cssi/SciSciGPT-SciSciNet huggingface dataset:
Test Gene-Environment Interactions for Multiple Traits in Sequencing Association Studies

Motivation The risk of many complex diseases is determined by an interplay of genetic and environmental factors. The examination of gene-environment interactions (G×Es) for multiple traits can yield valuable insights about the etiology of the disease and increase power in detecting disease-associated genes. However, the methods for testing G×Es for multiple traits are very limited. Method We developed novel approaches to test G×Es for multiple traits in sequencing association studies. We first perform a transformation of multiple traits by using either principal component analysis or standardization analysis. Then, we detect the effects of G×Es using novel proposed tests: testing the effect of an optimally weighted combination of G×Es (TOW-GE) and/or variable weight TOW-GE (VW-TOW-GE). Finally, we employ Fisher's combination test to combine the p values. Results Extensive simulation studies show that the type I error rates of the proposed methods are well controlled. Compared to the interaction sequence kernel association test (ISKAT), TOW-GE is more powerful when there are only rare risk and protective variants; VW-TOW-GE is more powerful when there are both rare and common variants. Both TOW-GE and VW-TOW-GE are robust to directions of effects of causal G×Es. Application to the COPDGene Study demonstrates that our proposed methods are very effective. Conclusions Our proposed methods are useful tools in the identification of G×Es for multiple traits. The proposed methods can be used not only to identify G×Es for common variants, but also for rare variants. Therefore, they can be employed in identifying G×Es in both genome-wide association studies and next-generation sequencing data analyses.
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models

Abstract–We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generali...
Coupling of Ca2+ and voltage activation in BK channels through the αB helix/voltage sensor interface

Large-conductance Ca2+ and voltage-activated K+ (BK) channels control membrane excitability in many cell types. BK channels are tetrameric. Each subunit is composed of a voltage sensor domain (VSD), a central pore-gate domain, and a large cytoplasmic domain (CTD) that contains the Ca2+ sensors. While it is known that BK channels are activated by voltage and Ca2+, and that voltage and Ca2+ activations interact, less is known about the mechanisms involved. We explore here these mechanisms by examining the gating contribution of an interface formed between the VSDs and the αB helices located at the top of the CTDs. Proline mutations in the αB helix greatly decreased voltage activation while having negligible effects on gating currents. Analysis with the Horrigan, Cui, and Aldrich model indicated a decreased coupling between voltage sensors and pore gate. Proline mutations decreased Ca2+ activation for both Ca2+ bowl and RCK1 Ca2+ sites, suggesting that both high-affinity Ca2+ sites transduce their effect, at least in part, through the αB helix. Mg2+ activation also decreased. The crystal structure of the CTD with proline mutation L390P showed a flattening of the first helical turn in the αB helix compared to wild type, without other notable differences in the CTD, indicating that structural changes from the mutation were confined to the αB helix. These findings indicate that an intact αB helix/VSD interface is required for effective coupling of Ca2+ binding and voltage depolarization to pore opening and that shared Ca2+ and voltage transduction pathways involving the αB helix may be involved.
Working Remotely and the Supply-side Impact of Covid-19

We analyze the supply-side disruptions associated with Covid-19 across firms and workers. To do so, we exploit differences in the ability of workers across industries to work remotely using data from the American Time Use Survey (ATUS). We find that sectors in which a higher fraction of the workforce is not able to work remotely experienced significantly greater declines in employment, significantly more reductions in expected revenue growth, worse stock market performance, and higher expected likelihood of default. In terms of individual employment outcomes, lower-paid workers, especially female workers with young children, were significantly more affected by these disruptions. Last, we combine these ex-ante heterogeneous industry exposures with daily financial market data to create a stock return portfolio that most closely replicate the supply-side disruptions resulting from the pandemic.
Vibrational Properties of Hard and Soft Spheres Are Unified at Jamming

The unconventional thermal properties of jammed amorphous solids are directly related to their density of vibrational states. While the vibrational spectrum of jammed soft sphere solids has been fully described, the vibrational spectrum of hard spheres, a model glass former often related to physical colloidal glasses, is still unknown due to the difficulty of treating the nonanalytic interaction potential. We bypass this difficulty using the recently described effective interaction potential for the free energy of thermal hard spheres. By minimizing this effective free energy, we mimic the rapid compression of hard spheres and produce typical configurations of the thermal system. We measure the resulting vibrational spectrum and characterize its evolution toward the jamming point where configurations of hard and soft spheres are trivially unified. For densities approaching jamming from below, we observe low-frequency modes which agree with those found in numerical simulations of jammed soft spheres. Our measurements of the vibrational structure demonstrate that the jamming universality extends away from jamming: hard sphere thermal systems below jamming exhibit the same vibrational spectra as thermal and athermal soft sphere systems above the transition.
Solder extrusion solution and mold adhesion to die surface improvement with PI isolation design for FCOL exposed die technology

Due to rapid growth of the microelectronics industry, the packaged device with smaller, low cost and high power performance becomes a high demand in the market nowadays. To fulfill the market development rate, flip chip interconnection is the most promising packaging solution. In this environment, the National Semiconductor Sdn. Bhd. (a subsidiary of Texas Instruments) performed a qualification run on Thin Shrink Small Outline Package (TSSOP) with Flip Chip on lead frame (FCOL) exposed die back (eDIE) technology. It has been reported that the most detrimental effect on reliability come from solder extrusion and mold adhesion. The solder extrusion observed like a thin sliver “flake” that partially adhered on the polyimide (PI) layer surface. The solder extrusion can be observed from Scanning Acoustical Microscopy (CSAM) image and SEM cross section image which shows as the delamination. The PI layer with isolation, “Island” is designed as a barrier in between two bumps to prevent solder extruded that connect together. To have better barrier effect by optimizing the PI layer thickness and the width size were further evaluated. Preconditioning was performed to screen out the samples with solder extrusion by doing the electrical testing (ATE). The thermal cycling test was proceeded to assess the reliability up to 500 cycles. The results indicated that the samples with the PI isolation passed the ATE without solder extrusion and no solder joint reliability issue observed.
Proliferation of HIV-infected renal epithelial cells following virus acquisition from infected macrophages

Objectives HIV-1 can infect and persist in different organs and tissues, resulting in the generation of multiple viral compartments and reservoirs. Increasing evidence supports the kidney as such a reservoir. Previous work demonstrated that HIV-1 infected CD4 T-cells transfer virus to renal tubule epithelial (RTE) cells through cell-to-cell contact. In addition to CD4 T cells, macrophages represent the other major target of HIV-1. Renal macrophages induce and regulate inflammatory responses and are critical to homeostatic regulation of the kidney environment. Combined with their ability to harbour virus, macrophages may also play an important role in the spread of HIV-1 infection in the kidney. Design and methods Multiparametric histochemistry analysis was performed on kidney biopsies from individuals with HIV-1 associated nephropathy (HIVAN). Primary monocyte-derived macrophages were infected with a GFP-expressing replication competent HIV-1. HIV-1 transfer from macrophages to RTE cells was carried out in a coculture system and evaluated by fluorescence-microscopy and flow-cytometry. Live imaging was performed to assess the fate of HIV-1 infected RTE cells over time. Results We show that macrophages are abundantly present in the renal inflammatory infiltrate of individuals with HIVAN. We observed contact-dependent HIV-1 transfer from infected macrophages to both primary and immortalized renal cells. Live imaging of HIV-1 infected RTE cells revealed four different fates: proliferation, hypertrophy, latency and cell death. Conclusion Our study suggests that macrophages may play a role in the dissemination of HIV-1 in the kidney and that proliferation of infected renal cells may contribute to HIV-1 persistence in this compartment.
Duality of Moduli and Quasiconformal Mappings in Metric Spaces

We prove a duality relation for the moduli of the family of curves connecting two sets and the family of surfaces separating the sets, in the setting of a complete metric space equipped with a doubling measure and supporting a Poincare inequality. Then we apply this to show that quasiconformal mappings can be characterized by the fact that they quasi-preserve the modulus of certain families of surfaces.
The Python's Lunch: geometric obstructions to decoding Hawking radiation

According to Harlow and Hayden [ arXiv:1301.4504 ] the task of distilling information out of Hawking radiation appears to be computationally hard despite the fact that the quantum state of the black hole and its radiation is relatively un-complex. We trace this computational difficulty to a geometric obstruction in the Einstein-Rosen bridge connecting the black hole and its radiation. Inspired by tensor network models, we conjecture a precise formula relating the computational hardness of distilling information to geometric properties of the wormhole — specifically to the exponential of the difference in generalized entropies between the two non-minimal quantum extremal surfaces that constitute the obstruction. Due to its shape, we call this obstruction the ‘Python’s Lunch’, in analogy to the reptile’s postprandial bulge.
On edge‐ordered Ramsey numbers

An edge-ordered graph is a graph with a linear ordering of its edges. Two edge-ordered graphs are equivalent if their is an isomorphism between them preserving the ordering of the edges. The edge-ordered Ramsey number $r_{edge}(H; q)$ of an edge-ordered graph $H$ is the smallest $N$ such that there exists an edge-ordered graph $G$ on $N$ vertices such that, for every $q$-coloring of the edges of $G$, there is a monochromatic subgraph of $G$ equivalent to $H$. Recently, Balko and Vizer announced that $r_{edge}(H;q)$ exists. However, their proof uses the Graham-Rothschild theorem and consequently gives an enormous upper bound on these numbers. We give a new proof giving a much better bound. We prove that for every edge-ordered graph $H$ on $n$ vertices, we have $r_{edge}(H;q) \leq 2^{c^qn^{2q-2}\log^q n}$, where $c$ is an absolute constant. We also explore the edge-ordered Ramsey number of sparser graphs and prove a polynomial bound for edge-ordered graphs of bounded degeneracy. We also prove a strengthening for edge-labeled graphs, graphs where every edge is given a label and the labels do not necessary have an ordering.
Exchange Rates and Liquidity Risk

I use Forex trading data to study how risks associated with the lack of liquidity contribute to the dynamics of 17 spot exchange rates through their time-varying contributions to risk premia. I find that liquidity risk matters. All the foreign exchange risk premia compensate investors for exposure to liquidity risk; and, for many currencies, exposure to liquidity risk appears to be more important than exposure to the traditional carry and momentum risk factors. I also find that variations in the price of liquidity risk make economically important contributions to the behavior of individual foreign currency returns: they account for approximately 34 percent, on average, of the variability in currency returns compared to the contribution of approximately 8 percent from the prices of carry and momentum risk.
A higher weight analogue of Ogg’s theorem on Weierstrass points

For a positive integer N, we say that ∞ is a Weierstrass point on the modular curve X0(N) if there is a non-zero cusp form of weight 2 on Γ0(N) which vanishes at ∞ to order greater than the genus o...
The Limit Point of the Pentagram Map and Infinitesimal Monodromy

The pentagram map takes a planar polygon $P$ to a polygon $P'$ whose vertices are the intersection points of consecutive shortest diagonals of $P$. The orbit of a convex polygon under this map is a sequence of polygons which converges exponentially to a point. Furthermore, as recently proved by Glick, coordinates of that limit point can be computed as an eigenvector of a certain operator associated with the polygon. In the present paper we show that Glick's operator can be interpreted as the infinitesimal monodromy of the polygon. Namely, there exists a certain natural infinitesimal perturbation of a polygon, which is again a polygon but in general not closed; what Glick's operator measures is the extent to which this perturbed polygon does not close up.
SmartBullets: A Cloud-Assisted Bullet Screen Filter based on Deep Learning

Bullet-screen is a technique that enables the website users to send real-time comment ‘bullet’ cross the screen. Since all the comments are shown on the screen publicly and simultaneously, low-quality bullets will reduce the watching enjoyment of the users. In this paper, we present SmartBullets, a user-centered bullet-screen filter based on deep learning techniques. A convolutional neural network is trained as the classifier to remove low-quality bullet comments. Moreover, to increase the scalability of our model, we employ a cloud-assisted framework by developing a backend cloud server and a front-end browser extension. We evaluate our framework with forty volunteers and the result shows that our framework can effectively remove the low-quality bullets and improve the overall watching experience of viewers.
Good reductions of Shimura varieties of Hodge type in arbitrary unramified mixed characteristic. Part I

We prove a conjecture of Milne pertaining to the existence of integral canonical models of Shimura varieties of abelian type in arbitrary unramified mixed char- acteristic (0, p).
Bond Synergy Model for Bond Energies in Alloy Oxides

In this work we introduce a metal-oxide bond-energy model for alloy oxides based on pure-phase bond energies and bond synergy factors that describe the effect of alloying on the bond energy between cations and oxygen, an important quantity to understand formation and stability of passive films. This model is parameterized for binary cation-alloy oxides using density-functional theory energies and is shown to be directly transferable to multi-component alloy oxides. We parameterized the model for alloy oxide energies with metal cations that form the basis of corrosion resistant alloys, including Fe, Ni, Cr, Mo, Mn, W, Co, and Ru. We find that isoelectronic solutes allow quantification of pure-phase bond energies in oxides and that the calculated bond energy values give sensible results compared to common experience, including the role of Cr as the passive-layer former in Fe-Ni-Cr alloys for corrosion applications. Additionally, the bond synergy factors give insights into the mutual strengthening and weakening effects of alloying on cation-oxygen bonds and can be related to enthalpy of mixing and charge neutrality constraints. We demonstrate how charge neutrality can be identified and achieved by the oxidation states that the different cations assume depending on alloy composition and the presence of defects.
Precision age indicators that exploit chemically peculiar stars

Context. The integrated light of distant star clusters and galaxies can yield information on the stellar formation epochs, chemical abundance mixtures, and initial stellar mass functions, and therefore improve our understanding of galaxy evolution. Aims. We would like to find a way to improve the determination of galaxy star formation history from integrated light spectroscopy. Methods. Several classes of chemically peculiar (CP) stars arise during the course of normal evolution in single stars and noninteracting binary stars. An aging stellar population has periods of time in which CP stars contribute to the integrated light, and others in which the contributions fade. The HgMn stars, for example, occupy a narrow temperature range of 10500 to 16000 K, which maps to a narrow range of ages. Wolf-Rayet stars, He-poor stars, Bp-Ap stars, Am-Fm stars, and C stars all become very common in a normal stellar population at various ages between zero and several Gyr, fading in and out in a way that is analogous to features used in stellar spectral classification. We examine population fractions and light fractions in order to assess the feasibility of using CP stars as age tracers. Results. Even though CP stars do not usually dominate in number, there are enough of them so that the CP spectral features are detectable in high-quality integrated spectra of young and intermediate age stellar populations. The new technique should be calibratable and useful. Furthermore, using CP signatures as age dating tools sidesteps reliance on photometry that is susceptible to dust and Balmer features that are susceptible to nebular fill-in.
New computer-based search strategies for extreme functions of the Gomory–Johnson infinite group problem

We describe new computer-based search strategies for extreme functions for the Gomory–Johnson infinite group problem. They lead to the discovery of new extreme functions, whose existence settles several open questions.
Domain state occurring in the de Haas-van Alphen effect in silver

Hysteresis has been observed in de Haas–van Alphen measurements of the Condon domains in silver, and it shows the first-order nature of the transition to the domain state. The hysteresis, and thus the first-order nature, is manifested in a nonlinear effect where a double-valued response of the amplitude with the applied external field is observed.
The CALSPEC Stars P177D and P330E

Multicolor photometric data are presented for the CALSPEC stars P177D and P330E. Together with previously published photometry for nine other CALSPEC standards, the photometric observations and synthetic photometry from Hubble Space Telescope/STIS spectrophotometry agree in the B, V, R, and I bands to better than ?1% (10 mmag). Photometry over the 1986 to1991 period indicates that BD+17?4708 brightened by ? 0.04 mag.
Boolean and non-Boolean computation with spin devices

Recently several device and circuit design techniques have been explored for applying nano-magnets and spin torque devices like spin valves and domain wall magnets in computational hardware. However, most of them have been focused on digital logic, and, their benefits over robust and high performance CMOS remains debatable. Ultra-low voltage, current-mode operation of magneto-metallic spin torque devices can potentially be more suitable for non-Boolean logic like neuromorphic computation, which involve analog processing. Device circuit co-design for different classes of neuromorphic architectures using spin-torque based neuron models along with DWM or other memristive synapses show that the spin-based neuromorphic designs can achieve 15X-100X lower computation energy for applications like, image processing, data-conversion, cognitive-computing, pattern matching and programmable-logic, as compared to state of art CMOS designs.
Renormalization group flow, Entropy and Eigenvalues

The irreversibility of the renormalization group flow is conjectured to be closely related to the concept of entropy. In this paper, the variation of eigenvalues of the Laplacian in the Polyakov action under the renormalization group flow will be studied. Based on the one-loop approximation to the effective field theory, we will use the heat kernel method and zeta function regularization. In even dimensions, the variation of eigenvalues is given by the top heat kernel coefficient, and the conformal anomaly is relevant. In odd dimensions, we will conjecture a formula for the variation of eigenvalues through the holographic renormalization in the setting of geometric AdS/CFT correspondence.
Compositional Explanations of Neurons

We describe a procedure for explaining neurons in deep representations by identifying compositional logical concepts that closely approximate neuron behavior. Compared to prior work that uses atomic labels as explanations, analyzing neurons compositionally allows us to more precisely and expressively characterize their behavior. We use this procedure to answer several questions on interpretability in models for vision and natural language processing. First, we examine the kinds of abstractions learned by neurons. In image classification, we find that many neurons learn highly abstract but semantically coherent visual concepts, while other polysemantic neurons detect multiple unrelated features; in natural language inference (NLI), neurons learn shallow lexical heuristics from dataset biases. Second, we see whether compositional explanations give us insight into model performance: vision neurons that detect human-interpretable concepts are positively correlated with task performance, while NLI neurons that fire for shallow heuristics are negatively correlated with task performance. Finally, we show how compositional explanations provide an accessible way for end users to produce simple "copy-paste" adversarial examples that change model behavior in predictable ways.
Contribution of structural variation to genome structure: TAD fusion discovery and ranking

The significant contribution of structural variants to function, disease, and evolution is widely reported. However, in many cases, the mechanism by which these variants contribute to the phenotype is not well understood. Recent studies reported structural variants that disrupted the three-dimensional genome structure by fusing two topologically associating domains (TADs), such that enhancers from one TAD interacted with genes from the other TAD, and could cause severe developmental disorders. However, no computational method exists for directly scoring and ranking structural variations based on their effect on the three-dimensional structure such as the TAD disruption to guide further studies of their biological function. In this paper, we formally define TAD fusion and provide a combinatorial approach for assigning a score to quantify the level of TAD fusion for each deletion denoted as TAD fusion score. We also show that our method outperforms the approaches which use predicted TADs and overlay the deletion on them to predict TAD fusion. Furthermore, we show that deletions that cause TAD fusion are rare and under negative selection in general population. Finally, we show that our method correctly gives higher scores to deletions reported to cause various disorders (developmental disorder and cancer) in comparison to the deletions reported in the 1000 genomes project.
Systems of Oscillators Designed for a Specific Conscious Percept

As put forward by neuroscientists, the mechanisms of consciousness can be elucidated by revealing correlations between neural dynamics and specific conscious percepts. Recently, I have elaborated o...
Learning from Richer Human Guidance: Augmenting Comparison-Based Learning with Feature Queries

We focus on learning the desired objective function for a robot. Although trajectory demonstrations can be very informative of the desired objective, they can also be difficult for users to provide. Answers to comparison queries, asking which of two trajectories is preferable, are much easier for users, and have emerged as an effective alternative. Unfortunately, comparisons are far less informative. We propose that there is much richer information that users can easily provide and that robots ought to leverage. We focus on augmenting comparisons with feature queries, and introduce a unified formalism for treating all answers as observations about the true desired reward. We derive an active query selection algorithm, and test these queries in simulation and on real users. We find that richer, feature-augmented queries can extract more information faster, leading to robots that better match user preferences in their behavior.
A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty

Author(s): Nachman, Benjamin | Abstract: Deep learning tools can incorporate all of the available information into a search for new particles, thus making the best use of the available data. This paper reviews how to optimally integrate information with deep learning and explicitly describes the corresponding sources of uncertainty. Simple illustrative examples show how these concepts can be applied in practice.
Quantifying Voter Biases in Online Platforms: An Instrumental Variable Approach

In content-based online platforms, use of aggregate user feedback (say, the sum of votes) is commonplace as the "gold standard" for measuring content quality. Use of vote aggregates, however, is at odds with the existing empirical literature, which suggests that voters are susceptible to different biases-reputation (e.g., of the poster), social influence (e.g., votes thus far), and position (e.g., answer position). Our goal is to quantify, in an observational setting, the degree of these biases in online platforms. Specifically, what are the causal effects of different impression signals-such as the reputation of the contributing user, aggregate vote thus far, and position of content-on a participant's vote on content? We adopt an instrumental variable (IV) framework to answer this question. We identify a set of candidate instruments, carefully analyze their validity, and then use the valid instruments to reveal the effects of the impression signals on votes. Our empirical study using log data from Stack Exchange websites shows that the bias estimates from our IV approach differ from the bias estimates from the ordinary least squares (OLS) method. In particular, OLS underestimates reputation bias (1.6-2.2x for gold badges) and position bias (up to 1.9x for the initial position) and overestimates social influence bias (1.8-2.3x for initial votes). The implications of our work include: redesigning user interface to avoid voter biases; making changes to platforms' policy to mitigate voter biases; detecting other forms of biases in online platforms.
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models

This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job (T2). Our best models using section encoding and a multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms.
From the Heart of the Ghoul: C and N Abundances in the Corona of Algol B

Chandra Low Energy Transmission Grating Spectrograph observations of Algol have been used to determine the abundances of C and N in the secondary star for the first time. In order to minimize errors arising from an uncertain coronal differential emission measure as a function of temperature, the analysis was performed relative to similar observations of an adopted "standard" star, HR 1099. It is demonstrated that HR 1099 and Algol are coronal twins in many respects and that their X-ray spectra are very similar in nearly all details, except for the observed strengths of C and N lines. The H-like 2p 2P3/2,1/2 → 1s 2S1/2 transitions of C and N in the coronae of Algol and HR 1099 demonstrate that the surface abundances of Algol B have been strongly modified by CN processing, as shown earlier by Schmitt & Ness. It is found that N is enhanced in Algol B by a factor of 3 compared with HR 1099. No C lines are detected in the Algol spectrum, indicating a C depletion relative to HR 1099 by a factor of 10 or more. These C and N abundances indicate that Algol B must have lost at least half of its initial mass and are consistent with predictions of evolutionary models that include nonconservative mass transfer and angular momentum loss through magnetic activity. Based on H-like and He-like transitions in O and Ne, it is estimated that Algol is slightly metal poor, by 0.2 dex in terms of the coronal abundances of light elements relative to HR 1099, while the Fe XVII 2p53d 1P1 → 2p6 1S0 transition indicates a very similar Fe abundance. In reviewing coronal abundance results for active stars in the literature and drawing on an earlier Chandra study of the coronal abundances of HR 1099, it is concluded that Fe is very likely depleted in the coronae of both Algol and HR 1099 by 0.5 dex relative to their photospheric compositions, but that Ne is enhanced by a similar magnitude. Light elements such as C, N, and O are likely depleted in both stars by of order 0.3 dex. The similarities in these large-abundance anomalies in HR 1099 and Algol are notable. Despite such compositional fractionation in these coronae, the relative C and N abundances in HR 1099, determined by comparing observed line strengths with theoretical C/N line ratios, are consistent with recent solar values, indicating that differential fractionation between these elements is not significant and that little or no dredge-up of material subjected to CN processing has occurred on the subgiant component.
A FAMILY OF PARAMETER-DEPENDENT DIFFEOMORPHISMS ACTING ON FUNCTION SPACES OVER A RIEMANNIAN MANIFOLD AND APPLICATIONS TO GEOMETRIC FLOWS

It is the purpose of this article to establish a technical tool to study regularity of solutions to parabolic equations on manifolds. As applications of this technique, we prove that solutions to the Ricci-DeTurck flow, the surface diffusion flow and the mean curvature flow enjoy joint analyticity in time and space, and solutions to the Ricci flow admit temporal analyticity.
p-bits for probabilistic spin logic

We introduce the concept of a probabilistic or p-bit, intermediate between the standard bits of digital electronics and the emerging q-bits of quantum computing. We show that low barrier magnets or LBMs provide a natural physical representation for p-bits and can be built either from perpendicular magnets designed to be close to the in-plane transition or from circular in-plane magnets. Magnetic tunnel junctions (MTJs) built using LBMs as free layers can be combined with standard NMOS transistors to provide three-terminal building blocks for large scale probabilistic circuits that can be designed to perform useful functions. Interestingly, this three-terminal unit looks just like the 1T/MTJ device used in embedded magnetic random access memory technology, with only one difference: the use of an LBM for the MTJ free layer. We hope that the concept of p-bits and p-circuits will help open up new application spaces for this emerging technology. However, a p-bit need not involve an MTJ; any fluctuating resistor could be combined with a transistor to implement it, while completely digital implementations using conventional CMOS technology are also possible. The p-bit also provides a conceptual bridge between two active but disjoint fields of research, namely, stochastic machine learning and quantum computing. First, there are the applications that are based on the similarity of a p-bit to the binary stochastic neuron (BSN), a well-known concept in machine learning. Three-terminal p-bits could provide an efficient hardware accelerator for the BSN. Second, there are the applications that are based on the p-bit being like a poor man's q-bit. Initial demonstrations based on full SPICE simulations show that several optimization problems, including quantum annealing are amenable to p-bit implementations which can be scaled up at room temperature using existing technology.
Large intersection properties in Diophantine approximation and dynamical systems

We investigate the large intersection properties of the set of points that are approximated at a certain rate by a family of affine subspaces. We then apply our results to various sets arising in the metric theory of Diophantine approximation, in the study of the homeomorphisms of the circle, and in the perturbation theory for Hamiltonian systems.
ALMA Observations of the Debris Disk around the Young Solar Analog HD 107146

We present the Atacama Large Millimeter/submillimeter Array (ALMA) continuum observations at a wavelength of 1.25 mm of the debris disk surrounding the ~100 Myr old solar analog HD 107146. The continuum emission extends from about 30 to 150 AU from the central star with a decrease in the surface brightness at intermediate radii. We analyze the ALMA interferometric visibilities using debris disk models with radial profiles for the dust surface density parameterized as (1) a single power law, (2) a single power law with a gap, and (3) a double power law. We find that models with a gap of radial width ~8 AU at a distance of ~80 AU from the central star, as well as double power-law models with a dip in the dust surface density at ~70 AU provide significantly better fits to the ALMA data than single power-law models. We discuss possible scenarios for the origin of the HD 107146 debris disk using models of planetesimal belts in which the formation of Pluto-sized objects trigger disruptive collisions of large bodies, as well as models that consider the interaction of a planetary system with a planetesimal belt and spatial variation of the dust opacity across the disk. If future observations with higher angular resolution and sensitivity confirm the fully depleted gap structure discussed here, a planet with a mass of approximately a few Earth masses in a nearly circular orbit at ~80 AU from the central star would be a possible explanation for the presence of the gap.
Evaluation of Docker Containers for Scientific Workloads in the Cloud

The HPC community is actively researching and evaluating tools to support execution of scientific applications in cloud-based environments. Among the various technologies, containers have recently gained importance as they have significantly better performance compared to full-scale virtualization, support for microservices and DevOps, and work seamlessly with workflow and orchestration tools. Docker is currently the leader in containerization technology because it offers low overhead, flexibility, portability of applications, and reproducibility. Singularity is another container solution that is of interest as it is designed specifically for scientific applications. It is important to conduct performance and feature analysis of the container technologies to understand their applicability for each application and target execution environment.This paper presents a (1) performance evaluation of Docker and Singularity on bare metal nodes in the Chameleon cloud (2) mechanism by which Docker containers can be mapped with InfiniBand hardware with RDMA communication and (3) analysis of mapping elements of parallel workloads to the containers for optimal resource management with container-ready orchestration tools. Our experiments are targeted toward application developers so that they can make informed decisions on choosing the container technologies and approaches that are suitable for their HPC workloads on cloud infrastructure. Our performance analysis shows that scientific workloads for both Docker and Singularity based containers can achieve near-native performance.Singularity is designed specifically for HPC workloads. However, Docker still has advantages over Singularity for use in clouds as it provides overlay networking and an intuitive way to run MPI applications with one container per rank for fine-grained resources allocation. Both Docker and Singularity make it possible to directly use the underlying network fabric from the containers for coarsegrained resource allocation.
A Rank-Preserving Generalized Matrix Inverse for Consistency With Respect to Similarity

There has recently been renewed recognition of the need to understand the consistency properties that must be preserved when a generalized matrix inverse is required. The most widely known generalized inverse, the Moore-Penrose pseudoinverse, provides consistency with respect to orthonormal transformations (e.g., rotations of a coordinate frame), and a recently derived inverse provides consistency with respect to diagonal transformations (e.g., a change of units on state variables). Another well-known and theoretically important generalized inverse is the Drazin inverse, which preserves consistency with respect to similarity transformations. In this letter we note that the Drazin inverse suffers a significant practical limitation in that it does not generally preserve the rank of the linear system of interest. We then introduce an alternative generalized inverse that both preserves rank and provides consistency with respect to similarity transformations. We discuss practical implementation considerations and demonstrate with an example.
DIRECT EXOPLANET DETECTION WITH BINARY DIFFERENTIAL IMAGING

Binaries are typically excluded from direct imaging exoplanet surveys. However, the recent findings of Kepler and radial velocity programs show that planets can and do form in binary systems. Here, we suggest that visual binaries offer unique advantages for direct imaging. We show that Binary Differential Imaging (BDI), whereby two stars are imaged simultaneously at the same wavelength within the isoplanatic patch at high Strehl ratio, offers improved point spread function (PSF) subtraction that can result in increased sensitivity to planets close to each star. We demonstrate this by observing a young visual binary separated by 4\asec ~with MagAO/Clio-2 at 3.9 \microns, where the Strehl ratio is high, the isoplanatic patch is large, and giant planets are bright. Comparing BDI to angular differential imaging (ADI), we find that BDI's 5$\sigma$ contrast is \about 0.5 mags better than ADI's within \about 1\asec ~for the particular binary we observed. Because planets typically reside close to their host stars, BDI is a promising technique for discovering exoplanets in stellar systems that are often ignored. BDI is also 2-4$\times$ more efficient than ADI and classical reference PSF subtraction, since planets can be detected around both the target and PSF reference simultaneously. We are currently exploiting this technique in a new MagAO survey for giant planets in 140 young nearby visual binaries. BDI on a space-based telescope would not be limited by isoplanatism effects and would therefore be an even more powerful tool for imaging and discovering planets.
VARIATIONS IN THE MASS FUNCTIONS OF CLUSTERED AND ISOLATED YOUNG STELLAR OBJECTS

We analyze high-quality, complete stellar catalogs for four young (roughly 1 Myr) and nearby (within {approx}300 pc) star-forming regions: Taurus, Lupus3, ChaI, and IC348, which have been previously shown to have stellar groups whose properties are similar to those of larger clusters such as the Orion Nebula Cluster (ONC). We find that stars at higher stellar surface densities within a region or belonging to groups tend to have a relative excess of more massive stars, over a wide range of masses. We find statistically significant evidence for this result in Taurus and IC348 as well as in the ONC. These differences correspond to having typically a {approx}10%-20% higher mean mass in the more clustered environment. Stars in ChaI show no evidence for a trend with either surface density or grouped status, and there are too few stars in Lupus3 to make any definitive interpretation. Models of clustered star formation do not typically extend to sufficiently low masses or small group sizes in order for their predictions to be tested, but our results suggest that this regime is important to consider.
Targeted sampling from massive block model graphs with personalized PageRank

The paper provides statistical theory and intuition for personalized PageRank (called ‘PPR’): a popular technique that samples a small community from a massive network. We study a setting where the entire network is expensive to obtain thoroughly or to maintain, but we can start from a seed node of interest and ‘crawl’ the network to find other nodes through their connections. By crawling the graph in a designed way, the PPR vector can be approximated without querying the entire massive graph, making it an alternative to snowball sampling. Using the degree‐corrected stochastic block model, we study whether the PPR vector can select nodes that belong to the same block as the seed node. We provide a simple and interpretable form for the PPR vector, highlighting its biases towards high degree nodes outside the target block. We examine a simple adjustment based on node degrees and establish consistency results for PPR clustering that allows for directed graphs. These results are enabled by recent technical advances showing the elementwise convergence of eigenvectors. We illustrate the method with the massive Twitter friendship graph, which we crawl by using the Twitter application programming interface. We find that the adjusted and unadjusted PPR techniques are complementary approaches, where the adjustment makes the results particularly localized around the seed node, and that the bias adjustment greatly benefits from degree regularization.
Impact of prenatal exome sequencing for fetal genetic diagnosis on maternal psychological outcomes and decisional conflict in a prospective cohort

To evaluate associations between prenatal trio exome sequencing (trio-ES) and psychological outcomes among women with an anomalous pregnancy. Trio-ES study enrolling patients with major fetal anomaly and normal microarray. Women completed self-reported measures and free response interviews at two timepoints: pre- (1) and post- (2) sequencing. Pre-sequencing responses were compared with post-sequencing responses; post-sequencing responses were stratified by women who received trio-ES results that may explain fetal findings, secondary findings (medically actionable or couples with heterozygous variants for the same recessive disorder), or negative results. One hundred fifteen trios were enrolled. Of those, 41/115 (35.7%) received results from trio-ES, including 36 (31.3%) who received results that may explain the fetal phenotype. These women had greater post-sequencing distress compared with women who received negative results, including generalized distress (p = 0.03) and test-related distress (p = 0.2); they also had worse psychological adaptation to results (p = 0.001). Genomic knowledge did not change from pre- to post-sequencing (p = 0.51). Women show more distress after receiving trio-ES results compared with those who do not, suggesting that women receiving results may need additional support or counseling to inform current and future reproductive decisions.
Sampling Chaotic Trajectories Quickly in Parallel

The parallel computational complexity of the quadratic map is studied. A parallel algorithm is described that generates typical pseudotrajectories of length t in a time that scales as log t and increases slowly in the accuracy demanded of the pseudotrajectory. Long pseudotrajectories are created in parallel by putting together many short pseudotrajectories; Monte Carlo procedures are used to eliminate the discontinuities between these short pseudotrajectories and then suitably randomize the resulting long pseudotrajectory. Numerical simulations are presented that show the scaling properties of the parallel algorithm. The existence of the fast parallel algorithm provides a way to formalize the intuitive notion that chaotic systems do not generate complex histories.
The opacity problem in social contagion

Abstract Many social phenomena can be modeled as cascades in networks, where nodes adopt a behavior in response to peers adopting. When studying cascades, researchers typically assume that the number of active peers when a node adopts is equivalent to the node's threshold for adoption. This assumption is rarely justified due to the “opacity problem”: networked cascades reveal intervals which contain thresholds, rather than point estimates. Existing approaches take the maximum of each node's threshold interval, which biases models of social influence. Opacity is inevitable in many small graphs when using the threshold model, resulting from the networked process itself rather than data collection techniques. Using simulation, we extend this finding to the probabilistic SI (independent cascade) model. We confirm these theoretical results by studying 50 large hashtag cascades among 3.2 million Twitter users, finding that 20% of adoptions suffer from opacity. Different assumptions in response to opacity qualitatively change conclusions about peer influence. While opacity is a far-reaching problem, it can be addressed. Using information from nodes who have tightly bounded intervals allows building models to reduce error in estimating node thresholds.
Experimental demonstration of a surface-electrode multipole ion trap

We report on the design and experimental characterization of a surface-electrode multipole ion trap. Individual microscopic sugar particles are confined in the trap. The trajectories of driven particle motion are compared with a theoretical model, both to verify qualitative predictions of the model and to measure the charge-to-mass ratio of the confined particle. The generation of harmonics of the driving frequency is observed as a key signature of the nonlinear nature of the trap. We remark on possible applications of our traps, including to mass spectrometry.