cssi/SciSciGPT-SciSciNet huggingface dataset:
Towards accurate and high-speed spiking neuromorphic systems with data quantization-aware deep networks

Deep Neural Networks (DNNs) have gained immense success in cognitive applications and greatly pushed today’s artificial intelligence forward. The biggest challenge in executing DNNs is their extremely data-extensive computations. The computing efficiency in speed and energy is constrained when traditional computing platforms are employed in such computational hungry executions. Spiking neuromorphic computing (SNC) has been widely investigated in deep networks implementation own to their high efficiency in computation and communication. However, weights and signals of DNNs are required to be quantized when deploying the DNNs on the SNC, which results in unacceptable accuracy loss. Previous works mainly focus on weights discretize while inter-layer signals are mainly neglected. In this work, we propose to represent DNNs with fixed integer inter-layer signals and fixed-point weights while holding good accuracy. We implement the proposed DNNs on the memristor-based SNC system as a deployment example. With 4-bit data representation, our results show that the accuracy loss can be controlled within 0.02% (2.3%) on MNIST (CIFAR-10). Compared with the 8-bit dynamic fixed-point DNNs, our system can achieve more than 9.8× speedup, 89.1% energy saving, and 30% area saving.
Two Local Volume Dwarf Galaxies Discovered in 21?cm Emission: Pisces A and B

We report the discovery of two dwarf galaxies, Pisces A and B, from a blind 21 cm H I search. These were the only two galaxies found via optical imaging and spectroscopy of 22 H I clouds identified in the GALFA-H I survey as dwarf galaxy candidates. They have properties consistent with being in the Local Volume (<10 Mpc), and one has resolved stellar populations such that it may be on the outer edge of the Local Group (~1 Mpc from M31). While the distance uncertainty makes interpretation ambiguous, these may be among the faintest star-forming galaxies known. Additionally, rough estimates comparing these galaxies to ΛCDM dark matter simulations suggest consistency in number density, implying that the dark matter halos likely to host these galaxies are primarily H I-rich. The galaxies may thus be indicative of a large population of dwarfs at the limit of detectability that are comparable to the faint satellites of the Local Group. Because they are outside the influence of a large dark matter halo to alter their evolution, these galaxies can provide critical anchors to dwarf galaxy formation models.
Environmental Dependence of the Kennicutt?Schmidt Relation in Galaxies

We present a detailed description of a phenomenological H2 formation model and local star formation prescription based on the density of molecular (rather than total) gas. Such an approach allows us to avoid the arbitrary density and temperature thresholds typically used in star formation recipes in galaxy formation simulations. We present results of the model based on realistic cosmological simulations of high-z galaxy formation for a grid of numerical models with varied dust-to-gas ratios and interstellar far-UV (FUV) fluxes. Our results show that both the atomic-to-molecular transition on small, tens-of-parsec scales and the Kennicutt-Schmidt (K-S) relation on large, kiloparsec scales are sensitive to the dust-to-gas ratio and the FUV flux. The atomic-to-molecular transition as a function of gas density or column density has a large scatter but is rather sharp and shifts to higher densities with decreasing dust-to-gas ratio and/or increasing FUV flux. Consequently, star formation is concentrated to higher gas surface density regions, resulting in steeper slope and lower amplitude of the K-S relation at a given ΣH, in less dusty and/or higher FUV flux environments. We parameterize the dependences observed in our simulations in convenient fitting formulae, which can be used to model the dependence of the K-S relation on the dust-to-gas ratio and FUV flux in semi-analytic models and in cosmological simulations that do not include radiative transfer and H2 formation. Finally, we show that ionized gas can contribute a significant fraction of the total gas surface density in environments typical for high-redshift galaxies.
Collider Phenomenology of a Gluino Continuum

Continuum supersymmetry is a class of models in which the supersymmetric partners together with part of the standard model come from a conformal sector, broken in the IR near the TeV scale. Such models not only open new doors for addressing the problems of the standard model, but also have unique signatures at hadron colliders, which might explain why we have not yet seen any superpartners at the LHC. Here we use gauge- gravity duality to model the conformal sector, generate collider simulations, and finally analyze continuum gluino signatures at the LHC. Due to the increase in the number of jets produced the bounds are weaker than for the minimal supersymmetric standard model with the same gluino mass threshold.
Resonant Excitation of Disk Oscillations in Deformed Disks. V. Effects of Dissipative Process

It is suggested that a set of positive- and negative-energy oscillations can be resonantly excited in the inner region of deformed (warped or eccentric) relativistic disks. In this paper we examine how a dissipative process affects this wave excitation. The results show that when the resonant condition in frequency is roughly satisfied, and thus the oscillations are excited, the introduction of a dissipative process works so as to decrease the growth rate of the oscillations. When the frequency difference between the two oscillations is larger than a certain amount that is required by the resonant condition, however, the oscillations are excited by introducing a dissipative process. This excitation by the dissipative process can be understood as a special example of a double-diffusive instability.
Understanding Lorentz violation with Rashba interaction

Rashba spin–orbit interaction is a well-studied effect in condensed matter physics and has important applications in spintronics. The standard model extension (SME) includes a CPT-even term with the coefficient Hμν, which leads to the Rashba interaction term. From the limit available on the coefficient Hμν in the SME we derive a limit on the Rashba coupling constant for Lorentz violation. In condensed matter physics the Rashba term is understood as resulting from an asymmetry in the confining potential at the interface of two different types of semiconductors. Based on this interpretation we suggest that a possible way of inducing the Hμν term in the SME is with an asymmetry in the potential that confines us to three spatial dimensions.
DYNAMICS OF TURBULENT CONVECTION AND CONVECTIVE OVERSHOOT IN A MODERATE-MASS STAR

We present results of realistic three-dimensional (3D) radiative hydrodynamic simulations of the outer layers of a moderate-mass star (1.47 M ⊙), including the full convection zone, the overshoot region, and the top layers of the radiative zone. The simulation results show that the surface granulation has a broad range of scales, from 2 to 12 Mm, and that large granules are organized in well-defined clusters, consisting of several granules. Comparison of the mean structure profiles from 3D simulations with the corresponding one-dimensional (1D) standard stellar model shows an increase of the stellar radius by ~800 km, as well as significant changes in the thermodynamic structure and turbulent properties of the ionization zones. Convective downdrafts in the intergranular lanes between granulation clusters reach speeds of more than 20 km s−1, penetrate through the whole convection zone, hit the radiative zone, and form an 8 Mm thick overshoot layer. Contrary to semi-empirical overshooting models, our results show that the 3D dynamic overshoot region consists of two layers: a nearly adiabatic extension of the convection zone and a deeper layer of enhanced subadiabatic stratification. This layer is formed because of heating caused by the braking of the overshooting convective plumes. This effect has to be taken into account in stellar modeling and the interpretation of asteroseismology data. In particular, we demonstrate that the deviations of the mean structure of the 3D model from the 1D standard model of the same mass and composition are qualitatively similar to the deviations for the Sun found by helioseismology.
ELECTRON ELECTRIC DIPOLE MOMENT EXPERIMENT WITH SLOW ATOMS

Discovering an electron electric dipole moment (e-EDM) would uncover new physics requiring an extension of the Standard Model. e-EDMs, large enough to be discovered by new experiments are now common predictions in extensions of the Standard Model, including extensions that describe baryogenesis, dark matter, and neutrino mass. A cesium slow-atom e-EDM experiment (which is similar to an atomic clock) can improve the sensitivity to the e-EDM. And, as with an atomic clock, it could be more sensitive in microgravity than on Earth. As a first step an Earth-based demonstration Cs fountain e-EDM experiment has been carried out at LBNL.
(t, i, f)-Neutrosophic Structures & I-Neutrosophic Structures (Revisited)

This paper is an improvement of our paper “(t, i, f)-Neutrosophic Structures” [1], where we introduced for the first time a new type of structures, called (t, i, f)-Neutrosophic Structures, presented from a neutrosophic logic perspective, and we showed particular cases of such structures in geometry and in algebra. In any field of knowledge, each structure is composed from two parts: space, and a set of axioms (or laws) acting (governing) on it. If the space, or at least one of its axioms (laws), has some indeterminacy of the form (t, i, f) ≠ (1, 0, 0), that structure is a (t, i, f)-Neutrosophic Structure. The (t, i, f)-Neutrosophic Structures [based on the components t = truth, i = numerical indeterminacy, f = falsehood] are different from the Neutrosophic Algebraic Structures [based on neutrosophic numbers of the form a + bI, where I = literal indeterminacy and In = I], that we rename as I-Neutrosophic Algebraic Structures (meaning algebraic structures based on indeterminacy “I” only). But we can combine both and obtain the (t, i, f)-I-Neutrosophic Algebraic Structures, i.e. algebraic structures based on neutrosophic numbers of the form a+bI, but also having indeterminacy of the form (t, i, f) ≠ (1, 0, 0) related to the structure space (elements which only partially belong to space, or elements we know nothing if they belong to space or not) or indeterminacy of the form (t, i, f) ≠ (1, 0, 0) related to at least one axiom (or law) acting on the structure space. Then we extend them to Refined (t, i, f)- Refined I-Neutrosophic Algebra-ic Structures.
Dynamic Deep Multi-modal Fusion for Image Privacy Prediction

With millions of images that are shared online on social networking sites, effective methods for image privacy prediction are highly needed. In this paper, we propose an approach for fusing object, scene context, and image tags modalities derived from convolutional neural networks for accurately predicting the privacy of images shared online. Specifically, our approach identifies the set of most competent modalities on the fly, according to each new target image whose privacy has to be predicted. The approach considers three stages to predict the privacy of a target image, wherein we first identify the neighborhood images that are visually similar and/or have similar sensitive content as the target image. Then, we estimate the competence of the modalities based on the neighborhood images. Finally, we fuse the decisions of the most competent modalities and predict the privacy label for the target image. Experimental results show that our approach predicts the sensitive (or private) content more accurately than the models trained on individual modalities (object, scene, and tags) and prior privacy prediction works. Also, our approach outperforms strong baselines, that train meta-classifiers to obtain an optimal combination of modalities.
Self-dual intervals in the Bruhat order

Bjorner and Ekedahl (Ann Math (2) 170(2):799–817, 2009) prove that general intervals [e, w] in Bruhat order are “top-heavy”, with at least as many elements in the i-th corank as the i-th rank. Well-known results of Carrell (in: Algebraic groups and their generalizations: classical methods (University Park, PA, 1991), volume 56 of proceedings of symposium on pure mathematics, pp 53–61. American Mathematical Society, Providence, RI, 1994) and of Lakshmibai and Sandhya (Proc Indian Acad Sci Math Sci 100(1):45–52, 1990) give the equality case: [e, w] is rank-symmetric if and only if the permutation w avoids the patterns 3412 and 4231 and these are exactly those w such that the Schubert variety $$X_w$$ is smooth. In this paper we study the finer structure of rank-symmetric intervals [e, w], beyond their rank functions. In particular, we show that these intervals are still “top-heavy” if one counts cover relations between different ranks. The equality case in this setting occurs when [e, w] is self-dual as a poset; we characterize these w by pattern avoidance and in several other ways.
The Detection of Inside-Out Disk Growth in M33

We present resolved stellar photometry of four fields along the major axis of the M33 disk from images taken with the Advanced Camera for Surveys aboard the Hubble Space Telescope. The photometry provides a detailed census of the red clump in all fields and reaches the ancient main sequence in the outermost field. Through detailed modeling of the color-magnitude diagrams, we find that the percentage of the stellar mass formed prior to z = 1 changes from 71 ± 9% in the innermost field to 16 ± 6% in the outermost field. The disk shows a clear trend of increasing scale length with time, evolving from rs = 1.0 ± 0.1 kpc 10 Gyr ago to rs = 1.8 ± 0.1 at times more recent than 5 Gyr ago, in agreement with analytical predictions for disk growth. Beyond the disk truncation radius, however, the stellar density profile steepens with time and the age gradient reverses, in agreement with recent simulations. The late and slow growth of the stellar disk may be due in part to the low mass of M33.
The electromagnetic Christodoulou memory effect and its application to neutron star binary mergers

Gravitational waves are predicted by the general theory of relativity. It has been shownthatgravitationalwaveshaveanonlinearmemory,displacingtestmasses permanently. This is called the Christodoulou memory. We proved that the electromagneticfieldcontributesathighestordertothenonlinearmemoryeffect of gravitational waves, enlarging the permanent displacement of test masses. In experiments like LISA or LIGO which measure distances of test masses, the Christodoulou memory will manifest itself as a permanent displacement of these objects. It has been suggested to detect the Christodoulou memory effect using radio telescopes investigating small changes in pulsar’s pulse arrival times. The latter experiments are based on present-day technology and measure changes in frequency. In the present paper, we study the electromagnetic Christodoulou memory effect and compute it for binary neutron star mergers. These are typical sources of gravitational radiation. During these processes, not only mass and momenta are radiated away in form of gravitational waves, but also very strong magnetic fields are produced and radiated away. Moreover, a large portion of the energy is carried away by neutrinos. We give constraints on the conditions, where the energy transported by electromagnetic radiation is of similar or slightly higher order than the energy radiated in gravitational waves or in form of neutrinos. We find that for coalescing neutron stars, large magnetic fields magnify the Christodoulou memory as long as the gaseous environment is sufficiently rarefied. Thus the observed effect on test masses of a laser interferometer gravitational wave detector will be enlarged by the contribution of the electromagnetic field. Therefore, the present results are important for the planned experiments. Looking at the null asymptotics of spacetimes, which are solutions of the Einstein‐Maxwell equations, we derive the electromagnetic Christodoulou memory effect. We obtain an exact solution of the full nonlinear problem, no approximations were used. Moreover, our results allow to answer astrophysical questions, as the knowledge about the
Efficient ab initio calculations of electron-defect scattering and defect-limited carrier mobility

The interactions between electrons and defects govern charge transport at low temperature and in materials with high doping or disorder. Such electron-defect (e-d) interactions are typically treated with simplified empirical models that neglect the electronic and atomistic structure of the material. Here, the authors develop an efficient method to compute e-d interactions from first principles, and show calculations of elastic e-d scattering and defect-limited mobility in silicon. Their study reveals that e-d scattering depends strongly on carrier energy and defect type, contrary to conventional wisdom. Their new approach sets the stage for accurate calculations of e-d interactions in complex materials.
The entropy efficiency of point-push mapping classes on the punctured disk

We study the maximal entropy per unit generator of point-push mapping classes on the punctured disk. Our work is motivated by fluid mixing by rods in a planar domain. If a single rod moves among N fixed obstacles, the resulting fluid diffeomorphism is in the point-push mapping class associated with the loop in 1.D 2 f N pointsg/ traversed by the single stirrer. The collection of motions where each stirrer goes around a single obstacle generate the group of point-push mapping classes, and the entropy efficiency with respect to these generators gives a topological measure of the mixing per unit energy expenditure of the mapping class. We give lower and upper bounds for Eff.N/, the maximal efficiency in the presence of N obstacles, and prove that Eff.N/! log.3/ as N!1. For the lower bound we compute the entropy efficiency of a specific point-push protocol, HSPN , which we conjecture achieves the maximum. The entropy computation uses the action on chains in a Z‐covering space of the punctured disk which is designed for point-push protocols. For the upper bound we estimate the exponential growth rate of the action of the point-push mapping classes on the fundamental group of the punctured disk using a collection of incidence matrices and then computing the generalized spectral radius of the collection. 37E30
Automatic Differentiation in ROOT

In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to evaluate the derivative of a function specified by a computer program. AD exploits the fact that every computer program, no matter how complicated, executes a sequence of elementary arithmetic operations (addition, subtraction, multiplication, division, etc.), elementary functions (exp, log, sin, cos, etc.) and control flow statements. AD takes source code of a function as input and produces source code of the derived function. By applying the chain rule repeatedly to these operations, derivatives of arbitrary order can be computed automatically, accurately to working precision, and using at most a small constant factor more arithmetic operations than the original program. This paper presents AD techniques available in ROOT, supported by Cling, to produce derivatives of arbitrary C/C++ functions through implementing source code transformation and employing the chain rule of differential calculus in both forward mode and reverse mode. We explain its current integration for gradient computation in TFormula. We demonstrate the correctness and performance improvements in ROOT’s fitting algorithms.
Numerical Aspects of Bubble Nucleation

Bubble nucleation has been studied on lattices using phenomenological Langevin equations. Recently there have been theoretical motivations for using these equations. These studies also conclude that the simple Langevin description requires some modification. We study bubble nucleation on a lattice and determine effects of the modified Langevin equations.
Parisi Formula, Disorder Chaos and Fluctuation for the Ground State Energy in the Spherical Mixed p-Spin Models

We show that the limiting ground state energy of the spherical mixed p-spin model can be identified as the infimum of certain variational problem. This complements the well-known Parisi formula for the limiting free energy in the spherical model. As an application, we obtain explicit formulas for the limiting ground state energy in the replica symmetry, one level of replica symmetry breaking and full replica symmetry breaking phases at zero temperature. In addition, our approach leads to new results on disorder chaos in spherical mixed even p-spin models. In particular, we prove that when there is no external field, the location of the ground state energy is chaotic under small perturbations of the disorder. We also establish that in the spherical mixed even p-spin model, the ground state energy superconcentrates in the absence of external field, while it obeys a central limit theorem if the external field is present.
The Sun’s position in the sky

We express the position of the Sun in the sky as a function of time and the observer?s geographic coordinates. Our method is based on applying rotation matrices to vectors describing points on the celestial sphere. We also derive direct expressions, as functions of date of the year and geographic latitude, for the duration of daylight, the maximum and minimum altitudes of the Sun, and the cardinal directions to sunrise and sunset. We discuss how to account for the eccentricity of the Earth?s orbit, the precessions of the equinoxes and the perihelion, the size of the solar disc and atmospheric refraction. We illustrate these results by computing the dates of ?Manhattanhenge? (when sunset aligns with the east?west streets on the main traffic grid for Manhattan, in New York City), by plotting the altitude of the Sun over representative cities as a function of time, and by showing plots (?analemmas?) for the position of the Sun in the sky at a given hour of the day.
GALAXY REDSHIFTS FROM DISCRETE OPTIMIZATION OF CORRELATION FUNCTIONS

We propose a new method of constraining the redshifts of individual extragalactic sources based on celestial coordinates and their ensemble statistics. Techniques from integer linear programming (ILP) are utilized to optimize simultaneously for the angular two-point cross- and autocorrelation functions. Our novel formalism introduced here not only transforms the otherwise hopelessly expensive, brute-force combinatorial search into a linear system with integer constraints but also is readily implementable in off-the-shelf solvers. We adopt Gurobi, a commercial optimization solver, and use Python to build the cost function dynamically. The preliminary results on simulated data show potential for future applications to sky surveys by complementing and enhancing photometric redshift estimators. Our approach is the first application of ILP to astronomical analysis.
Mining Brain Networks Using Multiple Side Views for Neurological Disorder Identification

Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information is available along with the graph data. For example, for neurological disorder identification, in addition to the brain networks derived from neuroimaging data, hundreds of clinical, immunologic, serologic and cognitive measures may also be documented for each subject. These measures compose multiple side views encoding a tremendous amount of supplemental information for diagnostic purposes, yet are often ignored. In this paper, we study the problem of discriminative subgraph selection using multiple side views and propose a novel solution to find an optimal set of subgraph features for graph classification by exploring a plurality of side views. We derive a feature evaluation criterion, named gSide, to estimate the usefulness of subgraph patterns based upon side views. Then we develop a branch-and-bound algorithm, called gMSV, to efficiently search for optimal subgraph features by integrating the subgraph mining process and the procedure of discriminative feature selection. Empirical studies on graph classification tasks for neurological disorders using brain networks demonstrate that subgraph patterns selected by the multi-side-view guided subgraph selection approach can effectively boost graph classification performances and are relevant to disease diagnosis.
A spectral method for elliptic equations: the Dirichlet problem

Let Ω be an open, simply connected, and bounded region in ℝd, d ≥ 2, and assume its boundary \(\partial\Omega\) is smooth. Consider solving an elliptic partial differential equation Lu = f over Ω with zero Dirichlet boundary values. The problem is converted to an equivalent elliptic problem over the unit ball B; and then a spectral Galerkin method is used to create a convergent sequence of multivariate polynomials un of degree ≤ n that is convergent to u. The transformation from Ω to B requires a special analytical calculation for its implementation. With sufficiently smooth problem parameters, the method is shown to be rapidly convergent. For \(u\in C^{\infty}( \overline{\Omega})\) and assuming \(\partial\Omega\) is a C ∞ boundary, the convergence of \(\left\Vert u-u_{n}\right\Vert _{H^{1}}\) to zero is faster than any power of 1/n. Numerical examples in ℝ2 and ℝ3 show experimentally an exponential rate of convergence.
Quantum groups and generalized circular elements

We show that with respect to the Haar state, the joint distributions of the generators of Van Daele and Wang's free orthogonal quantum groups are modeled by free families of generalized circular elements and semicircular elements in the large (quantum) dimension limit. We also show that this class of quantum groups acts naturally as distributional symmetries of almost-periodic free Araki-Woods factors.
Bayesian Learning Without Recall

We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents’ beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independent and identically distributed private signals as well as the actions of their neighboring agents at each time. Successive applications of Bayes rule to the entire history of past observations lead to forebodingly complex inferences: due to lack of knowledge about the global network structure, and unavailability of private observations, as well as third party interactions preceding every decision. Such difficulties make Bayesian updating of beliefs an implausible mechanism for social learning. To address these complexities, we consider a Bayesian without Recall model of inference. On the one hand, this model provides a tractable framework for analyzing the behavior of rational agents in social networks. On the other hand, this model also provides a behavioral foundation for the variety of non-Bayesian update rules in the literature. We present the implications of various choices for the structure of the action space and utility functions for such agents and investigate the properties of learning, convergence, and consensus in special cases.
Ensemble Theory for Stealthy Hyperuniform Disordered Ground States

Some materials exhibit ground states that are disordered, even in the zero-temperature limit. Researchers derive theoretical relations of thermodynamic and structural properties to describe these unexpected states.
Control of Stochastic and Induced Switching in Biophysical Networks.

Many realistic networks, including biological ones, are driven by noise, which can generate sudden changes in system behavior. Using a scalable algorithm for controlling the response to noise in complex biological systems, researchers identify gene targets for new cancer therapies.
Informed Group-Sparse Representation for Singing Voice Separation

Singing voice separation attempts to separate the vocal and instrumental parts of a music recording, which is a fundamental problem in music information retrieval. Recent work on singing voice separation has shown that the low-rank representation and informed separation approaches are both able to improve separation quality. However, low-rank optimizations are computationally inefficient due to the use of singular value decompositions. Therefore, in this letter, we propose a new linear-time algorithm called informed group-sparse representation, and use it to separate the vocals from music using pitch annotations as side information. Experimental results on the iKala dataset confirm the efficacy of our approach, suggesting that the music accompaniment follows a group-sparse structure given a pretrained instrumental dictionary. We also show how our work can be easily extended to accommodate multiple dictionaries using the DSD100 dataset.
Contact homology and virtual fundamental cycles

We give a construction of contact homology in the sense of Eliashberg–Givental– Hofer. Specifically, we construct coherent virtual fundamental cycles on the relevant compactified moduli spaces of pseudo-holomorphic curves. The aim of this work is to provide a rigorous construction of contact homology, an invariant of contact manifolds and exact symplectic cobordisms due to Eliashberg–Givental–Hofer [Eli98, EGH00]. The contact homology of (Y,ξ) is defined in terms of pseudo-holomorphic curve “counts” (in the sense of Gromov [Gro85]) in the symplectization R × Y . Unfortunately, the moduli spaces of such curves often suffer from a severe lack of transversality, and for this reason, it has, until now, remained an important open problem to define the relevant curve counts in full generality. We will use the framework developed in [Par15] to construct coherent virtual fundamental cycles on the moduli spaces in question, thus giving rise to the desired curve counts. Our methods are quite general, and should apply equally well to many other moduli spaces of interest. Previously, a number of important special cases of contact homology (and closely related invariants) have been constructed rigorously using generic and/or automatic transversality techniques. Cylindrical contact homology of some three-manifolds was constructed by Bao–
Diffusion K-means clustering on manifolds: Provable exact recovery via semidefinite relaxations

Abstract We introduce the diffusion K-means clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion K-means constructs a random walk on the similarity graph with vertices as data points randomly sampled on the manifolds and edges as similarities given by a kernel that captures the local geometry of manifolds. The diffusion K-means is a multi-scale clustering tool that is suitable for data with non-linear and non-Euclidean geometric features in mixed dimensions. Given the number of clusters, we propose a polynomial-time convex relaxation algorithm via the semidefinite programming (SDP) to solve the diffusion K-means. In addition, we also propose a nuclear norm regularized SDP that is adaptive to the number of clusters. In both cases, we show that exact recovery of the SDPs for diffusion K-means can be achieved under suitable between-cluster separability and within-cluster connectedness of the submanifolds, which together quantify the hardness of the manifold clustering problem. We further propose the localized diffusion K-means by using the local adaptive bandwidth estimated from the nearest neighbors. We show that exact recovery of the localized diffusion K-means is fully adaptive to the local probability density and geometric structures of the underlying submanifolds.
WFIRST PLANET MASSES FROM MICROLENS PARALLAX

I present a method using only a few ground-based observations of magnified microlensing events to routinely measure the parallaxes of WFIRST events if WFIRST is in an L2 orbit. This could be achieved for all events with A max > 30 using target-of-opportunity observations of select WFIRST events, or with a complementary, ground-based survey of the WFIRST field, which can push beyond this magnification limit. When combined with a measurement of the angular size of the Einstein ring, which is almost always measured in planetary events, these parallax measurements will routinely give measurements of the lens masses and hence the absolute masses of the planets. They can also lead to mass measurements for dark, isolated objects such as brown dwarfs, free-floating planets, and stellar remnants if the size of the Einstein ring is measured.
Mapping Population Synthesis Event Rates on Model Parameters. II. Convergence and Accuracy of Multidimensional Fits

Binary population synthesis calculations and associated predictions, especially event rates, are known to depend on a significant number of input model parameters with different degrees of sensitivity. At the same time, for systems with relatively low formation rates, such simulations are heavily computationally demanding, and therefore the needed explorations of the high-dimensional parameter space require major, often prohibitive, computational resources. In the present study, to better understand several key event rates involving binary evolution and binaries with two compact objects in Milky Way-like galaxies and to provide ways of reducing the computational costs of complete parameter space explorations, (1) we perform a methodical parameter study of the StarTrack population synthesis code, and (2) we develop a formalism and methodology for the derivation of multidimensional fits for event rates. We significantly generalize our earlier study, and we focus on ways of thoroughly assessing the accuracy of the fits. We anticipate that the efficient tools developed here can be applied in lieu of large-scale population calculations and will facilitate the exploration of the dependence of rate predictions on a wide range of binary evolution parameters. Such explorations can then allow one to derive the constraints on these parameters, given empirical rate constraints and accounting for fitting errors. Here we describe in detail the principles and practice behind constructing these fits, estimating their accuracy, and comparing them with observations in a manner that accounts for their errors.
Realization and properties of biochemical-computing biocatalytic XOR gate based on enzyme inhibition by a substrate.

We consider a realization of the XOR logic gate in a process biocatalyzed by an enzyme which can be inhibited by a substrate when the latter is inputted at large enough concentrations. A model is developed for describing such systems in an approach suitable for evaluation of the analog noise amplification properties of the gate. The obtained data are fitted for gate quality evaluation within the developed model, and we discuss aspects of devising XOR gates for functioning in “biocomputing” systems utilizing biomolecules for information processing.
Parametric Instance Classification for Unsupervised Visual Feature Learning

This paper presents parametric instance classification (PIC) for unsupervised visual feature learning. Unlike the state-of-the-art approaches which do instance discrimination in a dual-branch non-parametric fashion, PIC directly performs a one-branch parametric instance classification, revealing a simple framework similar to supervised classification and without the need to address the information leakage issue. We show that the simple PIC framework can be as effective as the state-of-the-art approaches, i.e. SimCLR and MoCo v2, by adapting several common component settings used in the state-of-the-art approaches. We also propose two novel techniques to further improve effectiveness and practicality of PIC: 1) a sliding-window data scheduler, instead of the previous epoch-based data scheduler, which addresses the extremely infrequent instance visiting issue in PIC and improves the effectiveness; 2) a negative sampling and weight update correction approach to reduce the training time and GPU memory consumption, which also enables application of PIC to almost unlimited training images. We hope that the PIC framework can serve as a simple baseline to facilitate future study.
Stability of underwater periodic locomotion

Most aquatic vertebrates swim by lateral flapping of their bodies and caudal fins. While much effort has been devoted to understanding the flapping kinematics and its influence on the swimming efficiency, little is known about the stability (or lack of) of periodic swimming. It is believed that stability limits maneuverability and body designs/flapping motions that are adapted for stable swimming are not suitable for high maneuverability and vice versa. In this paper, we consider a simplified model of a planar elliptic body undergoing prescribed periodic heaving and pitching in potential flow. We show that periodic locomotion can be achieved due to the resulting hydrodynamic forces, and its value depends on several parameters including the aspect ratio of the body, the amplitudes and phases of the prescribed flapping.We obtain closedform solutions for the locomotion and efficiency for small flapping amplitudes, and numerical results for finite flapping amplitudes. This efficiency analysis results in optimal parameter values that are in agreement with values reported for some carangiform fish. We then study the stability of the (finite amplitude flapping) periodic locomotion using Floquet theory. We find that stability depends nonlinearly on all parameters. Interesting trends of switching between stable and unstable motions emerge and evolve as we continuously vary the parameter values. This suggests that, for live organisms that control their flapping motion, maneuverability and stability need not be thought of as disjoint properties, rather the organism may manipulate its motion in favor of one or the other depending on the task at hand.
It's not a Non-Issue: Negation as a Source of Error in Machine Translation.

As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60%. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.
SUSY splits, but then returns

We study the phenomenon of accidental or “emergent” supersymmetry within gauge theory and connect it to the scenarios of Split Supersymmetry and Higgs compositeness. Combining these elements leads to a significant refinement and extension of the proposal of Partial Supersymmetry, in which supersymmetry is broken at very high energies but with a remnant surviving to the weak scale. The Hierarchy Problem is then solved by a non-trivial partnership between supersymmetry and compositeness, giving a promising approach for reconciling Higgs naturalness with the wealth of precision experimental data. We discuss aspects of this scenario from the AdS/CFT dual viewpoint of higher-dimensional warped compactification. It is argued that string theory constructions with high scale supersymmetry breaking which realize warped/composite solutions to the Hierarchy Problem may well be accompanied by some or all of the features described. The central phenomenological considerations and expectations are discussed, with more detailed modelling within warped effective field theory reserved for future work.
Confronting Models of Massive Star Evolution and Explosions with Remnant Mass Measurements

NSF [DGE-1143953, PHY-1404311]; John Simon Guggenheim Memorial Foundation; NASA [NNX16AC56G]
Stellar Velocity Dispersion and Black Hole Mass in the Blazar Markarian 501

The recently discovered correlation between black hole mass and stellar velocity dispersion provides a new method to determine the masses of black holes in active galaxies. We have obtained optical spectra of Mrk 501, a nearby γ-ray blazar with emission extending to TeV energies. The stellar velocity dispersion of the host galaxy, measured from the calcium triplet lines in a 2'' × 37 aperture, is 372 ± 18 km s-1. If Mrk 501 follows the MBH-σ* correlation defined for local galaxies, then its central black hole has a mass of (0.9-3.4) × 109 M☉. This is significantly larger than some previous estimates for the central mass in Mrk 501 that were based on models for its nonthermal emission. The host galaxy luminosity implies a black hole of ~6 × 108 M☉, but this is not in severe conflict with the mass derived from σ* because the MBH-Lbulge correlation has a large intrinsic scatter. Using the emission-line luminosity to estimate the bolometric luminosity of the central engine, we find that Mrk 501 radiates at an extremely sub-Eddington level of L/LEdd ≈ 10-4. Further applications of the MBH-σ* relation to radio-loud active galactic nuclei may be useful for interpreting unified models and understanding the relationship between radio galaxies and BL Lac objects.
An inverse problem formulation for parameter estimation of a reaction–diffusion model of low grade gliomas

We present a numerical scheme for solving a parameter estimation problem for a model of low-grade glioma growth. Our goal is to estimate the spatial distribution of tumor concentration, as well as the magnitude of anisotropic tumor diffusion. We use a constrained optimization formulation with a reaction–diffusion model that results in a system of nonlinear partial differential equations. In our formulation, we estimate the parameters using partially observed, noisy tumor concentration data at two different time instances, along with white matter fiber directions derived from diffusion tensor imaging. The optimization problem is solved with a Gauss–Newton reduced space algorithm. We present the formulation and outline the numerical algorithms for solving the resulting equations. We test the method using a synthetic dataset and compute the reconstruction error for different noise levels and detection thresholds for monofocal and multifocal test cases.
Latent Space Approaches to Community Detection in Dynamic Networks

Embedding dyadic data into a latent space has long been a popular approach to modeling networks of all kinds. While clustering has been done using this approach for static networks, this paper gives two methods of community detection within dynamic network data, building upon the distance and projection models previously proposed in the literature. Our proposed approaches capture the time-varying aspect of the data, can model directed or undirected edges, inherently incorporate transitivity and account for each actor’s individual propensity to form edges. We provide Bayesian estimation algorithms, and apply these methods to a ranked dynamic friendship network and world export/import data.
A Broader Study of Cross-Domain Few-Shot Learning

Recent progress on few-shot learning largely relies on annotated data for meta-learning: base classes sampled from the same domain as the novel classes. However, in many applications, collecting data for meta-learning is infeasible or impossible. This leads to the cross-domain few-shot learning problem, where there is a large shift between base and novel class domains. While investigations of the cross-domain few-shot scenario exist, these works are limited to natural images that still contain a high degree of visual similarity. No work yet exists that examines few-shot learning across different imaging methods seen in real world scenarios, such as aerial and medical imaging. In this paper, we propose the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, consisting of image data from a diverse assortment of image acquisition methods. This includes natural images, such as crop disease images, but additionally those that present with an increasing dissimilarity to natural images, such as satellite images, dermatology images, and radiology images. Extensive experiments on the proposed benchmark are performed to evaluate state-of-art meta-learning approaches, transfer learning approaches, and newer methods for cross-domain few-shot learning. The results demonstrate that state-of-art meta-learning methods are surprisingly outperformed by earlier meta-learning approaches, and all meta-learning methods underperform in relation to simple fine-tuning by 12.8% average accuracy. In some cases, meta-learning even underperforms networks with random weights. Performance gains previously observed with methods specialized for cross-domain few-shot learning vanish in this more challenging benchmark. Finally, accuracy of all methods tend to correlate with dataset similarity to natural images, verifying the value of the benchmark to better represent the diversity of data seen in practice and guiding future research. Code for the experiments in this work can be found at https://github.com/IBM/cdfsl-benchmark.
Geomagnetic response to solar activity.

The relationship between solar activity and geomagnetic variations is discussed in the light of spacecraft data obtained during the last decade. The effects of centers of solar activity responsible for producing geomagnetic activity on earth are believed to be transmitted through the solar wind, and there is usually a delay of two or three days before the onset of magnetic activity. Attempts to make a one-to-one correspondence between specific solar events and specific magnetic storms, however, are usually unsuccessful, because of the complex and indirect processes linking the two phenomena. Normally, only statistical tendencies can be shown.
Poisson Boundaries of Lamplighter Groups: Proof of the Kaimanovich-Vershik Conjecture

We answer positively a question of Kaimanovich and Vershik from 1979, showing that the final configuration of lamps for simple random walk on the lamplighter group over ${\Bbb Z}^d$ ($d \ge 3$) is the Poisson boundary. For $d \ge 5$, this had been shown earlier by Erschler (2011). We extend this to walks of more general types on more general groups.
Data-Driven Power Electronic Converter Modeling for Low Inertia Power System Dynamic Studies

A significant amount of converter-based generation is being integrated into the bulk electric power grid to fulfill the future electric demand through renewable energy sources, such as wind and photovoltaic. The dynamics of converter systems in the overall stability of the power system can no longer be neglected as in the past. Numerous efforts have been made in the literature to derive detailed dynamic models, but using detailed models becomes complicated and computationally prohibitive in large system level studies. In this paper, we use a data-driven, black-box approach to model the dynamics of a power electronic converter. System identification tools are used to identify the dynamic models, while a power amplifier controlled by a real-time digital simulator is used to perturb and control the converter. A set of linear dynamic models for the converter are derived, which can be employed for system level studies of converter-dominated electric grids.