cssi/SciSciGPT-SciSciNet huggingface dataset:
Hard-Coded Gaussian Attention for Neural Machine Translation

Recent work has questioned the importance of the Transformer's multi-headed attention for achieving high translation quality. We push further in this direction by developing a ``hard-coded'' attention variant without any learned parameters. Surprisingly, replacing all learned self-attention heads in the encoder and decoder with fixed, input-agnostic Gaussian distributions minimally impacts BLEU scores across four different language pairs. However, additionally, hard-coding cross attention (which connects the decoder to the encoder) significantly lowers BLEU, suggesting that it is more important than self-attention. Much of this BLEU drop can be recovered by adding just a single learned cross attention head to an otherwise hard-coded Transformer. Taken as a whole, our results offer insight into which components of the Transformer are actually important, which we hope will guide future work into the development of simpler and more efficient attention-based models.
Superpixel Segmentation With Fully Convolutional Networks

In computer vision, superpixels have been widely used as an effective way to reduce the number of image primitives for subsequent processing. But only a few attempts have been made to incorporate them into deep neural networks. One main reason is that the standard convolution operation is defined on regular grids and becomes inefficient when applied to superpixels. Inspired by an initialization strategy commonly adopted by traditional superpixel algorithms, we present a novel method that employs a simple fully convolutional network to predict superpixels on a regular image grid. Experimental results on benchmark datasets show that our method achieves state-of-the-art superpixel segmentation performance while running at about 50fps. Based on the predicted superpixels, we further develop a downsampling/upsampling scheme for deep networks with the goal of generating high-resolution outputs for dense prediction tasks. Specifically, we modify a popular network architecture for stereo matching to simultaneously predict superpixels and disparities. We show that improved disparity estimation accuracy can be obtained on public datasets.
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?

Algorithmic approaches to interpreting machine learning models have proliferated in recent years. We carry out human subject tests that are the first of their kind to isolate the effect of algorithmic explanations on a key aspect of model interpretability, simulatability, while avoiding important confounding experimental factors. A model is simulatable when a person can predict its behavior on new inputs. Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that combines explanations from each method. Clear evidence of method effectiveness is found in very few cases: LIME improves simulatability in tabular classification, and our Prototype method is effective in counterfactual simulation tests. We also collect subjective ratings of explanations, but we do not find that ratings are predictive of how helpful explanations are. Our results provide the first reliable and comprehensive estimates of how explanations influence simulatability across a variety of explanation methods and data domains. We show that (1) we need to be careful about the metrics we use to evaluate explanation methods, and (2) there is significant room for improvement in current methods.
Linear-central filtrations and the image of the Burau representation

The Burau representation is a fundamental bridge between the braid group and diverse other topics in mathematics. A 1974 question of Birman asks for a description of the image; in this paper we give an approximate answer. Since a 1984 paper of Squier it has been known that the Burau representation preserves a certain Hermitian form. We show that the Burau image is dense in this unitary group relative to a topology induced by a naturally-occurring filtration. We expect that the methods of the paper should extend to many other representations of the braid group.
Why case fatality ratios can be misleading: individual- and population-based mortality estimates and factors influencing them.

Different ways of calculating mortality during epidemics have yielded very different results, particularly during the current COVID-19 pandemic. For example, the "CFR" has been interchangeably called the case fatality ratio, case fatality rate, and case fatality risk, often without standard mathematical definitions. The most commonly used CFR is thecase fatality ratio, typically constructed using the estimated number of deaths to date divided by the estimated total number of confirmed infected cases to date. How does this CFR relate to an infected individual's probability of death? To explore such issues, we formulate both a survival probability model and an associated infection duration-dependent SIR model to define individual- and population-based estimates of dynamic mortality measures to show that neither of these are directly represented by the case fatality ratio. The key parameters that affect the dynamics of different mortality estimates are the incubation period and the time individuals were infected before confirmation of infection. Using data on the recent SARS-CoV-2 outbreaks, we estimate and compare the different dynamic mortality estimates and highlight their differences. Informed by our modeling, we propose more systematic methods to determine mortality during epidemic outbreaks and discuss sensitivity to confounding effects and uncertainties in the data such as undertesting and heterogeneous populations.
Free resolutions of function classes via order complexes

Abstract Function classes are collections of Boolean functions on a finite set, which are fundamental objects of study in theoretical computer science. We study algebraic properties of ideals associated to function classes previously defined by the third author. We consider the broad family of intersection-closed function classes, and describe cellular free resolutions of their ideals by order complexes of the associated posets. For function classes arising from matroids, polyhedral cell complexes, and more generally interval Cohen-Macaulay posets, we show that the multigraded Betti numbers are pure, and are given combinatorially by the Mobius functions. We then apply our methods to derive bounds on the VC dimension of some important families of function classes in learning theory.
Policy Synthesis for Factored MDPs with Graph Temporal Logic Specifications

We study the synthesis of policies for multi-agent systems to implement spatial-temporal tasks. We formalize the problem as a factored Markov decision process subject to so-called graph temporal logic specifications. The transition function and the spatial-temporal task of each agent depend on the agent itself and its neighboring agents. The structure in the model and the specifications enable to develop a distributed algorithm that, given a factored Markov decision process and a graph temporal logic formula, decomposes the synthesis problem into a set of smaller synthesis problems, one for each agent. We prove that the algorithm runs in time linear in the total number of agents. The size of the synthesis problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the algorithm in case studies on disease control and urban security. The numerical examples show that the algorithm can scale to hundreds of agents.
Self-consistent tomography and measurement-device independent cryptography

A recurring problem in quantum mechanics is to estimate either the state of a quantum system or the measurement operator applied to it. If we wish to estimate both, then the difficulty is that the ...
Learning to segment from misaligned and partial labels

To extract information at scale, researchers are increasingly applying semantic segmentation techniques to remotely-sensed imagery. While fully-supervised learning enables accurate pixelwise segmentation, compiling the exhaustive datasets required is often prohibitively expensive, and open-source datasets that do exists are frequently inexact and non-exhaustive. In this paper, we present a novel and generalizable two-stage framework that enables improved pixelwise image segmentation given misaligned and missing annotations. First, we introduce the Alignment Correction Network to rectify incorrectly registered open source labels. Next, we demonstrate a segmentation model - the Pointer Segmentation Network - that uses corrected labels to predict infrastructure footprints despite missing annotations. We demonstrate the transferability of our method to lower quality data sources by applying the Alignment Correction Network to correct OpenStreetMaps building footprints, and we show the accuracy of the Pointer Segmentation Network in predicting cropland boundaries in California. Overall, our methodology is robust for multiple applications with varied amounts of training data present, thus offering a method to extract reliable information from noisy, partial data.
A Probabilistic Approach to Explore Signal Execution Mechanisms With Limited Experimental Data

Mathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that relies on expected values for quantities of interest calculated from available information regarding the reaction network topology and parameters can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that in silico knockouts, represented by model subnetworks, successfully identify the most likely execution mode for specific concentrations of key molecular regulators. We then show that changes in molecular regulator concentrations alter the overall reaction flux through the network by shifting the primary route of signal flow between the direct caspase and mitochondrial pathways. Our work thus demonstrates that probabilistic approaches can be used to explore the qualitative dynamic behavior of model biochemical systems even with missing or sparse data.
The Subelliptic Heat Kernel of the Octonionic Hopf Fibration

We study the sub-Laplacian of the 15-dimensional unit sphere which is obtained by lifting with respect to the Hopf fibration the Laplacian of the octonionic projective space. We obtain in particular explicit formulas for its heat kernel and deduce an expression for the Green function of a related sub-Laplacian. As a byproduct we also obtain the spectrum of the sub-Laplacian, the small-time asymptotics of the heat kernel and explicitly compute the sub-Riemannian distance.
Quantifying Gerrymandering in North Carolina

By comparing a specific redistricting plan to an ensemble of plans, we evaluate whether the plan translates individual votes to election outcomes in an unbiased fashion. Explicitly, we evaluate if ...
QDeep: distance-based protein model quality estimation by residue-level ensemble error classifications using stacked deep residual neural networks.

Motivation Protein model quality estimation, in many ways, informs protein structure prediction. Despite their tight coupling, existing model quality estimation methods do not leverage inter-residue distance information or the latest technological breakthrough in deep learning that has recently revolutionized protein structure prediction. Results We present a new distance-based single-model quality estimation method called QDeep by harnessing the power of stacked deep residual neural networks (ResNets). Our method first employs stacked deep ResNets to perform residue-level ensemble error classifications at multiple predefined error thresholds, and then combines the predictions from the individual error classifiers for estimating the quality of a protein structural model. Experimental results show that our method consistently outperforms existing state-of-the-art methods including ProQ2, ProQ3, ProQ3D, ProQ4, 3DCNN, MESHI, and VoroMQA in multiple independent test datasets across a wide-range of accuracy measures; and that predicted distance information significantly contributes to the improved performance of QDeep. Availability and implementation https://github.com/Bhattacharya-Lab/QDeep. Supplementary information Supplementary data are available at Bioinformatics online.
Computational Methods for Predicting Functions at the mRNA Isoform Level

Multiple mRNA isoforms of the same gene are produced via alternative splicing, a biological mechanism that regulates protein diversity while maintaining genome size. Alternatively spliced mRNA isoforms of the same gene may sometimes have very similar sequence, but they can have significantly diverse effects on cellular function and regulation. The products of alternative splicing have important and diverse functional roles, such as response to environmental stress, regulation of gene expression, human heritable, and plant diseases. The mRNA isoforms of the same gene can have dramatically different functions. Despite the functional importance of mRNA isoforms, very little has been done to annotate their functions. The recent years have however seen the development of several computational methods aimed at predicting mRNA isoform level biological functions. These methods use a wide array of proteo-genomic data to develop machine learning-based mRNA isoform function prediction tools. In this review, we discuss the computational methods developed for predicting the biological function at the individual mRNA isoform level.
Angular and Spatial Light Modulation by Single Digital Micromirror Device for Multi-Image Output and Nearly-Doubled Étendue

The “Angular Spatial Light Modulator” (ASLM) uses a binary patterned programmable blazed grating to increase the etendue and number of output pixels of a DMD. We demonstrate an extended FOV display, a light-field projector, and a multi-view display.
Algorithms for mitigating the effect of uncertain geomagnetic disturbances in electric grids

Abstract Geomagnetic disturbances (GMDs), a result of space weather, pose a severe risk to electric grids. When GMDs occur, they can cause geomagnetically-induced currents (GICs), which saturate transformers, induce hot-spot heating, and increase reactive power losses in the transmission grid. Furthermore, uncertainty in the magnitude and orientation of the geo-electric field, and insufficient historical data make the problem of mitigating the effects of uncertain GMDs challenging. In this paper, we propose a novel distributionally robust optimization (DRO) approach that models uncertain GMDs and mitigates the effects of GICs on electric grids. This is achieved via a set of mitigation actions (e.g., line switching, locating blocking devices, generator re-dispatch and load shedding), prior to the GMD event, such that the worst-case expectation of the system cost is minimized. To this end, we develop a column-and-constraint generation algorithm that solves a sequence of mixed-integer second-order conic programs to handle the underlying convex support set of the uncertain GMDs. Also, we present a monolithic exact reformulation of our DRO model when the underlying support set can be approximated by a polytope with three extreme points. Numerical experiments on ‘epri-21’ system show the efficacy of the proposed algorithms and the exact reformulation of our DRO model.
Treatment Policy Learning in Multiobjective Settings with Fully Observed Outcomes

In several medical decision-making problems, such as antibiotic prescription, laboratory testing can provide precise indications for how a patient will respond to different treatment options. This enables us to "fully observe" all potential treatment outcomes, but while present in historical data, these results are infeasible to produce in real-time at the point of the initial treatment decision. Moreover, treatment policies in these settings often need to trade off between multiple competing objectives, such as effectiveness of treatment and harmful side effects. We present, compare, and evaluate three approaches for learning individualized treatment policies in this setting: First, we consider two indirect approaches, which use predictive models of treatment response to construct policies optimal for different trade-offs between objectives. Second, we consider a direct approach that constructs such a set of policies without intermediate models of outcomes. Using a medical dataset of Urinary Tract Infection (UTI) patients, we show that all approaches learn policies that achieve strictly better performance on all outcomes than clinicians, while also trading off between different objectives. We demonstrate additional benefits of the direct approach, including flexibly incorporating other goals such as deferral to physicians on simple cases.
Lumos: A Library for Diagnosing Metric Regressions in Web-Scale Applications

Web-scale applications can ship code on a daily to weekly cadence. These applications rely on online metrics to monitor the health of new releases. Regressions in metric values need to be detected and diagnosed as early as possible to reduce the disruption to users and product owners. Regressions in metrics can surface due to a variety of reasons: genuine product regressions, changes in user population and bias due to telemetry loss (or processing) are among the common causes. Diagnosing the cause of these metric regressions is costly for engineering teams as they need to invest time in finding the root cause of the issue as soon as possible. We presentLumos, a Python library built using the principles of A/B testing to systematically diagnose metric regressions to automate such analysis.Lumos has been deployed across the component teams in Microsoft's Real-Time Communication (RTC) applications Skype and Microsoft Teams. It has enabled engineering teams to detect 100s of real changes in metrics and reject 1000s of false alarms detected by anomaly detectors. The application ofLumos has resulted in freeing up as much as $95%$ of the time allocated to metric-based investigations. In this work, we open sourceLumos and present our results from applying it to two different components within the RTC group over millions of sessions. This general library can be coupled with any production system to manage the volume of alerting efficiently.
Magnesium ion catalyzed P-N bond hydrolysis in imidazolide activated nucleotides. Relevance to template-directed synthesis of polynucleotides

Results are presented from a detailed study of the P-N bond hydrolysis in guanosine 5-prime-monophosphate 2-methylimidazolide (2-MeImpG) and in guanosine 5-prime-imidazolide (ImpG) in the presence of 0-0.50 M Mg(2+). Pseudo-first-order rate constants of these compounds were obtained as a function of Mg(2+) concentration, for pH values between 6 and 10 and 37 C. It was found that Mg(2+) catalysis was most effective at pH 10, where a 15-fold increase in hydrolysis was achieved in 0.02 M Mg; at 0.2 M, a 115-fold increase was observed. Implication of these results for the mechanism of template-directed oligomerization is discussed.
Nucleation properties of isolated shear bands

Shear banding, or localization of intense strains along narrow bands, is a plastic instability in solids with important implications for material failure in a wide range of materials and across len...
A new non-linear conjugate gradient algorithm for destructive cure rate model and a simulation study: illustration with negative binomial competing risks

In this paper, we propose a new estimation methodology based on a projected non-linear conjugate gradient (PNCG) algorithm with an efficient line search technique. We develop a general PNCG algorit...
First-principles studies of strongly correlated states in defect spin qubits in diamond

Using a recently developed quantum embedding theory, we present first-principles calculations of strongly correlated states of spin defects in diamond. Using this theory, effective Hamiltonians are constructed, which can be solved by classical and quantum computers; the latter promise a much more favorable scaling as a function of system size than the former. In particular, we report a study on the neutral group-IV vacancy complexes in diamond, and we discuss their strongly correlated spin-singlet and spin-triplet excited states. Our results provide valuable predictions for experiments aimed at optical manipulation of these defects for quantum information technology applications.
The fine-tuning problems of particle physics and anthropic mechanisms

Many of the classic problems of particle physics appear in a very different light when viewed from the perspective of the multiverse. Most importantly the two great “fine tuning” problems that motivate the field are far less serious when one accounts for the required anthropic constraints which exist in a multiverse. However, the challenge then becomes to construct a realistic physical theory of the multiverse and test it. I describe some phenomenology of the quark and lepton masses that may provide a window to the theory that underlies the multiverse.
Observation of Topological Band Gap Solitons

We present the first experimental observation of solitons in the bulk of a photonic Floquet topological insulator. We probe a family of these nonlinear states residing on the topological band gap and performing cyclotron-like orbits.
Mucin Glycans Signal through the Sensor Kinase RetS to Inhibit Virulence-Associated Traits in Pseudomonas aeruginosa.

Summary Mucus is a densely populated ecological niche that coats all non-keratinized epithelia, and plays a critical role in protecting the human body from infections. Although traditionally viewed as a physical barrier, emerging evidence suggests that mucus can directly suppress virulence-associated traits in opportunistic pathogens including Pseudomonas aeruginosa. However, the molecular mechanisms by which mucus affords this protection are unclear. Here, we show that mucins, and particularly their associated glycans, signal through the Dismed2 domain of the sensor kinase RetS in P. aeruginosa. We find that this RetS-dependent signaling leads to the direct inhibition of the GacS-GacA two-component system, the activity of which is associated with a chronic infection state. This signaling includes downregulation of the type VI secretion system (T6SS), and prevents T6SS-dependent bacterial killing by P. aeruginosa. Overall, these results shed light on how mucus impacts P. aeruginosa behavior, and may inspire novel approaches for controlling P. aeruginosa infections.
Separating Varying Numbers of Sources with Auxiliary Autoencoding Loss

Many recent source separation systems are designed to separate a fixed number of sources out of a mixture. In the cases where the source activation patterns are unknown, such systems have to either adjust the number of outputs or to identify invalid outputs from the valid ones. Iterative separation methods have gain much attention in the community as they can flexibly decide the number of outputs, however (1) they typically rely on long-term information to determine the stopping time for the iterations, which makes them hard to operate in a causal setting; (2) they lack a "fault tolerance" mechanism when the estimated number of sources is different from the actual number. In this paper, we propose a simple training method, the auxiliary autoencoding permutation invariant training (A2PIT), to alleviate the two issues. A2PIT assumes a fixed number of outputs and uses auxiliary autoencoding loss to force the invalid outputs to be the copies of the input mixture, and detects invalid outputs in a fully unsupervised way during inference phase. Experiment results show that A2PIT is able to improve the separation performance across various numbers of speakers and effectively detect the number of speakers in a mixture.
simplePHENOTYPES: SIMulation of pleiotropic, linked and epistatic phenotypes

Advances in genotyping and phenotyping techniques have enabled the acquisition of a great amount of data. Consequently, there is an interest in multivariate statistical analyses that identify genomic regions likely to contain causal mutations affecting multiple traits (i.e., pleiotropy). As the demand for multivariate analyses increases, it is imperative that optimal tools are available to assess their performance. To facilitate the testing and validation of these multivariate approaches, we developed simplePHENOTYPES, an R/CRAN package that simulates pleiotropy, partial pleiotropy, and spurious pleiotropy in a wide range of genetic architectures, including additive, dominance and epistatic models. We illustrate simplePHENOTYPES’ ability to simulate thousands of phenotypes in less than one minute. We then provide two vignettes illustrating how to simulate sets of correlated traits in simplePHENOTYPES. Finally, we demonstrate the use of results from simplePHENOTYPES in a standard GWAS software, as well as the equivalence of simulated phenotypes from simplePHENOTYPES and other packages with similar capabilities. simplePHENOTYPES is a R/CRAN package that makes it possible to simulate multiple traits controlled by loci with varying degrees of pleiotropy. Its ability to interface with both commonly-used marker data formats and downstream quantitative genetics software and packages should facilitate a rigorous assessment of both existing and emerging statistical GWAS and GS approaches. simplePHENOTYPES is also available at https://github.com/samuelbfernandes/simplePHENOTYPES .
Nonlocal special relativity

In the special theory of relativity, Lorentz invariance is extended in Minkowski spacetime from ideal inertial observers to actual observers by means of the hypothesis of locality, which postulates that accelerated observers are always pointwise inertial. A critical examination of the locality assumption reveals its domain of validity: it is true for pointwise coincidences, but is in conflict with wave-particle duality. To remedy this situation, a nonlocal theory of accelerated systems is presented that reduces to the standard theory in the limit of small accelerations. Some of the main consequences of nonlocal special relativity are briefly outlined.
Efficient, compact and low loss thermo-optic phase shifter in silicon

We design a resistive heater optimized for efficient and low-loss optical phase modulation in a silicon-on-insulator (SOI) waveguide and characterize the fabricated devices. Modulation is achieved by flowing current perpendicular to a new ridge waveguide geometry. The resistance profile is engineered using different dopant concentrations to obtain localized heat generation and maximize the overlap between the optical mode and the high temperature regions of the structure, while simultaneously minimizing optical loss due to free-carrier absorption. A 61.6 μm long phase shifter was fabricated in a CMOS process with oxide cladding and two metal layers. The device features a phase-shifting efficiency of 24.77 ± 0.43 mW/π and a −3 dB modulation bandwidth of 130.0 ± 5.59 kHz; the insertion loss measured for 21 devices across an 8-inch wafer was only 0.23 ± 0.13 dB. Considering the prospect of densely integrated photonic circuits, we also quantify the separation necessary to isolate thermo-optic devices in the standard 220 nm SOI platform.
Trapping in the Random Conductance Model

We consider random walks on ℤ d among nearest-neighbor random conductances which are i.i.d., positive, bounded uniformly from above but whose support extends all the way to zero. Our focus is on the detailed properties of the paths of the random walk conditioned to return back to the starting point at time 2n. We show that in the situations when the heat kernel exhibits subdiffusive decay—which is known to occur in dimensions d≥4—the walk gets trapped for a time of order n in a small spatial region. This shows that the strategy used earlier to infer subdiffusive lower bounds on the heat kernel in specific examples is in fact dominant. In addition, we settle a conjecture concerning the worst possible subdiffusive decay in four dimensions.
Dynamics of Multiparticle Systems with non - Abelian Symmetry

We consider the dynamics governing the evolution of a many body system constrained by a nonabelian local symmetry. We obtain explicit forms of the global macroscopic condition assuring that at the microscopic level the evolution respects the overall symmetry constraint. We demonstrate the constraint mechanisms for the case of SU(2) system comprising particles in fundamental, and adjoint representations (‘nucleons’ and ‘pions’).
MESON CLOUDS AND NUCLEON ELECTROMAGNETIC FORM FACTORS

In contrast with common non-relativistic lore, the usual Sachs form factors are not the Fourier transforms of charge or magnetization densities. Instead, the two-dimensional Fourier transform of the electromagnetic F1 form factor is the charge charge density of partons in the transverse plane. An analysis of the available data for neutron form factors leads to the result that the neutron charge density is negative at the center, and that the square of the transverse charge radius is positive. This contrasts with many expectations. Additionally, the use of measured proton form factors leads to the result that the proton’s central d quark charge density is larger than that of the u quark by about 30%. The proton (neutron) charge density has a long range positively (negatively) charged component indicative of a pion cloud.
CHARACTERIZING THE COOL KOIs. VII. REFINED PHYSICAL PROPERTIES OF THE TRANSITING BROWN DWARF LHS 6343 C

We present an updated analysis of LHS 6343, a triple system in the Kepler field which consists of a brown dwarf transiting one member of a widely separated M+M binary system. By analyzing the full Kepler data set and 34 Keck/HIgh Resolution Echelle Spectrometer radial velocity observations, we measure both the observed transit depth and Doppler semiamplitude to 0.5% precision. With Robo-AO and Palomar/PHARO adaptive optics imaging as well as TripleSpec spectroscopy, we measure a model-dependent mass for LHS 6343 C of 62.1 ± 1.2 M_(Jup) and a radius of 0.783 ± 0.011 R_(Jup). We detect the secondary eclipse in the Kepler data at 3.5σ, measuring ecos ω = 0.0228 ± 0.0008. We also derive a method to measure the mass and radius of a star and transiting companion directly, without any direct reliance on stellar models. The mass and radius of both objects depend only on the orbital period, stellar density, reduced semimajor axis, Doppler semiamplitude, eccentricity, and inclination, as well as the knowledge that the primary star falls on the main sequence. With this method, we calculate a mass and radius for LHS 6343 C to a precision of 3% and 2%, respectively.
Phase-field model of vapor-liquid-solid nanowire growth

We present a multiphase-field model to describe quantitatively nanowire growth by the vapor-liquid-solid (VLS) process. The free-energy functional of this model depends on three nonconserved order parameters that distinguish the vapor, liquid, and solid phases and describe the energetic properties of various interfaces, including arbitrary forms of anisotropic $\ensuremath{\gamma}$ plots for the solid-vapor and solid-liquid interfaces. The evolution equations for those order parameters describe basic kinetic processes including the rapid (quasi-instantaneous) equilibration of the liquid catalyst to a droplet shape with constant mean curvature, the slow incorporation of growth atoms at the droplet surface, and crystallization within the droplet. The standard constraint that the sum of the phase fields equals unity and the conservation of the number of catalyst atoms, which relates the catalyst volume to the concentration of growth atoms inside the droplet, are handled via separate Lagrange multipliers. An analysis of the model is presented that rigorously maps the phase-field equations to a desired set of sharp-interface equations for the evolution of the phase boundaries under the constraint of force balance at three-phase junctions (triple points) given by the Young-Herring relation that includes torque term related to the anisotropy of the solid-liquid and solid-vapor interface excess free energies. Numerical examples of growth in two dimensions are presented for the simplest case of vanishing crystalline anisotropy and the more realistic case of a solid-liquid $\ensuremath{\gamma}$ plot with cusped minima corresponding to two sets of $(10)$ and $(11)$ facets. The simulations reproduce many of the salient features of nanowire growth observed experimentally, including growth normal to the substrate with tapering of the side walls, transitions between different growth orientations, and crawling growth along the substrate. They also reproduce different observed relationships between the nanowire growth velocity and radius depending on the growth condition. For the basic normal growth mode, the steady-state solid-liquid interface tip shape consists of a main facet intersected by two truncated side facets ending at triple points. The ratio of truncated and main facet lengths are in quantitative agreement with the prediction of sharp-interface theory that is developed here for faceted nanowire growth in two dimensions.
NEON INSIGHTS FROM OLD SOLAR X-RAYS: A PLASMA TEMPERATURE DEPENDENCE OF THE CORONAL NEON CONTENT

An analysis using modern atomic data of fluxes culled from the literature for O VIII and Ne IX lines observed in solar active regions by the P78 and Solar Maximum Mission satellites confirms that the coronal Ne/O abundance ratio varies by a factor of two or more, and finds an increase in Ne/O with increasing active region plasma temperature. The latter is reminiscent of evidence for increasing Ne/O with stellar activity in low-activity coronae that reaches a "neon saturation" in moderately active stars at approximately twice the historically accepted solar value of about 0.15 by number. We argue that neon saturation represents the underlying stellar photospheric compositions, and that low-activity coronae, including that of the Sun, are generally depleted in neon. The implication would be that the solar Ne/O abundance ratio should be revised upward by a factor of about two to n(Ne)/n(O) ~ 0.3. Diverse observations of neon in the local cosmos provide some support for such a revision. Neon would still be of some relevance for reconciling helioseismology with solar models computed using recently advocated chemical mixtures with lower metal content.
On some problems related to the Hilbert-Smith conjecture

We present a reduction of the Hilbert-Smith conjecture in the case of the finite dimensional orbit space to some algebraic topology problems.
Protected Multiplets of M-Theory on a Plane Wave

We show that the symmetry algebra governing the interacting part of the matrix model for M-theory on the maximally supersymmetric pp-wave is the basic classical Lie superalgebra SU(4|2). We determine the SU(4|2) multiplets present in the exact spectrum in the limit where \mu (the mass parameter) becomes infinite, and find that these include infinitely many BPS multiplets. Using the representation theory of SU(4|2), we demonstrate that some of these BPS multiplets, including all of the vacuum states of the matrix model plus certain infinite towers of excited states, have energies which are exactly protected non-perturbatively for any value of \mu > 0. In the large N limit, these lead to exact quantum states of M-theory on the pp-wave. We also show explicitly that there are certain BPS multiplets which do receive energy corrections by combining with other BPS multiplets to form ordinary multiplets.
Noncommutative Chern-Simons terms and the noncommutative vacuum

It is pointed out that the space noncommutativity parameters θμν in non-commutative gauge theory can be considered as a set of superselection parameters, in analogy with the θ-angle in ordinary gauge theories. As such, they do not need to enter explicitly into the action. A simple generic formula is then suggested to reproduce the Chern-Simons action in non-commutative gauge theory, which reduces to the standard action in the commutative limit but in general implies a cascade of lower-dimensional Chern-Simons terms. The presence of these terms in general alters the vacuum structure of the theory and non-standard gauge theories can emerge around the new vacua.
PHOTON FEEDBACK: SCREENING AND THE EDDINGTON LIMIT

Bright star-forming galaxies radiate well below their Eddington limits. The value of the flux-mean opacity that mediates the radiation force onto matter is orders of magnitude smaller than the UV or optical dust opacity. On empirical grounds, it is shown that high-redshift ULIRGs radiate at two orders of magnitude below their Eddington limits, while the local starbursters M82 and Arp 220 radiate at a few percent of their Eddington limits. A model for the radiative transfer of UV and optical light in dust-rich environments is considered. Radiation pressure on dust does not greatly affect the large-scale gas dynamics of star-forming galaxies.
Cargèse Lectures on Brane Induced Gravity

A brief introduction is given to the subject of brane induced gravity. The 5D example is discussed in detail. The 4D laws of gravity are obtained on a brane embedded in an infinite volume extra space, where the problem of stabilization of the volume modulus is absent. The theory has two classically disjoint branches of solutions – the conventional and self-accelerated one. The conventional branch is perturbatively stable, and gives rise to predictions that are potentially measurable within the Solar system. The self-accelerated branch, on the other hand, provides an existence proof for an idea that the accelerated expansion of the Universe could be due to modified gravity. This branch, however, exhibits negative mass states, suggesting that the self-accelerated solution may not be free of instabilities. A proposal to overcome this difficulty in an extension of the original model, that also allows for the quantum gravity scale to be unrestricted, is discussed.
Controls and Machine Protection Systems

Machine protection, as part of accelerator control systems, can be managed with a ‘functional safety’ approach, which takes into account product life cycle, processes, quality, industrial standards and cybersafety. This paper will discuss strategies to manage such complexity and the related risks, with particular attention to fail-safe design and safety integrity levels, software and hardware standards, testing, and verification philosophy. It will also discuss an implementation of a machine protection system at the SLAC National Accelerator Laboratory’s Linac Coherent Light Source (LCLS).
One-Shot Recognition of Manufacturing Defects in Steel Surfaces

Abstract Quality control is an essential process in manufacturing to make the product defect-free as well as to meet customer needs. The automation of this process is important to maintain high quality along with the high manufacturing throughput. With recent developments in deep learning and computer vision technologies, it has become possible to detect various features from the images with near-human accuracy. However, many of these approaches are data intensive. Training and deployment of such a system on manufacturing floors may become expensive and time-consuming. The need for large amounts of training data is one of the limitations of the applicability of these approaches in real-world manufacturing systems. In this work, we propose the application of a Siamese convolutional neural network to do one-shot recognition for such a task. Our results demonstrate how one-shot learning can be used in quality control of steel by identification of defects on the steel surface. This method can significantly reduce the requirements of training data and can also be run in real-time.
Multiplayer rock–paper–scissors

We study a class of algebras we regard as generalized rock–paper–scissors games. We determine when such algebras can exist, show that these algebras generate the varieties generated by hypertournament algebras, count these algebras, study their automorphisms, and determine their congruence lattices. We produce a family of finite simple algebras.
On cosmological perturbations of quasidilaton

A theory of the quasidilaton is an extension of massive gravity by a scalar field, nonlinearly realizing a certain new global symmetry of the Lagrangian. It has been shown that unlike pure massive gravity, this theory does admit homogeneous and isotropic spatially flat solutions. Among the latter, the self-accelerated solutions attract a special attention. Previous studies of perturbations, performed in the decoupling limit, revealed one healthy scalar mode, while the second relevant scalar was not captured in that limit. Here we study full cosmological perturbations above the simplest self-accelerated background. We show that the fluctuations of a mixed state of the quasidilaton and the helicity-0 graviton necessarily have a negative kinetic term at short distances, making this background unphysical. In addition, these cosmologies exhibit an order-1 sensitivity to higher dimensional terms suppressed by an energy scale that is parametrically higher than the strong coupling scale of the quasidilaton effective theory: such terms include Galileons, Goldstone-like self-interactions and derivatives of the quasidilaton coupled to curvature, none of which introduce extra Ostrogradsky states. As one consequence, cosmology at the Hubble distances for this particular class of solutions depends on an unknown extension of the quasidilaton below its strong coupling distance scale. We note that non-FRW solutions that are similar to those of the pure massive gravity should not necessarily suffer from these pathologies.
Tracking Triggers for the HL-LHC

Hardware-based track reconstruction in the CMS and ATLAS trigger systems for the high-luminosity upgrade of the LHC (HL-LHC) will provide unique capabilities. In this review, we present an overview...
Importance of Amplifier Physics in Maximizing the Capacity of Submarine Links

The throughput of submarine transport cables is approaching fundamental limits imposed by amplifier noise and Kerr nonlinearity. Energy constraints in ultra-long submarine links exacerbate this problem, as the throughput per fiber is further limited by the electrical power available to the undersea optical amplifiers. Recent works have studied how employing more spatial dimensions can mitigate these limitations. In this paper, we address the fundamental question of how to optimally use each spatial dimension. Specifically, we discuss how to optimize the channel power allocation in order to maximize the information-theoretic capacity under an electrical power constraint. Our formulation accounts for amplifier physics, Kerr nonlinearity, and power feed constraints. Whereas recent works assume that the optical amplifiers operate in deep saturation, where power conversion efficiency (PCE) is high, we show that given a power constraint, operating in a less saturated regime, where PCE is lower, supports a wider bandwidth and a larger number of spatial dimensions, thereby maximizing capacity. This design strategy increases the capacity of submarine links by about 70% compared to the theoretical capacity of a recently proposed high-capacity system.
Simulating a Dual-Recycled Gravitational Wave Interferometer with Realistically Imperfect Optics

We simulate the performance of a gravitational wave interferometer in the Dual Recycling (DR) configuration, as will be used for systems like Advanced-LIGO. Our grid-based simulation program models complex interferometric detectors with realistic optical deformations (e.g., fine-scale mirror surface roughness). Broadband and Tuned DR are modeled here; the results are also applied qualitatively to Resonant Sideband Extraction (RSE). Several beneficial properties anticipated for DR detectors are investigated: signal response tuning and narrowbanding, power loss reduction, and the reclamation of lost power as useful light for signal detection. It is shown that these benefits would be limited by large scattering losses in large (multi-kilometer) systems. Furthermore, losses may be resonantly enhanced (particularly for RSE), if the interferometer's modal resonance conditions are not well chosen. We therefore make two principal recommendations for DR/RSE interferometers: the DR/RSE cavity must be modally nondegenerate; and fabricated mirror surfaces and coatings must be as smooth as is practically feasible.
[C II] and [N II] from dense ionized regions in the Galaxy

Context. The interstellar medium (ISM) consists of highly ionized and neutral atomic, as well as molecular, components. Knowledge of their distribution is important for tracing the structure and lifecycle of the ISM. Aims. To determine the properties of the highly ionized gas and neutral weakly ionized gas in the Galaxy traced by the fine-structure lines of ionized nitrogen, [N ii], and ionized carbon, [C ii]. Methods. We utilize observations of the [C ii] 158 μ m and [N ii] 205 μ m fine-structure lines taken with the high spectral resolution Heterodyne Instrument in the Far-Infrared (HIFI) on the Herschel Space Observatory along ten lines of sight towards the inner Galaxy to analyze the ionized ISM. The [N ii] emission can be used to estimate the contribution of the highly ionized gas to the [C ii] emission and separate the contributions from highly ionized and weakly ionized neutral gas. Results. We find that [N ii] has strong emission in distinct spectral features along all lines of sight associated with strong [C ii] emission. The [N ii] arises from moderate density extended H ii regions or ionized boundary layers of clouds. Comparison of the [N ii] and [C ii] spectra in 31 separate kinematic features shows that many of the [C ii] spectra are affected by absorption from low excitation gas associated with molecular clouds, sometimes strongly so. The apparent fraction of the [C ii] associated with the [N ii] gas is unrealistically large in many cases, most likely due to the reduction of [C ii] by absorption. In a few cases the foreground absorption can be modeled to determine the true source intensity. In these sources we find that the foreground absorbing gas layer has C + column densities of order 10 18 cm -2 . Conclusions. [C ii] emission arising from strong sources of [N ii] emission is frequently absorbed by low excitation foreground gas complicating the interpretation of the properties of the ionized and neutral gas components that give rise to [C ii] emission.
Temporal switching to extend the bandwidth of thin absorbers

Wave absorption in time-invariant, passive thin films is fundamentally limited by a trade-off between bandwidth and overall thickness. In this work, we investigate the use of temporal switching to reduce signal reflections from a thin grounded slab over broader bandwidths. We extend quasi-normal mode theory to time switching, developing an ab initio formalism that can model a broad class of time-switched structures. Our formalism provides optimal switching strategies to maximize the bandwidth over which minimal reflection is achieved, showing promising prospects for time-switched nanophotonic and metamaterial systems to overcome the limits of time-invariant, passive structures.