cssi/SciSciGPT-SciSciNet huggingface dataset:
ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK.

Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking, matrix completion, among others. While a large variety of bounds are available for average errors between empirical and population statistics of eigenvectors, few results are tight for entrywise analyses, which are critical for a number of problems such as community detection. This paper investigates entrywise behaviors of eigenvectors for a large class of random matrices whose expectations are low rank, which helps settle the conjecture in Abbe, Bandeira and Hall (2014) that the spectral algorithm achieves exact recovery in the stochastic block model without any trimming or cleaning steps. The key is a first-order approximation of eigenvectors under the $\ell _{\infty }$ norm: \begin{equation*}u_{k}\approx \frac{Au_{k}^{*}}{\lambda _{k}^{*}},\end{equation*} where $\{u_{k}\}$ and $\{u_{k}^{*}\}$ are eigenvectors of a random matrix $A$ and its expectation $\mathbb{E}A$, respectively. The fact that the approximation is both tight and linear in $A$ facilitates sharp comparisons between $u_{k}$ and $u_{k}^{*}$. In particular, it allows for comparing the signs of $u_{k}$ and $u_{k}^{*}$ even if $\|u_{k}-u_{k}^{*}\|_{\infty }$ is large. The results are further extended to perturbations of eigenspaces, yielding new $\ell _{\infty }$-type bounds for synchronization ($\mathbb{Z}_{2}$-spiked Wigner model) and noisy matrix completion.
MMS SITL Ground Loop: Automating the Burst Data Selection Process.

Global-scale energy flow throughout Earth’s magnetosphere is catalyzed by processes that occur at Earth’s magnetopause (MP) in the electron diffusion region (EDR) of magnetic reconnection. Until the launch of the Magnetospheric Multiscale (MMS) mission, only rare, fortuitous circumstances permitted a glimpse of the electron dynamics that break magnetic field lines and energize plasma. MMS employs automated burst triggers onboard the spacecraft and a Scientist-in-the-Loop (SITL) on the ground to select intervals likely to contain diffusion regions. Only low-resolution survey data is available to the SITL, which is insufficient to resolve electron dynamics. A strategy for the SITL, then, is to select all MP crossings. This has resulted in over 35 potential MP EDR encounters but is labor- and resource-intensive; after manual reclassification, just $\sim$ 0.7\% of MP crossings, or ~0.0001\% of the mission lifetime during MMS’s first two years contained an EDR. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network (RNN) to detect MP crossings and automate the SITL classification process. The LSTM has been implemented in the MMS data stream to provide automated predictions to the SITL. This model facilitates EDR studies and helps free-up mission operation costs by consolidating manual classification processes into automated routines.
Demoting Racial Bias in Hate Speech Detection

In the task of hate speech detection, there exists a high correlation between African American English (AAE) and annotators’ perceptions of toxicity in current datasets. This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech (high false positive rate) by current hate speech classifiers. Here, we use adversarial training to mitigate this bias. Experimental results on one hate speech dataset and one AAE dataset suggest that our method is able to reduce the false positive rate for AAE text with only a minimal compromise on the performance of hate speech classification.
Mixed Autonomy in Ride-Sharing Networks

We consider ride-sharing networks served by human-driven vehicles (HVs) and autonomous vehicles (AVs). We propose a model for ride-sharing in this mixed autonomy setting for a multilocation equidistant network, in which a ride-sharing platform sets prices for riders, compensations for drivers of HVs, and operates AVs for a fixed price with the goal of maximizing profits. When there are more vehicles than riders at a location, we consider three vehicle-to-rider assignment possibilities: 1) rides are assigned to HVs first; 2) rides are assigned to AVs first; and 3) rides are assigned in proportion to the number of available HVs and AVs. Next, for each of these priority possibilities, we establish a nonconvex optimization problem characterizing the optimal profits for a network operating at a steady-state equilibrium. We then provide a convex problem which we show to have the same optimal profits, allowing for efficient computation of equilibria, and we show that all three priority possibilities result in the same maximum profits for the platform. Next, we show that, in some cases, there is a regime, for which the platform will choose to mix HVs and AVs in order to maximize its profit, while, in other cases, the platform will use only HVs or only AVs, depending on the relative cost of AVs. For a specific class of networks, we fully characterize these thresholds analytically and demonstrate our results on an example.
Stratification as a general variance reduction method for Markov chain Monte Carlo.

The eigenvector method for umbrella sampling (EMUS) [E. H. Thiede et al., J. Chem. Phys., 145 (2016), 084115] belongs to a popular class of methods in statistical mechanics which adapt the principl...
Synchronous Robotic Framework

We present a synchronous robotic testbed called SyROF that allows fast implementation of robotic swarms. Our main goal is to lower the entry barriers to cooperative-robot systems for undergraduate and graduate students. The testbed provides a high-level programming environment that allows the implementation of Timed Input/Output Automata (TIOA). Sy-ROF offers the following unique characteristics: 1) a transparent mechanism to synchronize robot maneuvers, 2) a membership service with a failure detector, and 3) a transparent service to provide common knowledge in every round. These characteristics are fundamental to simplifying the implementation of robotic swarms. The software is organized in five layers: The lower layer consists of a real-time publish-subscribe system that allows efficient communication between tasks. The next layer is an implementation of a Kalman filter to estimate the position, orientation, and speed of the robot. The third layer consists of a synchronizer that synchronously executes the robot maneuvers, provides common knowledge to all the active participants, and handles failures. The fifth layer consists of the programming environment.
Annihilation radiation from a hot e/+/-e/-/ plasma

The radiation from electron-positron annihilations in a plasma of temperature above 10 to the 8th K is investigated as a possible source of the emission line at energies between 400 and 460 keV frequently seen in gamma-ray bursts. The annihilation rate and luminosity of an optically thin electron-positron plasma and the energy distribution of the resulting annihilation radiation are calculated by the use of a Monte Carlo technique as functions of temperature. Results indicate the annihilation spectrum to be peaked at an energy of 0.511 MeV plus a temperature-dependent blueshift, and the annihilation line to be significantly temperature-broadened. The widths of the observed burst emission lines set an upper limit of 3 x 10 to the 8th K on the temperature of any pair annihilation region in burst sources, which is considerably lower than the typical kinetic temperatures of the radiating particles. It is thus inferred that either the annihilation region is nonthermal, or spatially distinct from the burst site.
When more is less: Nonmonotonic trends in adsorption on clusters in alloy surfaces

Single-atom alloys can be effective catalysts and have been compared to supported single-atom catalysts. To rationally design single-atom alloys and other surfaces with localized ensembles, it is crucial to understand variations in reactivity when varying the dopant and the ensemble size. Here, we examined hydrogen adsorption on surfaces embedded with localized clusters and discovered general trends. Counterintuitively, increasing the amount of a more reactive metal sometimes makes a surface site less reactive. This behavior is due to the hybridization and splitting of narrow peaks in the electronic density of states of many of these surfaces, making them analogous to free-standing nanoclusters. When a single-atom alloy has a peak just below the Fermi energy, the corresponding two-dopant cluster often has weaker adsorption than the single-atom alloy due to splitting of this peak across the Fermi energy. Furthermore, single-atom alloys have qualitatively different behaviors than larger ensembles. Specifically, the adsorption energy is a U-shaped function of the dopant’s group for single-atom alloys. Additionally, adsorption energies on single-atom alloys correlate more strongly with the dopant’s p-band center than with the d-band center.
SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation

A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called 'dropout'), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In simulations, SIMPLEs performed significantly better than prevailing scRNA-seq imputation methods according to various metrics. By applying SIMPLEs to several real datasets, we discovered gene modules that can further classify subtypes of cells. Our imputations successfully recovered the expression trends of marker genes in stem cell differentiation and can discover putative pathways regulating biological processes.
State vs. Local Management of Pension Assets: Effects of the Massachusetts Chapter 68 Public Pension Reform

By some estimates, underfunded public retirement systems in the United States represent liabilities that could impose $4 trillion on taxpayers and municipal employees, depending on how governments choose to respond to shortfalls resulting from a system’s inability to meet its pension obligations. While there has been a wide range of responses (and nonresponses) to state and local pension underfunding, it is instructive to examine Massachusetts, where in 2007, the state government enacted legislation to identify underperforming local systems and require them to cede control of their pension investments to the state’s Pension Reserves Investment Management (PRIM). The decade that has passed since enactment of this reform provides an opportunity to evaluate its economic impact. This article evaluates and quantifies the effect that PRIM management has had on the investment returns received by the local systems that transferred assets to PRIM after 2007, with a focus on within-system effects. Results indicate that underperforming local systems received substantial benefits from the shift to PRIM’s investment management. These findings provide lessons for other states in which locally managed pensions have fallen into a position of severe underfunding. TOPICS:Long-term/retirement investing, pension funds, performance measurement, retirement, wealth management Key Findings • After the Massachusetts state government enacted legislation (“Chapter 68”) in 2007 to identify underperforming local retirement systems, a substantial number of local systems transferred all, or nearly all, of their pension assets to the state’s Pension Reserves Investment Management (PRIM). • It is estimated that, on average, local systems that transferred assets to PRIM experienced an increase in annual gross returns of 8.9 basis points for every 10% of their assets transferred to PRIM. • In dollar terms, it is conservatively estimated that these local systems collectively gained $321 million (nearly 7% of their total unfunded liability in 2016) as a result of transferring their assets to PRIM after the implementation of Chapter 68.
Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking

We propose in this paper Periodic Interaction Primitives – a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21◦ in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning

The capability to learn and adapt to changes in the driving environment is crucial for developing autonomous driving systems that are scalable beyond geo-fenced operational design domains. Deep Reinforcement Learning (RL) provides a promising and scalable framework for developing adaptive learning based solutions. Deep RL methods usually model the problem as a (Partially Observable) Markov Decision Process in which an agent acts in a stationary environment to learn an optimal behavior policy. However, driving involves complex interaction between multiple, intelligent (artificial or human) agents in a highly non-stationary environment. In this paper, we propose the use of Partially Observable Markov Games(POSG) for formulating the connected autonomous driving problems with realistic assumptions. We provide a taxonomy of multi-agent learning environments based on the nature of tasks, nature of agents and the nature of the environment to help in categorizing various autonomous driving problems that can be addressed under the proposed formulation. As our main contributions, we provide MACAD-Gym, a Multi-Agent Connected, Autonomous Driving agent learning platform for furthering research in this direction. Our MACAD-Gym platform provides an extensible set of Connected Autonomous Driving (CAD) simulation environments that enable the research and development of Deep RL- based integrated sensing, perception, planning and control algorithms for CAD systems with unlimited operational design domain under realistic, multi-agent settings. We also share the MACAD-Agents that were trained successfully using the MACAD-Gym platform to learn control policies for multiple vehicle agents in a partially observable, stop-sign controlled, 3-way urban intersection environment with raw (camera) sensor observations.
On the Flow Problem in Water Distribution Networks: Uniqueness and Solvers

Increasing concerns on the security and quality of water distribution systems (WDS), call for computational tools with performance guarantees. To this end, this work revisits the physical laws governing water flow (WF) and provides a hierarchy of solvers of complementary value. Given the water injection or pressure at each WDS node, finding the WFs within pipes and pumps along with the pressures at all WDS nodes, constitutes the WF problem. The latter entails solving a set of (non)-linear equations. We extend uniqueness claims on the solution to the WF equations in setups with multiple fixed-pressure nodes and detailed pump models. For networks without pumps, the WF solution is already known to be the minimizer of a convex function. The latter approach is extended to networks with pumps but not in cycles, through a stitching algorithm. For networks with nonoverlapping cycles, a provably exact convex relaxation of the pressure drop equations yields a mixed-integer quadratically constrained quadratic program (MI-QCQP) solver. A hybrid scheme combining the MI-QCQP with the stitching algorithm can handle a WDS with overlapping cycles, but without pumps on them. Each solver is guaranteed to converge regardless of initialization, as numerically validated on a benchmark WDS.
Built to change: dominance strategy changes with life stage in a primitively eusocial bee

Abstract Access to reproduction is determined by an individual dominance’s rank in many species and is achieved through aggression and/or dominance signalling. In eusocial insects one or several dominant females (queens) monopolize reproduction but to what extent queens rely on aggression and signalling remains obscure. Aggression is costly and its efficiency depends on the group size, whereas signalling may reduce the risks and costs of aggression. Both strategies are used to regulate reproduction in social taxa, with aggression being more common in small social groups, compared to signalling in larger societies. Here, we examine the use of aggression and chemical signalling in a social species (Bombus impatiens) where the dominant queen interacts with increasing numbers of workers as she ages. We found that the queens’ strategy to monopolize reproduction changes with life stage, shifting from overt aggression to chemical signalling as the queen gets older. Particularly, old queens exhibited a higher ratio of short to long cuticular hydrocarbons compared to young queens, an endogenous shift that was attributed to age, as all egg-laying queens were fecund and kept with the same number of workers. Our findings contribute to the understanding of reproductive dominance in the context of an individual’s life history.
Effects of high intensity interval exercise on cerebrovascular function: A systematic review

High intensity interval exercise (HIIE) improves aerobic fitness with decreased exercise time compared to moderate continuous exercise. A gap in knowledge exists regarding the effects of HIIE on cerebrovascular function such as cerebral blood velocity and autoregulation. The objective of this systematic review was to ascertain the effect of HIIE on cerebrovascular function in healthy individuals. We searched PubMed and the Cumulative Index to Nursing and Allied Health Literature databases with apriori key words. We followed the Preferred Reporting Items for Systematic Reviews. Twenty articles were screened and thirteen articles were excluded due to not meeting the apriori inclusion criteria. Seven articles were reviewed via the modified Sackett's quality evaluation. Outcomes included middle cerebral artery blood velocity (MCAv) (n = 4), dynamic cerebral autoregulation (dCA) (n = 2), cerebral de/oxygenated hemoglobin (n = 2), cerebrovascular reactivity to carbon dioxide (CO2) (n = 2) and cerebrovascular conductance/resistance index (n = 1). Quality review was moderate with 3/7 to 5/7 quality criteria met. HIIE acutely lowered exercise MCAv compared to moderate intensity. HIIE decreased dCA phase following acute and chronic exercise compared to rest. HIIE acutely increased de/oxygenated hemoglobin compared to rest. HIIE acutely decreased cerebrovascular reactivity to higher CO2 compared to rest and moderate intensity. The acute and chronic effects of HIIE on cerebrovascular function vary depending on the outcomes measured. Therefore, future research is needed to confirm the effects of HIIE on cerebrovascular function in healthy individuals and better understand the effects in individuals with chronic conditions. In order to conduct rigorous systematic reviews in the future, we recommend assessing MCAv, dCA and CO2 reactivity during and post HIIE.
Inverse Stability Problem and Applications to Renewables Integration

In modern power systems, the operating point, at which the demand and supply are balanced, may take different values due to changes in loads and renewable generation levels. Understanding the dynamics of stressed power systems with a range of operating points would be essential to assuring their reliable operation, and possibly allow higher integration of renewable resources. This letter introduces a non-traditional way to think about the stability assessment problem of power systems. Instead of estimating the set of initial states leading to a given operating condition, we characterize the set of operating conditions that a power grid converges to from a given initial state under changes in power injections and lines. We term this problem as “inverse stability,” a problem which is rarely addressed in the control and systems literature, and hence, poorly understood. Exploiting quadratic approximations of the system’s energy function, we introduce an estimate of the inverse stability region. Also, we briefly describe three important applications of the inverse stability notion: 1) robust stability assessment of power systems with respect to different renewable generation levels; 2) stability-constrained optimal power flow; and 3) stability-guaranteed corrective action design.
Prospect of undoped inorganic crystals at 77 Kelvin for low-mass dark matter search at Spallation Neutron Source

Investigated in this work were sensitivities of a prototype detector for the detection of low-mass dark matter particles produced at the Spallation Neutron Source at the Oak Ridge National Laboratory in 2 years of data taking. The presumed prototype consisted of 10 kg undoped CsI or NaI scintillation crystals directly coupled with SiPM arrays operated at 77 K. Compared to the COHERENT CsI(Na) detector, a much higher light yield was assumed for the prototype. An experiment with a cylindrical 1 kg undoped CsI crystal coupled directly to two photomultiplier tubes at about 77 K was conducted as the first step to verify the idea. A light yield of $$26.0 \pm 0.4$$ photoelectrons per keV electron-equivalent was achieved. This eliminated the concern of self light absorption in large crystals raised in some of the early studies.
BLACK HOLES, SPACE-FILLING CHAINS AND RANDOM WALKS

Many approaches to a semiclassical description of gravity lead to an integer black hole entropy. In four dimensions this implies that the Schwarzschild radius obeys a formula which describes the distance covered by a Brownian random walk. For the higher-dimensional Schwarzschild–Tangherlini black hole, its radius relates similarly to a fractional Brownian walk. We propose a possible microscopic explanation for these random walk structures based on microscopic chains which fill the interior of the black hole.
The Rise of ROME. I. A Multiwavelength Analysis of the Star-Planet Interaction in the HD 189733 System

Several "hot Jupiter" host stars appear to exhibit enhanced chromospheric activity, coronal flaring, and starspot development synchronized to their planetary orbits. These effects have been attributed to tidal and/or magnetic interactions between the star and exoplanet. The best studied among such systems is HD 189733, which has previously been observed from radio to X-ray wavelengths. Here, I present ~4.75 GHz Arecibo radio telescope observations of HD 189733 during a fraction of the exoplanet orbital phase range previously associated with enhanced coronal X-ray flaring. No radio flares from the lower corona were detected. I also leverage Microvariability and Oscillations of Stars, Automated Photoelectric Telescope, and Wise photometry to measure the occurences of minima associated with enhanced starspot activity. The phasing of these minima with the exoplanet orbit does not reveal any synchronized active region behavior. Moreover, for the first time, I combine X-ray, ultraviolet, photometric, Ca II H&K, H-alpha, and radio observations of HD 189733 to conduct an extensive, holistic reexamination of stellar activity in this system. Through the presentation of new data, and by merging physical and statistical analytic approaches, I demonstrate that the previously asserted enhancements in activity, allegedly synchronized to certain exoplanet orbital phases, are likely the result of inadequately sampled intrinsic stellar activity from an active star, rather than star-planet interactions.
Mass-in-mass lattices with small internal resonators

We consider the mass-in-mass (MiM) lattice when the internal resonators are very small. When there are no internal resonators the lattice reduces to a standard Fermi-Pasta-Ulam-Tsingou (FPUT) system. We show that the solution of the MiM system, with suitable initial data, shadows the FPUT system for long periods of time. Using some classical oscillatory integral estimates we can conclude that the error of the approximation is (in some settings) higher than one may expect.
Holographic calculations of Euclidean Wilson loop correlator in Euclidean anti-de Sitter space

The correlation functions of two or more Euclidean Wilson loops of various shapes in Euclidean anti-de Sitter space are computed by considering the minimal area surfaces connecting the loops. The surfaces are parametrized by Riemann theta functions associated with genus three hyperelliptic Riemann surfaces. In the case of two loops, the distance L by which they are separated can be adjusted by continuously varying a specific branch point of the auxiliary Riemann surface. When L is much larger than the characteristic size of the loops, then the loops are approximately regarded as local operators and their correlator as the correlator of two local operators. Similarly, when a loop is very small compared to the size of another loop, the small loop is considered as a local operator corresponding to a light supergravity mode.
Cosmic Ray Sampling of a Clumpy Interstellar Medium

How cosmic rays sample the multi-phase interstellar medium (ISM) in starburst galaxies has important implications for many science goals, including evaluating the cosmic ray calorimeter model for these systems, predicting their neutrino fluxes, and modeling their winds. Here, we use Monte Carlo simulations to study cosmic ray sampling of a simple, two-phase ISM under conditions similar to those of the prototypical starburst galaxy M82. The assumption that cosmic rays sample the mean density of the ISM in the starburst region is assessed over a multi-dimensional parameter space where we vary the number of molecular clouds, the galactic wind speed, the extent to which the magnetic field is tangled, and the cosmic ray injection mechanism. We evaluate the ratio of the emissivity from pion production in molecular clouds to the emissivity that would be observed if the cosmic rays sampled the mean density, and seek areas of parameter space where this ratio differs significantly from unity. The assumption that cosmic rays sample the mean density holds over much of parameter space; however, this assumption begins to break down for high cloud density, injection close to the clouds, and a very tangled magnetic field. We conclude by evaluating the extent to which our simulated starburst region behaves as a proton calorimeter and constructing the time-dependent spectrum of a burst of cosmic rays.
Detection methods for stochastic gravitational-wave backgrounds: a unified treatment

We review detection methods that are currently in use or have been proposed to search for a stochastic background of gravitational radiation. We consider both Bayesian and frequentist searches using ground-based and space-based laser interferometers, spacecraft Doppler tracking, and pulsar timing arrays; and we allow for anisotropy, non-Gaussianity, and non-standard polarization states. Our focus is on relevant data analysis issues, and not on the particular astrophysical or early Universe sources that might give rise to such backgrounds. We provide a unified treatment of these searches at the level of detector response functions, detection sensitivity curves, and, more generally, at the level of the likelihood function, since the choice of signal and noise models and prior probability distributions are actually what define the search. Pedagogical examples are given whenever possible to compare and contrast different approaches. We have tried to make the article as self-contained and comprehensive as possible, targeting graduate students and new researchers looking to enter this field.
Normalization models of contextualized touch

Divisive normalization is a canonical computation that explains contextual modulation of visual perception and neural responses in the visual system. Conceivably, normalization also underlies contextual modulation of bimanual touch, a perceptual process that likely requires combining what is felt on the hands with where the hands are located in space. We found that touch experienced on one hand systematically modulates how touch is perceived on the other hand. Notably, bimanual interaction patterns and their sensitivity to hand locations differed depending on whether participants directed attention to the frequency or intensity of the cues, which were always mechanical vibrations. These idiosyncratic perceptual patterns were well explained by distinct cue combination models that each comprise divisive normalization. Our findings indicate that, while feature-specific rules govern bimanual touch, normalization underlies contextual modulation between the hands.
Strong planar subsystem symmetry-protected topological phases and their dual fracton orders

We classify subsystem symmetry-protected topological (SSPT) phases in 3 + 1 dimensions (3 + 1D) protected by planar subsystem symmetries: short-range entangled phases which are dual to long-range entangled Abelian fracton topological orders via a generalized “gauging” duality. We distinguish between weak SSPTs, which can be constructed by stacking 2 + 1D SPTs, and strong SSPTs, which cannot. We identify signatures of strong phases, and show by explicit construction that such phases exist. A classification of strong phases is presented for an arbitrary finite Abelian group. Finally, we show that fracton orders realizable via p-string condensation are dual to weak SSPTs, while those dual to strong SSPTs exhibit statistical interactions prohibiting such a realization.
Rotating Radio Transients and Their Place Among Pulsars

Six years ago, the discovery of Rotating Radio Transients (RRATs) marked what appeared to be a new type of sparsely-emitting pulsar. Since 2006, more than 70 of these objects have been discovered in single-pulse searches of archival and new surveys. With a continual inflow of new information about the RRAT population in the form of new discoveries, multi-frequency follow ups, coherent timing solutions, and pulse rate statistics, a view is beginning to form of the place in the pulsar population RRATs hold. Here we review the properties of neutron stars discovered through single pulse searches. We first seek to clarify the definition of the term RRAT, emphasising that "the RRAT population" encompasses several phenomenologies. A large subset of RRATs appears to represent the tail of an extended distribution of pulsar nulling fractions and activity cycles; these objects present several key open questions remaining in this field.
Detailing the equivalence between real equiangular tight frames and certain strongly regular graphs

An equiangular tight frame (ETF) is a set of unit vectors whose coherence achieves the Welch bound, and so is as incoherent as possible. They arise in numerous applications. It is well known that real ETFs are equivalent to a certain subclass of strongly regular graphs. In this note, we give some alternative techniques for understanding this equivalence. In a later document, we will use these techniques to further generalize this theory.
A majorization-minimization approach to design of power transmission networks

We propose an optimization approach to design cost-effective electrical power transmission networks. That is, we aim to select both the network structure and the line conductances (line sizes) so as to optimize the trade-off between network efficiency (low power dissipation within the transmission network) and the cost to build the network. We begin with a convex optimization method based on the paper “Minimizing Effective Resistance of a Graph” [Ghosh, Boyd & Saberi]. We show that this (DC) resistive network method can be adapted to the context of AC power flow. However, that does not address the combinatorial aspect of selecting network structure. We approach this problem as selecting a subgraph within an over-complete network, posed as minimizing the (convex) network power dissipation plus a non-convex cost on line conductances that encourages sparse networks where many line conductances are set to zero. We develop a heuristic approach to solve this non-convex optimization problem using: (1) a continuation method to interpolate from the smooth, convex problem to the (non-smooth, non-convex) combinatorial problem, (2) the majorization-minimization algorithm to perform the necessary intermediate smooth but non-convex optimization steps. Ultimately, this involves solving a sequence of convex optimization problems in which we iteratively reweight a linear cost on line conductances to fit the actual non-convex cost. Several examples are presented which suggest that the overall method is a good heuristic for network design. We also consider how to obtain sparse networks that are still robust against failures of lines and/or generators.
Directional Multivariate Ranking

User-provided multi-aspect evaluations manifest users' detailed feedback on the recommended items and enable fine-grained understanding of their preferences. Extensive studies have shown that modeling such data greatly improves the effectiveness and explainability of the recommendations. However, as ranking is essential in recommendation, there is no principled solution yet for collectively generating multiple item rankings over different aspects. In this work, we propose a directional multi-aspect ranking criterion to enable a holistic ranking of items with respect to multiple aspects. Specifically, we view multi-aspect evaluation as an integral effort from a user that forms a vector of his/her preferences over aspects. Our key insight is that the direction of the difference vector between two multi-aspect preference vectors reveals the pairwise order of comparison. Hence, it is necessary for a multi-aspect ranking criterion to preserve the observed directions from such pairwise comparisons. We further derive a complete solution for the multi-aspect ranking problem based on a probabilistic multivariate tensor factorization model. Comprehensive experimental analysis on a large TripAdvisor multi-aspect rating dataset and a Yelp review text dataset confirms the effectiveness of our solution.
BRST hamiltonian for bulk quantized gauge theory

By treating the bulk-quantized Yang-Mills theory as a constrained system we obtain a consistent gauge-fixed BRST hamiltonian in the minimal sector. This provides an independent derivation of the 5-d lagrangian bulk action. The ground state is independent of the (anti)ghosts and is interpreted as the solution of the Fokker-Planck equation, thus establishing a direct connection to the Fokker-Planck hamiltonian. The vacuum state correlators are shown to be in agreement with correlators in lagrangian 5-d formulation. It is verified that the complete propagators remain parabolic in one-loop dimensional regularization.
Metastable rocksalt ZnO is p -type dopable

Despite decades of efforts, achieving $p$-type conductivity in the wide band gap ZnO in its ground-state wurtzite structure continues to be a challenge. Here we detail how $p$-type ZnO can be realized in a known metastable, high-pressure rocksalt phase (also wide-gap) with Li as an external dopant. Using modern defect theory, we predict Li to dope the rocksalt phase $p$-type by preferentially substituting for Zn and introducing shallow acceptor levels, resulting in predicted hole concentrations to exceed ${10}^{19}\phantom{\rule{4pt}{0ex}}{\mathrm{cm}}^{\ensuremath{-}3}$. Formation of compensating donors like interstitial Li and unintentional hydrogen, ubiquitous in wurtzite phase, is inhibited by the close-packed nature of the rocksalt polymorph. Also, relatively high absolute valence band edge of rocksalt ZnO benefits low hole effective masses and hole delocalization. In addition to the technological significance, our results reveal polymorphism as a promising route to overcome strong doping asymmetry of wide band gap oxides.
Infinite Volume Limit for the Dipole Gas

We consider a classical lattice dipole gas with low activity and show that the pressure has a limit as the volume goes to infinity. The result is obtained by a renormalization group analysis of the model.
Generalized fluid theory including non-Maxwellian kinetic effects

The results obtained by the plasma physics community for the validation and the prediction of turbulence and transport in magnetized plasmas come mainly from the use of very central processing unit (CPU)-consuming particle-in-cell or (gyro)kinetic codes which naturally include non-Maxwellian kinetic effects. To date, fluid codes are not considered to be relevant for the description of these kinetic effects. Here, after revisiting the limitations of the current fluid theory developed in the 19th century, we generalize the fluid theory including kinetic effects such as non-Maxwellian super-thermal tails with as few fluid equations as possible. The collisionless and collisional fluid closures from the nonlinear Landau Fokker–Planck collision operator are shown for an arbitrary collisionality. Indeed, the first fluid models associated with two examples of collisionless fluid closures are obtained by assuming an analytic non-Maxwellian distribution function (e.g. the INMDF (Izacard, O. 2016b Kinetic corrections from analytic non-Maxwellian distribution functions in magnetized plasmas. Phys. Plasmas 23, 082504) that stands for interpreted non-Maxwellian distribution function). One of the main differences with the literature is our analytic representation of the distribution function in the velocity phase space with as few hidden variables as possible thanks to the use of non-orthogonal basis sets. These new non-Maxwellian fluid equations could initiate the next generation of fluid codes including kinetic effects and can be expanded to other scientific disciplines such as astrophysics, condensed matter or hydrodynamics. As a validation test, we perform a numerical simulation based on a minimal reduced INMDF fluid model. The result of this test is the discovery of the origin of particle and heat diffusion. The diffusion is due to the competition between a growing INMDF on short time scales due to spatial gradients and the thermalization on longer time scales. The results shown here could provide the insights to break some of the unsolved puzzles of turbulence.
Inducing vortices in a Bose-Einstein condensate using holographically produced light beams

In this paper we demonstrate a technique that can create non-equilibrium vortex configurations with almost arbitrary charge and geometry in a Bose-Einstein condensate. We coherently transfer orbital angular momentum from a holographically generated light beam to a 87Rb condensate using a two-photon stimulated Raman process. Using matter wave interferometry, we verify the phase pattern imprinted onto the atomic wave function for a single vortex and a vortex-antivortex pair. In addition to their phase winding, the vortices created with this technique have an associated hyperfine spin texture.
Intergalactic Magnetic Fields from Quasar Outflows

Outflows from quasars inevitably pollute the intergalactic medium (IGM) with magnetic fields. The short-lived activity of a quasar leaves behind an expanding magnetized bubble in the IGM. We model the expansion of the remnant quasar bubbles and calculate their distribution as a function of size and magnetic field strength at different redshifts. We generically find that by a redshift z ~ 3, about 5%-20% of the IGM volume is filled by magnetic fields with an energy density 10% of the mean thermal energy density of a photoionized IGM (at ~104 K). As massive galaxies and X-ray clusters condense out of the magnetized IGM, the adiabatic compression of the magnetic field could result in the field strength observed in these systems without a need for further dynamo amplification. The intergalactic magnetic field could also provide a nonthermal contribution to the pressure of the photoionized gas that may account for the claimed discrepancy between the simulated and observed Doppler width distributions of the Ly? forest.
Impact of Detector Simulation in Particle Physics Collider Experiments

Abstract Through the last three decades, accurate simulation of the interactions of particles with matter and modeling of detector geometries has proven to be of critical importance to the success of the international high-energy physics (HEP) experimental programs. For example, the detailed detector modeling and accurate physics of the Geant4-based simulation software of the CMS and ATLAS particle physics experiments at the European Center of Nuclear Research (CERN) Large Hadron Collider (LHC) was a determinant factor for these collaborations to deliver physics results of outstanding quality faster than any hadron collider experiment ever before. This review article highlights the impact of detector simulation on particle physics collider experiments. It presents numerous examples of the use of simulation, from detector design and optimization, through software and computing development and testing, to cases where the use of simulation samples made a difference in the precision of the physics results and publication turnaround, from data-taking to submission. It also presents estimates of the cost and economic impact of simulation in the CMS experiment. Future experiments will collect orders of magnitude more data with increasingly complex detectors, taxing heavily the performance of simulation and reconstruction software. Consequently, exploring solutions to speed up simulation and reconstruction software to satisfy the growing demand of computing resources in a time of flat budgets is a matter that deserves immediate attention. The article ends with a short discussion on the potential solutions that are being considered, based on leveraging core count growth in multicore machines, using new generation coprocessors, and re-engineering HEP code for concurrency and parallel computing.
High mobility back-gated InAs/GaSb double quantum well grown on GaSb substrate

We report a backgated InAs/GaSb double quantum well device grown on GaSb substrate. The use of the native substrate allows for high materials quality with electron mobility in excess of 500 000 cm2/Vs at sheet charge density of 8 × 1011 cm−2 and approaching 100 000 cm2/Vs near the charge neutrality point. Lattice matching between the quantum well structure and the substrate eliminates the need for a thick buffer, enabling large back gate capacitance and efficient coupling with the conduction channels in the quantum wells. As a result, quantum Hall effects are observed in both electron and hole regimes across the hybridization gap.
Smooth parametric dependence of asymptotics of the semiclassical focusing NLS

We consider the one dimensional focusing (cubic) Nonlinear Schr\"odinger equation (NLS) in the semiclassical limit with exponentially decaying complex-valued initial data, whose phase is multiplied by a real parameter. We prove smooth dependence of the asymptotic solution on the parameter. Numerical results supporting our estimates of important quantities are presented.
On the accuracy of the Pade-resummed master equation approach to dissipative quantum dynamics

Well-defined criteria are proposed for assessing the accuracy of quantum master equations whose memory functions are approximated by Pade resummation of the first two moments in the electronic coupling. These criteria partition the parameter space into distinct levels of expected accuracy, ranging from quantitatively accurate regimes to regions of parameter space where the approach is not expected to be applicable. Extensive comparison of Pade-resummed master equations with numerically exact results in the context of the spin–boson model demonstrates that the proposed criteria correctly demarcate the regions of parameter space where the Pade approximation is reliable. The applicability analysis we present is not confined to the specifics of the Hamiltonian under consideration and should provide guidelines for other classes of resummation techniques.
Quantum Transport in Disordered Systems Under Magnetic Fields: A Study Based on Operator Algebras

The linear conductivity tensor for generic homogeneous, microscopic quantum models was formulated as a noncommutative Kubo formula in Refs. \cite{BELLISSARD:1994xj,Schulz-Baldes:1998vm,Schulz-Baldes:1998oq}. This formula was derived directly in the thermodynamic limit, within the framework of $C^*$-algebras and noncommutative calculi defined over infinite spaces. As such, the numerical implementation of the formalism encountered fundamental obstacles. The present work defines a $C^*$-algebra and an approximate noncommutative calculus over a finite real-space torus, which naturally leads to an approximate finite-volume noncommutative Kubo formula, amenable on a computer. For finite temperatures and dissipation, it is shown that this approximate formula converges exponentially fast to its thermodynamic limit, which is the exact noncommutative Kubo formula. The approximate noncommutative Kubo formula is then deconstructed to a form that is implementable on a computer and simulations of the quantum transport in a 2-dimensional disordered lattice gas in a magnetic field are presented.
High performance computing of gene regulatory networks using a message-passing model

Gene regulatory network reconstruction is a fundamental problem in computational biology. We recently developed an algorithm, called PANDA (Passing Attributes Between Networks for Data Assimilation), that integrates multiple sources of omics data and estimates regulatory network models. This approach was initially implemented in the C++ programming language and has since been applied to a number of biological systems. In our current research we are beginning to expand the algorithm to incorporate larger and most diverse data-sets, to reconstruct networks that contain increasing numbers of elements, and to build not only single network models, but sets of networks. In order to accomplish these “Big Data” applications, it has become critical that we increase the computational efficiency of the PANDA implementation. In this paper we show how to recast PANDA's similarity equations as matrix operations. This allows us to implement a highly readable version of the algorithm using the MATLAB/Octave programming language. We find that the resulting M-code much shorter (103 compared to 1128 lines) and more easily modifiable for potential future applications. The new implementation also runs significantly faster, with increasing efficiency as the network models increase in size. Tests comparing the C-code and M-code versions of PANDA demonstrate that this speed-up is on the order of 20–80 times faster for networks of similar dimensions to those we find in current biological applications.