cssi/SciSciGPT-SciSciNet huggingface dataset:
Topological circuits of inductors and capacitors

Abstract Alternating current (ac) circuits can have electromagnetic edge modes protected by symmetries, analogous to topological band insulators or semimetals. How to make such a topological circuit? This paper illustrates a particular design idea by analyzing a series of topological circuits consisting purely of inductors (L) and capacitors (C) connected to each other by wires to form periodic lattices. All the examples are treated using a unifying approach based on Lagrangians and the dynamical H -matrix. First, the building blocks and permutation wiring are introduced using simple circuits in one dimension, the SSH transmission line and a braided ladder analogous to the ice-tray model also known as the π -flux ladder. Then, more general building blocks (loops and stars) and wiring schemes ( m -shifts) are introduced. The key concepts of emergent pseudo-spin degrees of freedom and synthetic gauge fields are discussed, and the connection to quantum lattice Hamiltonians is clarified. A diagrammatic notation is introduced to simplify the design and presentation of more complicated circuits. These building blocks are then used to construct topological circuits in higher dimensions. The examples include the circuit analog of Haldane’s Chern insulator in two dimensions and quantum Hall insulator in four dimensions featuring finite second Chern numbers. The topological invariants and symmetry protection of the edge modes are discussed based on the H -matrix.
Towards applied theories based on computability logic

Computability logic (CL) is a recently launched program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth that logic has more traditionally been. Formulas in it represent computational problems, "truth" means existence of an algorithmic solution, and proofs encode such solutions. Within the line of research devoted to finding axiomatizations for ever more expressive fragments of CL, the present paper introduces a new deductive system CL12 and proves its soundness and completeness with respect to the semantics of CL. Conservatively extending classical predicate calculus and offering considerable additional expressive and deductive power, CL12 presents a reasonable, computationally meaningful, constructive alternative to classical logic as a basis for applied theories. To obtain a model example of such theories, this paper rebuilds the traditional, classical-logic based Peano arithmetic into a computability-logic-based counterpart. Among the purposes of the present contribution is to provide a starting point for what, as the author wishes to hope, might become a new line of research with a potential of interesting findings?an exploration of the presumably quite unusual metatheory of CL-based arithmetic and other CL-based applied systems. ?
Multiwavelength investigation of pulsar wind nebula da 495 with hawc, veritas, and nustar

Pulsar Wind Nebula (PWN) DA 495 (G65.7+1.2) was detected in TeV gamma-rays by the High Altitude Water Cherenkov Observatory (HAWC) in 2017 (2HWC J1953+294). Follow-up observations by the Very Energetic Radiation Imaging Telescope Array System (VERITAS) confirmed the association between 2HWC J1953+294 and DA 495 and found the TeV emission to be spatially coincident with the radio emission first reported in 1968. The detection of TeV gamma-rays from DA 495, along with past X-ray detection up to 10 keV, prompted high energy X-ray observations as part of the NuSTAR Galactic Legacy Survey. We present the results of these NuSTAR observations, combined with archival Chandra and XMM-Newton observations, and confirm the previous X-ray photon index of $\Gamma_{2-20 \rm\ keV} = 2.0 \pm 0.1$. We find no spectral cutoff up to 20 keV. With the spectral information for DA 495 extended to TeV gamma-rays, we were able to perform analytical modeling to test leptonic and hadronic emission scenarios. The leptonic models can explain the broadband emission, but also imply a diffuse X-ray nebula of similar extent to the radio and TeV nebulae, which cannot be confirmed by our observations. The hadronic models can simultaneously explain the spectrum and the spatial extent in all wavelengths; however, we need a very high magnetic field strength pervading the radio and TeV nebulae and a surprisingly high particle kinetic energy. These requirements deepen the mystery of the physical nature of DA 495. Future observations in radio to infrared bands and spatially resolved $\gamma$-rays can further constrain the physical conditions and radiation mechanisms in DA 495.
Identification of Wearable Devices with Bluetooth

With wearable devices such as smartwatches on the rise in the consumer electronics market, securing these wearables is vital. However, the current security mechanisms only focus on validating the user not the device itself. Indeed, wearables can be (1) unauthorized wearable devices with correct credentials accessing valuable systems and networks, (2) passive insiders or outsider wearable devices, or (3) information-leaking wearables devices. Fingerprinting via machine learning can provide necessary cyber threat intelligence to address all these cyber attacks. In this work, we introduce a wearable fingerprinting technique focusing on Bluetooth classic protocol, which is a common protocol used by the wearables and other IoT devices. Specifically, we propose a non-intrusive wearable device identification framework which utilizes 20 different Machine Learning (ML) algorithms in the training phase of the classification process and selects the best performing algorithm for the testing phase. Furthermore, we evaluate the performance of proposed wearable fingerprinting technique on real wearable devices, including various off-the-shelf smartwatches. Our evaluation demonstrates the feasibility of the proposed technique to provide reliable cyber threat intelligence. Specifically, our detailed accuracy results show on average 98.5 percent, 98.3 percent precision and recall for identifying wearables using the Bluetooth classic protocol.
Two-dimensional photonic crystal slab nanocavities on bulk single-crystal diamond

Color centers in diamond are promising spin qubits for quantum computing and quantum networking. In photon-mediated entanglement distribution schemes, the efficiency of the optical interface ultimately determines the scalability of such systems. Nano-scale optical cavities coupled to emitters constitute a robust spin-photon interface that can increase spontaneous emission rates and photon extraction efficiencies. In this work, we introduce the fabrication of 2D photonic crystal slab nanocavities with high quality factors and cubic wavelength mode volumes—directly in bulk diamond. This planar platform offers scalability and considerably expands the toolkit for classical and quantum nanophotonics in diamond.
Recent results from the ARIANNA neutrino experiment

The ARIANNA experiment is currently taking data in its pilot-phase on the Ross ice-shelf. Fully autonomous stations measure radio signals in the frequency range from 100 MHz to 1 GHz. The seven station hexagonal radio-array (HRA) was completed in December 2014, and augmented by two special purpose stations with unique configurations. In its full extent ARIANNA is targeted at detecting interactions of cosmogenic neutrinos (> 10 16 eV) in the ice-shelf. Downward-pointing antennas installed at the surface will record the radio emission created by neutrino-induced showers in the ice and exploit the fact that the ice-water surface acts as a mirror for radio emission. ARIANNA stations are independent, low-powered, easy to install and equipped with real-time communication via satellite modems. We report on the current status of the HRA, as well as air shower detections that have been made over the past year. Furthermore, we will discuss the search for neutrino emission, future plans of the experiment.
Mapping between long-time molecular and Brownian dynamics

We use computer simulations to test a simple idea for mapping between long-time self diffusivities obtained from molecular and Brownian dynamics. The strategy we explore is motivated by the behavior of fluids comprising particles that interact via inverse-power-law pair potentials, which serve as good reference models for dense atomic or colloidal materials. Based on our simulation data, we present an empirical expression that semi-quantitatively describes the “atomic” to “colloidal” diffusivity mapping for inverse-power-law fluids, but also for model complex fluids with considerably softer (star-polymer, Gaussian-core, or Hertzian) interactions. As we show, the anomalous structural and dynamic properties of these latter ultrasoft systems pose problems for other strategies designed to relate Newtonian and Brownian dynamics of hard-sphere-like particles.
The Dynamics of Internet Traffic: Self-Similarity, Self-Organization, and Complex Phenomena

The Internet is one of the largest and most complex communication and information exchange networks ever created. Therefore, its dynamics and traffic unsurprisingly take on a rich variety of complex dynamics, self-organization, and other phenomena that have been researched for years. This paper is a review of the complex dynamics of Internet traffic. Departing from normal treatises, we will take a view from both the network engineering and physics perspectives showing the strengths and weaknesses as well as insights of both. In addition, many less covered phenomena such as traffic oscillations, BGP storms, and comparisons of the Internet and biological models will be covered.
Design of a Trigger Data Serializer ASIC for the Upgrade of the ATLAS Forward Muon Spectrometer

A small-strip thin gap chamber (sTGC) will be used for both triggering and precision tracking purposes in the upgrade of the ATLAS forward muon spectrometer. Both sTGC pad and strip detectors are read out by a trigger data serializer (TDS) application-specific integrated circuit (ASIC) in the trigger path. This ASIC has two operation modes to prepare trigger data from pad and strip detectors, respectively. The pad mode (pad TDS) collects the firing status for up to 104 pads from one detector layer and transmits the data at 4.8 Gbps to the pad trigger extractor every 25 ns. The pad trigger extractor collects pad-TDS data from eight detector layers and defines a region of interest (ROI) along the path of a muon candidate. Data defining the ROIs is returned to the strip TDS. In the strip mode (strip TDS), the deposited charges from up to 128 strips are buffered and time-stamped, and a trigger matching procedure is performed to read out strips underneath the ROI. The strip-TDS output is also transmitted at 4.8 Gbps to the following field-programmable gate array (FPGA) processing circuits. Details of ASIC design and test results are presented in this paper.
A Disk-based Dynamical Constraint on the Mass of the Young Binary DQ Tau

We present new ALMA observations of CO $J$=2$-$1 line emission from the DQ Tau circumbinary disk. These data are used to tomographically reconstruct the Keplerian disk velocity field in a forward-modeling inference framework, and thereby provide a dynamical constraint on the mass of the DQ Tau binary of $M_\ast = 1.27_{-0.27}^{+0.46} \,M_\odot$. Those results are compared with an updated and improved orbital solution for this double-lined system based on long-term monitoring of its stellar radial velocities. Both of these independent dynamical constraints on the binary mass are in excellent agreement: taken together, they demonstrate that the DQ Tau system mass is $1.21\pm0.26\,M_\odot$ and that the disk and binary orbital planes are aligned within $3^\circ$ (at 3$\sigma$ confidence). The predictions of various theoretical models for pre-main sequence stellar evolution are also consistent with these masses, although more detailed comparisons are difficult due to lingering uncertainties in the photospheric properties of the individual components. DQ Tau is the third nearly equal-mass double-lined spectroscopic binary with a circumbinary disk that has been dynamically "weighed" with these two independent techniques: all show consistent results, validating the overall accuracy of the disk-based approach and demonstrating that it can be robustly applied to large samples of young, single stars as ALMA ramps up to operations at full capacity.
KIC 9832227: Using Vulcan Data to Negate The 2022 Red Nova Merger Prediction

KIC 9832227 is a contact binary whose 11 hr orbital period is rapidly changing. Based on the apparent exponential decay of its period, the two stars were predicted to merge in early 2022 resulting in a rare red nova outburst. Fortunately KIC 832227 was observed in 2003 as part of the NASA Ames pre-Kepler Vulcan Project to search for transiting exoplanets. We find that the Vulcan timing measurement does not agree with the previous exponential decay model. This led us to re-evaluate the other early epoch non-Kepler data sets, the Northern Sky Variability Survey (NSVS) and Wide Angle Search for Planets (WASP) survey. We find that the WASP times are in good agreement with the previous prediction, but the NSVS eclipse time differs by nearly an hour. The very large disagreement of the Vulcan and NSVS eclipse times with an exponentially decaying model forces us to reject the merger hypothesis. Although period variations are common in contact binaries, the physical cause of the period changes in KIC 9832227 remains unexplained; a third star scenario is unlikely. This study shows the data collected by the Vulcan photometer to be extremely valuable for extending the baseline for measurements of variable stars in the Kepler field.
Advancing System Performance with Redundancy: From Biological to Artificial Designs

Redundancy is a fundamental characteristic of many biological processes such as those in the genetic, visual, muscular, and nervous systems, yet its driven mechanism has not been fully comprehended...
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks

Self-supervised pre-training of transformer models has revolutionized NLP applications. Such pre-training with language modeling objectives provides a useful initial point for parameters that generalize well to new tasks with fine-tuning. However, fine-tuning is still data inefficient --- when there are few labeled examples, accuracy can be low. Data efficiency can be improved by optimizing pre-training directly for future fine-tuning with few examples; this can be treated as a meta-learning problem. However, standard meta-learning techniques require many training tasks in order to generalize; unfortunately, finding a diverse set of such supervised tasks is usually difficult. This paper proposes a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. This is achieved using a cloze-style objective, but creating separate multi-class classification tasks by gathering tokens-to-be blanked from among only a handful of vocabulary terms. This yields as many unique meta-training tasks as the number of subsets of vocabulary terms. We meta-train a transformer model on this distribution of tasks using a recent meta-learning framework. On 17 NLP tasks, we show that this meta-training leads to better few-shot generalization than language-model pre-training followed by finetuning. Furthermore, we show how the self-supervised tasks can be combined with supervised tasks for meta-learning, providing substantial accuracy gains over previous supervised meta-learning.
Analysis of perfectly matched layer operators for acoustic scattering on manifolds with quasicylindrical ends

Abstract In this paper we prove stability and exponential convergence of the Perfectly Matched Layer (PML) method for acoustic scattering on manifolds with axial analytic quasicylindrical ends. These manifolds model long-range geometric perturbations (e.g. bending or stretching) of tubular waveguides filled with homogeneous or inhomogeneous media. We construct non-reflective infinite PMLs replacing the metric on a part of the manifold by a non-degenerate complex symmetric tensor field. We prove that the problem with PMLs of finite length is uniquely solvable and solutions to this problem locally approximate scattered solutions with an error that exponentially tends to zero as the length of PMLs tends to infinity.
The runaway quiver

We point out that some recently proposed string theory realizations of dynamical supersymmetry breaking actually do not break supersymmetry in the usual desired sense. Instead, there is a runaway potential, which slides down to a supersymmetric vacuum at infinite expectation values for some fields. The runaway direction is not on a separated branch; rather, it shows up as a``tadpole" everywhere on the moduli space of field expectation values.
Efficient Bayesian Learning in Social Networks with Gaussian Estimators

We consider a group of Bayesian agents who try to estimate a state of the world θ through interaction on a social network. Each agent v initially receives a private measurement of θ: a number S v picked from a Gaussian distribution with mean θ and standard deviation σ. Then, in each discrete time iteration, each reveals its estimate of θ to its neighbors, and, observing its neighbors' actions, updates its belief using Bayes' Law. This process aggregates information efficiently, in the sense that all the agents converge to the belief that they would have, had they access to all the private measurements. We show that this process is computationally efficient, so that each agent's calculation can be easily carried out. We also show that on any graph the process converges after at most 2N · D steps, where N is the number of agents and D is the diameter of the network. Finally, we show that on trees and on distance-transitive graphs the process converges after D steps, and that it preserves privacy, so that agents learn very little about the private signal of most other agents, despite the efficient aggregation of information. Our results extend those in an unpublished manuscript of the first and last authors.
The moving discontinuous Galerkin finite element method with interface condition enforcement for compressible viscous flows

The moving discontinuous Galerkin finite element method with interface condition enforcement (MDG-ICE) is applied to the case of viscous flows. This method uses a weak formulation that separately enforces the conservation law, constitutive law, and the corresponding interface conditions in order to provide the means to detect interfaces or under-resolved flow features. To satisfy the resulting overdetermined weak formulation, the discrete domain geometry is introduced as a variable, so that the method implicitly fits a priori unknown interfaces and moves the grid to resolve sharp, but smooth, gradients, achieving a form of anisotropic curvilinear $r$-adaptivity. This approach avoids introducing low-order errors that arise using shock capturing, artificial dissipation, or limiting. The utility of this approach is demonstrated with its application to a series of test problems culminating with the compressible Navier-Stokes solution to a Mach 5 viscous bow shock for a Reynolds number of $10^{5}$ in two-dimensional space. Time accurate solutions of unsteady problems are obtained via a space-time formulation, in which the unsteady problem is formulated as a higher dimensional steady space-time problem. The method is shown to accurately resolve and transport viscous structures without relying on numerical dissipation for stabilization.
Predictions for Dusty Mass Loss from Asteroids During Close Encounters with Solar Probe Plus

The Solar Probe Plus (SPP) mission will explore the Sun’s corona and innermost solar wind starting in 2018. The spacecraft will also come close to a number of Mercury-crossing asteroids with perihelia less than 0.3 AU. At small heliocentric distances, these objects may begin to lose mass, thus becoming “active asteroids” with comet-like comae or tails. This paper assembles a database of 97 known Mercury-crossing asteroids that may be encountered by SPP, and it presents estimates of their time-dependent visible-light fluxes and mass loss rates. Assuming a similar efficiency of sky background subtraction as was achieved by STEREO , we find that approximately 80 % of these asteroids are bright enough to be observed by the Wide-field Imager for SPP (WISPR). A model of gas/dust mass loss from these asteroids is developed and calibrated against existing observations. This model is used to estimate the visible-light fluxes and spatial extents of spherical comae. Observable dust clouds occur only when the asteroids approach the Sun closer than 0.2 AU. The model predicts that during the primary SPP mission between 2018 and 2025, there should be 113 discrete events (for 24 unique asteroids) during which the modeled comae have angular sizes resolvable by WISPR. The largest of these correspond to asteroids 3200 Phaethon, 137924, 155140, and 289227, all with angular sizes of roughly 15–30 arcminutes. We note that the SPP trajectory may still change, but no matter the details there should still be multiple opportunities for fruitful asteroid observations.
Convex Denoising using Non-Convex Tight Frame Regularization

This letter considers the problem of signal denoising using a sparse tight-frame analysis prior. The ${\ell _1}$ norm has been extensively used as a regularizer to promote sparsity; however, it tends to under-estimate non-zero values of the underlying signal. To more accurately estimate non-zero values, we propose the use of a non-convex regularizer, chosen so as to ensure convexity of the objective function. The convexity of the objective function is ensured by constraining the parameter of the non-convex penalty. We use ADMM to obtain a solution and show how to guarantee that ADMM converges to the global optimum of the objective function. We illustrate the proposed method for 1D and 2D signal denoising.
Towards Understanding Sample Variance in Visually Grounded Language Generation: Evaluations and Observations.

A major challenge in visually grounded language generation is to build robust benchmark datasets and models that can generalize well in real-world settings. To do this, it is critical to ensure that our evaluation protocols are correct, and benchmarks are reliable. In this work, we set forth to design a set of experiments to understand an important but often ignored problem in visually grounded language generation: given that humans have different utilities and visual attention, how will the sample variance in multi-reference datasets affect the models' performance? Empirically, we study several multi-reference datasets and corresponding vision-and-language tasks. We show that it is of paramount importance to report variance in experiments; that human-generated references could vary drastically in different datasets/tasks, revealing the nature of each task; that metric-wise, CIDEr has shown systematically larger variances than others. Our evaluations on reference-per-instance shed light on the design of reliable datasets in the future.
Properties of a future susy universe

In the string landscape picture, the effective potential is characterized by an enormous number of local minima of which only a minuscule fraction are suitable for the evolution of life. In this "multiverse", random transitions are continually made between the various minima with the most likely transitions being to minima of lower vacuum energy. The inflationary era in the very early universe ended with such a transition to our current phase which is described by a broken supersymmetry and a small, positive vacuum energy. However, it is likely that an exactly supersymmetric (susy) phase of zero vacuum energy as in the original superstring theory also exists and that, at some time in the future, there will be a transition to this susy world. In this article we make some preliminary estimates of the consequences of such a transition.
Starburst Driven Galactic Superbubbles Radiating to 10 K

Our three-dimensional hydro-dynamical simulations of starbursts examine the formation of superbubbles over a range of driving luminosities and mass loadings that determine superbubble growth and wind velocity. From this we determine the relationship between the velocity of a galactic wind and the power of the starburst. We find a threshold for the formation of a wind, above which the speed of the wind is not affected by grid resolution or the temperature floor of our radiative cooling. We investigate the effect two different temperature floors in our radiative cooling prescription have on wind kinematics and content. We find that cooling to $10$ K instead of to $10^4$ K increases the mass fraction of cold neutral and hot X-ray gas in the galactic wind while halving that in warm H$\alpha$. Our simulations show the mass of cold gas transported into the lower halo does not depend on the starburst strength. Optically bright filaments form at the edge of merging superbubbles, or where a cold dense cloud has been disrupted by the wind. Filaments formed by merging superbubbles will persist and grow to $>400$ pc in length if anchored to a star forming complex. Filaments embedded in the hot galactic wind contain warm and cold gas that moves $300-1200$ km s$^{-1}$ slower than the surrounding wind, with the coldest gas hardly moving with respect to the galaxy. Warm and cold matter in the galactic wind show asymmetric absorption profiles consistent with observations, with a thin tail up to the wind velocity.
Global Melnikov Theory in Hamiltonian Systems with General Time-Dependent Perturbations

We consider a mechanical system consisting of n-penduli and a d-degree-of-freedom rotator. The phase space of the rotator defines a normally hyperbolic invariant manifold $$\Lambda _0$$ . We apply a time-dependent perturbation, which is not assumed to be either Hamiltonian, or periodic, or quasi-periodic, as we allow for rather general time dependence. The strength of the perturbation is given by a parameter $$\varepsilon \in \mathbb {R}$$ . For all $$|\varepsilon |$$ sufficiently small, the augmented flow—obtained by making the time into a new variable—has a normally hyperbolic locally invariant manifold $$\tilde{\Lambda }_\varepsilon $$ . For $$\varepsilon =0$$ , $$\tilde{\Lambda }_0=\Lambda _0\times \mathbb {R}$$ . We define a Melnikov-type vector, which gives the first-order expansion of the displacement of the stable and unstable manifolds of $$\tilde{\Lambda }_0$$ under the perturbation. We provide an explicit formula for the Melnikov vector in terms of convergent improper integrals of the perturbation along homoclinic orbits of the unperturbed system. We show that if the perturbation satisfies some explicit non-degeneracy conditions, then the stable and unstable manifolds of $$\tilde{\Lambda }_\varepsilon $$ , $$W^\mathrm{s}(\tilde{\Lambda }_\varepsilon )$$ and $$W^\mathrm{u}(\tilde{\Lambda }_\varepsilon )$$ , respectively, intersect along a transverse homoclinic manifold, and, moreover, the splitting of $$W^\mathrm{s}(\tilde{\Lambda }_\varepsilon )$$ and $$W^\mathrm{u}(\tilde{\Lambda }_\varepsilon )$$ can be explicitly computed, up to the first order, in terms of the Melnikov-type vector. This implies that the excursions along some homoclinic trajectories yield a non-trivial increase of order $$O(\varepsilon )$$ in the action variables of the rotator, for all sufficiently small perturbations. The formulas that we obtain are independent of the unperturbed motions in $$\Lambda _0$$ , and give, at the same time, the effects on periodic, quasi-periodic, or general-type orbits. When the perturbation is Hamiltonian, we express the effects of the perturbation, up to the first order, in terms of a Melnikov potential. In addition, if the perturbation is periodic, we obtain that the non-degeneracy conditions on the Melnikov potential are generic.
Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method

We consider the task of solving generic inverse problems, where one wishes to determine the hidden parameters of a natural system that will give rise to a particular set of measurements. Recently many new approaches based upon deep learning have arisen generating impressive results. We conceptualize these models as different schemes for efficiently, but randomly, exploring the space of possible inverse solutions. As a result, the accuracy of each approach should be evaluated as a function of time rather than a single estimated solution, as is often done now. Using this metric, we compare several state-of-the-art inverse modeling approaches on four benchmark tasks: two existing tasks, one simple task for visualization and one new task from metamaterial design. Finally, inspired by our conception of the inverse problem, we explore a solution that uses a deep learning model to approximate the forward model, and then uses backpropagation to search for good inverse solutions. This approach, termed the neural-adjoint, achieves the best performance in many scenarios.
Small-Scale Structure, Missing Galaxies and Gravitational Lensing

The gravitational lensing constraints on the small mass end of the LCDM mass function are discussed. Here a conservative approach is taken where only the most difficult to explain image flux anomalies in strong QSO lenses are emphasized. Numerical simulations are performed to compare predictions for the LCDM small scale mass function with the observed flux ratios. It is found that the cusp caustic lens anomalies and the disagreements between monochromatic flux ratios and simple lens models can be explained without any substructure in the primary lenses' dark matter halos. Extragalactic LCDM halos of mass < 10^9 Msun are enough to naturally explain these anomalies. This does not mean that substructure within the host lens is not contributing. In fact, it could dominate the lensing depending on how much of substructure survives in the centers of galactic halos. Spectroscopic gravitational lensing provides more information on the nature of these substructures. Thus far, the one relevant case in which this technique has been used, observations of Q2237+0305, shows evidence of more small mass halos (~ 10^6 Msun$) than is expected in the LCDM model.
Bound on the Jordan type of a generic nilpotent matrix commuting with a given matrix

It is well known that a nilpotent n×n matrix B is determined up to conjugacy by a partition of n formed by the sizes of the Jordan blocks of B. We call this partition the Jordan type of B. We obtain partial results on the following problem: for any partition P of n describe the type Q(P) of a generic nilpotent matrix commuting with a given nilpotent matrix of type P. A conjectural description for Q(P) was given by P. Oblak and restated by L. Khatami. In this paper we prove “half” of this conjecture by showing that this conjectural type is less than or equal to Q(P) in the dominance order on partitions.
Successive Convexification for 6-DoF Mars Rocket Powered Landing with Free-Final-Time

In this paper, we employ successive convexification to solve the minimum-time 6-DoF rocket powered landing problem. The contribution of this paper is the development and demonstration of a free-final-time problem formulation that can be solved iteratively using a successive convexification framework. This paper is an extension of our previous work on the 3-DoF free-final-time and the 6-DoF fixed-final-time minimum-fuel problems. Herein, the vehicle is modeled as a 6-DoF rigid-body controlled by a single gimbaled rocket engine. The trajectory is subject to a variety of convex and non-convex state- and control-constraints, and aerodynamic effects are assumed negligible. The objective of the problem is to determine the optimal thrust commands that will minimize the time-of-flight while satisfying the aforementioned constraints. Solving this problem quickly and reliably is challenging because (a) it is nonlinear and non-convex, (b) the validity of the solution is heavily dependent on the accuracy of the discretization scheme, and (c) it can be difficult to select a suitable reference trajectory to initialize an iterative solution process. To deal with these issues, our algorithm (a) uses successive convexification to eliminate non-convexities, (b) computes the discrete linear-time-variant system matrices to ensure that the converged solution perfectly satisfies the original nonlinear dynamics, and (c) can be initialized with a simple, dynamically inconsistent reference trajectory. Using the proposed convex formulation and successive convexification framework, we are able to convert the original non-convex problem into a sequence of convex second-order cone programming (SOCP) sub-problems. Through the use of Interior Point Method (IPM) solvers, this sequence can be solved quickly and reliably, thus enabling higher fidelity real-time guidance for rocket powered landings on Mars.
A metabolic coincidence mechanism controls winter photoperiodism in plants

Plants have served as a preeminent study system for photoperiodism because of their propensity to flower in concordance with the seasons. A nearly singular focus on understanding seasonal flowering has been to the detriment of discovering other photoperiod measuring mechanisms that may be necessary for vegetative health. Here we use bioinformatics to identify a group of winter photoperiod-induced genes in Arabidopsis and show that one, PP2-A13, is critical for fitness and survival, exclusively in winter-like photoperiods. We create a real-time photoperiod reporter, using the PP2-A13 promoter driving luciferase, and show that winter photoperiod genes are regulated independent of the canonical CO/FT mechanism for photoperiodic flowering. The reporter then allows us to identify the first genetic and cellular drivers of winter photoperiodism and reveal a mechanism that relies on coincidence between light capture through photosynthesis and rhythmic metabolism. This work demonstrates that plants have distinct photoperiod measuring mechanisms that enact critical biological and developmental processes in different seasons.
THE STELLAR POPULATIONS OF STRIPPED SPIRAL GALAXIES IN THE VIRGO CLUSTER

We present an analysis of the stellar populations of the gas-stripped outer disks of ten Virgo cluster spiral galaxies, utilizing SparsePak integral field spectroscopy on the WIYN 3.5 m telescope and Galaxy Evolution Explorer (GALEX) UV photometry. The galaxies in our sample show evidence for being gas-stripped spiral galaxies, with star formation within a truncation radius and a passive population beyond the truncation radius. We find that all of the galaxies with spatially truncated star formation have outer-disk stellar populations consistent with star formation ending within the last 500 Myr. The synthesis of optical spectroscopy and GALEX observations demonstrate that star formation was relatively constant until the quenching time, after which the galaxies passively evolved. Large starbursts at the time of quenching are excluded for all galaxies, but there is evidence of a modest starburst in at least one galaxy. For approximately half of our galaxies, the timescales derived from our observations are consistent with galaxies being stripped in or near the cluster core, where simple ram-pressure estimates can explain the observed stripping. However, the other half of our sample galaxies were clearly stripped outside the cluster core. Such galaxies provide evidence that the intra-cluster medium is not static and smooth. For three of the most recently stripped galaxies, there are estimates for the stripping timescales from detailed gas-stripping simulations. For all three of these galaxies, our stripping timescales agree with those from the gas-stripping simulations, suggesting that star formation is quenched near the time of peak pressure. While the stripping of star-forming gas in the outer disk creates a passive population in our sample of galaxies, there is still normal star formation in the center of these galaxies. It may be that Virgo is not massive enough to completely strip these spiral galaxies and, in a more dynamically active cluster or a cluster with a higher density intracluster medium, such a process would lead to passive spirals and/or S0s.
Heteroglossia: In-Situ Story Ideation with the Crowd

Ideation is essential for creative writing. Many authors struggle to come up with ideas throughout the writing process, yet modern writing tools fail to provide on-the-spot assistance for writers when they get stuck. This paper introduces Heteroglossia, an add-on for Google Docs that allows writers to elicit story ideas from the online crowd using their text editors. Writers can share snippets of their working drafts and ask the crowd to provide follow-up story ideas based on it. Heteroglossia employs a strategy called "role play", where each worker is assigned a fictional character in a story and asked to brainstorm plot ideas from that character's perspective. Our deployment with two experienced story writers shows that Heteroglossia is easy to use and can generate interesting ideas. Heteroglossia allows us to gain insight into how future technologies can be developed to support ideation in creative writing.
DETECTION OF NEUTRAL PHOSPHORUS IN THE NEAR-ULTRAVIOLET SPECTRA OF LATE-TYPE STARS

We report the detection of several absorption lines of neutral phosphorus (P, Z = 15) in archival near-ultraviolet spectra obtained with the Space Telescope Imaging Spectrograph on board the Hubble Space Telescope. We derive phosphorus abundances or interesting upper limits in 14 late-type stars with metallicities spanning –3.8 –1.0, the [P/Fe] ratio decreases toward the solar value with increasing metallicity, in agreement with previous observational studies. In stars with [Fe/H] <–1.0, ([P/Fe]) = +0.04 ± 0.10, which overlaps with the [P/Fe] ratios found in several high-redshift damped Lyman-α systems. This behavior hints at a primary origin in massive stars.
TWO-DIMENSIONAL CORE-COLLAPSE SUPERNOVA MODELS WITH MULTI-DIMENSIONAL TRANSPORT

We present new two-dimensional (2D) axisymmetric neutrino radiation/hydrodynamic models of core-collapse supernova (CCSN) cores. We use the CASTRO code, which incorporates truly multi-dimensional, multi-group, flux-limited diffusion (MGFLD) neutrino transport, including all relevant terms. Our main motivation for carrying out this study is to compare with recent 2D models produced by other groups who have obtained explosions for some progenitor stars and with recent 2D VULCAN results that did not incorporate terms. We follow the evolution of 12, 15, 20, and 25 solar-mass progenitors to approximately 600 ms after bounce and do not obtain an explosion in any of these models. Though the reason for the qualitative disagreement among the groups engaged in CCSN modeling remains unclear, we speculate that the simplifying ray-by-ray approach employed by all other groups may be compromising their results. We show that ray-by-ray calculations greatly exaggerate the angular and temporal variations of the neutrino fluxes, which we argue are better captured by our multi-dimensional MGFLD approach. On the other hand, our 2D models also make approximations, making it difficult to draw definitive conclusions concerning the root of the differences between groups. We discuss some of the diagnostics often employed in the analyses of CCSN simulations and highlight the intimate relationship between the various explosion conditions that have been proposed. Finally, we explore the ingredients that may be missing in current calculations that may be important in reproducing the properties of the average CCSNe, should the delayed neutrino-heating mechanism be the correct mechanism of explosion.
Fluid flows of mixed regimes in porous media

In porous media, there are three known regimes of fluid flows, namely, pre-Darcy, Darcy, and post-Darcy. Because of their different natures, these are usually treated separately in the literature. To study complex flows when all three regimes may be present in different portions of a same domain, we use a single equation of motion to unify them. Several scenarios and models are then considered for slightly compressible fluids. A nonlinear parabolic equation for the pressure is derived, which is degenerate when the pressure gradient is either small or large. We estimate the pressure and its gradient for all time in terms of initial and boundary data. We also obtain their particular bounds for large time which depend on the asymptotic behavior of the boundary data but not on the initial one. Moreover, the continuous dependence of the solutions on initial and boundary data and the structural stability for the equation are established.
Deeply Uncertain: Comparing Methods of Uncertainty Quantification in Deep Learning Algorithms

We present a comparison of methods for uncertainty quantification (UQ) in deep learning algorithms in the context of a simple physical system. Three of the most common uncertainty quantification methods - Bayesian Neural Networks (BNN), Concrete Dropout (CD), and Deep Ensembles (DE) - are compared to the standard analytic error propagation. We discuss this comparison in terms endemic to both machine learning ("epistemic" and "aleatoric") and the physical sciences ("statistical" and "systematic"). The comparisons are presented in terms of simulated experimental measurements of a single pendulum - a prototypical physical system for studying measurement and analysis techniques. Our results highlight some pitfalls that may occur when using these UQ methods. For example, when the variation of noise in the training set is small, all methods predicted the same relative uncertainty independently of the inputs. This issue is particularly hard to avoid in BNN. On the other hand, when the test set contains samples far from the training distribution, we found that no methods sufficiently increased the uncertainties associated to their predictions. This problem was particularly clear for CD. In light of these results, we make some recommendations for usage and interpretation of UQ methods.
Comparison results for derived Deligne-Mumford stacks

In this short note we write down a comparison between the notion of derived Deligne-Mumford stack in the sense of B. Toen and G. Vezzosi [HAG-II] and the one introduced by J. Lurie in [DAG-V]. It is folklore that the two theories yield essentially the same objects, but it is difficult to locate in the literature a precise result, despite being sometimes useful to be able to switch between the two frameworks.
Enzyme-based logic: OR gate with double-sigmoid filter response.

The first realization of a biomolecular OR gate function with double-sigmoid response (sigmoid in both inputs) is reported. Two chemical inputs activate the enzymatic gate processes, resulting in the output signal: chromogen oxidation, which occurs when either one of the inputs or both are present (corresponding to the OR binary function), and can be optically detected. High-quality gate functioning in handling of sources of noise is enabled by “filtering” involving pH control with an added buffer. The resulting gate response is sigmoid in both inputs when proper system parameters are chosen, and the gate properties are theoretically analyzed within a model devised to evaluate its noise-handling properties.
Characterizing Sparse Connectivity Patterns in Neural Networks

We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training. Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than half percent classification layer connection density, or less than 5 percent overall network connection density. We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers. Based on our sparsifying technique, we introduce the ‘scatter’ metric to characterize the quality of a particular connection pattern. As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.
Shake slice and shake concordant knots

A crucial step in the surgery-theoretic program to classify smooth manifolds is that of representing a middle--dimensional homology class by a smoothly embedded sphere. This step fails even for the simple 4-manifolds obtained from the 4-ball by adding a 2-handle with framing r along some knot K in S^3. An r-shake slice knot is one for which a generator of the second homology of this 4-manifold can be represented by a smoothly embedded 2-sphere. It is not known whether there exist 0-shake slice knots that are not slice. We define a relative notion of shake sliceness of knots, which we call shake concordance, which is easily seen to be a generalization of classical concordance, and we give the first examples of knots that are 0-shake concordant but not concordant; these may be chosen to be topologically slice. Additionally, for each r we completely characterize r-shake slice and r-shake concordant knots in terms of concordance and satellite operators. Our characterization allows us to construct new families of possible r-shake slice knots that are not slice.
Finding almost squares

We study short intervals which contain an ``almost square'', an integer $n$ that can be factored as $n = ab$ with $a$, $b$ close to $\sqrt{n}$. This is related to the problem on distribution of $n^2 \alpha \pmod 1$ and the problem on gaps between sums of two squares.
Effect of Live Chat on Traffic-to-Sales Conversion: Evidence from an Online Marketplace

Over the years, online retailers have increasingly embraced live chat to improve customer service and facilitate transactions. We examine how online sellers’ use of live chat influences their online traffic conversion into sales. Like brick-and-mortar stores, a fundamental task for online stores is to convert traffic into purchases, an important problem in retail service operations management. We argue that live chat can increase online sales conversion by performing the functions of informing and persuading. We explore how the two roles of live chat interact with existing information regarding sellers and their products, such as product descriptions, ratings, price, and reviews, as these types of information also influence consumers’ perceived product quality and help mitigate uncertainties in product fit to their needs. We apply a random-coefficient model in a Bayesian hierarchical framework and test the model using a panel dataset from Taobao, the leading C2C platform in China. Our results suggest that live chat has a positive impact on conversion and that the strength of this positive effect depends on seller and product characteristics. The positive effect is stronger when product information on web pages is less comprehensive (where live chat manifests an informative role), and when the perceived value of the product is higher (where live chat manifests a persuasive role) with either a higher product rating or a lower product price. Our results provide relevant and useful implications for online merchants and platform owners.
Discovery of new, dust-poor B[E] supergiants in the small magellanic cloud

We present the discovery of three new B[e] supergiants (sgB[e] stars) in the Small Magellanic Cloud (SMC). All three stars (R15, R38, and R48) were identified in the course of our Runaways and Isolated O Type Star Spectroscopic Survey of the SMC. The stars show optical spectra that closely resemble those of previously known B[e] stars, presenting numerous low-ionization forbidden and permitted emission lines such as [Fe II] and Fe II. Furthermore, our stars have luminosities of log (L/L ☉) ≥ 4, demonstrating that they are supergiants. However, we find lower infrared excesses and weaker forbidden emission lines than for previously identified B[e] supergiants. Thus our stars appear either to have less material in their circumstellar disks than other sgB[e] stars, or the circumstellar material has lower dust content. We suggest that these may constitute a new subclass of dust-poor sgB[e] stars.