cssi/SciSciGPT-SciSciNet huggingface dataset:
Improved design and analysis of practical minimizers.

Motivation Minimizers are methods to sample k-mers from a string, with the guarantee that similar set of k-mers will be chosen on similar strings. It is parameterized by the k-mer length k, a window length w and an order on the k-mers. Minimizers are used in a large number of softwares and pipelines to improve computation efficiency and decrease memory usage. Despite the method's popularity, many theoretical questions regarding its performance remain open. The core metric for measuring performance of a minimizer is the density, which measures the sparsity of sampled k-mers. The theoretical optimal density for a minimizer is 1/w, provably not achievable in general. For given k and w, little is known about asymptotically optimal minimizers, that is minimizers with density O(1/w). Results We derive a necessary and sufficient condition for existence of asymptotically optimal minimizers. We also provide a randomized algorithm, called the Miniception, to design minimizers with the best theoretical guarantee to date on density in practical scenarios. Constructing and using the Miniception is as easy as constructing and using a random minimizer, which allows the design of efficient minimizers that scale to the values of k and w used in current bioinformatics software programs. Availability and implementation Reference implementation of the Miniception and the codes for analysis can be found at https://github.com/kingsford-group/miniception. Supplementary information Supplementary data are available at Bioinformatics online.
Modeling Communication Costs in Blade Servers

Datacenters demand big memory servers for big data. For blade servers, which disaggregate memory across multiple blades, we derive technology and architectural models to estimate communication delay and energy. These models permit new case studies in refusal scheduling to mitigate NUMA and improve the energy efficiency of data movement. Preliminary results show that our model helps researchers coordinate NUMA mitigation and queueing dynamics. We find that judiciously permitting NUMA reduces queueing time, benefiting throughput, latency and energy efficiency for datacenter workloads like Spark. These findings highlight blade servers' strengths and opportunities when building distributed shared memory machines for data analytics.
Trends in conditional survival and predictors of late death in neuroblastoma.

PURPOSE Significant advances in the treatment of neuroblastoma have been made in the past several decades. There are scant data examining how these improvements have changed over time and differentially affected conditional survival among high-risk and non-high-risk patient groups. METHODS We conducted a retrospective cohort study using the Surveillance, Epidemiology, and End Results database. We analyzed clinical characteristics and survival outcomes for 4717 neuroblastoma patients. Kaplan-Meier methods were used to estimate overall survival (OS) and conditional overall survival (COS) with estimates compared between groups using log-rank tests. RESULTS Five-year OS was 41.46% (95% CI 38.77-44.13) for the high-risk group and 91.13% (95% CI 89.49-92.53) for the non-high-risk group. Both groups saw significant improvements in OS by decade (P < .001). Five-year COS among 1-year survivors was 52.69% (CI 49.54-55.73) for the high-risk group and 96.75% (95% CI 95.57-97.62) for the non-high-risk group. One-year survivors in the high-risk group showed a statistically significant improvement in COS over time. No difference in COS was observed among 5-year high-risk survivors. In the high-risk and non-high-risk groups, 82% and 32% of late deaths were attributable to cancer, respectively. Statistically significant adverse prognostic factors for late death were age ≥ 1 year at diagnosis, metastatic disease, and nonthoracic primary site (P = .001). CONCLUSIONS Improvements in COS over time have largely benefited high-risk patients, though they are still at higher risk for late death due to cancer when compared to non-high-risk patients. Age, stage, and primary site, but not treatment decade, influence outcomes among 5-year survivors.
The color of shareholders' money: Institutional shareholders' political values and corporate environmental disclosure

Abstract In this study, we investigate whether and to what extent institutional shareholders' political values influence their investees' environmental disclosure and performance. Using employees' political donation data, we construct institutional investors' political ideology score, which higher (lower) value represents a more Republican- (Democratic)-leaning culture. We find that firms led by institutional shareholders with a more Republican-oriented political ideology are less likely to issue environmental reports. Such a negative effect is more pronounced for firms with institutional shareholders with long-term horizons, with high corporate Republican ideology scores, and without an environmental committee. We further find that institutional shareholders' Republican-oriented political values are negatively associated with their investee firms' environmental performance and green innovations. Overall, our results indicate that institutional shareholders' internal political polarization significantly influences corporate environmental disclosure policies.
Autonomous magnetic resonance imaging.

Abstract Access to Magnetic Resonance Imaging (MRI) across developing countries ranges from being prohibitive to scarcely available. For example, eleven countries in Africa have no scanners. One critical limitation is the absence of skilled manpower required for MRI usage. Some of these challenges can be mitigated using autonomous MRI (AMRI) operation. In this work, we demonstrate AMRI to simplify MRI workflow by separating the required intelligence and user interaction from the acquisition hardware. AMRI consists of three components: user node, cloud and scanner. The user node voice interacts with the user and presents the image reconstructions at the end of the AMRI exam. The cloud generates pulse sequences and performs image reconstructions while the scanner acquires the raw data. An AMRI exam is a custom brain screen protocol comprising of one T1-, T2- and T2*-weighted exams. A neural network is trained to incorporate Intelligent Slice Planning (ISP) at the start of the AMRI exam. A Look Up Table was designed to perform intelligent protocolling by optimizing for contrast value while satisfying signal to noise ratio and acquisition time constraints. Data were acquired from four healthy volunteers for three experiments with different acquisition time constraints to demonstrate standard and self-administered AMRI. The source code is available online. AMRI achieved an average SNR of 22.86 ± 0.89 dB across all experiments with similar contrast. Experiment #3 (33.66% shorter table time than experiment #1) yielded a SNR of 21.84 ± 6.36 dB compared to 23.48 ± 7.95 dB for experiment #1. AMRI can potentially enable multiple scenarios to facilitate rapid prototyping and research and streamline radiological workflow. We believe we have demonstrated the first Autonomous MRI of the brain.
Functional gene categories differentiate maize leaf drought-related microbial epiphytic communities

The phyllosphere epiphytic microbiome is composed of microorganisms that colonize the external aerial portions of plants. Relationships of plant responses to specific microorganisms-both pathogenic and beneficial-have been examined, but the phyllosphere microbiome functional and metabolic profile responses are not well described. Changing crop growth conditions, such as increased drought, can have profound impacts on crop productivity. Also, epiphytic microbial communities provide a new target for crop yield optimization. We compared Zea mays leaf microbiomes collected under drought and well-watered conditions by examining functional gene annotation patterns across three physically disparate locations each with and without drought treatment, through the application of short read metagenomic sequencing. Drought samples exhibited different functional sequence compositions at each of the three field sites. Maize phyllosphere functional profiles revealed a wide variety of metabolic and regulatory processes that differed in drought and normal water conditions and provide key baseline information for future selective breeding.
Altering the Solubility of the Antibiotic Candidate Nisin—A Computational Study

The growing bacterial resistance to available antibiotics makes it necessary to look for new drug candidates. An example is the lanthionine-containing nisin, which has a broad spectrum of antimicro...
Uncertainty quantification in imaging and automatic horizon tracking – A Bayesian deep-prior based approach

In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as "deep prior". Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.
Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games

Many problems in robotics involve multiple decision making agents. To operate efficiently in such settings, a robot must reason about the impact of its decisions on the behavior of other agents. Differential games offer an expressive theoretical framework for formulating these types of multi-agent problems. Unfortunately, most numerical solution techniques scale poorly with state dimension and are rarely used in real-time applications. For this reason, it is common to predict the future decisions of other agents and solve the resulting decoupled, i.e., single-agent, optimal control problem. This decoupling neglects the underlying interactive nature of the problem; however, efficient solution techniques do exist for broad classes of optimal control problems. We take inspiration from one such technique, the iterative linear-quadratic regulator (ILQR), which solves repeated approximations with linear dynamics and quadratic costs. Similarly, our proposed algorithm solves repeated linear-quadratic games. We experimentally benchmark our algorithm in several examples with a variety of initial conditions and show that the resulting strategies exhibit complex interactive behavior. Our results indicate that our algorithm converges reliably and runs in real-time. In a three-player, 14-state simulated intersection problem, our algorithm initially converges in < 0.25 s. Receding horizon invocations converge in < 50 ms in a hardware collision-avoidance test.
A distributed look-up architecture for text mining applications using MapReduce

We study text analysis algorithms that use global optimization methods to compute local characteristics that are consistent with properties of the entire corpus rather than computed locally based on exogenous parameters. In the iterative implementations that we consider, each step both reads and updates a database of parameter values. Motivated by a need for rapid analysis of large corpora, we have developed methods for efficient access to such databases on parallel computers. These methods combine Bloom filters, in-memory caches, and an HBase cluster to reduce communication costs greatly relative to simpler approaches that either fully distribute or fully replicate the database. We also describe how this method can be incorporated into the MapReduce programming model, and illustrate its use within phrase segmentation programs. Our design can achieve considerable run time, latency and storage space improvements relative to other methods. In one phrase segmentation application, we improve performance by a factor of six relative to an HBase-based implementation.
A hierarchy of multilayered plate models

We derive a hierarchy of plate theories for heterogeneous multilayers from three dimensional nonlinear elasticity by means of Γ-convergence. We allow for layers composed of different materials whose constitutive assumptions may vary significantly in the small film direction and which also may have a (small) pre-stress. By computing the Γ-limits in the energy regimes in which the scaling of the pre-stress is non-trivial, we arrive at linearised Kirchhoff, von Karman, and fully linear plate theories, respectively, which contain an additional spontaneous curvature tensor. The effective (homogenised) elastic constants of the plates will turn out to be given in terms of the moments of the pointwise elastic constants of the materials.
A distributed active subspace method for scalable surrogate modeling of function valued outputs

We present a distributed active subspace method for training surrogate models of complex physical processes with high-dimensional inputs and function valued outputs. Specifically, we represent the model output with a truncated Karhunen–Loeve (KL) expansion, screen the structure of the input space with respect to each KL mode via the active subspace method, and finally form an overall surrogate model of the output by combining surrogates of individual output KL modes. To ensure scalable computation of the gradients of the output KL modes, needed in active subspace discovery, we rely on adjoint-based gradient computation. The proposed method combines benefits of active subspace methods for input dimension reduction and KL expansions used for spectral representation of the output field. We provide a mathematical framework for the proposed method and conduct an error analysis of the mixed KL active subspace approach. Specifically, we provide an error estimate that quantifies errors due to active subspace projection and truncated KL expansion of the output. We demonstrate the numerical performance of the surrogate modeling approach with an application example from biotransport.
A Generalization of Otsu’s Method and Minimum Error Thresholding

We present Generalized Histogram Thresholding (GHT), a simple, fast, and effective technique for histogram-based image thresholding. GHT works by performing approximate maximum a posteriori estimation of a mixture of Gaussians with appropriate priors. We demonstrate that GHT subsumes three classic thresholding techniques as special cases: Otsu’s method, Minimum Error Thresholding (MET), and weighted percentile thresholding. GHT thereby enables the continuous interpolation between those three algorithms, which allows thresholding accuracy to be improved significantly. GHT also provides a clarifying interpretation of the common practice of coarsening a histogram’s bin width during thresholding. We show that GHT outperforms or matches the performance of all algorithms on a recent challenge for handwritten document image binarization (including deep neural networks trained to produce per-pixel binarizations), and can be implemented in a dozen lines of code or as a trivial modification to Otsu’s method or MET.
Symmetries and Collective Excitations in Large Superconducting Circuits

Theoretical calculations describe the behavior of a large quantum circuit by taking advantage of certain symmetrical relationships between superconducting elements.
A cosmological solution to the Impossibly Early Galaxy Problem

Abstract To understand the formation and evolution of galaxies at redshifts 0 ≲ z ≲ 10 , one must invariably introduce specific models (e.g., for the star formation) in order to fully interpret the data. Unfortunately, this tends to render the analysis compliant to the theory and its assumptions, so consensus is still somewhat elusive. Nonetheless, the surprisingly early appearance of massive galaxies challenges the standard model, and the halo mass function estimated from galaxy surveys at z ≳ 4 appears to be inconsistent with the predictions of Λ CDM, giving rise to what has been termed “The Impossibly Early Galaxy Problem” by some workers in the field. A simple resolution to this question may not be forthcoming. The situation with the halos themselves, however, is more straightforward and, in this paper, we use linear perturbation theory to derive the halo mass function over the redshift range 0 ≲ z ≲ 10 for the R h = c t universe. We use this predicted halo distribution to demonstrate that both its dependence on mass and its very weak dependence on redshift are compatible with the data. The difficulties with Λ CDM may eventually be overcome with refinements to the underlying theory of star formation and galaxy evolution within the halos. For now, however, we demonstrate that the unexpected early formation of structure may also simply be due to an incorrect choice of the cosmology, rather than to yet unknown astrophysical issues associated with the condensation of mass fluctuations and subsequent galaxy formation.
Measuring Water Vapor and Ash in Volcanic Eruptions With a Millimeter-Wave Radar/Imager

Millimeter-wave remote sensing technology can significantly improve measurements of volcanic eruptions, yielding new insights into eruption processes and improving forecasts of drifting volcanic ash for aviation safety. Radiometers can measure water vapor density and temperature inside eruption clouds, improving on existing measurements with infrared cameras that are limited to measuring the outer cloud surface. Millimeter-wave radar can measure the 3-D mass distribution of volcanic ash inside eruption plumes and their nearby drifting ash clouds. Millimeter wavelengths are better matched to typical ash particle sizes, offering better sensitivity than longer wavelength existing weather radar measurements, as well as the unique ability to directly measure ash particle size in situ . Here we present sensitivity calculations in the context of developing the water and ash millimeter-wave spectrometer (WAMS) instrument. WAMS, a radar/radiometer system designed to use off-the-shelf components, would be able to measure water vapor and ash throughout an entire eruption cloud, a unique capability.
Computational Complexity of Motion Planning of a Robot through Simple Gadgets.

We initiate a general theory for analyzing the complexity of motion planning of a single robot through a graph of "gadgets", each with their own state, set of locations, and allowed traversals between locations that can depend on and change the state. This type of setup is common to many robot motion planning hardness proofs. We characterize the complexity for a natural simple case: each gadget connects up to four locations in a perfect matching (but each direction can be traversable or not in the current state), has one or two states, every gadget traversal is immediately undoable, and that gadget locations are connected by an always-traversable forest, possibly restricted to avoid crossings in the plane. Specifically, we show that any single nontrivial four-location two-state gadget type is enough for motion planning to become PSPACE-complete, while any set of simpler gadgets (effectively two-location or one-state) has a polynomial-time motion planning algorithm. As a sample application, our results show that motion planning games with "spinners" are PSPACE-complete, establishing a new hard aspect of Zelda: Oracle of Seasons.
SPDEs law equivalence and the compact support property: applications to the Allen–Cahn SPDE

Abstract Using our uniqueness in law transfer result for SPDEs, described in a recent note, we prove the equivalence of laws of SPDEs differing by a drift, under vastly applicable conditions. This gives us the equivalence in the compact support property among a large class of SPDEs. As an important application, we prove the equivalence in law of the Allen–Cahn and the associated heat SPDEs; and we give a criterion for the compact support property to hold for the Allen–Cahn SPDE with diffusion function a(t,x,u)=C u γ , with C≠0 and 1/2≤γ
Risks for Life on Habitable Planets from Superflares of Their Host Stars

We explore some of the ramifications arising from superflares on the evolutionary history of Earth, other planets in the Solar system, and exoplanets. We propose that the most powerful superflares can serve as plausible drivers of extinction events, and that their periodicity could correspond to certain patterns in the terrestrial fossil diversity record. On the other hand, weaker superflares may play a positive role in enabling the origin of life through the formation of key organic compounds. Superflares could also prove to be quite detrimental to the evolution of complex life on present-day Mars and exoplanets in the habitable zone of M- and K-dwarfs. We conclude that the risk posed by superflares has not been sufficiently appreciated, and that humanity might potentially witness a superflare event in the next $\sim 10^3$ years leading to devastating economic and technological losses. In light of the many uncertainties and assumptions associated with our analysis, we recommend that these results should be viewed with due caution.
An optically modulated zero-field atomic magnetometer with suppressed spin-exchange broadening

We demonstrate an optically pumped 87Rb magnetometer in a microfabricated vapor cell based on a zero-field dispersive resonance generated by optical modulation of the 87Rb ground state energy levels. The magnetometer is operated in the spin-exchange relaxation-free regime where high magnetic field sensitivities can be achieved. This device can be useful in applications requiring array-based magnetometers where radio frequency magnetic fields can induce cross-talk among adjacent sensors or affect the source of the magnetic field being measured.
The Interaction between Shear and Fingering (Thermohaline) Convection

Fingering convection is a turbulent mixing process that can occur in stellar radiative regions whenever the mean molecular weight increases with radius. In some cases, it can have a significant observable impact on stellar structure and evolution. The efficiency of mixing by fingering convection as a standalone process has been studied by Brown et al. (2013), but other processes such as rotation, magnetic fields and shear can affect it. In this paper, we present a first study of the effect of shear on fingering (thermohaline) convection in astrophysics. Using Direct Numerical Simulations we find that a moderate amount of shear (that is not intrinsically shear-unstable) always decreases the mixing efficiency of fingering convection, as a result of the tilt it imparts to the fingering structures. We propose a simple analytical extension of the Brown et al. (2013) model in the presence of shear that satisfactorily explains the numerically-derived turbulent compositional mixing coefficient for moderate shearing rates, and can trivially be implemented in stellar evolution codes. We also measure from the numerical simulations a turbulent viscosity, and find that the latter is strongly tied to the turbulent compositional mixing coefficient. Observational implications and caveats of the model are discussed.
Emission from Bow Shocks of Beamed Gamma-Ray Bursts

Beamed gamma-ray burst (GRB) sources produce a bow shock in their gaseous environment. The emitted flux from this bow shock may dominate over the direct emission from the jet for lines of sight that are outside the angular radius of the jet emission, Θb. The event rate for these lines of sight is increased by a factor of ~260(Θb/5°)-2. For typical GRB parameters, we find that the bow shock synchrotron emission from a jet of half-angle ~5° is visible out to tens of megaparsecs in the radio and hundreds of megaparsecs in the X-ray. If GRBs are linked to supernovae (SNe), studies of peculiar SNe in the local universe should reveal this nonthermal bow shock emission for weeks to months following the explosion. Roughly one out of ~103(Θb/5°)2 SNe should display nonthermal bow shock emission, assuming a SN rate of ~(100 yr)-1 per L* galaxy.
A heavy Higgs particle in the TeV mass range

Abstract The first simulation results are presented on Higgs mass calculations in the spontaneously broken phase of the Higgs sector in the minimal Standard Model with higher derivative regulator. A heavy Higgs particle is found in the TeV mass range in the presence of a complex conjugate ghost pair at higher energies. The ghost pair evades easy experimental detection and a nonperturbative reinterpretation of the triviality Higgs mass bound becomes necessary.
COMPARISON OF CONVECTIVE OVERSHOOTING MODELS AND THEIR IMPACT ON ABUNDANCES FROM INTEGRATED LIGHT SPECTROSCOPY OF YOUNG (<3 Gyr) STAR CLUSTERS

As part of an ongoing program to measure detailed chemical abundances in nearby galaxies, we use a sample of young- to intermediate-age clusters in the Large Magellanic Cloud with ages of 10 Myr-2 Gyr to evaluate the effect of isochrone parameters, specifically core convective overshooting, on Fe abundance results from high-resolution, integrated light spectroscopy. In this work we also obtain fiducial Fe abundances from high-resolution spectroscopy of the cluster individual member stars. We compare the Fe abundance results for the individual stars to the results from isochrones and integrated light spectroscopy to determine whether isochrones with convective overshooting should be used in our integrated light analysis of young- to intermediate-age (10 Myr-3 Gyr) star clusters. We find that when using the isochrones from the Teramo group, we obtain more accurate results for young- and intermediate-age clusters over the entire age range when using isochrones without convective overshooting. While convective overshooting is not the only uncertain aspect of stellar evolution, it is one of the most readily parameterized ingredients in stellar evolution models, and thus important to evaluate for the specific models used in our integrated light analysis. This work demonstrates that our method for integrated light spectroscopy of star clustersmore » can provide unique tests for future constraints on stellar evolution models of young- and intermediate-age clusters.« less
Exact Lagrangian fillings of Legendrian (2,n) torus links

For a Legendrian $(2,n)$ torus knot or link with maximal Thurston-Bennequin number, Ekholm, Honda, and Kalman constructed $C_n$ exact Lagrangian fillings, where $C_n$ is the $n$-th Catalan number. We show that these exact Lagrangian fillings are pairwise non-isotopic through exact Lagrangian isotopy. To do that, we compute the augmentations induced by the exact Lagrangian fillings $L$ to $\mathbb{Z}_2[H_1(L)]$ and distinguish the resulting augmentations.
Neo: a learned query optimizer

Query optimization is one of the most challenging problems in database systems. Despite the progress made over the past decades, query optimizers remain extremely complex components that require a great deal of hand-tuning for specific workloads and datasets. Motivated by this shortcoming and inspired by recent advances in applying machine learning to data management challenges, we introduce Neo (Neural Optimizer), a novel learning-based query optimizer that relies on deep neural networks to generate query executions plans. Neo bootstraps its query optimization model from existing optimizers and continues to learn from incoming queries, building upon its successes and learning from its failures. Furthermore, Neo naturally adapts to underlying data patterns and is robust to estimation errors. Experimental results demonstrate that Neo, even when bootstrapped from a simple optimizer like PostgreSQL, can learn a model that offers similar performance to state-of-the-art commercial optimizers, and in some cases even surpass them.
Establishing the quantum supremacy frontier with a 281 Pflop/s simulation

Noisy Intermediate-Scale Quantum (NISQ) computers are entering an era in which they can perform computational tasks beyond the capabilities of the most powerful classical computers, thereby achieving "Quantum Supremacy", a major milestone in quantum computing. NISQ Supremacy requires comparison with a state-of-the-art classical simulator. We report HPC simulations of hard random quantum circuits (RQC), which have been recently used as a benchmark for the first experimental demonstration of Quantum Supremacy, sustaining an average performance of 281 Pflop/s (true single precision) on Summit, currently the fastest supercomputer in the World. These simulations were carried out using qFlex, a tensor-network-based classical high-performance simulator of RQCs. Our results show an advantage of many orders of magnitude in energy consumption of NISQ devices over classical supercomputers. In addition, we propose a standard benchmark for NISQ computers based on qFlex.
Deep Reinforcement Learning for Cybersecurity Assessment of Wind Integrated Power Systems

The integration of renewable energy sources (RES) is rapidly increasing in electric power systems (EPS). While the inclusion of intermittent RES coupled with the wide-scale deployment of communication and sensing devices is important towards a fully smart grid, it has also expanded the cyber-threat landscape, effectively making power systems vulnerable to cyberattacks. This article proposes a cybersecurity assessment approach designed to assess the cyberphysical security of EPS. The work takes into consideration the intermittent generation of RES, vulnerabilities introduced by microprocessor-based electronic information and operational technology (IT/OT) devices, and contingency analysis results. The proposed approach utilizes deep reinforcement learning (DRL) and an adapted Common Vulnerability Scoring System (CVSS) score tailored to assess vulnerabilities in EPS in order to identify the optimal attack transition policy based on $N-2$ contingency results, i.e., the simultaneous failure of two system elements. The effectiveness of the work is validated via numerical and real-time simulation experiments performed on literature-based power grid test cases. The results demonstrate how the proposed method based on deep $Q$ -network ( $\text{D}Q\text{N}$ ) performs closely to a graph-search approach in terms of the number of transitions needed to find the optimal attack policy, without the need for full observation of the system. In addition, the experiments present the method’s scalability by showcasing the number of transitions needed to find the optimal attack transition policy in a large system such as the Polish 2383 bus test system. The results exhibit how the proposed approach requires one order of magnitude fewer transitions when compared to a random transition policy.
Polarization of Astronomical Maser Radiation. III. Arbitrary Zeeman Splitting and Anisotropic Pumping

General solutions of the maser polarization problem are presented for arbitrary absorption coefficients. The results are used to calculate polarization for masers permeated by magnetic fields with arbitrary values of \xB, the ratio of Zeeman splitting to Doppler linewidth, and for anisotropic pumping. The $\xb \to 0$ limit of the magnetic solution reproduces the linear polarization derived in previous studies, which were always conducted at this unphysical limit. While terms of higher order in \xb\ have a negligible effect on the magnitude of $q$, they produce some major new results. In particular, the linear polarization is accompanied by circular polarization, proportional to \xb. Because \xb\ is proportional to the transition wavelength, the circular polarization of SiO masers should decrease with rotation quantum number, as observed. In the absence of theory for $\xb < 1$, previous estimates of magnetic fields from detected maser circular polarization had to rely on conjectures in this case and generally need to be revised downward. The fields in SiO masers are \about\ 2--10 G and were overestimated by a factor of 8. The OH maser regions around supergiants have fields of \about\ 0.1--0.5 mG, which were overestimated by factors of 10--100. The fields were properly estimated for OH/IR masers ($\la$ 0.1 mG) and \H2O masers in star-forming regions (\about\ 15--50 mG). Spurious solutions that required stability analysis for their removal in all previous studies are never reproduced here; in particular, there are no stationary physical solutions for propagation at $\sin^2\theta < \third$, where $\theta$ is the angle from the direction of the magnetic field, so such radiation is unpolarized. These spurious solutions can be identified as the \xb\ = 0 limits of non-physical solutions and they never arise at finite
Quantum Behavior of Graphene Transistors near the Scaling Limit

The superior intrinsic properties of graphene have been a key research focus for the past few years. However, external components, such as metallic contacts, serve not only as essential probing elements, but also give rise to an effective electron cavity, which can form the basis for new quantum devices. In previous studies, quantum interference effects were demonstrated in graphene heterojunctions formed by a top gate. Here phase coherent transport behavior is demonstrated in a simple two terminal graphene structure with clearly resolved Fabry-Perot oscillations in sub-100 nm devices. By aggressively scaling the channel length down to 50 nm, we study the evolution of the graphene transistor from the channel-dominated diffusive regime to the contact-dominated ballistic regime. Key issues such as the current asymmetry, the question of Fermi level pinning by the contacts, the graphene screening determining the heterojunction barrier width, the scaling of minimum conductivity, and of the on/off current ratio are investigated.
SPIN TILTS IN THE DOUBLE PULSAR REVEAL SUPERNOVA SPIN ANGULAR-MOMENTUM PRODUCTION

The system PSR J0737–3039 is the only binary pulsar known to consist of two radio pulsars (PSR J0737–3039 A and PSR J0737–3039 B). This unique configuration allows measurements of spin orientation for both pulsars: pulsar A's spin is tilted from the orbital angular momentum by no more than 14 deg at 95% confidence; pulsar B's by 130 ± 1 deg at 99.7% confidence. This spin-spin misalignment requires that the origin of most of B's present-day spin is connected to the supernova that formed pulsar B. Under the simplified assumption of a single, instantaneous kick during the supernova, the spin could be thought of as originating from the off-center nature of the kick, causing pulsar B to tumble to its misaligned state. With this assumption, and using current constraints on the kick magnitude, we find that pulsar B's instantaneous kick must have been displaced from the center of mass of the exploding star by at least 1 km and probably 5-10 km. Regardless of the details of the kick mechanism and the process that produced pulsar B's current spin, the measured spin-spin misalignment in the double pulsar system provides an empirical, direct constraint on the angular momentum production in this supernova. This constraint can be used to guide core-collapse simulations and the quest for understanding the spins and kicks of compact objects.
Semantics and Algorithms for Parametric Monitoring

Analysis of execution traces plays a fundamental role in many program anal- ysis approaches, such as runtime verication, testing, monitoring, and specication min- ing. Execution traces are frequently parametric, i.e., they contain events with param- eter bindings. Each parametric trace usually consists of many meaningful trace slices merged together, each slice corresponding to one parameter binding. For example, a Java program creating iterator objects i1 and i2 over collection object c1 may yield a trace createIterhc1i1i nexthi1i createIterhc1i2i updateCollhc1i nexthi1i parametric in collection c and iterator i, whose slices corresponding to instances \c;i 7! c1;i1" and \c;i 7! c1;i2" are createIterhc1i1i nexthi1i updateCollhc1i nexthi1i and, respectively, createIterhc1i2i updateCollhc1i. Several approaches have been proposed to specify and dynamically analyze parametric properties, but they have limitations: some in the specication
How two neutrino superbeam experiments do better than one

Abstract We examine the use of two superbeam neutrino oscillation experiments with baselines ≲1000 km to resolve parameter degeneracies inherent in the three-neutrino analysis of such experiments. We find that with appropriate choices of neutrino energies and baselines two experiments with different baselines can provide a much better determination of the neutrino mass ordering than a single experiment alone. Two baselines are especially beneficial when the mass scale for solar neutrino oscillations δm 2 sol is ≳5×10 −5 eV 2 . We also examine CP violation sensitivity and the resolution of other parameter degeneracies. We find that the combined data of superbeam experiments with baselines of 295 and 900 km can provide sensitivity to both the neutrino mass ordering and CP violation for sin 2 2 θ 13 down to 0.03 for | δm 2 atm |≃3×10 −3 eV 2 . It would be advantageous to have a 10% determination of | δm 2 atm | before the beam energies and baselines are finalized, although if | δm 2 atm | is not that well known, the neutrino energies and baselines can be chosen to give fairly good sensitivity for a range of | δm 2 atm |.
Flat entanglement spectra in fixed-area states of quantum gravity

We use the Einstein-Hilbert gravitational path integral to investigate gravita- tional entanglement at leading order O(1/G). We argue that semiclassical states prepared by a Euclidean path integral have the property that projecting them onto a subspace in which the Ryu-Takayanagi or Hubeny-Rangamani-Takayanagi surface has definite area gives a state with a flat entanglement spectrum at this order in gravitational perturbation theory. This means that the reduced density matrix can be approximated as proportional to the identity to the extent that its Renyi entropies Sn are independent of n at this order. The n-dependence of Sn in more general states then arises from sums over the RT/HRT- area, which are generally dominated by different values of this area for each n. This provides a simple picture of gravitational entanglement, bolsters the connection between holographic systems and tensor network models, clarifies the bulk interpretation of alge- braic centers which arise in the quantum error-correcting description of holography, and strengthens the connection between bulk and boundary modular Hamiltonians described by Jafferis, Lewkowycz, Maldacena, and Suh.
Is WISEP J060738.65+242953.4 Really a Magnetically Active, Pole-on L Dwarf?

The interplay of rotation and manifested magnetic activity on ultracool dwarfs (UCDs) is of key importance for gathering clues as to the operation of the dynamos within these objects. A number of magnetized UCDs host kG-strength magnetic fields. It was recently reported that the L8 dwarf WISEP J060738.65+242953.4 is a radio-emitting UCD that is likely observed pole-on, due to its lack of photometric variability and narrow spectral lines. Follow-up radio observations at Arecibo Observatory, together with careful analysis of previously published details, however, suggest that the scientific and statistical significance of the radio and spectroscopic data have been overstated. If the UCD is observed along its aligned spin/magnetic axis, the absence of observed H$\alpha$ activity may present challenges to the auroral model of UCD magnetism, although short or long-term cyclic magnetic activity may explain this behavior. Monte Carlo simulations presented here suggest that the source probably rotates with v sin i=6-12 km s$^{-1}$, indicating that its inclination angle and rotational velocity are unexceptional and that its angular momentum has evolved as expected for brown dwarfs observed in ~1 Myr-old clusters. The discovery and verification of the most rapidly and slowest rotating brown dwarfs place valuable constraints on the angular momentum evolution and magnetic activity history of these objects.
Line and point defects in nonlinear anisotropic solids

In this paper, we present some analytical solutions for the stress fields of nonlinear anisotropic solids with distributed line and point defects. In particular, we determine the stress fields of (i) a parallel cylindrically symmetric distribution of screw dislocations in infinite orthotropic and monoclinic media, (ii) a cylindrically symmetric distribution of parallel wedge disclinations in an infinite orthotropic medium, (iii) a distribution of edge dislocations in an orthotropic medium, and (iv) a spherically symmetric distribution of point defects in a transversely isotropic spherical ball.
Curriculum Guidelines for Undergraduate Programs in Data Science

The Park City Math Institute 2016 Summer Undergraduate Faculty Program met for the purpose of composing guidelines for undergraduate programs in data science. The group consisted of 25 undergraduate faculty from a variety of institutions in the United States, primarily from the disciplines of mathematics, statistics, and computer science. These guidelines are meant to provide some structure for institutions planning for or revising a major in data science.
Path Integral Representation for Interface States of the Anisotropic Heisenberg Model

We develop a geometric representation for the ground state of the spin-1/2 quantum XXZ ferromagnetic chain in terms of suitably weighted random walks in a two-dimensional lattice. The path integral model so obtained admits a genuine classical statistical mechanics interpretation with a translation invariant Hamiltonian. This new representation is used to study the interface ground states of the XXZ model. We prove that the probability of having a number of down spins in the up phase decays exponentially with the sum of their distances to the interface plus the square of the number of down spins. As an application of this bound, we prove that the total third component of the spin in a large interval of even length centered on the interface does not fluctuate, i.e. has zero variance. We also show how to construct a path integral representation in higher dimensions and obtain a reduction formula for the partition functions in two dimensions in terms of the partition function of the one-dimensional model.
Designing Commercial Therapeutic Robots for Privacy Preserving Systems and Ethical Research Practices Within the Home

The migration of robots from the laboratory into sensitive home settings as commercially available therapeutic agents represents a significant transition for information privacy and ethical imperatives. We present new privacy paradigms and apply the fair information practices (FIPs) to investigate concerns unique to the placement of therapeutic robots in private home contexts. We then explore the importance and utility of research ethics as operationalized by existing human subjects research frameworks to guide the consideration of therapeutic robotic users—a step vital to the continued research and development of these platforms. Together, privacy and research ethics frameworks provide two complementary approaches to protect users and ensure responsible yet robust information sharing for technology development. We make recommendations for the implementation of these principles—paying particular attention to specific principles that apply to vulnerable individuals (i.e., children, disabled, or elderly persons)—to promote the adoption and continued improvement of long-term, responsible, and research-enabled robotics in private settings.
Self-perpetuating Spiral Arms in Disk Galaxies

The causes of spiral structure in galaxies remain uncertain. Leaving aside the grand bisymmetric spirals with their own well-known complications, here we consider the possibility that multi-armed spiral features originate from density inhomogeneities orbiting within disks. Using high-resolution N-body simulations, we follow the motions of stars under the influence of gravity, and show that mass concentrations with properties similar to those of giant molecular clouds can induce the development of spiral arms through a process termed swing amplification. However, unlike in earlier work, we demonstrate that the eventual response of the disk can be highly non-linear, significantly modifying the formation and longevity of the resulting patterns. Contrary to expectations, ragged spiral structures can thus survive at least in a statistical sense long after the original perturbing influence has been removed.
The Bloch equation for spin dynamics in electron storage rings: Computational and theoretical aspects

In this paper, we describe our work on spin polarization in high-energy electron storage rings which we base on the Full Bloch equation (FBE) for the polarization density and which aims towards the...
The Density and Spectral Energy Distributions of Red Galaxies at z ~ 3.7

We use the deep NIR imaging of the FIRES survey to investigate trends with redshift of the properties of galaxies selected to have strong Balmer/4000 A breaks at 2 1.3 (AB) color criterion designed to select red galaxies at z > 2, we propose two color criteria, J - H > 0.9 and H - K > 0.9, to select red galaxies in two redshift bins at 2 0.9, H 0.9, Ks < 24.6, where the flux limits are chosen to match the limiting rest-frame luminosities at the different median redshifts of the two samples. The space densities of the Js - H and H - Ks samples are (1.5 ± 0.5) × 10-4 and (1.2 ± 0.4) × 10-4 Mpc-3, respectively. The rest-frame U - B colors of galaxies in both samples are similarly red (as expected from the definition of the color criteria), but the rest-frame UV properties are different: galaxies in the higher redshift H - Ks selected sample have blue NUV-optical colors and UV slopes similar to those of Lyman break galaxies, while the Js - H galaxies are generally red over the entire wavelength range observed. Synthetic template fits indicate that the distinct rest-NUV properties of the two samples are primarily a result of dust: we find AVJH = 1 mag and AVHK = 0.2 mag. The median stellar mass determined from the template fits decreases by a factor of ~5 from z = 2.4 to 3.7, which, coupled with the fact that the space density of such galaxies remains roughly constant, may imply that the stellar mass density in red galaxies decreases by a similar factor over this redshift range.
Enhanced input modeling for construction simulation using bayesian deep neural networks

This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multi-source data generated from construction operations, which provides rich information for input modeling. The framework implements Bayesian deep neural networks to facilitate the purpose of incorporating richer information in input modeling. A case study on road paving operation is performed to test the feasibility and applicability of the proposed framework. Overall, this research enhances input modeling by deriving detailed input models, thereby, augmenting the decision-making processes in construction operations. This research also sheds lights on prompting data-driven simulation through incorporating machine learning techniques.