cssi/SciSciGPT-SciSciNet huggingface dataset:
A quantitative sequencing framework for absolute abundance measurements of mucosal and lumenal microbial communities

A fundamental goal in microbiome studies is determining which microbes affect host physiology. Standard methods for determining changes in microbial taxa measure relative, rather than absolute abundances. Moreover, studies often analyze only stool, despite microbial diversity differing substantially among gastrointestinal (GI) locations. Here, we develop a quantitative framework to measure absolute abundances of individual bacterial taxa by combining the precision of digital PCR with the high-throughput nature of 16S rRNA gene amplicon sequencing. In a murine ketogenic-diet study, we compare microbial loads in lumenal and mucosal samples along the GI tract. Quantitative measurements of absolute (but not relative) abundances reveal decreases in total microbial loads on the ketogenic diet and enable us to determine the differential effects of diet on each taxon in stool and small-intestine mucosa samples. This rigorous quantitative microbial analysis framework, appropriate for diverse GI locations enables mapping microbial biogeography of the mammalian GI tract and more accurate analyses of changes in microbial taxa in microbiome studies.
Replicated umbilical cord blood DNA methylation loci associated with gestational age at birth.

DNA methylation is highly sensitive to in utero perturbations and has an established role in both embryonic development and regulation of gene expression. The foetal genetic component has been prev...
Genetic Redundancy in Iron and Manganese Transport in the Metabolically Versatile Bacterium Rhodopseudomonas palustris TIE-1.

ABSTRACT The purple nonsulfur bacterium Rhodopseudomonas palustris TIE-1 can produce useful biochemicals such as bioplastics and biobutanol. Production of such biochemicals requires intracellular electron availability, which is governed by the availability and the transport of essential metals such as iron (Fe). Because of the distinct chemical properties of ferrous [Fe(II)] and ferric iron [Fe(III)], different systems are required for their transport and storage in bacteria. Although Fe(III) transport systems are well characterized, we know much less about Fe(II) transport systems except for the FeoAB system. Iron transporters can also import manganese (Mn). We studied Fe and Mn transport by five putative Fe transporters in TIE-1 under metal-replete, metal-depleted, oxic, and anoxic conditions. We observed that by overexpressing feoAB, efeU, and nramp1AB, the intracellular concentrations of Fe and Mn can be enhanced in TIE-1 under oxic and anoxic conditions, respectively. The deletion of a single gene/operon does not attenuate Fe or Mn uptake in TIE-1 regardless of the growth conditions used. This indicates that genetically dissimilar yet functionally redundant Fe transporters in TIE-1 can complement each other. Relative gene expression analysis shows that feoAB and efeU are expressed during Fe and Mn depletion under both oxic and anoxic conditions. The promoters of these transporter genes contain a combination of Fur and Fnr boxes, suggesting that their expression is regulated by both Fe and oxygen availability. The findings from this study will help us modulate intracellular Fe and Mn concentrations, ultimately improving TIE-1’s ability to produce desirable biomolecules. IMPORTANCERhodopseudomonas palustris TIE-1 is a metabolically versatile bacterium that can use various electron donors, including Fe(II) and poised electrodes, for photoautotrophic growth. TIE-1 can produce useful biomolecules, such as biofuels and bioplastics, under various growth conditions. Production of such reduced biomolecules is controlled by intracellular electron availability, which, in turn, is mediated by various iron-containing proteins in the cell. Several putative Fe transporters exist in TIE-1’s genome. Some of these transporters can also transport Mn, part of several important cellular enzymes. Therefore, understanding the ability to transport and respond to various levels of Fe and Mn under different conditions is important to improve TIE-1’s ability to produce useful biomolecules. Our data suggest that by overexpressing Fe transporter genes via plasmid-based expression, we can increase the import of Fe and Mn in TIE-1. Future work will leverage these data to improve TIE-1 as an attractive microbial chassis and future biotechnological workhorse.
A Distributional Framework For Data Valuation

Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework -- distributional Shapley -- where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non-distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.
SAFER: A Structure-free Approach for Certified Robustness to Adversarial Word Substitutions

State-of-the-art NLP models can often be fooled by human-unaware transformations such as synonymous word substitution. For security reasons, it is of critical importance to develop models with certified robustness that can provably guarantee that the prediction is can not be altered by any possible synonymous word substitution. In this work, we propose a certified robust method based on a new randomized smoothing technique, which constructs a stochastic ensemble by applying random word substitutions on the input sentences, and leverage the statistical properties of the ensemble to provably certify the robustness. Our method is simple and structure-free in that it only requires the black-box queries of the model outputs, and hence can be applied to any pre-trained models (such as BERT) and any types of models (world-level or subword-level). Our method significantly outperforms recent state-of-the-art methods for certified robustness on both IMDB and Amazon text classification tasks. To the best of our knowledge, we are the first work to achieve certified robustness on large systems such as BERT with practically meaningful certified accuracy.
Toward a neuro-inspired creative decoder

Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain. Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space. The proposed approach is fully unsupervised and can be used off-the-shelf. Several novelty metrics and human evaluation were used to evaluate the creative capacity of the deep decoder. Our experiments on different image datasets (MNIST, FMNIST, MNIST+FMNIST, WikiArt and CelebA) reveal that atypical co-activation of highly activated and weakly activated neurons in a deep decoder promotes generation of novel and meaningful artifacts.
Population Distributions from Native Mass Spectrometry Titrations Reveal Nearest-Neighbor Cooperativity in the Ring-Shaped Oligomeric Protein TRAP.

Allostery pervades macromolecular function and drives cooperative binding of ligands to macromolecules. To decipher the mechanisms of cooperative ligand binding, it is necessary to define, at a mic...
Approximate Bayesian Bootstrap procedures to estimate multilevel treatment effects in observational studies with application to type 2 diabetes treatment regimens.

Randomized clinical trials are considered as the gold standard for estimating causal effects. Nevertheless, in studies that are aimed at examining adverse effects of interventions, randomized trial...
Markov chain Monte Carlo algorithms with sequential proposals

We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain. This sequential-proposal framework can be applied to various existing MCMC methods, including Metropolis–Hastings algorithms using random proposals and methods that use deterministic proposals such as Hamiltonian Monte Carlo (HMC) or the bouncy particle sampler. Sequential-proposal MCMC methods construct the same Markov chains as those constructed by the delayed rejection method under certain circumstances. In the context of HMC, the sequential-proposal approach has been proposed as extra chance generalized hybrid Monte Carlo (XCGHMC). We develop two novel methods in which the trajectories leading to proposals in HMC are automatically tuned to avoid doubling back, as in the No-U-Turn sampler (NUTS). The numerical efficiency of these new methods compare favorably to the NUTS. We additionally show that the sequential-proposal bouncy particle sampler enables the constructed Markov chain to pass through regions of low target density and thus facilitates better mixing of the chain when the target density is multimodal.
Reinforcement Learning for Safety-Critical Control under Model Uncertainty, using Control Lyapunov Functions and Control Barrier Functions

In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-ouput linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model-based CBF-CLF-QP, resulting in the Reinforcement Learning-based CBF-CLF-QP (RL-CBF-CLF-QP), which addresses the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.
CGPOPS: A C++ Software for Solving Multiple-Phase Optimal Control Problems Using Adaptive Gaussian Quadrature Collocation and Sparse Nonlinear Programming

A general-purpose C++ software program called CGPOPS is described for solving multiple-phase optimal control problems using adaptive direct orthogonal collocation methods. The software employs a Legendre-Gauss-Radau direct orthogonal collocation method to transcribe the continuous optimal control problem into a large sparse nonlinear programming problem (NLP). A class of hp mesh refinement methods are implemented that determine the number of mesh intervals and the degree of the approximating polynomial within each mesh interval to achieve a specified accuracy tolerance. The software is interfaced with the open source Newton NLP solver IPOPT. All derivatives required by the NLP solver are computed via central finite differencing, bicomplex-step derivative approximations, hyper-dual derivative approximations, or automatic differentiation. The key components of the software are described in detail, and the utility of the software is demonstrated on five optimal control problems of varying complexity. The software described in this article provides researchers a transitional platform to solve a wide variety of complex constrained optimal control problems.
Transient laser-induced molecular reorientation and laser heating in a nematic liquid crystal

Transient molecular reorientation induced in a liquid crystal by a giant laser pulse was observed for the first time in a nematic substance 4\ensuremath{'}-$n$-pentyl-4-cyanobiphenyl (5CB). Even though the response time of molecular reorientation was several orders of magnitude longer than the laser pulsewidth, the effect on the refractive indices was still large enough to be easily detectable by a Mach-Zehnder interferometer. Transient laser heating of the medium also affected the refractive indices, but with proper geometric and polarization arrangement, it could be decoupled from molecular reorientation and measured separately. Theoretical calculations using the Ericksen-Leslie theory for molecular reorientation in liquid crystals and the heat-diffusion theory for the laser heating effect were able to explain the experimental observations quantitatively.
The Differential Effect of Corruption on Growth: Does Firm Origin Matter?

This study investigates whether the impact of firm-level corruption on growth varies based on origin of the firm. More specifically, I examine how corruption in the business environment affects gro...
Visual judgments of length in the economics laboratory: Are there brains in stochastic choice?

We design an induced value choice experiment where the objects are valued according to only a single attribute with a continuous measure. Subjects have an imperfect perception of the choice objects but can reduce their imperfect perception with cognitive effort. Subjects are given a choice set involving several lines of various lengths and are told to select one of them. They strive to select the longest line because they are paid an amount that is increasing in the length of their selection. This "idealized" choice experiment produces a dataset that is uniquely suited to study apparently random choice. We also manipulate the available cognitive resources of the subjects by imposing either a high or low cognitive load. We find that both choices and the allocation of effort are affected by the material incentives in the choice problem and the available cognitive resources. We find evidence that optimal choices have shorter deliberation times than suboptimal choices, which is consistent with previous theoretical predictions. The distribution of errors can have significant implications for the specification of stochastic choice models. Specifications where errors have a Gumbel distribution appear to provide a better fit than those with a normal distribution. Despite that the cognitive load manipulation affects both choice and search, it is notable that neither the Gumbel distribution results nor the relationship between optimal choice and deliberation time appear to be affected by the available cognitive resources. This perhaps suggests that these results are general and persistent features of choice.
Benchmarking risk predictions and uncertainties in the NSCR model of GCR cancer risks with revised low let risk coefficients.

Abstract We report on the contributions of model factors that appear in projection models to the overall uncertainty in cancer risks predictions for exposures to galactic cosmic ray (GCR) in deep space, including comparisons with revised low LET risks coefficients. Annual GCR exposures to astronauts at solar minimum are considered. Uncertainties in low LET risk coefficients, dose and dose-rate modifiers, quality factors (QFs), space radiation organ doses, non-targeted effects (NTE) and increased tumor lethality at high LET compared to low LET radiation are considered. For the low LET reference radiation parameters we use a revised assessment of excess relative risk (ERR) and excess additive risk (EAR) for radiation induced cancers in the Life-Span Study (LSS) of the Atomic bomb survivors that was recently reported, and also consider ERR estimates for males from the International Study of Nuclear Workers (INWORKS). For 45-y old females at mission age the risk of exposure induced death (REID) per year and 95% confidence intervals is predicted as 1.6% [0.71, 1.63] without QF uncertainties and 1.64% [0.69, 4.06] with QF uncertainties. However, fatal risk predictions increase to 5.83% [2.56, 9.7] based on a sensitivity study of the inclusion of non-targeted effects on risk predictions. For males a comparison using LSS or INWORKS lead to predictions of 1.24% [0.58, 3.14] and 2.45% [1.23, 5.9], respectively without NTEs. The major conclusion of our report is that high LET risk prediction uncertainties due to QFs parameters, NTEs, and possible increase lethality at high LET are dominant contributions to GCR uncertainties and should be the focus of space radiation research.
Analysis of amplification mechanisms and cross-frequency interactions in nonlinear flows via the harmonic resolvent

We propose a framework that elucidates the input–output characteristics of flows with complex dynamics arising from nonlinear interactions between different time scales. More specifically, we consider a periodically time-varying base flow, and perform a frequency-domain analysis of periodic perturbations about this base flow. The response of these perturbations is governed by the harmonic resolvent, which is a linear operator similar to the harmonic transfer function introduced by Wereley (1991 Analysis and control of linear periodically time-varying systems, PhD thesis, Massachusetts Institute of Technology). This approach makes it possible to explicitly capture the triadic interactions that are responsible for the energy transfer between different time scales in the flow. For instance, perturbations at frequency are coupled with perturbations at frequency through the base flow at frequency . We draw a connection with resolvent analysis, which is a special case of the harmonic resolvent when evaluated about a steady base flow. We show that the left and right singular vectors of the harmonic resolvent are the optimal response and forcing modes, which can be understood as full spatio-temporal signals that reveal space–time amplification characteristics of the flow. Finally, we illustrate the method on examples, including a three-dimensional system of ordinary differential equations and the flow over an airfoil at near-stall angle of attack.
Efficient hedging under ambiguity in continuous time

It is well known that the minimal superhedging price of a contingent claim is too high for practical use. In a continuous-time model uncertainty framework, we consider a relaxed hedging criterion based on acceptable shortfall risks. Combining existing aggregation and convex dual representation theorems, we derive duality results for the minimal price on the set of upper semicontinuous discounted claims.
Fully implanted adaptive deep brain stimulation in freely moving essential tremor patients.

Objective Deep brain stimulation (DBS) is a safe and established treatment for essential tremor (ET) and several other movement disorders. One approach to improving DBS therapy is adaptive DBS (aDBS), in which stimulation parameters are modulated in real time based on biofeedback from either external or implanted sensors. Previously tested systems have fallen short of translational applicability due to the requirement for patients to continuously wear the necessary sensors or processing devices, as well as privacy and security concerns. Approach We designed and implemented a translation-ready training data collection system for fully implanted aDBS. Two patients chronically implanted with electrocorticography strips over the hand portion of M1 and DBS probes in the ipsilateral ventral intermediate nucleus of the thalamus for treatment of ET were recruited for this study. Training was conducted using a translation-ready distributed training procedure, allowing a substantially higher degree of control over data collection than previous works. A linear classifier was trained using this system, biased towards activating stimulation in accordance with clinical considerations. Main results The clinically relevant average false negative rate, defined as fraction of time during which stimulation dropped below [Formula: see text] clinical levels during movement epochs, was 0.036. Tremor suppression, calculated through analysis of gyroscope data, was 33.2% more effective on average with aDBS than with continuous DBS. During a period of free movement with aDBS, one patient reported a slight paresthesia; patients noticed no difference in treatment efficacy between systems. Significance Here is presented the first translation-ready training procedure for a fully embedded aDBS control system for MDs and one of the first examples of such a system in ET, adding to the consensus that fully implanted aDBS systems are sufficiently mature for broader deployment in treatment of movement disorders.
Caregivers’ Expectations, Reflected Appraisals, and Arrests among Adolescents Who Experienced Parental Incarceration:

This research sought to identify a potential process by which intergenerational crime occurs, focusing on the effect of parental incarceration on adolescents’ subsequent arrests. We drew from Matsu...
Multivariate functional data modeling with time-varying clustering

We consider the setting of multivariate functional data collected over time at each of a set of sites. Our objective is to implement model-based clustering of the functions across the sites where we allow such clustering to vary over time. Anticipating dependence between the functions within a site as well as across sites, we model the collection of functions using a multivariate Gaussian process. With many sites and several functions at each site, we use dimension reduction to provide a computationally manageable stochastic process specification. To jointly cluster the functions, we use the Dirichlet process which enables shared labeling of the functions across the sites. Specifically, we cluster functions based on their response to exogenous variables. Though the functions arise over continuous time, clustering in continuous time is extremely computationally demanding and not of practical interest. Therefore, we employ partitioning of the timescale to capture time-varying clustering. Our illustrative setting is bivariate, monitoring ozone and PM $$_{10}$$ levels over time for one year at a set of monitoring sites. The data we work with is from 24 monitoring sites in Mexico City for 2017 which record hourly ozone and PM $$_{10}$$ levels. Hence, we have 48 functions to work with across 8760 hours. We provide a Gaussian process model for each function using continuous-time meteorological variables as regressors along with adjustment for daily periodicity. We interpret the similarity of functions in terms of their shape, captured through site-specific coefficients, and use these coefficients to develop the clustering.
Entire solutions of diffusive Lotka-Volterra system

Abstract This work is concerned with the existence of entire solutions of the diffusive Lotka-Volterra competition system on the real line. We prove the existence of some entire solutions that are asymptotic, as t → − ∞ , to a traveling wave consisting of a single species. Depending on system parameters, these entire solutions evolve into two ore more stacked invasion waves as t → + ∞ . Our results cover both the weak and strong competition case. In the weak competition case, the existence of a class of entire solutions that forms a 4-dimensional manifold is proved.
Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data.

We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual segmentation using diffusion weighted imaging (DWI) data. NF1 Patients were recruited from a phase II clinical trial for the treatment of PN. Multiple b-value DWI was imaged over the largest PN. All DWI datasets were registered and intensity normalized prior to segmentation with a multi-spectral neural network classifier (MSNN). Manual volumes of PN were performed on 3D-T2 images registered to diffusion images and compared to MSNN volumes with the Sorensen-Dice coefficient. Intravoxel incoherent motion (IVIM) parameters were calculated from resulting volumes. 35 MRI scans were included from 14 subjects. Sorensen-Dice coefficient between the semi-automated and manual segmentation was 0.77 ± 0.016. Perfusion fraction (f) was significantly higher for tumor versus normal tissue (0.47 ± 0.42 vs. 0.30 ± 0.22, p = 0.02), similarly, true diffusion (D) was significantly higher for PN tumor versus normal (0.0018 ± 0.0003 vs. 0.0012 ± 0.0002, p < 0.0001). By contrast, the pseudodiffusion coefficient (D*) was significantly lower for PN tumor versus normal (0.024 ± 0.01 vs. 0.031 ± 0.005, p < 0.0001). Volumes generated by a neural network from multiple diffusion data on PNs demonstrated good correlation with manual volumes. IVIM analysis of multiple b-value diffusion data demonstrates significant differences between PN and normal tissue.
Patients' compliance behavior in a personalized mobile patient education system (PMPES) setting: Rational, social, or personal choices?

Abstract Objective With the advancement of mobile technologies, patients can access medical and patient educational information anytime and anywhere. Computer-aided patient education has been advocated as a key means of interventions for improving patient knowledge and compliance (i.e., adherence). However, evidence of the efficacy of computer-aided patient education remains relatively limited. For example, little is known about how the latest mobile technologies influence patients’ compliance intention and their actual compliance behavior. The objective of this study is to investigate patients’ compliance intention and behavior using a personalized mobile patient education system (PMPES) as a novel technological intervention for patients based on rational choice theory (RCT) and the theory of planned behavior (TPB). Materials and methods We conducted a field survey with 125 actual patients in U.S. who obtained their patient education through PMPES while seeking medical treatment advice from their doctors. We used partial least squares (PLS) regression path modeling to test our model. Results We found that, based on RCT, the benefits of compliance and cost/threat of noncompliance positively influenced intention toward treatment compliance; in contrast, costs of compliance negatively influenced intention toward treatment compliance. However, the benefits of noncompliance had no effect on intention toward treatment compliance. The results also indicated that intention toward treatment compliance, response efficacy, and self-efficacy related to TPB jointly influenced the degree of actual compliance behaviors. Social influence factors including subjective norms and descriptive norms had no influence on patients’ actual treatment compliance behavior. Conclusion Overall, the research model explains 69.2 % of the variance in patients’ actual compliance behavior. We find our model robust in using RCT as a key theoretical lens for the assessment of patients’ compliance intention to follow medical recommendations enabled by the PMPES and delivered to mobile devices. The factors associated with RCT and TPB jointly influence patients’ actual compliance behavior. Future mobile patient education programs should consider patients’ age groups, mixed-gender groups, different medical settings, and cross-cultural contexts.
Ignition criteria and the effect of boundary layers on wedge-stabilized oblique detonation waves

Abstract Simulations of a supersonic, premixed, reacting flow over a wedge were performed to investigate the effect of a boundary layer on the wedge surface on ignition and stability of oblique detonation waves (ODWs). Two computational domains were used: one containing a wedge of a single angle with a straight after-body, and the other containing a double-angle wedge geometry. Both domains were channels with a supersonic inflow of stoichiometric hydrogen-air and a nonreflecting outflow, and the wedge was modeled using an immersed boundary method. The compressible reactive Navier–Stokes equations were solved using a high-order numerical algorithm on an adapting grid. Inviscid and viscous wedge surfaces were modeled using slip and no-slip adiabatic boundary conditions, respectively. Inviscid wedge surface cases are presented for a range of inflow conditions and compared to previous work outlining several different ODW structures. An ignition criterion is established as an accurate method of predicting the formation of an ODW for a given inflow temperature, Mach number, wedge angle, and length of the inviscid wedge surface. A viscous wedge surface is then considered for a Mach 5 inflow at temperatures of 600 K, 700 K, and 800 K. The 600 K flow ignites in the boundary layer, but does not detonate, while the 700 K and 800 K flows ignite and form ODWs. It was determined that ODW formation depends on the degree of augmentation of the leading oblique shock wave by the burning boundary layer and that ODW formation is therefore predictable based on the ignition criterion. The 700 K flow produces a unique oscillatory mode of a receding ODW followed by a redetonation event occurring near the leading edge. A mechanism for this cycle, which repeats indefinitely, is proposed.
Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists

Robotic painting has been a subject of interest among both artists and roboticists since the 1970s. Researchers and interdisciplinary artists have employed various painting techniques and human-robot collaboration models to create visual mediums on canvas. One of the challenges of robotic painting is to apply a desired artistic style to the painting. Style transfer techniques with machine learning models have helped us address this challenge with the visual style of a specific painting. However, other manual elements of style, i.e., painting techniques and brushstrokes of an artist, have not been fully addressed.We propose a method to integrate an artistic style to the brushstrokes and the painting process through collaboration with a human artist. In this paper, we describe our approach to 1) collect brushstrokes and hand-brush motion samples from an artist, and 2) train a generative model to generate brushstrokes that pertains to the artist’s style, and 3) fine tune a stroke-based rendering model to work with our robotic painting setup. We will report on the integration of these three steps in a separate publication. In a preliminary study, 71% of human evaluators find our reconstructed brushstrokes are pertaining to the characteristics of the artist’s style. Moreover, 58% of participants could not distinguish a painting made by our method from a visually similar painting created by a human artist.
A Sylvester–Gallai Result for Concurrent Lines in the Complex Plane

We show that if a finite non-collinear set of points in $$\mathbb {C}^2$$ lies on a family of m concurrent lines, and if one of those lines contains more than $$m-2$$ points, there exists a line passing through exactly two points of the set. The bound $$m-2$$ in our result is optimal. Our main theorem resolves a conjecture of Frank de Zeeuw, and generalizes a result of Kelly and Nwankpa.
The Electromagnetic Counterpart of the Binary Neutron Star Merger LIGO/Virgo GW170817. VII. Properties of the Host Galaxy and Constraints on the Merger Timescale

We present the properties of NGC 4993, the host galaxy of GW170817, the first gravitational wave (GW) event from the merger of a binary neutron star (BNS) system and the first with an electromagnetic (EM) counterpart. We use both archival photometry and new optical/near-IR imaging and spectroscopy, together with stellar population synthesis models to infer the global properties of the host galaxy. We infer a star formation history peaked at $\gtrsim 10$ Gyr ago, with subsequent exponential decline leading to a low current star formation rate of 0.01 M$_{\odot}$ yr$^{-1}$, which we convert into a binary merger timescale probability distribution. We find a median merger timescale of $11.2^{+0.7}_{-1.4}$ Gyr, with a 90% confidence range of $6.8-13.6$ Gyr. This in turn indicates an initial binary separation of $\approx 4.5$ R$_{\odot}$, comparable to the inferred values for Galactic BNS systems. We also use new and archival $Hubble$ $Space$ $Telescope$ images to measure a projected offset of the optical counterpart of $2.1$ kpc (0.64$r_{e}$) from the center of NGC 4993 and to place a limit of $M_{r} \gtrsim -7.2$ mag on any pre-existing emission, which rules out the brighter half of the globular cluster luminosity function. Finally, the age and offset of the system indicates it experienced a modest natal kick with an upper limit of $\sim 200$ km s$^{-1}$. Future GW$-$EM observations of BNS mergers will enable measurement of their population delay time distribution, which will directly inform their viability as the dominant source of $r$-process enrichment in the Universe.
Charge-stripe crystal phase in an insulating cuprate.

High-temperature (high-Tc) superconductivity in cuprates arises from carrier doping of an antiferromagnetic Mott insulator. This carrier doping leads to the formation of electronic liquid-crystal phases1. The insulating charge-stripe crystal phase is predicted to form when a small density of holes is doped into the charge-transfer insulator state1–3, but this phase is yet to be observed experimentally. Here, we use surface annealing to extend the accessible doping range in Bi-based cuprates and realize the lightly doped charge-transfer insulating state of the cuprate Bi2Sr2CaCu2O8+x. In this insulating state with a charge transfer gap on the order of ~1 eV, our spectroscopic imaging scanning tunnelling microscopy measurements provide strong evidence for a unidirectional charge-stripe order with a commensurate 4a0 period along the Cu–O–Cu bond. Notably, this insulating charge-stripe crystal phase develops before the onset of the pseudogap and formation of the Fermi surface. Our work provides fresh insight into the microscopic origin of electronic inhomogeneity in high-Tc cuprates. A surface annealing method is used to access an insulating phase of Bi2Sr2CaCu2O8+x and unidirectional charge order is observed.
Coated conductors containing grains with big aspect ratios

It is shown that the critical currents of high-Tc superconducting tapes fabricated by the coated conductor technologies are enhanced considerably if grain arrangements with large effective grain boundary areas are used. Increasing the aspect ratios of the grains reduces the deleterious effects of the grain boundaries. A practical road to competitive high-Tc cables is proposed.
Quantum information geometry in the space of measurements

We introduce a new approach to evaluating entangled quantum networks using information geometry. Quantum computing is powerful because of the enhanced correlations from quantum entanglement. For example, larger entangled networks can enhance quantum key distribution (QKD). Each network we examine is an n-photon quantum state with a degree of entanglement. We analyze such a state within the space of measured data from repeated experiments made by n observers over a set of identically-prepared quantum states – a quantum state interrogation in the space of measurements. Each observer records a 1 if their detector triggers, otherwise they record a 0. This generates a string of 1’s and 0’s at each detector, and each observer can define a binary random variable from this sequence. We use a well-known information geometry-based measure of distance that applies to these binary strings of measurement outcomes,1–3 and we introduce a generalization of this length to area, volume and higher-dimensional volumes.4 These geometric equations are defined using the familiar Shannon expression for joint and mutual entropy.5 We apply our approach to three distinct tripartite quantum states: the |GHZi state, the |Wi state, and a separable state |Pi. We generalize a well-known information geometry analysis of a bipartite state to a tripartite state. This approach provides a novel way to characterize quantum states, and it may have favorable scaling with increased number of photons.
OBSERVATIONAL DIAGNOSTICS OF SELF-GRAVITATING MHD TURBULENCE IN GIANT MOLECULAR CLOUDS

We study the observable signatures of self-gravitating MHD turbulence by applying the probability density functions (PDFs) and the spatial density power spectrum to synthetic column density maps. We find that there exists three characterizable stages of the evolution of the collapsing cloud which we term “early,” “intermediate,” and “advanced.” At early times, i.e. t 0.35tff, the power spectral slope is positive valued, and a dramatic increase is observed in the PDF moments and the Tsallis incremental PDF parameters, which gives rise to deviations between PDF-sonic Mach number relations. Finally, we show that the imprint of gravity on the density power spectrum can be replicated in non-gravitating turbulence by introducing a delta-function with amplitude equivalent to the maximum valued point in a given self-gravitating map. We find that the turbulence power spectrum restored through spatial filtering of the high density material. Subject headings: methods: numerical — AMR, MHD
NPTFit: A Code Package for Non-Poissonian Template Fitting

We present NPTFit, an open-source code package, written in Python and Cython, for performing non-Poissonian template fits (NPTFs). The NPTF is a recently developed statistical procedure for characterizing the contribution of unresolved point sources (PSs) to astrophysical data sets. The NPTF was first applied to Fermi gamma-ray data to provide evidence that the excess of ~GeV gamma-rays observed in the inner regions of the Milky Way likely arises from a population of sub-threshold point sources, and the NPTF has since found additional applications studying sub-threshold extragalactic sources at high Galactic latitudes. The NPTF generalizes traditional astrophysical template fits to allow for the ability to search for populations of unresolved PSs that may follow a given spatial distribution. NPTFit builds upon the framework of the fluctuation analyses developed in X-ray astronomy, thus it likely has applications beyond those demonstrated with gamma-ray data. The NPTFit package utilizes novel computational methods to perform the NPTF efficiently. The code is available at http://github.com/bsafdi/NPTFit and up-to-date and extensive documentation may be found at http://nptfit.readthedocs.io.
The motivation and status of two-body resonance decays after the LHC Run 2 and beyond

Searching for two-body resonance decays is a central component of the high energy physics energy frontier research program. While many of the possibilities are covered when the two bodies are Standard Model (SM) particles, there are still significant gaps. If one or both of the bodies are themselves non-SM particles, there is very little coverage from existing searches. We review the status of two-body searches and motivate the need to search for the missing combinations. It is likely that the search program of the future will be able to cover all possibilities with a combination of dedicated and model agnostic search approaches.
Minimal hypersurfaces with arbitrarily large area

For 3 $\leq$ n $\leq$ 7, we prove that a bumpy closed Riemannian n-manifold contains a sequence of connected embedded closed minimal surfaces with unbounded area.
Multimodal brain visualization

Current connectivity diagrams of human brain image data are either overly complex or overly simplistic. In this work we introduce simple yet accurate interactive visual representations of multiple brain image structures and the connectivity among them. We map cortical surfaces extracted from human brain magnetic resonance imaging (MRI) data onto 2D surfaces that preserve shape (angle), extent (area), and spatial (neighborhood) information for 2D (circular disk) and 3D (spherical) mapping, split these surfaces into separate patches, and cluster functional and diffusion tractography MRI connections between pairs of these patches. The resulting visualizations are easier to compute on and more visually intuitive to interact with than the original data, and facilitate simultaneous exploration of multiple data sets, modalities, and statistical maps.
Anisotropic dependence of tune-out wavelength near Dy 741-nm transition

We report the first measurement of a tune-out wavelength for ground-state bosonic Dy and linearly polarized light. The tune-out wavelength is measured as a detuning from the nearby narrow-line 741-nm transition in 162Dy, and is the wavelength at which the total Stark shift of the ground state vanishes. We find that it strongly depends on the relative angle between the optical field and quantization axis due to Dy’s large tensor polarizability. This anisotropy provides a wide, 22-GHz tunability of the tune-out frequency for linearly polarized light, in contrast to Rb and Cs whose near-infrared tune-out wavelengths do not exhibit large anisotropy. The measurements of the total light shift are performed by measuring the contrast of multipulse Kapitza-Dirac diffraction. The calculated wavelengths are within a few GHz of the measured values using known Dy electronic transition data. The lack of hyperfine structure in bosonic Dy implies that the tune-out wavelengths for the other bosonic Dy isotopes should be related to this 162Dy measurement by the known isotope shifts.
Radiative Neutralino Decay in Supersymmetric Models

The radiative decay 2?1? proceeds at the one-loop level in the MSSM. It can be the dominant decay mode for the second lightest neutralino 2 in certain regions of parameter space of supersymmetric models, where either a dynamical and/or kinematic enhancement of the branching fraction occurs. We perform an updated numerical study of this decay mode in both the minimal supergravity model (mSUGRA) and in the more general MSSM framework. In mSUGRA, the largest rates are found in the ``focus point'' region, where the ? parameter becomes small, and the lightest neutralinos become higgsino-like; in this case, radiative branching fraction can reach the 1% level. Our MSSM analysis includes a scan over independent positive and negative gaugino masses. We show branching fractions can reach the 10-100% level even for large values of the parameter tan ?. These regions of parameter space are realized in supergravity models with non-universal gaugino masses. Measurement of the radiative neutralino branching fraction may help pin down underlying parameters of the fundamental supersymmetric model.
Vector-valued Eisenstein series of small weight

We study the (mock) Eisenstein series Ek of weight k ∈{1, 3/2, 2} for the Weil representation on an even lattice, defined as the result of Bruinier and Kuss’s coefficient formula for the Eisenstein...
Induced p-wave superfluidity in two dimensions: Brane world in cold atoms and nonrelativistic defect CFTs

Abstract We propose to use a two-species Fermi gas with the interspecies s-wave Feshbach resonance to realize p-wave superfluidity in two dimensions. By confining one species of fermions in a two-dimensional plane immersed in the background three-dimensional Fermi sea of the other species, an attractive interaction is induced between two-dimensional fermions. We compute the pairing gap in the weak-coupling regime and show that it has the symmetry of p x + ip y . Because the magnitude of the pairing gap increases toward the unitarity limit, it is possible that the critical temperature for the p x + ip y -wave superfluidity becomes within experimental reach. The resulting system has a potential application to topological quantum computation using vortices with non-Abelian statistics. We also discuss aspects of our system in the unitarity limit as a “nonrelativistic defect conformal field theory (CFT)”. The reduced Schrodinger algebra, operator-state correspondence, scaling dimensions of composite operators, and operator product expansions are investigated.
The FLASHES Survey. I. Integral Field Spectroscopy of the CGM around 48 z ≃ 2.3-3.1 QSOs

We present the pilot study of the Fluorescent Lyman-Alpha Structures in High-z Environments Survey; the largest integral field spectroscopy survey to date of the circumgalactic medium at z = 2.3–3.1. We observed 48 quasar fields with the Palomar Cosmic Web Imager to an average (2σ) limiting surface brightness of 6 × 10⁻¹⁸ erg s⁻¹ cm⁻² arcsec⁻² (in a 1'' aperture and ~20 A bandwidth). Extended H I Lyα emission is discovered around 37/48 of the observed quasars, ranging in projected radius from 14 to 55 proper kiloparsecs (pkpc), with one nebula exceeding 100 pkpc in effective diameter. The dimming-adjusted circularly averaged surface brightness profile peaks at 1 × 10⁻¹⁵ erg s⁻¹ cm⁻² arcsec⁻² at R⊥ ~ 20 pkpc and integrated luminosities range from 0.4 to 9.4 × 10⁴³ erg s⁻¹. The emission appears to have an eccentric morphology and an average covering factor of ~30%–40% at small radii. On average, the nebular spectra are redshifted with respect to both the systemic redshift and Lyα peak of the quasar spectrum. The integrated spectra of the nebulae mostly have single- or double-peaked profiles with global dispersions ranging from 143 to 708 km s⁻¹, though the individual Gaussian components of lines with complex shapes mostly have dispersions ≤400 km s⁻¹, and the flux-weighted velocity centroids of the lines vary by thousands of km s⁻¹ with respect to the QSO redshifts. Finally, the root-mean-square velocities of the nebulae are found to be consistent with those expected from gravitational motions in dark matter halos of mass Log₁₀(M_h[M⊙]) ≃ 12.2^(+0.7)_(-1.2). We compare these results to existing surveys at higher and lower redshift.
Oscillating about coplanarity in the 4 body problem

For the Newtonian 4-body problem in space we prove that any zero angular momentum bounded solution suffers infinitely many coplanar instants, that is, times at which all 4 bodies lie in the same plane. This result generalizes a known result for collinear instants (“syzygies”) in the zero angular momentum planar 3-body problem, and extends to the $$d+1$$ body problem in d-space. The proof begins by identifying the translation-reduced configuration space with real $$d \times d$$ matrices, the degeneration locus (set of coplanar configurations when $$d=3$$ ) with the set of matrices having determinant zero, and the mass metric with the Frobenius (standard Euclidean) norm. Let S denote the signed distance from a matrix to the hypersurface of matrices with determinant zero. The proof hinges on establishing a harmonic oscillator type ODE for S along solutions. Bounds on inter-body distances then yield an explicit lower bound $$\omega $$ for the frequency of this oscillator, guaranteeing a degeneration within every time interval of length $$\pi /\omega $$ . The non-negativity of the curvature of oriented shape space (the quotient of the translation-reduced configuration space by the rotation group) plays a crucial role in the proof.
Unitarily inequivalent vacua and long-range forces: Phenomenology with scalar boson mass-shift

We explore the impact of a sudden shift in the mass of a scalar boson field on long-range forces mediated by this field under the framework of unitarily inequivalent vacua. Since the search for new...