cssi/SciSciGPT-SciSciNet huggingface dataset:
Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures.

Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to attain good accuracy in various machine learning tasks. A DNN's architecture (i.e., its hyper-parameters) broadly determines the DNN's accuracy and performance, and is often confidential. Attacking a DNN in the cloud to obtain its architecture can potentially provide major commercial value. Further, attaining a DNN's architecture facilitates other, existing DNN attacks. This paper presents Cache Telepathy: a fast and accurate mechanism to steal a DNN's architecture using the cache side channel. Our attack is based on the insight that DNN inference relies heavily on tiled GEMM (Generalized Matrix Multiply), and that DNN architecture parameters determine the number of GEMM calls and the dimensions of the matrices used in the GEMM functions. Such information can be leaked through the cache side channel. This paper uses Prime+Probe and Flush+Reload to attack VGG and ResNet DNNs running OpenBLAS and Intel MKL libraries. Our attack is effective in helping obtain the architectures by very substantially reducing the search space of target DNN architectures. For example, for VGG using OpenBLAS, it reduces the search space from more than $10^{35}$ architectures to just 16.
Meta-analysis of several epidemic characteristics of COVID-19

As the COVID-19 pandemic has strongly disrupted people's daily work and life, a great amount of scientific research has been conducted to understand the key characteristics of this new epidemic. In this manuscript, we focus on four crucial epidemic metrics with regard to the COVID-19, namely the basic reproduction number, the incubation period, the serial interval and the epidemic doubling time. We collect relevant studies based on the COVID-19 data in China and conduct a meta-analysis to obtain pooled estimates on the four metrics. From the summary results, we conclude that the COVID-19 has stronger transmissibility than SARS, implying that stringent public health strategies are necessary.
Integral transforms and quantum correspondences

Abstract We reframe a collection of well-known comparison results in genus zero Gromov–Witten theory in order to relate these to integral transforms between derived categories. This implies that various comparisons among Gromov–Witten theories and FJRW theory are compatible with the integral structure introduced by Iritani. We conclude with a proof that a version of the LG/CY correspondence relating quantum D-modules with Orlov's equivalence is implied by a version of the crepant transformation conjecture.
Synthesis of di- and triacrylamides with tertiary amine cores and their evaluation as monomers in dental adhesive interfaces.

Abstract Purpose/aim In an attempt to increase the service life of dental adhesive interfaces, more hydrolytically and enzymatically-stable methacrylate alternatives, such as methacrylamides, have been proposed. The aim of this study was to investigate polymerization behavior, as well as mechanical and biological properties of experimental adhesives containing multi-functional acrylamides. Materials and methods Multi-functional acrylamides (N,N-Bis[(3-methylaminoacryl)propyl]methylamine - BMAAPMA, Tris[(2-methylaminoacryl)ethyl]amine - TMAAEA, N,N’-bis(acrylamido) 1,4-diazepane - BAADA, N,N-Diethyl-1,3-bis(acrylamido)propane - DEBAAP) or HEMA (2-Hydroxyethyl methacrylate - control) were added at 40 wt% to UDMA. 0.2 wt% DMPA and 0.4 wt% DPI-PF6 were used as initiators. Polymerization kinetics was followed in real-time in near-IR during photoactivation (320–500 nm, at 630 mW/cm2). Water sorption/solubility and flexural strength/modulus were measured according to ISO 4049. 1H NMR was used to assess monomer degradation kinetics. MTT assay was used to assess cytotoxicity against OD-21 and DPSC cells. Biofilm formation and adhesion were assessed by Luciferase Assay and Impingement technique, respectively. Solvated adhesives (40 vol% ethanol) were used to test interfacial adhesion strength. The results were analyzed by ANOVA/Tukey's test (α = 0.05). Results In general, the pure methacrylate mixture had higher rate of polymerization (Rpmax), degree of conversion (DC) at Rpmax, and final DC than the acrylamides. Flexural properties after water storage decreased between 11 and 65%, more markedly for acrylamides. Interfacial bond strength was greater and more stable long-term for the newly synthesized acrylamide formulations (less than 4% reduction at 6 months) compared to the methacrylate experimental control (42% reduction at 6 months). HEMA degraded by almost 90%, while the acrylamides showed no degradation in acidic conditions. Cytotoxicity and biofilm formation, in general, were similar for all groups. Conclusions Despite demonstrating high water sorption, the acrylamide-containing materials had similar mechanical and biological properties and enhanced interfacial bond strength stability compared to the methacrylate control.
Interdependence-Aware Game-Theoretic Framework for Secure Intelligent Transportation Systems

The operation of future intelligent transportation systems (ITSs), communications infrastructure (CI), and power grids (PGs) will be highly interdependent. In particular, autonomous connected vehicles require CI resources to operate, and, thus, communication failures can result in nonoptimality in the ITS flow in terms of traffic jams and fuel consumption. Similarly, CI components, e.g., base stations (BSs) can be impacted by failures in the electric grid that is powering them. Thus, malicious attacks on the PG can lead to failures in both the CI and the ITSs. To this end, in this article, the security of an ITS against indirect attacks carried out through the PG is studied in an interdependent PG-CI-ITS scenario. In the considered scenario, an attacker can induce nonoptimality in the ITS or disrupt a particular set of streets by attacking the PG components while remaining stealthy from the ITS administrators. To defend against such attacks, the administrator of the interdependent critical infrastructure can allocate backup power sources (BPSs) at every BS to compensate for the power loss caused by the attacker. However, due to budget limitations, the administrator must consider the importance of each BS in light of the PG risk of failure, while allocating the BPSs. In this regard, a rigorous analytical framework is proposed to model the interdependencies between the ITS, CI, and PG. Next, a one-to-one relationship between the PG components and ITS streets is derived in order to capture the effect of the PG components’ failure on the optimality of the traffic flow in the streets. Moreover, the problem of BPS allocation is formulated using a Stackelberg game framework and the Stackelberg equilibrium (SE) of the game is characterized. Simulation results show that the derived SE outperforms any other BPS allocation strategy and can be scalable in linear time with respect to the size of the interdependent infrastructure.
Single-Chain Lanthanide Luminescence Biosensors for Cell-Based Imaging and Screening of Protein-Protein Interactions.

Lanthanide-based, Forster resonance energy transfer (LRET) biosensors enabled sensitive, time-gated luminescence (TGL) imaging or multiwell plate analysis of protein-protein interactions (PPIs) in living cells. We prepared stable cell lines that expressed polypeptides composed of an alpha helical linker flanked by a Tb(III) complex-binding domain, GFP, and two interacting domains at each terminus. The PPIs examined included those between FKBP12 and the rapamycin-binding domain of m-Tor (FRB) and between p53 (1-92) and HDM2 (1-128). TGL microscopy revealed dramatic differences (>500%) in donor- or acceptor-denominated, Tb(III)-to-GFP LRET ratios between open (unbound) and closed (bound) states of the biosensors. We observed much larger signal changes (>2,500%) and Z'-factors of 0.5 or more when we grew cells in 96- or 384-well plates and analyzed PPI changes using a TGL plate reader. The modular design and exceptional dynamic range of lanthanide-based LRET biosensors will facilitate versatile imaging and cell-based screening of PPIs.
Convex Optimization for the Densest Subgraph and Densest Submatrix Problems

We consider the densest k-subgraph problem, which seeks to identify the k-node subgraph of a given input graph with maximum number of edges. This problem is well-known to be NP-hard, by reduction to the maximum clique problem. We propose a new convex relaxation for the densest k-subgraph problem, based on a nuclear norm relaxation of a low-rank plus sparse decomposition of the adjacency matrices of k-node subgraphs to partially address this intractability. We establish that the densest k-subgraph can be recovered with high probability from the optimal solution of this convex relaxation if the input graph is randomly sampled from a distribution of random graphs constructed to contain an especially dense k-node subgraph with high probability. Specifically, the relaxation is exact when the edges of the input graph are added independently at random, with edges within a particular k-node subgraph added with higher probability than other edges in the graph. We provide a sufficient condition on the size of this subgraph k and the expected density under which the optimal solution of the proposed relaxation recovers this k-node subgraph with high probability. Further, we propose a first-order method for solving this relaxation based on the alternating direction method of multipliers, and empirically confirm our predicted recovery thresholds using simulations involving randomly generated graphs, as well as graphs drawn from social and collaborative networks.
Inexact SARAH Algorithm for Stochastic Optimization

We develop and analyse a variant of the SARAH algorithm, which does not require computation of the exact gradient. Thus this new method can be applied to general expectation minimization problems r...
MFGs for partially reversible investment

Abstract This paper analyzes a class of infinite-time-horizon stochastic games with singular controls motivated from the partially reversible problem. It provides an explicit solution for the mean-field game (MFG), and presents sensitivity analysis to compare the solution for the MFG with that for the single-agent control problem. It shows that in the MFG, model parameters not only affect the optimal strategies as in the single-agent case, but also influence the equilibrium price. It then establishes that the solution to the MFG is an ϵ -Nash Equilibrium to the corresponding N -player game, with ϵ = O 1 N .
Inducing metallicity in graphene nanoribbons via zero-mode superlattices

The design and fabrication of robust metallic states in graphene nanoribbons (GNRs) are challenging because lateral quantum confinement and many-electron interactions induce electronic band gaps when graphene is patterned at nanometer length scales. Recent developments in bottom-up synthesis have enabled the design and characterization of atomically precise GNRs, but strategies for realizing GNR metallicity have been elusive. Here we demonstrate a general technique for inducing metallicity in GNRs by inserting a symmetric superlattice of zero-energy modes into otherwise semiconducting GNRs. We verify the resulting metallicity using scanning tunneling spectroscopy as well as first-principles density-functional theory and tight-binding calculations. Our results reveal that the metallic bandwidth in GNRs can be tuned over a wide range by controlling the overlap of zero-mode wave functions through intentional sublattice symmetry breaking.
The Role of Neural Network Activation Functions

A wide variety of activation functions have been proposed for neural networks. The Rectified Linear Unit (ReLU) is especially popular today. There are many practical reasons that motivate the use of the ReLU. This paper provides new theoretical characterizations that support the use of the ReLU, its variants such as the leaky ReLU, as well as other activation functions in the case of univariate, single-hidden layer feedforward neural networks. Our results also explain the importance of commonly used strategies in the design and training of neural networks such as “weight decay” and “path-norm” regularization, and provide a new justification for the use of “skip connections” in network architectures. These new insights are obtained through the lens of spline theory. In particular, we show how neural network training problems are related to infinite-dimensional optimizations posed over Banach spaces of functions whose solutions are well-known to be fractional and polynomial splines, where the particular Banach space (which controls the order of the spline) depends on the choice of activation function.
Overview of SIM wide-angle astrometric calibration strategies

This paper summarizes two different strategies envisioned for calibrating the systematic field dependent biases present in the Space Interferometry Mission (SIM) instrument. The Internal Calibration strategy is based on pre-launch measurements combined with a set of on-orbit measurements generated by a source internal to the instrument. The External Calibration strategy uses stars as an external source for generating the calibration function. Both approaches demand a significant amount of innovation given that SIM's calibration strategy requires a post-calibration error of 100 picometers over a 15 degree field of regard while the uncalibrated instrument introduces tens to hundreds of nanometers of error. The calibration strategies are discussed in the context of the wide angle astrometric mode of the instrument, although variations on both strategies have been proposed for doing narrow angle astrometry.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Abstractions for computing all robotic sensors that suffice to solve a planning problem

Whether a robot can perform some specific task depends on several aspects, including the robot’s sensors and the plans it possesses. We are interested in search algorithms that treat plans and sensor designs jointly, yielding solutions—i.e., plan and sensor characterization pairs—if and only if they exist. Such algorithms can help roboticists explore the space of sensors to aid in making design trade-offs. Generalizing prior work where sensors are modeled abstractly as sensor maps on p-graphs, the present paper increases the potential sensors which can be sought significantly. But doing so enlarges a problem currently on the outer limits of being considered tractable. Toward taming this complexity, two contributions are made: (1) we show how to represent the search space for this more general problem and describe data structures that enable whole sets of sensors to be summarized via a single special representative; (2) we give a means by which other structure (either task domain knowledge, sensor technology or fabrication constraints) can be incorporated to reduce the sets to be enumerated. These lead to algorithms that we have implemented and which suffice to solve particular problem instances, albeit only of small scale. Nevertheless, the algorithm aids in helping understand what attributes sensors must possess and what information they must provide in order to ensure a robot can achieve its goals despite non-determinism.
Overview: Computer Vision and Machine Learning for Microstructural Characterization and Analysis

Microstructural characterization and analysis is the foundation of microstructural science, connecting materials structure to composition, process history, and properties. Microstructural quantification traditionally involves a human deciding what to measure and then devising a method for doing so. However, recent advances in computer vision (CV) and machine learning (ML) offer new approaches for extracting information from microstructural images. This overview surveys CV methods for numerically encoding the visual information contained in a microstructural image using either feature-based representations or convolutional neural network (CNN) layers, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.
Efficiency Wages, Unemployment, and Environmental Policy

Abstract We study the incidence of pollution taxes and their impact on unemployment in an analytical general equilibrium efficiency wage model. We find closed-form solutions for the effect of a pollution tax on unemployment, factor prices, and output prices, and we identify and isolate different channels through which these general equilibrium effects arise. An effect arising from the efficiency wage specification depends on the form of the workers' effort function. Numerical simulations further illustrate our results and show that this efficiency wage effect can fully offset the sources-side incidence results found in models that omit it.
Parallel single-shot measurement and coherent control of solid-state spins below the diffraction limit

Solid-state spin defects are a promising platform for quantum science and technology. The realization of larger-scale quantum systems with solid-state defects will require high-fidelity control over multiple defects with nanoscale separations, with strong spin-spin interactions for multi-qubit logic operations and the creation of entangled states. We demonstrate an optical frequency-domain multiplexing technique, allowing high-fidelity initialization and single-shot spin measurement of six rare-earth (Er3+) ions, within the subwavelength volume of a single, silicon photonic crystal cavity. We also demonstrate subwavelength control over coherent spin rotations by using an optical AC Stark shift. Our approach may be scaled to large numbers of ions with arbitrarily small separation and is a step toward realizing strongly interacting atomic defect ensembles with applications to quantum information processing and fundamental studies of many-body dynamics.
Why Did the Robot Cross the Road? - A User Study of Explanation in Human-Robot Interaction.

This work documents a pilot user study evaluating the effectiveness of contrastive, causal and example explanations in supporting human understanding of AI in a hypothetical commonplace human robot interaction HRI scenario. In doing so, this work situates explainable AI XAI in the context of the social sciences and suggests that HRI explanations are improved when informed by the social sciences.
COVID-19-CT-CXR: A Freely Accessible and Weakly Labeled Chest X-Ray and CT Image Collection on COVID-19 From Biomedical Literature

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature, including those that report findings on radiographs. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. Because a large portion of figures in COVID-19 articles are not CXR or CT, we designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved deep-learning (DL) performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza, another common infectious respiratory illness that may present similarly to COVID-19, and fine-tuned a baseline deep neural network to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We fine-tuned an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared 15 clinical symptoms and 20 clinical findings of COVID-19 versus those of influenza to demonstrate the disease differences in the scientific publications. Our database is unique, as the figures are retrieved along with relevant text with fine-grained descriptions, and it can be extended easily in the future. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR .
A variational method in the problem of screening an external charge in strongly correlated metals

We describe a variational calculation for the problem of screening of a point charge in a layered correlated metal close to the Mott transition where the screening is non-linear due to the proximity to the incompressible insulating state. This analysis can robustly account for locally incompressible regions induced by external charge and gives further insights, such as overscreening in the nearest nearby metallic layers while preserving overall charge neutrality.
Nonintersecting Brownian Motions on the Half-Line and Discrete Gaussian Orthogonal Polynomials

We study the distribution of the maximal height of the outermost path in the model of N nonintersecting Brownian motions on the half-line as N→∞, showing that it converges in the proper scaling to the Tracy-Widom distribution for the largest eigenvalue of the Gaussian orthogonal ensemble. This is as expected from the viewpoint that the maximal height of the outermost path converges to the maximum of the Airy2 process minus a parabola. Our proof is based on Riemann-Hilbert analysis of a system of discrete orthogonal polynomials with a Gaussian weight in the double scaling limit as this system approaches saturation. We consequently compute the asymptotics of the free energy and the reproducing kernel of the corresponding discrete orthogonal polynomial ensemble in the critical scaling in which the density of particles approaches saturation. Both of these results can be viewed as dual to the case in which the mean density of eigenvalues in a random matrix model is vanishing at one point.
THE PRESENCE OF WEAK ACTIVE GALACTIC NUCLEI IN HIGH REDSHIFT STAR-FORMING GALAXIES

We present [O III 5007 A] observations of the star-forming galaxy (SFG) HDF-BMZ1299 (z = 1.598) using Keck Observatory's adaptive optics system with the near-infrared integral field spectrograph OSIRIS. Using previous Hα and [N II] measurements of the same source, we are able for the first time to use spatially resolved observations to place a high-redshift galaxy's substructure on a traditional H II diagnostic diagram. We find that HDF-BMZ1299's spatially concentrated nebular ratios in the central ~1.5 kpc (02) are best explained by the presence of an active galactic nucleus (AGN): log ([N II]/Hα) = –0.22 ± 0.05 and 2σ limit of log ([O III]/Hβ) 0.26. The dominant energy source of this galaxy is star formation, and integrating a single aperture across the galaxy yields nebular ratios that are composite spectra from both AGN and H II regions. The presence of an embedded AGN in HDF-BMZ1299 may suggest a potential contamination in a fraction of other high-redshift SFGs, and we suggest that this may be a source of the elevated nebular ratios previously seen in seeing-limited metallicity studies. HDF-BMZ1299's estimated AGN luminosity is L Hα = (3.7 ± 0.5) × 1041 erg s–1 and = (5.8 ± 1.9) × 1041 erg s–1, making it one of the lowest luminosity AGNs discovered at this early epoch.
Estimation of the true evolutionary distance under the fragile breakage model

The ability to estimate the evolutionary distance between extant genomes plays a crucial role in many phylogenomic studies. Often such estimation is based on the parsimony assumption, implying that the distance between two genomes can be estimated as the rearrangement distance equal the minimal number of genome rearrangements required to transform one genome into the other. However, in reality the parsimony assumption may not always hold, emphasizing the need for estimation that does not rely on the rearrangement distance. The distance that accounts for the actual (rather than minimal) number of rearrangements between two genomes is often referred to as the true evolutionary distance. While there exists a method for the true evolutionary distance estimation, it however assumes that genomes can be broken by rearrangements equally likely at any position in the course of evolution. This assumption, known as the random breakage model, has recently been refuted in favor of the more rigorous fragile breakage model postulating that only certain “fragile” genomic regions are prone to rearrangements. We propose a new method for estimating the true evolutionary distance between two genomes under the fragile breakage model. We evaluate the proposed method on simulated genomes, which show its high accuracy. We further apply the proposed method for estimation of evolutionary distances within a set of five yeast genomes and a set of two fish genomes. The true evolutionary distances between the five yeast genomes estimated with the proposed method reveals that some pairs of yeast genomes violate the parsimony assumption. The proposed method further demonstrates that the rearrangement distance between the two fish genomes underestimates their evolutionary distance by about 20%. These results demonstrate how drastically the two distances can differ and justify the use of true evolutionary distance in phylogenomic studies.
Modeling The GRB Host Galaxy Mass Distribution: Are GRBs Unbiased Tracers of Star Formation?

We model the mass distribution of long gamma-ray burst (GRB) host galaxies given recent results suggesting that GRBs occur in low metallicity environments. By utilizing measurements of the redshift evolution of the mass-metallicity (M-Z) relationship for galaxies, along with a sharp host metallicity cut-off suggested by Modjaz and collaborators, we estimate an upper limit on the stellar mass of a galaxy that can efficiently produce a GRB as a function of redshift. By employing consistent abundance indicators, we find that sub-solar metallicity cut-offs effectively limit GRBs to low stellar mass spirals and dwarf galaxies at low redshift. At higher redshifts, as the average metallicity of galaxies in the Universe falls, the mass range of galaxies capable of hosting a GRB broadens, with an upper bound approaching the mass of even the largest spiral galaxies. We compare these predicted limits to the growing number of published GRB host masses and find that extremely low metallicity cut-offs of 0.1 to 0.5 Z{sub {circle_dot}} are effectively ruled out by a large number of intermediate mass galaxies at low redshift. A mass function that includes a smooth decrease in the efficiency of producing GRBs in galaxies of metallicity above 12+log(O/H){sub KK04} = 8.7 can, however, accommodate a majority of the measured host galaxy masses. We find that at z {approx} 1, the peak in the observed GRB host mass distribution is inconsistent with the expected peak in the mass of galaxies harboring most of the star formation. This suggests that GRBs are metallicity biased tracers of star formation at low and intermediate redshifts, although our model predicts that this bias should disappear at higher redshifts due to the evolving metallicity content of the universe.
Irreversible reactions and diffusive escape: stationary properties

We study three basic diffusion-controlled reaction processes—annihilation, coalescence, and aggregation. We examine the evolution starting with the most natural inhomogeneous initial configuration where a half-line is uniformly filled by particles, while the complementary half-line is empty. We show that the total number of particles that infiltrate the initially empty half-line is finite and has a stationary distribution. We determine the evolution of the average density from which we derive the average total number N of particles in the initially empty half-line; e.g. for annihilation . For the coalescence process, we devise a procedure that in principle allows one to compute P(N), the probability to find exactly N particles in the initially empty half-line; we complete the calculations in the first non-trivial case (N = 1). As a by-product we derive the distance distribution between the two leading particles.
New and old fermionic dualities from 3d bosonization

We construct novel fermion-fermion dualities in 2 + 1-dimensions using 3d bosonization dualities. This is achieved by relating two-node quiver theories using both the flavor-bounded and flavor-violated 3d bosonization dualities. Such quivers can be viewed as a generalization of the fermionic particle-vortex duality. A special case of these quivers exhibits a ℤ2 symmetry under interchange of the two nodes. Using orbifold techniques, we show that such dualities provide a novel way of deriving known 3d bosonization dualities with adjoint matter, thus unifying the non-Abelian bosonization dualities in an even larger duality web. We then use this construction to derive new dualities involving adjoint matter.
Low Rossby limiting dynamics for stably stratified flow with finite Froude number

In this paper, we explore the strong rotation limit of the rotating and stratified Boussinesq equations with periodic boundary conditions when the stratification is order 1 ([Rossby number] Ro = ∈, [Froude number] Fr = O(1), as ∈ → 0). Using the same framework of Embid & Majda (Geophys. Astrophys. Fluid Dyn., vol. 87, 1998, p. 1), we show that the slow dynamics decouples from the fast. Furthermore, we derive equations for the slow dynamics and their conservation laws. The horizontal momentum equations reduce to the two-dimensional Navier―Stokes equations. The equation for the vertically averaged vertical velocity includes a term due to the vertical average of the buoyancy. The buoyancy equation, the only variable to retain its three-dimensionality, is advected by all three two-dimensional slow velocity components. The conservation laws for the slow dynamics include those for the two-dimensional Navier―Stokes equations and a new conserved quantity that describes dynamics between the vertical kinetic energy and the buoyancy. The leading order potential enstrophy is slow while the leading order total energy retains both fast and slow dynamics. We also perform forced numerical simulations of the rotating Boussinesq equations to demonstrate support for three aspects of the theory in the limit Ro → 0: (i) we find the formation and persistence of large-scale columnar Taylor―Proudman flows in the presence of O(1) Froude number; after a spin-up time, (ii) the ratio of the slow total energy to the total energy approaches a constant and that at the smallest Rossby numbers that constant approaches 1 and (iii) the ratio of the slow potential enstrophy to the total potential enstrophy also approaches a constant and that at the lowest Rossby numbers that constant is 1. The results of the numerical simulations indicate that even in the presence of the low wavenumber white noise forcing the dynamics exhibit characteristics of the theory.
S-CHIRP: Secure communication for heterogeneous IoTs with round-robin protection

This work introduces CHIRP — an algorithm for communication between ultra-portable heterogeneous IoT devices with a type of round-robin protection mechanism. This algorithm is presented both in its basic form as well as in a secured form in order to secure and maintain trust boundaries and communication within specific groups of heterogeneous devices. The specific target application scenarios includes resource constrained environments where a co-located swarm of devices (adversarial in mission or objective) is also present. CHIRP, and its secured version (S-CHIRP), enables complete peer-to-peer communication of a n-agent network of devices in as few as n rounds. In addition to the n-round cycle length, the proposed communication mechanism has the following major properties: nodes communication is entirely decentralized, communication is resilient to the loss of nodes, and finally communication is resilient to the (re)-entry of nodes. Theoretical models show that even the secure implementation of this mechanism is capable of scaling to IoT swarms in the million device range with memory constraints in the < 10 MB range.
TransForm: formally specifying transistency models and synthesizing enhanced litmus tests

Memory consistency models (MCMs) specify the legal ordering and visibility of shared memory accesses in a parallel program. Traditionally, instruction set architecture (ISA) MCMs assume that relevant program-visible memory ordering behaviors only result from shared memory interactions that take place between user-level program instructions. This assumption fails to account for virtual memory (VM) implementations that may result in additional shared memory interactions between user-level program instructions and both 1) system-level operations (e.g., address remappings and translation lookaside buffer invalidations initiated by system calls) and 2) hardware-level operations (e.g., hardware page table walks and dirty bit updates) during a user-level program’ s execution. These additional shared memory interactions can impact the observable memory ordering behaviors of user-level programs. Thus, memory transistency models (MTMs) have been coined as a superset of MCMs to additionally articulate VM-aware consistency rules. However, no prior work has enabled formal MTM specifications, nor methods to support their automated analysis.To fill the above gap, this paper presents the TransForm framework. First, TransForm features an axiomatic vocabulary for formally specifying MTMs. Second, TransForm includes a synthesis engine to support the automated generation of litmus tests enhanced with MTM features (i.e., enhanced litmus tests, or ELTs) when supplied with a TransForm MTM specification. As a case study, we formally define an estimated MTM for Intel x86 processors, called x86t_elt, that is based on observations made by an ELT-based evaluation of an Intel x86 MTM implementation from prior work and available public documentation [23], [29]. Given x86t_elt and a synthesis bound (on program size) as input, TransForm’ s synthesis engine successfully produces a complete set of ELTs (within a 9-instruction bound) including relevant hand-curated ELTs from prior work, plus 100 more.
FlipTest: fairness testing via optimal transport

We present FlipTest, a black-box technique for uncovering discrimination in classifiers. FlipTest is motivated by the intuitive question: had an individual been of a different protected status, would the model have treated them differently? Rather than relying on causal information to answer this question, FlipTest leverages optimal transport to match individuals in different protected groups, creating similar pairs of in-distribution samples. We show how to use these instances to detect discrimination by constructing a flipset: the set of individuals whose classifier output changes post-translation, which corresponds to the set of people who may be harmed because of their group membership. To shed light on why the model treats a given subgroup differently, FlipTest produces a transparency report: a ranking of features that are most associated with the model's behavior on the flipset. Evaluating the approach on three case studies, we show that this provides a computationally inexpensive way to identify subgroups that may be harmed by model discrimination, including in cases where the model satisfies group fairness criteria.
Kinetic Expression for Optimal Catalyst Electronic Configuration: The Case of Ammonia Decomposition

A new steady-state kinetic model of ammonia decomposition is presented and analyzed regarding the electronic properties of metal catalysts. The model is based on the classical Temkin–Ertl mechanism...
Singular Chern Classes of Schubert Varieties via Small Resolution

We discuss a method for calculating the Chern-Schwartz-MacPherson (CSM) class of a Schubert variety in the Grassmannian using small resolutions introduced by Zelevinsky. As a consequence, we show how to compute the Chern-Mather class and local Euler obstructions using small resolutions instead of the Nash blowup. The algorithm obtained for CSM classes also allows us to prove new cases of a positivity conjecture of Aluffi and Mihalcea.
Quantum fluctuations at the Planck scale

The recently measured cutoff, $$k_\mathrm{min}=4.34\pm 0.50/r_{\mathrm{cmb}}$$ (with $$r_{\mathrm{cmb}}$$ the comoving distance to the last scattering surface), in the fluctuation spectrum of the cosmic microwave background, appears to disfavor slow-roll inflation and the associated transition of modes across the horizon. We show in this Letter that $$k_{\mathrm{min}}$$ instead corresponds to the first mode emerging out of the Planck domain into the semi-classical universe. The required scalar-field potential is exponential, though not inflationary, and satisfies the zero active mass condition, $$\rho _\phi +3p_\phi =0$$ . Quite revealingly, the observed amplitude of the temperature anisotropies requires the quantum fluctuations in $$\phi $$ to have classicalized at $$\sim 3.5\times 10^{15}$$ GeV, consistent with the energy scale in grand unified theories. Such scalar-field potentials are often associated with Kaluza–Klein cosmologies, string theory and even supergravity.
DETECTION OF A LIGHT ECHO FROM THE OTHERWISE NORMAL SN 2007af

We present the discovery of a light echo from SN 2007af, a normal Type Ia supernova (SN Ia) in NGC 5584. Hubble Space Telescope (HST) images taken three years post explosion reveal two separate echoes; an outer echo and extended central region, which we propose as an unresolved inner echo. Multiple images were obtained in the F160W, F350LP, F555W, and F814W using the Wide Field Camera 3. If the outer echo is produced by an interstellar dust sheet perpendicular to the line of sight, it is located ~800 pc in front of the SN. The dust for the inner echo is 0.45 pc < d < 90 pc away from the SN. The inner echo color is consistent with typical interstellar dust wavelength-dependent scattering cross-sections, while the outer echo color does not match the predictions. Both dust sheets, if in the foreground, are optically thin for scattering, with the outer echo sheet thickness consistent with the inferred extinction from peak brightness. Whether the inner echo is from interstellar or circumstellar dust is ambiguous. Overall, the echo characteristics are quite similar to previously observed SN Ia echoes.
Measuring the self-assembly kinetics of individual viral capsids

Self-assembly is widely used by biological systems to build complex and functional nanoscale structures, but the assembly pathways are difficult to observe because of the small length scales and wide range of time scales involved. We describe a label-free optical method to directly probe the assembly of individual nanostructures, and we apply it to measure the kinetics of assembly of viral capsids 28 nm in diameter and consisting of 90 protein subunits. We use a rigid flow cell to inject a solution of the coat protein of bacteriophage MS2 over multiple MS2 RNA strands that are tethered to a microscope coverslip by specific DNA linkages. Using an interferometric detection scheme, we measure changes in the intensity of light scattered from the proteins while they are assembling around each RNA. The low photodamage afforded by elastic scattering enables high illumination intensities and temporal resolutions down to 1 ms, while 3D-active stabilization of the microscope extends the measurement duration to 600 s or longer. With this wide range of timescales, we find that the assembly is characterized by an exponential distribution of wait times preceding a rapid growth phase, suggesting that the pathway under the conditions we investigate is nucleation followed by growth. Because the method can measure the assembly of many individual capsids in parallel, from start to finish, it offers a direct view of the self-assembly process not accessible to bulk scattering or spectroscopic techniques. It can be adapted to study the assembly of other viruses, biomolecular assemblies, and synthetic nanostructures.
Eigenvector method for umbrella sampling enables error analysis

Umbrella sampling efficiently yields equilibrium averages that depend on exploring rare states of a model by biasing simulations to windows of coordinate values and then combining the resulting data with physical weighting. Here, we introduce a mathematical framework that casts the step of combining the data as an eigenproblem. The advantage to this approach is that it facilitates error analysis. We discuss how the error scales with the number of windows. Then, we derive a central limit theorem for averages that are obtained from umbrella sampling. The central limit theorem suggests an estimator of the error contributions from individual windows, and we develop a simple and computationally inexpensive procedure for implementing it. We demonstrate this estimator for simulations of the alanine dipeptide and show that it emphasizes low free energy pathways between stable states in comparison to existing approaches for assessing error contributions. Our work suggests the possibility of using the estimator and, more generally, the eigenvector method for umbrella sampling to guide adaptation of the simulation parameters to accelerate convergence.
Spatial Action Maps for Mobile Manipulation

Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e.g., step forward, turn left, turn right, etc.) from images of the current state (e.g., a bird's-eye view of a SLAM reconstruction). Instead, we show that it can be advantageous to learn with dense action representations defined in the same domain as the state. In this work, we present "spatial action maps," in which the set of possible actions is represented by a pixel map (aligned with the input image of the current state), where each pixel represents a local navigational endpoint at the corresponding scene location. Using ConvNets to infer spatial action maps from state images, action predictions are thereby spatially anchored on local visual features in the scene, enabling significantly faster learning of complex behaviors for mobile manipulation tasks with reinforcement learning. In our experiments, we task a robot with pushing objects to a goal location, and find that policies learned with spatial action maps achieve much better performance than traditional alternatives.
A carbon nanotube immunosensor for Salmonella

Antibody-functionalized carbon nanotube devices have been suggested for use as bacterial detectors for monitoring of food purity in transit from the farm to the kitchen. Here we report progress towards that goal by demonstrating specific detection of Salmonella in complex nutrient broth solutions using nanotube transistors functionalized with covalently-bound anti-Salmonella antibodies. The small size of the active device region makes them compatible with integration in large-scale arrays. We find that the on-state current of the transistor is sensitive specifically to the Salmonella concentration and saturates at low concentration (<1000 cfu/ml). In contrast, the carrier mobility is affected comparably by Salmonella and other bacteria types, with no sign of saturation even at much larger concentrations (108 cfu/ml).
Polyatomic molecules as quantum sensors for fundamental physics

Precision measurements in molecules have advanced rapidly in recent years through developments in techniques to cool, trap, and control. The complexity of molecules makes them a challenge to study, but also offers opportunities for enhanced sensitivity to many interesting effects. Polyatomic molecules offer additional complexity compared to diatomic molecules, yet are still "simple" enough to be laser-cooled and controlled. While laser cooling molecules is still a research frontier itself, there are many proposed and ongoing experiments seeking to combine the advanced control enabled by ultracold temperatures with the intrinsic sensitivity of molecules. In this perspective, we discuss some applications where laser-cooled polyatomic molecules may offer advantages for precision measurements of fundamental physics, both within and beyond the Standard Model.
Quantum-Enhanced Fiber-Optic Gyroscopes Using Quadrature Squeezing and Continuous-Variable Entanglement

We evaluate the fundamental performance of a fiber-optic gyroscope (FOG) design that is enhanced by the injection of a quantum-optical squeezed vacuum. In the presence of fiber loss, we compute the optimum attainable enhancement below the standard quantum limit in terms of the angular velocity estimator variance from a homodyne measurement. We find that currently realizable amounts of single-mode squeezing are sufficient to access the maximum quantitative improvement, but that this gain in maximum rotation sensitivity is limited to a marginal constant factor. We then propose an entanglement-enhanced FOG design that segments a fixed amount of available fiber into multiple fiber interferometers and feeds this sensor array with a multimode-entangled squeezed vacuum resource. Our design raises the fundamental improvement in sensitivity to an appreciable factor of $e\ensuremath{\approx}2.718$.
Polarization of Vacuum Fluctuations: Source of the Vacuum Permittivity and Speed of Light

There are two types of fluctuations in the quantum vacuum: type 1 vacuum fluctuations are on shell and can interact with matter in specific, limited ways that have observable consequences; type 2 vacuum fluctuations are off shell and cannot interact with matter. A photon will polarize a type 1, bound, charged lepton–antilepton vacuum fluctuation in much the same manner that it would polarize a dielectric, suggesting the method used here for calculating the permittivity $$\epsilon _{0}$$ of the vacuum. In a model that retains only leading terms, $$\epsilon _{0} \cong (6\mu _{0}/\pi )(8e^{2}/\hbar )^{2}= 9.10\times 10^{-12}$$ C/(Vm). The calculated value for $$\epsilon _{0}$$ is 2.7% more than the accepted value. The permittivity of the vacuum, in turn, determines the speed c of light in the vacuum. Since the vacuum is at rest with respect to every inertial frame of reference, c is the same in every inertial reference frame.
Fluctuations in the Composite Regime of a Disordered Growth Model

We continue to study a model of disordered interface growth in two dimensions. The interface is given by a height function on the sites of the one-dimensional integer lattice and grows in discrete time: (1) the height above the site x adopts the height above the site to its left if the latter height is larger, (2) otherwise, the height above x increases by 1 with probability px. We assume that px are chosen independently at random with a common distribution F, and that the initial state is such that the origin is far above the other sites. Provided that the tails of the distribution F at its right edge are sufficiently thin, there exists a nontrivial composite regime in which the fluctuations of this interface are governed by extremal statistics of px. In the quenched case, the said fluctuations are asymptotically normal, while in the annealed case they satisfy the appropriate extremal limit law.