New Kinetic Equation for Compton Scattering

A kinetic equation for Compton scattering is given that differs from the Kompaneets equation in several significant ways. By using an inverse differential operator, this equation allows treatment of problems for which the radiation field varies rapidly on the scale of the width of the Compton kernel. This inverse operator method describes, among other effects, the thermal Doppler broadening of spectral lines and continuum edges and automatically incorporates the process of Compton heating/cooling. It is well adapted for inclusion into a numerical iterative solution of radiative transfer problems. The equivalent kernel of the new method is shown to be a positive function and with reasonable accuracy near the initial frequency, unlike the Kompaneets kernel, which is singular and not wholly positive. It is shown that iterations of the inverse operator kernel can be easily calculated numerically, and a simple summation formula over these iterations is derived that can be efficiently used to compute Comptonized spectra. It is shown that the new method can be used for initial-value and other problems with no more numerical effort than the Kompaneets equation and that it more correctly describes the solution over times comparable to the mean scattering time.
Strongly coupled striped superconductor with large modulation

Using gauge/gravity duality, we analytically calculate properties of a strongly coupled striped superconductor, with the charge density wave sourced by a modulated chemical potential, in the large modulation wavenumber Q limit. In the absence of a homogeneous term in the chemical potential, we show that the critical temperature scales as a negative power of Q for scaling dimensions $ \Delta < \frac{3}{2} $ , whereas for $ \Delta > \frac{3}{2} $ , there is no phase transition above a certain critical value of Q. The condensate is found to scale as a positive power of Q such that the gap is proportional to Q. We discuss how these results change if a homogeneous term is added to the chemical potential. We compare our analytic results with numerical calculations whenever the latter are available and find good agreement.
Explicitly broken supersymmetry with exactly massless moduli

The AdS/CFT correspondence is applied to an analogue of the little hierarchy problem in three-dimensional supersymmetric theories. The bulk is governed by a super-gravity theory in which a U(1) × U(1) R-symmetry is gauged by Chern-Simons fields. The bulk theory is deformed by a boundary term quadratic in the gauge fields. It breaks SUSY completely and sources an exactly marginal operator in the dual CFT. SUSY breaking is communicated by gauge interactions to bulk scalar fields and their spinor superpartners. The bulk-to-boundary propagator of the Chern-Simons fields is a total derivative with respect to the bulk coordinates. Integration by parts and the Ward identity permit evaluation of SUSY breaking effects to all orders in the strength of the deformation. The R-charges of scalars and spinors differ so large SUSY breaking mass shifts are generated. Masses of R-neutral particles such as scalar moduli are not shifted to any order in the deformation strength, despite the fact that they may couple to R-charged fields running in loops. We also obtain a universal deformation formula for correlation functions under an exactly marginal deformation by a product of holomorphic and anti-holomorphic U(1) currents.
Asymptotic Stability of the Relativistic Boltzmann Equation for the Soft Potentials

In this paper it is shown that unique solutions to the relativistic Boltzmann equation exist for all time and decay with any polynomial rate towards their steady state relativistic Maxwellian provided that the initial data starts out sufficiently close in \({L^\infty_\ell}\). If the initial data are continuous then so is the corresponding solution. We work in the case of a spatially periodic box. Conditions on the collision kernel are generic in the sense of Dudynski and Ekiel-Jezewska (Commun Math Phys 115(4):607–629, 1985); this resolves the open question of global existence for the soft potentials.
Double-soft graviton amplitudes and the extended BMS charge algebra

We discuss how scattering amplitudes in 4d Minkowski spacetime which involve multiple soft gravitons realize the algebra of BMS charges on the null boundary. In particular, we show how the commutator of two such charges is realized by the antisymmetrized consecutive soft limit of the double soft amplitude. The commutator is expected to be robust even in the presence of quantum corrections, and the associated Lie algebra has an extension, which breaks the BMS symmetry if the BMS algebra is taken to include the Virasoro algebra of local superrotations. We discuss the implications of this structure for the existence of a 2d CFT dual description for 4d scattering amplitudes.
Modeling Fashion Influence From Photos

The evolution of clothing styles and their migration across the world is intriguing, yet difficult to describe quantitatively. We propose to discover and quantify fashion influences from catalog and social media photos. We explore fashion influence along two channels: geolocation and fashion brands. We introduce an approach that detects which of these entities influence which other entities in terms of propagating their styles. We then leverage the discovered influence patterns to inform a novel forecasting model that predicts the future popularity of any given style within any given city or brand. To demonstrate our idea, we leverage public large-scale datasets of 7.7M Instagram photos from 44 major world cities (where styles are worn with variable frequency) as well as 41K Amazon product photos (where styles are purchased with variable frequency). Our model learns directly from the image data how styles move between locations and how certain brands affect each other's designs in a predictable way. The discovered influence relationships reveal how both cities and brands exert and receive fashion influence for an array of visual styles inferred from the images. Furthermore, the proposed forecasting model achieves state-of-the-art results for challenging style forecasting tasks. Our results indicate the advantage of grounding visual style evolution both spatially and temporally, and for the first time, they quantify the propagation of inter-brand and inter-city influences. Project page: https://www.cs.utexas.edu/~ziad/influence_from_photos.html
Joint distribution of Busemann functions in the exactly solvable corner growth model

The 1+1 dimensional corner growth model with exponential weights is a centrally important exactly solvable model in the Kardar-Parisi-Zhang class of statistical mechanical models. While significant progress has been made on the fluctuations of the growing random shape, understanding of the optimal paths, or geodesics, is less developed. The Busemann function is a useful analytical tool for studying geodesics. This paper describes the joint distribution of the Busemann functions, simultaneously in all directions of growth. As applications of this description we derive a marked point process representation for the Busemann function across a single lattice edge and calculate some marginal distributions of Busemann functions and semi-infinite geodesics.
The Quantum Alternating Operator Ansatz on Maximum k-Vertex Cover

The Quantum Alternating Operator Ansatz is a generalization of the Quantum Approximate Optimization Algorithm (QAOA) designed for finding approximate solutions to combinatorial optimization problems with hard constraints. In this paper, we study Maximum k-Vertex Cover under this ansatz due to its modest complexity, while still being more complex than the well studied problems of Max-Cut and Max E3-LIN2. Our approach includes (i) a performance comparison between easy-to-prepare classical states and Dicke states as starting states, (ii) a performance comparison between two XY-Hamiltonian mixing operators: the ring mixer and the complete graph mixer, (iii) an analysis of the distribution of solutions via Monte Carlo sampling, and (iv) the exploration of efficient angle selection strategies. Our results are: (i) Dicke states improve performance compared to easy-to-prepare classical states, (ii) an upper bound on the simulation of the complete graph mixer, (iii) the complete graph mixer improves performance relative to the ring mixer, (iv) numerical results indicating the standard deviation of the distribution of solutions decreases exponentially in $p$ (the number of rounds in the algorithm), requiring an exponential number of random samples to find a better solution in the next round, and (v) a correlation of angle parameters which exhibit high quality solutions that behave similarly to a discretized version of the Quantum Adiabatic Algorithm.
Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation

In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted third party. The concept of context-awareness is incorporated in LIP, which can be viewed as exploiting of data prior (both in privatizing and post-processing) to enhance data utility. We present an optimization framework to minimize the mean square error of data aggregation while protecting the privacy of each user's input data or a correlated latent variable by satisfying LIP constraints. Then, we study optimal mechanisms under different scenarios considering the prior uncertainty and correlation with a latent variable. Three types of mechanisms are studied in this paper, including randomized response (RR), unary encoding (UE), and local hashing (LH), and we derive closed-form solutions for the optimal perturbation parameters that are prior-dependent. We compare LIP based mechanisms with those based on LDP, and theoretically show that the former achieves enhanced utility. We then study two applications: (weighted) summation and histogram estimation, and show how proposed mechanisms can be applied to each application. Finally, we validate our analysis by simulations using both synthetic and real-world data.
Extending LOUPE for K-Space Under-Sampling Pattern Optimization in Multi-coil MRI

The previously established LOUPE (Learning-based Optimization of the Under-sampling Pattern) framework for optimizing the k-space sampling pattern in MRI was extended in three folds: firstly, fully sampled multi-coil k-space data from the scanner, rather than simulated k-space data from magnitude MR images in LOUPE, was retrospectively under-sampled to optimize the under-sampling pattern of in-vivo k-space data; secondly, binary stochastic k-space sampling, rather than approximate stochastic k-space sampling of LOUPE during training, was applied together with a straight-through (ST) estimator to estimate the gradient of the threshold operation in a neural network; thirdly, modified unrolled optimization network, rather than modified U-Net in LOUPE, was used as the reconstruction network in order to reconstruct multi-coil data properly and reduce the dependency on training data. Experimental results show that when dealing with the in-vivo k-space data, unrolled optimization network with binary under-sampling block and ST estimator had better reconstruction performance compared to the ones with either U-Net reconstruction network or approximate sampling pattern optimization network, and once trained, the learned optimal sampling pattern worked better than the hand-crafted variable density sampling pattern when deployed with other conventional reconstruction methods.
Concept whitening for interpretable image recognition

What does a neural network encode about a concept as we traverse through the layers? Interpretability in machine learning is undoubtedly important, but the calculations of neural networks are very challenging to understand. Attempts to see inside their hidden layers can be misleading, unusable or rely on the latent space to possess properties that it may not have. Here, rather than attempting to analyse a neural network post hoc, we introduce a mechanism, called concept whitening (CW), to alter a given layer of the network to allow us to better understand the computation leading up to that layer. When a concept whitening module is added to a convolutional neural network, the latent space is whitened (that is, decorrelated and normalized) and the axes of the latent space are aligned with known concepts of interest. By experiment, we show that CW can provide us with a much clearer understanding of how the network gradually learns concepts over layers. CW is an alternative to a batch normalization layer in that it normalizes, and also decorrelates (whitens), the latent space. CW can be used in any layer of the network without hurting predictive performance. There is much interest in ‘explainable’ AI, but most efforts concern post hoc methods. Instead, a neural network can be made inherently interpretable, with an approach that involves making human-understandable concepts (aeroplane, bed, lamp and so on) align along the axes of its latent space.
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-Mixing

Abstract This paper presents a physics-informed machine learning (ML) framework to construct reduced-order models (ROMs) for reactive-transport quantities of interest (QoIs) based on high-fidelity numerical simulations. QoIs include species decay, product yield, and degree of mixing. The ROMs for QoIs are applied to quantify and understand how the chemical species evolve over time. First, high-resolution datasets for constructing ROMs are generated by solving anisotropic reaction–diffusion equations using a non-negative finite element formulation for different input parameters. The reactive-mixing model input parameters are: time-scale associated with flipping of velocity, spatial-scale controlling small/large vortex structures of velocity, perturbation parameter of the vortex-based velocity, anisotropic dispersion contrast, and molecular diffusion. Second, random forests, F-test, and mutual information criterion are used to evaluate the importance of model inputs/features with respect to QoIs. We observed that anisotropic dispersion contrast is the most important feature and time-scale associated with flipping of velocity is the least important feature. Third, Support Vector Machines (SVM) and Support Vector Regression (SVR) are used to construct ROMs based on the model inputs. The constructed SVR-ROMs are then used to predict scaling of QoIs. We also present estimates and inequalities on the QoIs, which inform that the species decay, mix, and produce in an exponential fashion. These inequalities also inform that a radial basis function is the most suitable kernel for the SVM/SVR models for QoIs. It is observed that R 2 -score for SVR-ROMs on unseen data is greater than 0.9, implying that the SVR-ROMs are able to predict the reaction–diffusion system state reasonably well. Finally, in terms of the computational cost, the proposed SVM-ROMs are O ( 1 0 7 ) times faster than running a high-fidelity finite element simulation for evaluating QoIs. This makes the proposed ML-based ROMs attractive for reactive-transport sensing and real-time monitoring applications as they are significantly faster yet reasonably accurate.
Totally p-adic numbers of degree 3

The height of an algebraic number $\alpha$ is a measure of how arithmetically complicated $\alpha$ is. We say $\alpha$ is totally $p$-adic if the minimal polynomial of $\alpha$ splits completely over the field $\mathbb{Q}_p$ of $p$-adic numbers. In this paper, we investigate what can be said about the smallest nonzero height of a degree $3$ totally $p$-adic number. In particular, we provide an algorithm to determine, given a prime $p$, the smallest height of a degree $3$ totally $p$-adic number.
Switch‐1 Instability at the Active Site Decouples ATP Hydrolysis from Force Generation in Myosin II

Myosin active site elements (i.e., switch-1) bind both ATP and a divalent metal to coordinate ATP hydrolysis. ATP hydrolysis at the active site is linked via allosteric communication to the actin polymer binding site and lever arm movement, thus coupling the free energy of ATP hydrolysis to force generation. How active site motifs are functionally linked to actin binding and the power stroke is still poorly understood. We hypothesize that destabilizing switch-1 movement at the active site will negatively affect the tight coupling of the ATPase catalytic cycle to force production. Using a metal-switch system, we tested the effect of interfering with switch-1 coordination of the divalent metal cofactor on force generation. We found that while ATPase activity increased, motility was inhibited. Our results demonstrate that a single atom change that affects the switch-1 interaction with the divalent metal directly affects actin binding and productive force generation. Even slight modification of the switch-1 divalent metal coordination can decouple ATP hydrolysis from motility. Switch-1 movement is therefore critical for both structural communication with the actin binding site, as well as coupling the energy of ATP hydrolysis to force generation.
Power Grid State Estimation under General Cyber-Physical Attacks

Effective defense against cyber-physical attacks in power grid requires the capability of accurate damage assessment within the attacked area. While some solutions have been proposed to recover the phase angles and the breaker status of lines within the attacked area, existing solutions made the limiting assumption that the grid stays connected after the attack. To fill this gap, we study the problem of recovering the phase angles and the breaker status under a general cyber-physical attack that may partition the grid into islands. To this end, we (i) show that the existing solutions and recovery conditions still hold if the post-attack power injections in the attacked area are known, and (ii) propose a linear programming-based algorithm that can perfectly recover the breaker status under certain conditions even if the post-attack power injections are unknown. Our numerical evaluations based on the Polish power grid demonstrate that the pro-posed algorithm is highly accurate in localizing failed lines even though the conditions for perfect recovery can be hard to satisfy.