cssi/SciSciGPT-SciSciNet huggingface dataset:
K-Athena : A Performance Portable Structured Grid Finite Volume Magnetohydrodynamics Code

Large scale simulations are a key pillar of modern research and require ever-increasing computational resources. Different novel manycore architectures have emerged in recent years on the way towards the exascale era. Performance portability is required to prevent repeated non-trivial refactoring of a code for different architectures. We combine Athena++ , an existing magnetohydrodynamics (MHD) CPU code, with Kokkos , a performance portable on-node parallel programming paradigm, into K-Athena to allow efficient simulations on multiple architectures using a single codebase. We present profiling and scaling results for different platforms including Intel Skylake CPUs, Intel Xeon Phis, and NVIDIA GPUs. K-Athena achieves $>10^8$ > 10 8 cell-updates/s on a single V100 GPU for second-order double precision MHD calculations, and a speedup of 30 on up to 24 576 GPUs on Summit (compared to 172,032 CPU cores), reaching $1.94\times 10^{12}$ 1 . 94 × 10 12 total cell-updates/s at 76 percent parallel efficiency. Using a roofline analysis we demonstrate that the overall performance is currently limited by DRAM bandwidth and calculate a performance portability metric of 62.8 percent. Finally, we present the implementation strategies used and the challenges encountered in maximizing performance. This will provide other research groups with a straightforward approach to prepare their own codes for the exascale era. K-Athena is available at https://gitlab.com/pgrete/kathena .
In vivo repair of a protein underlying a neurological disorder by programmable RNA editing

Summary Programmable RNA editing is gaining momentum as an approach to repair mutations, but its efficiency in repairing endogenous mutant RNA in complex tissue is unknown. Here we apply this approach to the brain and successfully repair a guanosine-to-adenosine mutation in methyl CpG binding protein 2 RNA that causes the neurodevelopmental disease Rett syndrome. Repair is mediated by hippocampal injections of juvenile Mecp2317G>A mice with an adeno-associated virus expressing the hyperactive catalytic domain of adenosine deaminase acting on RNA 2 and Mecp2 guide. After 1 month, 50% of Mecp2 RNA is recoded in three different hippocampal neuronal populations. MeCP2 protein localization to heterochromatin is restored in neurons to 50% of wild-type levels. Whole-transcriptome RNA analysis of one neuronal population indicates that the majority of off-target editing sites exhibit rates of 30% or less. This study demonstrates that programmable RNA editing can be utilized to repair mutations in mouse models of neurological disease.
Actions of homeomorphism groups of manifolds admitting a nontrivial finite free action

In this paper, we study the action of Homeo₀ (M), the identity component of the group of homeomorphisms of an n-dimensional manifold M with an F_p-free action, on another manifold N of dimension n+k < 2n. We prove that if M is not an F_p-homology sphere, then N≅M×K for a homology manifold K such that the action of Homeo₀ (M) on M is standard and on K is trivial. In particular, for M = S_n a sphere, any nontrivial action is a generalization of the "coning-off" construction.
treeheatr: an R package for interpretable decision tree visualizations.

Summary treeheatr is an R package for creating interpretable decision tree visualizations with the data represented as a heatmap at the tree's leaf nodes. The integrated presentation of the tree structure along with an overview of the data efficiently illustrates how the tree nodes split up the feature space and how well the tree model performs. This visualization can also be examined in depth to uncover the correlation structure in the data and importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students' understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. Availability and implementation The treeheatr package is freely available under the permissive MIT license at https://trang1618.github.io/treeheatr and https://cran.r-project.org/package=treeheatr. It comes with a detailed vignette that is automatically built with GitHub Actions continuous integration.
A Dynamic Process Reference Model for Sparse Networks with Reciprocity

Many social and other networks exhibit stable size scaling relationships, such that features such as mean degree or reciprocation rates change slowly or are approximately constant as the number of ...
An Audio-Based Fault Diagnosis Method for Quadrotors Using Convolutional Neural Network and Transfer Learning.

Quadrotor unmanned aerial vehicles (UAVs) have been developed and applied into several types of workplaces, such as warehouses, which usually involve human workers. The co-existence of human and UAVs brings new challenges to UAVs: potential failure of UAVs may cause risk and danger to surrounding human. Effective and efficient detection of such failure may provide early warning to the surrounding human workers and reduce such risk to human beings as much as possible. One of the most common reasons that cause the failure of the UAV's flight is the physical damage to the propellers. This paper presents a method to detect the propellers damage only based on the audio noise caused by the UAV's flight. The diagnostic model is developed based on convolutional neural network (CNN) and transfer learning techniques. The audio data is collected from the UAVs in real time, transformed into the time-frequency spectrogram, and used to train the CNN-based diagnostic model. The developed model is able to detect the abnormal features of the spectrogram and thus the physical damage of the propellers. To reduce the data dependence on the UAV's dynamic models and enable the utilization of the training data from UAVs with different dynamic models, the CNN-based diagnostic model is further augmented by transfer learning. As such, the refinement of the well-trained diagnostic model ground on other UAVs only requires a small amount of UAV's training data. Experimental tests are conducted to validate the diagnostic model with an accuracy of higher than 90%.
Scalable Computation of Controlled Invariant Sets for Discrete-Time Linear Systems with Input Delays

In this paper, we first propose a method that can efficiently compute the maximal robust controlled invariant set for discrete-time linear systems with pure delay in input. The key to this method is to construct an auxiliary linear system (without delay) with the same state-space dimension of the original system in consideration and to relate the maximal invariant set of the auxiliary system to that of the original system. When the system is subject to disturbances, guaranteeing safety is harder for systems with input delays. Ability to incorporate any additional information about the disturbance becomes more critical in these cases. Motivated by this observation, in the second part of the paper, we generalize the proposed method to take into account additional preview information on the disturbances, while maintaining computational efficiency. Compared with the naive approach of constructing a higher dimensional system by appending the state-space with the delayed inputs and previewed disturbances, the proposed approach is demonstrated to scale much better with the increasing delay time.
Standardized flux seasonality metrics: A companion dataset for FLUXNET annual product

Abstract. Phenological events are integrative and sensitive indicators of ecosystem processes that respond to climate, water and nutrient availability, disturbance, and environmental change. The seasonality of ecosystem processes, including biogeochemical fluxes, can similarly be decomposed to identify key transition points and phase durations, which can be determined with high accuracy, and are specific to the processes of interest. As the seasonality of different processes differ, it can be argued that the interannual trends and responses to environmental forcings can be better described through the fluxes' own temporal characteristics than through correlation to traditional phenological events like bud break or leaf coloration. Here we present a global dataset of seasonality or phenological metrics calculated for gross primary productivity (GPP), ecosystem respiration (RE), latent heat (LE), and sensible heat ( H ) calculated for the FLUXNET2015 Dataset of about 200 sites and 1500 site years of data. The database includes metrics (i) on an absolute flux scale for comparisons with flux magnitudes and (ii) on a normalized scale for comparisons of change rates across different fluxes. Flux seasonality was characterized by fitting a single-pass double-logistic model to daily flux integrals, and the derivatives of the fitted time series were used to extract the phenological metrics marking key turning points, season lengths, and rates of change. Seasonal transition points could be determined with a 90 % confidence interval of 6–11 d for GPP, 8–14 d for RE, 10–15 d for LE, and 15–23 d for H . The phenology metrics derived from different partitioning methods diverged, at times significantly. This Flux Seasonality Metrics Database (FSMD) can be accessed at the US Department of Energy's (DOE) Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE, https://doi.org/10.15485/1602532 ; Yang and Noormets, 2020). We hope that it will facilitate new lines of research, including (1) validating and benchmarking ecosystem process models, (2) parameterizing satellite remote sensing phenology and PhenoCam products, (3) optimizing phenological models, and (4) generally expanding the toolset for interpreting ecosystems responses to changing climate.
Evolution of the acoustic startle response of Mexican cavefish.

The ability to detect threatening stimuli and initiate an escape response is essential for survival and under stringent evolutionary pressure. In diverse fish species, acoustic stimuli activate Mauthner neurons, which initiate a C-start escape response. This reflexive behavior is highly conserved across aquatic species and provides a model for investigating the neural mechanism underlying the evolution of escape behavior. Here, we characterize evolved differences in the C-start response between populations of the Mexican cavefish, Astyanax mexicanus. Cave populations of A. mexicanus inhabit an environment devoid of light and macroscopic predators, resulting in evolved differences in various morphological and behavioral traits. We find that the C-start is present in river-dwelling surface fish and multiple populations of cavefish, but that response kinematics and probability differ between populations. The Pachon population of cavefish exhibits an increased response probability, a slower response latency and speed, and reduction of the maximum bend angle, revealing evolved differences between surface and cave populations. Analysis of the responses of two other independently evolved populations of cavefish, revealed the repeated evolution of reduced angular speed. Investigation of surface-cave hybrids reveals a correlation between angular speed and peak angle, suggesting these two kinematic characteristics are related at the genetic or functional levels. Together, these findings provide support for the use of A. mexicanus as a model to investigate the evolution of escape behavior.
The Rise of Embedded Analytics: Empowering Manufacturing and Service Industry With Big Data

This article describes how in today's hyper-competitive environment, business leaders around the world are using analytics technologies to create business values, and to gain a better understanding...
Reconstructing Biological and Digital Phylogenetic Trees in Parallel.

In this paper, we study the parallel query complexity of reconstructing biological and digital phylogenetic trees from simple queries involving their nodes. This is motivated from computational biology, data protection, and computer security settings, which can be abstracted in terms of two parties, a responder, Alice, who must correctly answer queries of a given type regarding a degree-d tree, T, and a querier, Bob, who issues batches of queries, with each query in a batch being independent of the others, so as to eventually infer the structure of T. We show that a querier can efficiently reconstruct an n-node degree-d tree, T, with a logarithmic number of rounds and quasilinear number of queries, with high probability, for various types of queries, including relative-distance queries and path queries. Our results are all asymptotically optimal and improve the asymptotic (sequential) query complexity for one of the problems we study. Moreover, through an experimental analysis using both real-world and synthetic data, we provide empirical evidence that our algorithms provide significant parallel speedups while also improving the total query complexities for the problems we study.
SSGMT: A Secure Smart Grid Monitoring Technique

Critical infrastructure systems like power grid require an improved critical information infrastructure (CII) that can not only help in monitoring of the critical entities but also take part in failure analysis and self-healing. Efficient designing of a CII is challenging as each kind of communication technology has its own advantages and disadvantages. Wired networks are highly scalable and secure, but they are neither cost effective nor dynamic in nature. Wireless communication technologies on the other hand are easy to deploy, low cost etc. but they are vulnerable to cyber-attacks. In order to optimize cost, power consumption, dynamic nature, accuracy and scalability a hybrid communication network is designed in this paper where a portion of the communication network is built using wireless sensor networks (WSN) and the rest is a wired network of fiber optic channels. To offer seamless operation of the hybrid communication network and provide security a Secure Smart Grid Monitoring Technique (SSGMT) is also proposed. The performance of the proposed hybrid CII for the generation and transmission system of power grid coupled with the SSGMT during different cyber-attacks is tested using NS2 simulator. The simulation results show that the SSGMT for a joint power communication network of IEEE 118-Bus system performs better than the prevailing wireless CIIs like Lo-ADI and Modified AODV.
Bernstein-Bézier weight-adjusted discontinuous Galerkin methods for wave propagation in heterogeneous media

Abstract This paper presents an efficient discontinuous Galerkin method to simulate wave propagation in heterogeneous media with sub-cell variations. This method is based on a weight-adjusted discontinuous Galerkin method (WADG), which achieves high order accuracy for arbitrary heterogeneous media [1] . However, the computational cost of WADG grows rapidly with the order of approximation. In this work, we propose a Bernstein-Bezier weight-adjusted discontinuous Galerkin method (BBWADG) to address this cost. By approximating sub-cell heterogeneities by a fixed degree polynomial, the main steps of WADG can be expressed as polynomial multiplication and L 2 projection, which we carry out using fast Bernstein algorithms. The proposed approach reduces the overall computational complexity from O ( N 2 d ) to O ( N d + 1 ) in d dimensions. Numerical experiments illustrate the accuracy of the proposed approach, and computational experiments for a GPU implementation of BBWADG verify that this theoretical complexity is achieved in practice.
Predicting with Proxies: Transfer Learning in High Dimension

Predictive analytics is increasingly used to guide decision making in many applications. However, in practice, we often have limited data on the true predictive task of interest and must instead re...
Double transition-metal MXenes: Atomistic design of two-dimensional carbides and nitrides

MXenes are a large family of two-dimensional (2D) transition-metal carbides, nitrides, and carbonitrides. The MXene family has expanded since their original discovery in 2011, and has grown larger with the discovery of ordered double transition-metal (DTM) MXenes. These DTM MXenes differ from their counterpart mono-transition-metal (mono-M) MXenes, where two transition metals can occupy the metal sites. Ordered DTM MXenes are comprised of transition metals in either an in-plane or out-of-plane ordered structure. Additionally, some DTM MXenes are in the form of random solid solutions, which are defined by two randomly distributed transition metals throughout the 2D structure. Their different structures and array of transition-metal pairs provide the ability to tune DTM MXenes for specific optical, magnetic, electrochemical, thermoelectric, catalytic, or mechanical behavior. This degree of control over their composition and structure is unique in the field of 2D materials and offers a new avenue for application-driven design of functional nanomaterials. In this article, we review the synthesis, structure, and properties of DTM MXenes and provide an outlook for future research in this field.
End-to-End Trainable Deep Active Contour Models for Automated Image Segmentation: Delineating Buildings in Aerial Imagery

The automated segmentation of buildings in remote sensing imagery is a challenging task that requires the accurate delineation of multiple building instances over typically large image areas. Manual methods are often laborious and current deep-learning-based approaches fail to delineate all building instances and do so with adequate accuracy. As a solution, we present Trainable Deep Active Contours (TDACs), an automatic image segmentation framework that intimately unites Convolutional Neural Networks (CNNs) and Active Contour Models (ACMs). The Eulerian energy functional of the ACM component includes per-pixel parameter maps that are predicted by the backbone CNN, which also initializes the ACM. Importantly, both the ACM and CNN components are fully implemented in TensorFlow and the entire TDAC architecture is end-to-end automatically differentiable and backpropagation trainable without user intervention. TDAC yields fast, accurate, and fully automatic simultaneous delineation of arbitrarily many buildings in the image. We validate the model on two publicly available aerial image datasets for building segmentation, and our results demonstrate that TDAC establishes a new state-of-the-art performance.
A Pyramid Recurrent Network for Predicting Crowdsourced Speech-Quality Ratings of Real-World Signals.

The real-world capabilities of objective speech quality measures are limited since current measures (1) are developed from simulated data that does not adequately model real environments; or they (2) predict objective scores that are not always strongly correlated with subjective ratings. Additionally, a large dataset of real-world signals with listener quality ratings does not currently exist, which would help facilitate real-world assessment. In this paper, we collect and predict the perceptual quality of real-world speech signals that are evaluated by human listeners. We first collect a large quality rating dataset by conducting crowdsourced listening studies on two real-world corpora. We further develop a novel approach that predicts human quality ratings using a pyramid bidirectional long short term memory (pBLSTM) network with an attention mechanism. The results show that the proposed model achieves statistically lower estimation errors than prior assessment approaches, where the predicted scores strongly correlate with human judgments.
Heavy flavor in heavy-ion collisions at RHIC and RHIC II

Abstract In the initial years of operation, experiments at the Relativistic Heavy Ion Collider (RHIC) have identified a new form of matter formed in nucleus–nucleus collisions at energy densities more than 100 times that of a cold atomic nucleus. Measurements and comparison with relativistic hydrodynamic models indicate that the matter thermalizes in an unexpectedly short time, has an energy density at least 15 times larger than needed for color deconfinement, has a temperature about twice the critical temperature predicted by lattice QCD, and appears to exhibit collective motion with ideal hydrodynamic properties–a “perfect liquid” that appears to flow with a near-zero viscosity to entropy ratio–lower than any previously observed fluid and perhaps close to a universal lower bound. However, a fundamental understanding of the medium seen in heavy-ion collisions at RHIC does not yet exist. The most important scientific challenge for the field in the next decade is the quantitative exploration of the new state of nuclear matter. That will require new data that will, in turn, require enhanced capabilities of the RHIC detectors and accelerator. In this report we discuss the scientific opportunities for an upgraded RHIC facility–RHIC II–in conjunction with improved capabilities of the two large RHIC detectors, PHENIX and STAR. We focus solely on heavy flavor probes. Their production rates are calculable using the well-established techniques of perturbative QCD and their sizable interactions with the hot QCD medium provide unique and sensitive measurements of its crucial properties making them one of the key diagnostic tools available to us.
Superconducting Optoelectronic Circuits for Neuromorphic Computing

To realize functionality similar to that of their biological inspirations, advanced neuromorphic systems require massive interconnectivity, extreme energy efficiency, and complex signaling mechanisms. The authors propose an integrated optoelectronic platform combining superconducting electronics with photonic signaling, to enable neuromorphic computing beyond the scale of the human brain.
Computational Assessment of Silicon Quantum Gate Based on Detuning Mechanism for Quantum Computing

Silicon-based quantum computing has the potential advantages of low cost, high integration density, and compatibility with CMOS technologies. The detuning mechanism has been used to experimentally achieve silicon two-qubit quantum gates and programmable quantum processors. In this paper, the scaling behaviors and variability issues are explored by numerical device simulations of a model silicon quantum gate based on the detuning mechanism. The device physics of quantum gates modulation, tradeoff between device speed and quantum fidelity, and impact of variability on the implementation of a quantum algorithm are examined. The results indicate the attractive potential to achieve high speed and fidelity silicon quantum gates with a low operation voltage. To scale up, reducing the device variability and mitigating the variability effect are identified to be indispensable for reliable implementing a quantum computing algorithm with the silicon quantum gates based on the detuning mechanism. A scheme to use the control electronics for mitigating the variability of quantum gates is proposed.
Self-conjugate QCD

We carry out a systematic study of SU(6) Yang-Mills theory endowed with fermions in the adjoint and 3-index antisymmetric mixed-representation. The fermion bilinear in the 3-index antisymmetric representation vanishes identically, which leads to interesting new phenomena. We first study the theory on a small circle, i.e., on $$ {\mathrm{\mathbb{R}}}^3\times {\mathbbm{S}}_L^1 $$ , employing symmetry-twisted boundary conditions and semi-classical techniques. We find that the ground state is 3-fold degenerate, which can be explained as a consequence of a 1-form/0-form mixed ’t Hooft anomaly. In addition, the theory may admit massless bosonic and fermionic degrees of freedom, depending on the number of flavors, and confines the electric probes in the infrared. Empowered by ’t Hooft anomaly matching conditions along with the 2-loop β-function, we further examine the possible infrared symmetry realizations on ℝ4 for various number of adjoint and 3-index antisymmetric fermions. The infrared theory is either a conformal field theory, which is expected for a large number of flavors, or it is confining with or without chiral symmetry breaking. In a few cases, we are able to give enough evidence for adiabatic continuity between the small- and large-circle limits.
A bordered Chekanov-Eliashberg algebra

Given a front projection of a Legendrian knot K in R 3 , which has been cut into several pieces along vertical lines, we assign a differential graded algebra to each piece and prove a van Kampen theorem describing the Chekanov–Eliashberg invariant of K as a pushout of these algebras. We then use this theorem to construct maps between the invariants of Legendrian knots related by certain tangle replacements, and to describe the linearized contact homology of Legendrian Whitehead doubles. Other consequences include a Mayer–Vietoris sequence for linearized contact homology and a van Kampen theorem for the characteristic algebra of a Legendrian knot.
Simulated Quantum Annealing Can Be Exponentially Faster Than Classical Simulated Annealing

Can quantum computers solve optimization problems much more quickly than classical computers? One major piece of evidence for this proposition has been the fact that Quantum Annealing (QA) finds the minimum of some cost functions exponentially more quickly than classical Simulated Annealing (SA). One such cost function is the simple "Hamming weight with a spike" function in which the input is an n-bit string and the objective function is simply the Hamming weight, plus a tall thin barrier centered around Hamming weight n/4. While the global minimum of this cost function can be found by inspection, it is also a plausible toy model of the sort of local minima that arise in real-world optimization problems. It was shown by Farhi, Goldstone and Gutmann that for this example SA takes exponential time and QA takes polynomial time, and the same result was generalized by Reichardt to include barriers with width and height scaling as positive powers of n such that the total area under the barrier is at most the square root of n. This advantage could be explained in terms of quantum-mechanical "tunneling." Our work considers a classical algorithm known as Simulated Quantum Annealing (SQA) which relates certain quantum systems to classical Markov chains. By proving that these chains mix rapidly, we show that SQA runs in polynomial time on the Hamming weight with spike problem in much of the parameter regime where QA achieves an exponential advantage over SA. While our analysis only covers this toy model, it can be seen as evidence against the prospect of exponential quantum speedup using tunneling. Our technical contributions include extending the canonical path method for analyzing Markov chains to cover the case when not all vertices can be connected by low-congestion paths. We also develop methods for taking advantage of warm starts and for relating the quantum state in QA to the probability distribution in SQA. These techniques may be of use in future studies of SQA or of rapidly mixing Markov chains in general.
Tractions and Stress Fibers Control Cell Shape and Rearrangements in Collective Cell Migration

Traction between a living cell and its underlying surface controls the cell's shape and motion, revealing one of the dominant factors underlying cell migration.
Magnetic Field Measurements of T Tauri Stars in the Orion Nebula Cluster

We present an analysis of high-resolution (R ~ 50, 000) infrared K-band echelle spectra of 14 T Tauri stars (TTSs) in the Orion Nebula Cluster. We model Zeeman broadening in three magnetically sensitive Ti I lines near 2.2 μm and consistently detect kilogauss-level magnetic fields in the stellar photospheres. The data are consistent in each case with the entire stellar surface being covered with magnetic fields, suggesting that magnetic pressure likely dominates over gas pressure in the photospheres of these stars. These very strong magnetic fields might themselves be responsible for the underproduction of X-ray emission of TTSs relative to what is expected based on main-sequence star calibrations. We combine these results with previous measurements of 14 stars in Taurus and 5 stars in the TW Hydrae association to study the potential variation of magnetic field properties during the first 10 million years of stellar evolution, finding a steady decline in total magnetic flux with age.
Hybrid Graphene-Plasmonic Gratings to Achieve Enhanced Nonlinear Effects at Terahertz Frequencies

The demonstration of enhanced nonlinear optical effects at terahertz frequencies is important for the development of reconfigurable planar THz devices. However, high input intensities are usually required to efficiently excite nonlinear optical effects in ultrathin structures---an acute problem here, as high-power THz sources are not available. This work presents a class of ultrathin, nonlinear, hybrid planar THz devices based on graphene-covered plasmonic gratings. These devices exhibit very large nonlinear responses to low input power, and are expected to be useful for THz nonlinear spectroscopy, noninvasive subwavelength imaging, and communication applications.
Metadynamics for training neural network model chemistries: A competitive assessment

Neural network model chemistries (NNMCs) promise to facilitate the accurate exploration of chemical space and simulation of large reactive systems. One important path to improving these models is to add layers of physical detail, especially long-range forces. At short range, however, these models are data driven and data limited. Little is systematically known about how data should be sampled, and “test data” chosen randomly from some sampling techniques can provide poor information about generality. If the sampling method is narrow, “test error” can appear encouragingly tiny while the model fails catastrophically elsewhere. In this manuscript, we competitively evaluate two common sampling methods: molecular dynamics (MD), normal-mode sampling, and one uncommon alternative, Metadynamics (MetaMD), for preparing training geometries. We show that MD is an inefficient sampling method in the sense that additional samples do not improve generality. We also show that MetaMD is easily implemented in any NNMC soft...
Electronic structure and optical properties of metallic nanoshells

The electronic structure and optical properties of metallic nanoshells are investigated using a jellium model and the Time Dependent Local Density Approximation (TDLDA). An efficient numerical implementation enables applications to nanoshells of realistic size with up to a million electrons. We demonstrate how a frequency dependent background polarizability of the jellium shell can be included in the TDLDA formalism. The energies of the plasmon resonances are calculated for nanoshells of different sizes and with different dielectric cores, dielectric embedding media, and dielectric shell backgrounds. The plasmon energies are found to be in good agreement with the results from classical Mie scattering theory using a Drude dielectric function. A comparison with experimental data shows excellent agreement between theory and the measured frequency dependent absorption spectra.
Unsupervised Text Style Transfer with Padded Masked Language Models

We propose Masker, an unsupervised text-editing method for style transfer. To tackle cases when no parallel source--target pairs are available, we train masked language models (MLMs) for both the source and the target domain. Then we find the text spans where the two models disagree the most in terms of likelihood. This allows us to identify the source tokens to delete to transform the source text to match the style of the target domain. The deleted tokens are replaced with the target MLM, and by using a padded MLM variant, we avoid having to predetermine the number of inserted tokens. Our experiments on sentence fusion and sentiment transfer demonstrate that Masker performs competitively in a fully unsupervised setting. Moreover, in low-resource settings, it improves supervised methods' accuracy by over 10 percentage points when pre-training them on silver training data generated by Masker.
Petabyte-scale data mining: dream or reality?

Science is becoming very data intensive. Today's astronomy datasets with tens of millions of galaxies already present substantial challenges for data mining. In less than 10 years the catalogs are expected to grow to billions of objects, and image archives will reach Petabytes. Imagine having a 100GB database in 1996, when disk scanning speeds were 30MB/s, and database tools were immature. Such a task today is trivial, almost manageable with a laptop. We think that the issue of a PB database will be very similar in six years. In this paper we scale our current experiments in data archiving and analysis on the Sloan Digital Sky Survey data six years into the future. We analyze these projections and look at the requirements of performing data mining on such data sets. We conclude that the task scales rather well: we could do the job today, although it would be expensive. There do not seem to be any show-stoppers that would prevent us from storing and using a Petabyte dataset six years from today.
Deterministic tuning of slow-light in photonic-crystal waveguides through the C and L bands by atomic layer deposition

We demonstrate digital tuning of the slow-light regime in silicon photonic-crystal waveguides by performing atomic layer deposition of hafnium oxide. The high group-index regime was deterministically controlled (redshift of 140±10 pm per atomic layer) without affecting the group-velocity dispersion and third-order dispersion. Additionally, differential tuning of 110±30 pm per monolayer of the slow-light TE-like and TM-like modes was observed. This passive postfabrication process has potential applications including the tuning of chip-scale optical interconnects, as well as Raman and parametric amplification.
Velocity profile in a two-layer Kolmogorov-like flow

In this article, we discuss flows in shallow, stratified horizontal layers of two immiscible fluids. The top layer is an electrolyte which is electromagnetically driven and the bottom layer is a dielectric fluid. Using a quasi-two-dimensional approximation, which assumes a horizontal flow whose direction is independent of the vertical coordinate, we derive a generalized two-dimensional vorticity equation describing the evolution of the horizontal flow. Also, we derive an expression for the vertical profile of the horizontal velocity field. Measuring the horizontal velocity fields at the electrolyte-air and electrolyte-dielectric interfaces using particle image velocimetry, we validate the theoretical predictions of the horizontal velocity and its vertical profile for steady as well as for freely decaying Kolmogorov-like flows. Our analysis shows that by increasing the viscosity of the electrolyte relative to that of the dielectric, one may significantly improve the uniformity of the flow in the electrolyte, yielding excellent agreement between the analytical predictions and the experimental measurements.
ANOMALOUS DISSIPATION AND ENERGY CASCADE IN 3D INVISCID FLOWS

Adopting the setting for the study of existence and scale locality of the energy cascade in 3D viscous flows in physical space recently introduced by the authors to 3D inviscid flows, it is shown that the anomalous dissipation is – in the case of decaying turbulence – indeed capable of triggering the cascade which then continues ad infinitum, confirming Onsager’s predictions.
Numerical modeling of the disruption of Comet D/1993 F2 Shoemaker-Levy 9 representing the progenitor by a gravitationally bound assemblage of randomly shaped polyhedra

We advance the modeling of rubble-pile solid bodies by re-examining the tidal breakup of comet Shoemaker-Levy 9, an event that occurred during a encounter with Jupiter in 1992 July. Tidal disruption of the comet nucleus led to a chain of sub-nuclei ~100-1000 m diameter; these went on to collide with the planet two years later. They were intensively studied prior to and during the collisions, making SL9 the best natural benchmark for physical models of small-body disruption. For the first time in the study of this event, we use numerical codes treating rubble piles as collections of polyhedra. This introduces forces of dilatation and friction, and inelastic response. As in our previous studies we conclude that the progenitor must have been a rubble pile, and we obtain approximately the same pre-breakup diameter (~1.5 km) in our best fits to the data. We find that the inclusion of realistic fragment shapes leads to grain locking and dilatancy, so that even in the absence of friction or other dissipation we find that disruption is overall more difficult than in our spheres-based simulations. We constrain the comet's bulk density at ρbulk ~ 300-400 kg m–3, half that of our spheres-based predictions and consistent with recent estimates derived from spacecraft observations.
Surveys without Questions: A Reinforcement Learning Approach

The ‘old world’ instrument, survey, remains a tool of choice for firms to obtain ratings of satisfaction and experience that customers realize while interacting online with firms. While avenues for survey have evolved from emails and links to pop-ups while browsing, the deficiencies persist. These include - reliance on ratings of very few respondents to infer about all customers’ online interactions; failing to capture a customer’s interactions over time since the rating is a one-time snapshot; and inability to tie back customers’ ratings to specific interactions because ratings provided relate to all interactions. To overcome these deficiencies we extract proxy ratings from clickstream data, typically collected for every customer’s online interactions, by developing an approach based on Reinforcement Learning (RL). We introduce a new way to interpret values generated by the value function of RL, as proxy ratings. Our approach does not need any survey data for training. Yet, on validation against actual survey data, proxy ratings yield reasonable performance results. Additionally, we offer a new way to draw insights from values of the value function, which allow associating specific interactions to their proxy ratings. We introduce two new metrics to represent ratings - one, customer-level and the other, aggregate-level for click actions across customers. Both are defined around proportion of all pairwise, successive actions that show increase in proxy ratings. This intuitive customer-level metric enables gauging the dynamics of ratings over time and is a better predictor of purchase than customer ratings from survey. The aggregate-level metric allows pinpointing actions that help or hurt experience. In sum, proxy ratings computed unobtrusively from clickstream, for every action, for each customer, and for every session can offer interpretable and more insightful alternative to surveys.
Manipulating Chiral Transmission by Gate Geometry: Switching in Graphene with Transmission Gaps

We explore the chiral transmission of electrons across graphene heterojunctions for electronic switching using gate geometry alone. A sequence of gates is used to collimate and orthogonalize the chiral transmission lobes across multiple junctions, resulting in negligible overall current. The resistance of the device is enhanced by several orders of magnitude by biasing the gates into the bipolar npn doping regime, even as the ON state in the homogeneous nnn regime remains highly conductive. The mobility is preserved because the switching involves the suppression of transmission over a range of energy (transmission gap) instead of a structural band gap that would reduce the number of available channels of conduction. Under a different biasing scheme (npn to npp), this transmission gap can be made highly gate tunable, allowing a subthermal turn-on that beats the Landauer bound on switching energy, limiting present-day digital electronics.
The XYZs of Charmonium at Bes

In this contribution I review some recent developments in charmonium spectroscopy, and discuss related theoretical predictions. The spectrum of states, strong decays of states above open charm threshold, electromagnetic transitions, and issues related to the recent discoveries of the "XYZ" states are discussed. Contributions that BES can make to our understanding of charmonium and related states are stressed in particular.
Circumplanetary Disk Dynamics in the Isothermal and Adiabatic Limits

Circumplanetary disks (CPDs) may be essential to the formation of planets, regulating their spin and accretion evolution. We perform a series of 3D hydrodynamics simulations in both the isothermal and adiabatic limits to systematically measure the rotation rates, sizes, and masses of CPDs as functions of $q_{\rm thermal}$, the ratio of the planet mass to the disk thermal mass. Our $q_{\rm thermal}$ ranges from 0.1 to 4; for our various disk temperatures, this corresponds to planet masses between 1 Earth mass and 4 Jupiter masses. Within this parameter space, we find that isothermal CPDs are disky and bound within $\sim$10\% of the planet's Bondi radius $r_{\rm B}$, with the innermost $\sim0.05\,r_{\rm B}$ in full rotational support. Adiabatic CPDs are spherical (therefore not actually "disks"), bound within $\sim0.2\,r_{\rm B}$, and mainly pressure-supported with rotation rates scaling linearly with $q_{\rm thermal}$; extrapolation suggests full rotational support of adiabatic envelopes at $\sim10\,q_{\rm thermal}$. Fast rotation and 3D supersonic flow render isothermal CPDs significantly different in structure from --- and orders of magnitude less massive than --- their 1D isothermal hydrostatic counterparts. Inside a minimum-mass solar nebula, even a maximally cooled, isothermal CPD around a 10 Earth-mass core may have less than 1 Earth mass, suggesting that gas giant formation may hinge on angular momentum transport processes in CPDs. Our CPD sizes and masses appear consistent with the regular satellites orbiting solar system giants.
LOW MACH NUMBER FLUCTUATING HYDRODYNAMICS OF DIFFUSIVELY MIXING FLUIDS

Aleksandar Donev, ∗ Andy Nonaka, Yifei Sun, 3 Thomas G. Fai, Alejandro L. Garcia, and John B. Bell Courant Institute of Mathematical Sciences, New York University, New York, NY 10012 Center for Computational Science and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720 Leon H. Charney Division of Cardiology, Department of Medicine, New York University School of Medicine, New York, NY 10016 Department of Physics and Astronomy, San Jose State University, San Jose, California, 95192
The z = 0 Galaxy Luminosity Function. I. Techniques for Identification of Dwarf Galaxies at ∼10 Mpc

We present a program to study the galaxy luminosity function of the Leo I and Coma I Groups at ~10 Mpc. We have surveyed over 7 deg2 in Leo I and ~11 deg2 in Coma I. In this paper, we detail the method we have developed and implemented for identifying on morphological grounds low surface brightness, MR < -10, dwarf galaxies at a distance of 10 Mpc. We also describe extensive Monte Carlo simulations of artificial galaxies, which we use to tune our detection algorithms and evaluate our detection efficiency and parameter recovery as a function of R-band central surface brightness μR(0) and total magnitude RT. We find for a subset of our Leo I data that at the 90% completeness level, we can detect dwarfs comparable to Antlia and Sculptor. Finally, we describe preliminary follow-up observations that confirm we are detecting dwarf spheroidal galaxies in Leo I at 10 Mpc.
Metric Mahler measures over number fields

For an algebraic number α, the metric Mahler measure \({m_1(\alpha)}\) was first studied by Dubickas and Smyth [4] and was later generalized to the t-metric Mahler measure \({m_t(\alpha)}\) by the author [16]. The definition of \({m_t(\alpha)}\) involves taking an infimum over a certain collection N-tuples of points in \(\overline{\mathbb{Q}}\), and from previous work of Jankauskas and the author, the infimum in the definition of \({m_t(\alpha)}\) is attained by rational points when \({\alpha\in \mathbb{Q}}\). As a consequence of our main theorem in this article, we obtain an analog of this result when \({\mathbb{Q}}\) is replaced with any imaginary quadratic number field of class number equal to 1. Further, we study examples of other number fields to which our methods may be applied, and we establish various partial results in those cases.
The Third Law of Galactic Rotation

I review the connection between dynamics and the baryonic mass distribution in rotationally-supported galaxies. The enclosed dynamical mass-to-light ratio increases with decreasing galaxy luminosity and surface brightness. The correlation with surface brightness appears to be the more fundamental, with the dependence on luminosity following simply from the weaker correlation between luminosity and surface brightness. In addition to this global relation, there is also a local relation between the amplitude of the mass discrepancy and the acceleration predicted by the observed distribution of baryons. I provide an empirical calibration of this mass discrepancy-acceleration relation. The data are consistent with the operation of a singe effective force law in disk galaxies, making this relation tantamount to a natural law. I further provide formulae by which the radial dark matter distribution can be estimated from surface photometry. The form of the dark matter halo depends uniquely on the distribution of baryons in each galaxy and, in general, is neither a cusp nor a core. It remains difficult to see how galaxy formation models can reproduce the observed behavior, which is uniquely predicted by MOND.
The Dispersion Bias

We identify and correct excess dispersion in the leading eigenvector of a sample covariance matrix, when the number of variables vastly exceeds the number of observations. Our correction is data-driven, and it materially diminishes the substantial impact of estimation error on weights and risk forecasts of minimum variance portfolios. We quantify that impact with a novel metric, the optimization bias, which has a positive lower bound prior to correction and tends to zero almost surely after correction. The sample eigenvalues are used to correct excess dispersion in the leading eigenvector. However, the sample eigenvalues have no direct bearing on large minimum variance portfolios: correcting the sample eigenvalues to their population counterparts does nothing to diminish the optimization bias.
Gapped PVBS models for all species numbers and dimensions

Product vacua with boundary states (PVBS) are cousins of the Heisenberg XXZ spin model and feature n particle species on ℤd. The PVBS models were originally introduced as toy models for the classif...
On the probabilities of local behaviors in abelian field extensions

For a number field K and a finite abelian group G, we determine the probabilities of various local completions of a random G-extension of K when extensions are ordered by conductor. In particular, for a fixed prime ℘ of K, we determine the probability that ℘ splits into r primes in a random G-extension of K that is unramified at ℘. We find that these probabilities are nicely behaved and mostly independent. This is in analogy to Chebotarev’s density theorem, which gives the probability that in a fixed extension a random prime of K splits into r primes in the extension. We also give the asymptotics for the number of G-extensions with bounded conductor. In fact, we give a class of extension invariants, including conductor, for which we obtain the same counting and probabilistic results. In contrast, we prove that neither the analogy with the Chebotarev probabilities nor the independence of probabilities holds when extensions are ordered by discriminant.