cssi/SciSciGPT-SciSciNet huggingface dataset:
Real-time 3D single molecule tracking.

To date, single molecule studies have been reliant on tethering or confinement to achieve long duration and high temporal resolution measurements. Here, we present a 3D single-molecule active real-time tracking method (3D-SMART) which is capable of locking on to single fluorophores in solution for minutes at a time with photon limited temporal resolution. As a demonstration, 3D-SMART is applied to actively track single Atto 647 N fluorophores in 90% glycerol solution with an average duration of ~16 s at count rates of ~10 kHz. Active feedback tracking is further applied to single proteins and nucleic acids, directly measuring the diffusion of various lengths (99 to 1385 bp) of single DNA molecules at rates up to 10 µm2/s. In addition, 3D-SMART is able to quantify the occupancy of single Spinach2 RNA aptamers and capture active transcription on single freely diffusing DNA. 3D-SMART represents a critical step towards the untethering of single molecule spectroscopy. Single molecule observation has been limited to tethered molecules to ensure that the target remains in the field of view (FOV). Here, the authors develop a real-time tracking method that locks onto rapidly diffusing targets and tracks them in a 3D volume, enabling single molecules to remain in the FOV for minutes at a time.
Flip and Neimark-Sacker Bifurcations in a Coupled Logistic Map System

In this paper, we consider a system of strongly coupled logistic maps involving two parameters. We classify and investigate the stability of its fixed points. A local bifurcation analysis of the system using center manifold theory is undertaken and then supported by numerical computations. This reveals the existence of a flip and Neimark–Sacker bifurcations.
On the braid group representations coming from weakly group-theoretical fusion categories

We prove that representations of the braid groups coming from weakly group-theoretical braided fusion categories have finite images.
Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

Background: Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. Objective: The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. Methods: We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. Results: We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. Conclusions: This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
New Potential-Based Bounds for Prediction with Expert Advice

This work addresses the classic machine learning problem of online prediction with expert advice. We consider the finite-horizon version of this zero-sum, two-person game. Using verification arguments from optimal control theory, we view the task of finding better lower and upper bounds on the value of the game (regret) as the problem of finding better sub- and supersolutions of certain partial differential equations (PDEs). These sub- and supersolutions serve as the potentials for player and adversary strategies, which lead to the corresponding bounds. To get explicit bounds, we use closed-form solutions of specific PDEs. Our bounds hold for any given number of experts and horizon; in certain regimes (which we identify) they improve upon the previous state of the art. For two and three experts, our bounds provide the optimal leading order term.
Sarcopenia, frailty and cachexia patients detected in a multisystem electronic health record database

Sarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls. We conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables. Cohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n = 8285 (86.4%). All cases detected by ICD codes (total n = 1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p < 0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements. Patients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions.
Breaking the Word Bank: Measurement and Effects of Bank Level Uncertainty

Banks differ from non-financial firms as banks must communicate to both regulators and shareholders. Also, unlike non-financial firms, banks possess opaque and complex balance sheets and are the main providers of credit to the real economy. In this paper, I propose a new index to detect the idiosyncratic uncertainty banks face at the bank-quarter level by applying natural language processing techniques to earnings conference call transcripts. The index reveals which banks at a given quarter signal more uncertainty about their balance sheets. Higher uncertainty is associated with lower lending and higher liquidity the next quarter, suggesting active management of uncertainty. The active management of uncertainty is more pronounced during periods of high aggregate volatility and for banks with more skin in the game. Using loan level data and firm fixed effects, I control for demand-side factors and find that higher bank level uncertainty is associated with lower loan issuances the following quarter.
System inference for the spatio-temporal evolution of infectious diseases: Michigan in the time of COVID-19

We extend the classical SIR model of infectious disease spread to account for time dependence in the parameters, which also include diffusivities. The temporal dependence accounts for the changing characteristics of testing, quarantine and treatment protocols, while diffusivity incorporates a mobile population. This model has been applied to data on the evolution of the COVID-19 pandemic in the US state of Michigan. For system inference, we use recent advances; specifically our framework for Variational System Identification (Wang et al. in Comput Methods Appl Mech Eng 356:44–74, 2019; arXiv:2001.04816 [cs.CE]) as well as Bayesian machine learning methods.
Improving Transaction Success Rate via Smart Gateway Selection in Cryptocurrency Payment Channel Networks

The last decade has experienced a vast interest in Blockchain-based cryptocurrencies. However, slow confirmation times of transactions hamper their wide adoption for micropayments. The idea of establishing payment channel networks is one of the many proposed solutions to address this scalability. Due to the way these channels are created, both sides have a certain one-way capacity for making transactions. In this paper, we aim to increase the overall success rate of payments by effectively exploiting the fact that end-users are usually connected to the network at multiple points. The evaluation of the proposed method shows that compared to greedy and maxflow-based approaches, we can achieve much higher success rates.
Oscillatory functions vanish on a large set

Let $(M,g)$ be a $n-$dimensional, compact Riemannian manifold. We define the frequency scale $\lambda$ of a function $f \in C^{0}(M)$ as the largest number such that $\left\langle f, \phi_k \right\rangle =0$ for all Laplacian eigenfunctions with eigenvalue $\lambda_k \leq \lambda$. If $\lambda$ is large, then the function $f$ has to vanish on a large set $$ \mathcal{H}^{n-1} \left\{x:f(x) =0\right\} \gtrsim_{} \left( \frac{ \|f\|_{L^1}}{\|f\|_{L^{\infty}}} \right)^{2 - \frac{1}{n}} \frac{ \sqrt{\lambda}}{(\log{\lambda})^{n/2}}.$$ Trigonometric functions on the flat torus $\mathbb{T}^d$ show that the result is sharp up to a logarithm if $\|f\|_{L^1} \sim \|f\|_{L^{\infty}}$. We also obtain a stronger result conditioned on the geometric regularity of $\left\{x:f(x) = 0\right\}$. This may be understood as a very general higher-dimensional extension of the Sturm oscillation theorem.
Mortality Attributed to COVID-19 in High-Altitude Populations.

Woolcott, Orison O., and Richard N. Bergman. Mortality attributed to COVID-19 in high-altitude populations. High Alt Med Biol. 21:409–416, 2020. Background: Since partial oxygen pressure decreases ...
A code within the genetic code: codon usage regulates co-translational protein folding.

The genetic code is degenerate, and most amino acids are encoded by two to six synonymous codons. Codon usage bias, the preference for certain synonymous codons, is a universal feature of all genomes examined. Synonymous codon mutations were previously thought to be silent; however, a growing body evidence now shows that codon usage regulates protein structure and gene expression through effects on co-translational protein folding, translation efficiency and accuracy, mRNA stability, and transcription. Codon usage regulates the speed of translation elongation, resulting in non-uniform ribosome decoding rates on mRNAs during translation that is adapted to co-translational protein folding process. Biochemical and genetic evidence demonstrate that codon usage plays an important role in regulating protein folding and function in both prokaryotic and eukaryotic organisms. Certain protein structural types are more sensitive than others to the effects of codon usage on protein folding, and predicted intrinsically disordered domains are more prone to misfolding caused by codon usage changes than other domain types. Bioinformatic analyses revealed that gene codon usage correlates with different protein structures in diverse organisms, indicating the existence of a codon usage code for co-translational protein folding. This review focuses on recent literature on the role and mechanism of codon usage in regulating translation kinetics and co-translational protein folding.
Wind Speed Prediction and Visualization Using Long Short-Term Memory Networks (LSTM)

Climate change is one of the most concerning issues of this century. Emission from electric power generation is a crucial factor that drives the concern to the next level. Renewable energy sources are widespread and available globally, however, one of the major challenges is to understand their characteristics in a more informative way. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy. The results show a deep learning approach, long short-term memory (LSTM) outperforms other models with the highest accuracy of 97.8%.
FLN-1/Filamin is required to anchor the actomyosin cytoskeleton and for global organization of sub-cellular organelles in a contractile tissue

Actomyosin networks are organized in space, direction, size, and connectivity to produce coordinated contractions across cells. We use the C. elegans spermatheca, a tube composed of contractile myoepithelial cells, to study how actomyosin structures are organized. FLN-1/filamin is required for the formation and stabilization of a regular array of parallel, contractile, actomyosin fibers in this tissue. Loss of fln-1 results in the detachment of actin fibers from the basal surface, which then accumulate along the cell junctions and are stabilized by spectrin. In addition, actin and myosin are captured at the nucleus by the linker of nucleoskeleton and cytoskeleton complex (LINC) complex, where they form large foci. Nuclear positioning and morphology, distribution of the endoplasmic reticulum and the mitochondrial network are also disrupted. These results demonstrate that filamin is required to prevent large actin bundle formation and detachment, to prevent excess nuclear localization of actin and myosin, and to ensure correct positioning of organelles.
Lieb─Robinson Bounds and Strongly Continuous Dynamics for a Class of Many-Body Fermion Systems in {\mathbb {R}}(d)

We introduce a class of UV-regularized two-body interactions for fermions in $${\mathbb {R}}^d$$ and prove a Lieb–Robinson estimate for the dynamics of this class of many-body systems. As a step toward this result, we also prove a propagation bound of Lieb–Robinson type for Schrodinger operators. We apply the propagation bound to prove the existence of infinite-volume dynamics as a strongly continuous group of automorphisms on the CAR algebra.
Weak deep priors for seismic imaging

Incorporating prior knowledge on model unknowns of interest is essential when dealing with ill-posed inverse problems due to the nonuniqueness of the solution and data noise. Unfortunately, it is not trivial to fully describe our priors in a convenient and analytical way. Parameterizing the unknowns with a convolutional neural network (CNN), and assuming an uninformative Gaussian prior on its weights, leads to a variational prior on the output space that favors "natural" images and excludes noisy artifacts, as long as overfitting is prevented. This is the so-called deep-prior approach. In seismic imaging, however, evaluating the forward operator is computationally expensive, and training a randomly initialized CNN becomes infeasible. We propose, instead, a weak version of deep priors, which consists of relaxing the requirement that reflectivity models must lie in the network range, and letting the unknowns deviate from the network output according to a Gaussian distribution. Finally, we jointly solve for the reflectivity model and CNN weights. The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap. Our synthetic numerical experiments demonstrate that the weak deep prior is more robust with respect to noise than conventional least-squares imaging approaches, with roughly twice the computational cost of reverse-time migration, which is the affordable computational budget in large-scale imaging problems.
Pair correlations in strongly coupled dusty plasmas

Pair correlation functions and static structure factors for dust particles are calculated in a strongly coupled plasma using the Kirkwood approximation [J. Chem. Phys. 3, 300 (1935); 14, 180 (1946)]. The kinetic integral equation is solved in long- as well as short-wavelength regimes. In the long-wavelength regime, the pair correlation function exhibits Debye-H\"uckel behavior and decays exponentially with distance. This type of behavior occurs not only when the dust particles are weakly coupled but also when they are unannealed and in a moderate strongly coupled regime (characterized by dust coupling constants of order unity). In the short-wavelength regime, when the characteristic spatial scale is of the order of the interdust distance, the static structure factor for strongly coupled dust particles (characterized by coupling constants much larger than unity) exhibits pronounced oscillations and liquidlike behavior, as seen in recent experiments and particle-in-cell simulations.
Defining facets of social distancing during the COVID-19 pandemic: Twitter analysis.

Abstract Objectives Using Twitter, we aim to (1) define and quantify the prevalence and evolution of facets of social distancing during the COVID-19 pandemic in the US in a spatiotemporal context and (2) examine amplified tweets among social distancing facets. Materials and methods We analyzed English and US-based tweets containing “coronavirus” between January 23-March 24, 2020 using the Twitter API. Tweets containing keywords were grouped into six social distancing facets: implementation, purpose, social disruption, adaptation, positive emotions, and negative emotions. Results A total of 259,529 unique tweets were included in the analyses. Social distancing tweets became more prevalent from late January to March but were not geographically uniform. Early facets of social distancing appeared in Los Angeles, San Francisco, and Seattle: the first cities impacted by the COVID-19 outbreak. Tweets related to the “implementation” and “negative emotions” facets largely dominated in combination with topics of “social disruption” and “adaptation”, albeit to lesser degree. Social disruptiveness tweets were most retweeted, and implementation tweets were most favorited. Discussion Social distancing can be defined by facets that respond to and represent certain events in a pandemic, including travel restrictions and rising case counts. For example, Miami had a low volume of social distancing tweets but grew in March corresponding with the rise of COVID-19 cases. Conclusion The evolution of social distancing facets on Twitter reflects actual events and may signal potential disease hotspots. Our facets can also be used to understand public discourse on social distancing which may inform future public health measures.
Long Time Behavior of 2D Water Waves with Point Vortices

In this paper, we study the motion of the two dimensional inviscid incompressible, infinite depth water waves with point vortices in the fluid. We show that the Taylor sign condition $$-\frac{\partial P}{\partial \vec {n}}\geqslant 0$$ can fail if the point vortices are sufficiently close to the free boundary, so the water waves could be subject to Taylor instability. Assuming the Taylor sign condition, we prove that the water wave system is locally wellposed in Sobolev spaces. Moreover, we show that if the water waves is symmetric with a symmetric vortex pair traveling downward initially, then the free interface remains smooth for a long time, and for initial data of size $$\epsilon \ll 1$$ , the lifespan is at least $$O(\epsilon ^{-2})$$ .
The Effects of Cannabis Use on Cognitive Function in Healthy Aging: A Systematic Scoping Review.

Background Older adults (≥50 years) represent the fastest-growing population of people who use cannabis, potentially due to the increasing promotion of cannabis as medicine by dispensaries and cannabis websites. Given healthy aging and cannabis use are both associated with cognitive decline, it is important to establish the effects of cannabis on cognition in healthy aging. Objective This systematic scoping review used preferred reporting items for systematic reviews and meta-analyses guidelines to critically examine the extent of literature on this topic and highlight areas for future research. Method A search of six databases (PubMed, EMBASE, PsycINFO, Web of Science, Family and Society Studies Worldwide, and CINAHL) for articles published by September 2019, yielded 1,014 unique results. Results Six articles reported findings for older populations (three human and three rodent studies), highlighting the paucity of research in this area. Human studies revealed largely null results, likely due to several methodological limitations. Better-controlled rodent studies indicate that the relationship between ∆9-tetrahydrocannabinol (THC) and cognitive function in healthy aging depends on age and level of THC exposure. Extremely low doses of THC improved cognition in very old rodents. Somewhat higher chronic doses improved cognition in moderately aged rodents. No studies examined the effects of cannabidiol (CBD) or high-CBD cannabis on cognition. Conclusions This systematic scoping review provides crucial, timely direction for future research on this emerging issue. Future research that combines neuroimaging and cognitive assessment would serve to advance understanding of the effects of age and quantity of THC and CBD on cognition in healthy aging.
Effect of Technical and Social Factors on Pull Request Quality for the NPM Ecosystem

Background: Pull request (PR) based development, which is a norm for the social coding platforms, entails the challenge of evaluating the contributions of, often unfamiliar, developers from across the open source ecosystem and, conversely, submitting a contribution to a project with unfamiliar maintainers. Previous studies suggest that the decision of accepting or rejecting a PR may be influenced by a diverging set of technical and social factors, but often focus on relatively few projects, do not consider ecosystem-wide measures, or the possible non-monotonic relationships between the predictors and PR acceptance probability. Aim: We aim to shed light on this important decision making process by testing which measures significantly affect the probability of PR acceptance on a significant fraction of a large ecosystem, rank them by their relative importance in predicting PR acceptance, and determine the shape of the functions that map each predictor to PR acceptance. Method: We proposed seven hypotheses regarding which technical and social factors might affect PR acceptance and created 17 measures based on them. Our dataset consisted of 470,925 PRs from 3349 popular NPM packages and 79,128 GitHub users who created those. We tested which of the measures affect PR acceptance and ranked the significant measures by their importance in a predictive model. Results: Our predictive model had and AUC of 0.94, and 15 of the 17 measures were found to matter, including five novel ecosystem-wide measures. Measures describing the number of PRs submitted to a repository and what fraction of those get accepted, and signals about the PR review phase were most significant. We also discovered that only four predictors have a linear influence on the PR acceptance probability while others showed a more complicated response. Conclusion: Our findings should be helpful for PR creators, integrators, as well as tool designers to focus on the important factors affecting PR acceptance.
SurReal: Complex-Valued Learning as Principled Transformations on a Scaling and Rotation Manifold.

Complex-valued data are ubiquitous in signal and image processing applications, and complex-valued representations in deep learning have appealing theoretical properties. While these aspects have long been recognized, complex-valued deep learning continues to lag far behind its real-valued counterpart. We propose a principled geometric approach to complex-valued deep learning. Complex-valued data could often be subject to arbitrary complex-valued scaling; as a result, real and imaginary components could covary. Instead of treating complex values as two independent channels of real values, we recognize their underlying geometry: we model the space of complex numbers as a product manifold of nonzero scaling and planar rotations. Arbitrary complex-valued scaling naturally becomes a group of transitive actions on this manifold. We propose to extend the property instead of the form of real-valued functions to the complex domain. We define convolution as the weighted Frechet mean on the manifold that is equivariant to the group of scaling/rotation actions and define distance transform on the manifold that is invariant to the action group. The manifold perspective also allows us to define nonlinear activation functions, such as tangent ReLU and G-transport, as well as residual connections on the manifold-valued data. We dub our model SurReal, as our experiments on MSTAR and RadioML deliver high performance with only a fractional size of real- and complex-valued baseline models.
Root system chip-firing I: interval-firing

Jim Propp recently introduced a variant of chip-firing on a line where the chips are given distinct integer labels. Hopkins, McConville, and Propp showed that this process is confluent from some (but not all) initial configurations of chips. We recast their set-up in terms of root systems: labeled chip-firing can be seen as a root-firing process which allows the moves for $$\alpha \in \Phi ^{+}$$ whenever $$\langle \lambda ,\alpha ^\vee \rangle = 0$$ , where $$\Phi ^{+}$$ is the set of positive roots of a root system of Type A and $$\lambda $$ is a weight of this root system. We are thus motivated to study the exact same root-firing process for an arbitrary root system. Actually, this central root-firing process is the subject of a sequel to this paper. In the present paper, we instead study the interval root-firing processes determined by for $$\alpha \in \Phi ^{+}$$ whenever $$\langle \lambda ,\alpha ^\vee \rangle \in [-k-1,k-1]$$ or $$\langle \lambda ,\alpha ^\vee \rangle \in [-k,k-1]$$ , for any $$k \ge 0$$ . We prove that these interval-firing processes are always confluent, from any initial weight. We also show that there is a natural way to consistently label the stable points of these interval-firing processes across all values of k so that the number of weights with given stabilization is a polynomial in k. We conjecture that these Ehrhart-like polynomials have nonnegative integer coefficients.
Torsion contact forms in three dimensions have two or infinitely many Reeb orbits

We prove that every nondegenerate contact form on a closed connected three-manifold such that the associated contact structure has torsion first Chern class has either two or infinitely many simple Reeb orbits. By previous results it follows that under the above assumptions, there are infinitely many simple Reeb orbits if the three-manifold is not the three-sphere or a lens space. We also show that for nontorsion contact structures, every nondegenerate contact form has at least four simple Reeb orbits.
Shortcuts to adiabaticity in a time-dependent box

A method is proposed to drive an ultrafast non-adiabatic dynamics of an ultracold gas trapped in a time-dependent box potential. The resulting state is free from spurious excitations associated with the breakdown of adiabaticity and preserves the quantum correlations of the initial state up to a scaling factor. The process relies on the existence of an adiabatic invariant and the inversion of the dynamical self-similar scaling law dictated by it. Its physical implementation generally requires the use of an auxiliary expulsive potential. The method is extended to a broad family of interacting many-body systems. As illustrative examples we consider the ultrafast expansion of a Tonks-Girardeau gas and of Bose-Einstein condensates in different dimensions, where the method exhibits an excellent robustness against different regimes of interactions and the features of an experimentally realizable box potential.
Modeling tunnel field effect transistors—From interface chemistry to non-idealities to circuit level performance

We present a quasi-analytical model for Tunnel Field Effect Transistors (TFETs) that includes the microscopic physics and chemistry of interfaces and non-idealities. The ballistic band-to-band tunneling current is calculated by modifying the well known Simmons equation for oxide tunneling, where we integrate the Wentzel-Kramers-Brillouin tunneling current over the transverse modes. We extend the Simmons equation to finite temperature and non-rectangular barriers using a two-band model for the channel material and an analytical channel potential profile obtained from Poisson’s equation. The two-band model is parametrized first principles by calibrating with hybrid Density Functional Theory calculations and extended to random alloys with a band unfolding technique. Our quasi-analytical model shows quantitative agreement with ballistic quantum transport calculations. On top of the ballistic tunnel current, we incorporate higher order processes arising at junctions coupling the bands, specifically interface trap assisted tunneling and Auger generation processes. Our results suggest that both processes significantly impact the off-state characteristics of the TFETs—Auger, in particular, being present even for perfect interfaces. We show that our microscopic model can be used to quantify the TFET performance on the atomistic interface quality. Finally, we use our simulations to quantify circuit level metrics such as energy consumption.We present a quasi-analytical model for Tunnel Field Effect Transistors (TFETs) that includes the microscopic physics and chemistry of interfaces and non-idealities. The ballistic band-to-band tunneling current is calculated by modifying the well known Simmons equation for oxide tunneling, where we integrate the Wentzel-Kramers-Brillouin tunneling current over the transverse modes. We extend the Simmons equation to finite temperature and non-rectangular barriers using a two-band model for the channel material and an analytical channel potential profile obtained from Poisson’s equation. The two-band model is parametrized first principles by calibrating with hybrid Density Functional Theory calculations and extended to random alloys with a band unfolding technique. Our quasi-analytical model shows quantitative agreement with ballistic quantum transport calculations. On top of the ballistic tunnel current, we incorporate higher order processes arising at junctions coupling the bands, specifically interface t...
A physically-motivated photometric calibration of M dwarf metallicity

The location of M dwarfs in the (V - K s ) - M Ks color-magnitude diagram (CMD) has been shown to correlate with metallicity. We demonstrate that previous empirical photometric calibrations of M dwarf metallicity exploiting this correlation systematically underestimate or overestimate metallicity at the extremes of their range. We improve upon previous calibrations in three ways. First, we use both a volume-limited and kinematically-matched sample of F and G dwarfs from the Geneva-Copehnagen Survey (GCS) to infer the mean metallicity of M dwarfs in the Solar Neighborhood. Second, we use theoretical models of M dwarf interiors and atmospheres to determine the effect of metallicity on M dwarfs in the (V - K s ) - M Ks CMD. Third, though we use the GCS to infer the mean metallicity of M dwarfs in the Solar Neighborhood, our final calibration is based purely on high-resolution spectroscopy of FGK primaries with M dwarf companions as well as the trigonometric parallaxes and apparent V- and K s -band magnitudes of those M dwarf companions. As a result, our photometric calibration explains an order of magnitude more of the variance in the calibration sample than previous photometric calibrations. We use our calibration to non-parametrically quantify the significance of the observation that M dwarfs that host exoplanets are preferentially in a region of the (V - K s ) - M Ks , plane populated by metal-rich M dwarfs. We find that the probability p that planet-hosting M dwarfs are distributed across the (V - K s ) - M Ks CMD in the same way as field M dwarfs is p = 0.06 ± 0.008. Interestingly, the subsample of M dwarfs that host Neptune and sub-Neptune mass planets may also be preferentially located in the region of the (V - K s ) - M Ks , plane populated by high-metallicity M dwarfs. The probability of this occurrence by chance is p = 0.40 ± 0.02, and this observation hints that low-mass planets may be more likely to be found around metal-rich M dwarfs. The confirmation of this hint would be in contrast to the result obtained for FGK stars, where it appears that metal-rich and metal-poor stars hosts Neptune-mass planets with approximately equal probability. An increased rate of low-mass planet occurrence around metal-rich M dwarfs would be a natural consequence of the core-accretion model of planet formation.
Boundary dynamics in gravitational theories

We present a foliation-focused critical review of the boundary conditions and dynamics of 4D gravitational theories. A general coordinate transformation introduces a new foliation and changes the hypersurface on which a natural boundary condition is imposed; in this sense gauge transformations must be viewed as changing the boundary conditions. The issue of a gauge invariant boundary condition is nontrivial and has been extensively studied in the literature. We turn around the difficulty in obtaining such a boundary condition (and subtleties observed in the main body) and take it as one of the indications of an enlarged Hilbert space so as to include the states satisfying different boundary conditions. Through the systematical reduction procedure we obtain, up to some peculiarities, the explicit form of the reduced Lagrangian that describes the dynamics of the physical states. We examine the new insights offered by the 3D Lagrangian on BMS-type symmetry and black hole information. In particular we confirm that the boundary dynamics is an indispensable part of the system information.
Super-resolution MIMO radar

A multiple input, multiple output (MIMO) radar emits probings signals with multiple transmit antennas and records the reflections from targets with multiple receive antennas. Estimating the relative angles, delays, and Doppler shifts from the received signals allows to determine the locations and velocities of the targets. Standard approaches to MIMO radar based on digital matched filtering or compressed sensing only resolve the angle-delay-Doppler triplets on a (1/(N T N R ), 1/B, 1/T ) grid, where N T and N R are the number of transmit and receive antennas, B is the bandwidth of the probing signals, and T is the length of the time interval over which the reflections are observed. In this work, we show that the continuous angle-delay-Doppler triplets and the corresponding attenuation factors can be recovered perfectly by solving a convex optimization problem. This result holds provided that the angle-delay-Doppler triplets are separated either by 10/(N T N R − 1) in angle, 10.01/B in delay, or 10.01/T in Doppler direction. Furthermore, this result is optimal (up to log factors) in the number of angle-delay-Doppler triplets that can be recovered.
On Krein-Like Theorems for Noncanonical Hamiltonian Systems with Continuous Spectra: Application to Vlasov-Poisson

The notions of spectral stability and the spectrum for the Vlasov-Poisson system linearized about homogeneous equilibria, f 0(v), are reviewed. Structural stability is reviewed and applied to perturbations of the linearized Vlasov operator through perturbations of f 0. We prove that for each f 0 there is an arbitrarily small δf′0 in such that f 0+δf 0 is unstable. When f 0 is perturbed by an area preserving rearrangement, f 0 will always be stable if the continuous spectrum is only of positive signature, where the signature of the continuous spectrum is defined as in Morrison and Pfirsch (1992) and Morrison (2000). If there is a signature change, then there is a rearrangement of f 0 that is unstable and arbitrarily close to f 0 with f′0 in W.1,1 This result is analogous to Krein's theorem for the continuous spectrum. We prove that if a discrete mode embedded in the continuous spectrum is surrounded by the opposite signature there is an infinitesimal perturbation in Cn norm that makes f 0 unstable. If f 0 ...
Pressure Dependent Shear Response of Jammed Packings of Frictionless Spherical Particles

The mechanical response of packings of purely repulsive, spherical particles to athermal, quasistatic simple shear near jamming onset is highly nonlinear. Previous studies have shown that, at small pressure $p$, the ensemble-averaged static shear modulus $⟨G\ensuremath{-}{G}_{0}⟩$ scales with ${p}^{\ensuremath{\alpha}}$, where $\ensuremath{\alpha}\ensuremath{\approx}1$, but above a characteristic pressure ${p}^{**}$, $⟨G\ensuremath{-}{G}_{0}⟩\ensuremath{\sim}{p}^{\ensuremath{\beta}}$, where $\ensuremath{\beta}\ensuremath{\approx}0.5$. However, we find that the shear modulus ${G}^{i}$ for an individual packing typically decreases linearly with $p$ along a geometrical family where the contact network does not change. We resolve this discrepancy by showing that, while the shear modulus does decrease linearly within geometrical families, $⟨G⟩$ also depends on a contribution from discontinuous jumps in $⟨G⟩$ that occur at the transitions between geometrical families. For $pg{p}^{**}$, geometrical-family and rearrangement contributions to $⟨G⟩$ are of opposite signs and remain comparable for all system sizes. $⟨G⟩$ can be described by a scaling function that smoothly transitions between two power-law exponents $\ensuremath{\alpha}$ and $\ensuremath{\beta}$. We also demonstrate the phenomenon of compression unjamming, where a jammed packing unjams via isotropic compression.
The asymptotic distance between an ultraflat unimodular polynomial and its conjugate reciprocal

Let $${\mathcal K}_n := \left\{p_n: p_n(z) = \sum_{k=0}^n{a_k z^k}, \enspace a_k \in {\mathbb C}\,,\enspace |a_k| = 1 \right\}\,.$$ A sequence $(P_n)$ of polynomials $P_n \in {\mathcal K}_n$ is called ultraflat if $(n + 1)^{-1/2}|P_n(e^{it})|$ converge to $1$ uniformly in $t \in {\mathbb R}$. In this paper we prove that $$\frac{1}{2\pi} \int_0^{2\pi}{\left| (P_n - P_n^*)(e^{it}) \right|^q \, dt} \sim \frac{{2}^q \Gamma \left(\frac{q+1}{2} \right)}{\Gamma \left(\frac q2 + 1 \right) \sqrt{\pi}} \,\, n^{q/2}$$ for every ultraflat sequence $(P_n)$ of polynomials $P_n \in {\mathcal K}_n$ and for every $q \in (0,\infty)$, where $P_n^*$ is the conjugate reciprocal polynomial associated with $P_n$, $\Gamma$ is the usual gamma function, and the $\sim$ symbol means that the ratio of the left and right hand sides converges to $1$ as $n \rightarrow \infty$. Another highlight of the paper states that $$\frac{1}{2\pi}\int_0^{2\pi}{\left| (P_n^\prime - P_n^{*\prime})(e^{it}) \right|^2 \, dt} \sim \frac{2n^3}{3}$$ for every ultraflat sequence $(P_n)$ of polynomials $P_n \in {\mathcal K}_n$. We prove a few other new results and reprove some interesting old results as well.
FreeDOM: A Transferable Neural Architecture for Structured Information Extraction on Web Documents

Extracting structured data from HTML documents is a long-studied problem with a broad range of applications like augmenting knowledge bases, supporting faceted search, and providing domain-specific experiences for key verticals like shopping and movies. Previous approaches have either required a small number of examples for each target site or relied on carefully handcrafted heuristics built over visual renderings of websites. In this paper, we present a novel two-stage neural approach, named FreeDOM, which overcomes both these limitations. The first stage learns a representation for each DOM node in the page by combining both the text and markup information. The second stage captures longer range distance and semantic relatedness using a relational neural network. By combining these stages, FreeDOM is able to generalize to unseen sites after training on a small number of seed sites from that vertical without requiring expensive hand-crafted features over visual renderings of the page. Through experiments on a public dataset with 8 different verticals, we show that FreeDOM beats the previous state of the art by nearly 3.7 F1 points on average without requiring features over rendered pages or expensive hand-crafted features.
Cognitive and motor deficits contribute to longer braking time in stroke

Braking is a critical determinant of safe driving that depends on the integrity of cognitive and motor processes. Following stroke, both cognitive and motor capabilities are impaired to varying degrees. The current study examines the combined impact of cognitive and motor impairments on braking time in chronic stroke. Twenty stroke survivors and 20 aged-matched healthy controls performed cognitive, motor, and simulator driving assessments. Cognitive abilities were assessed with processing speed, divided attention, and selective attention. Motor abilities were assessed with maximum voluntary contraction (MVC) and motor accuracy of the paretic ankle. Driving performance was examined with the braking time in a driving simulator and self-reported driving behavior. Braking time was 16% longer in the stroke group compared with the control group. The self-reported driving behavior in stroke group was correlated with braking time (r = − 0.53, p = 0.02). The stroke group required significantly longer time for divided and selective attention tasks and showed significant decrease in motor accuracy. Together, selective attention time and motor accuracy contributed to braking time (R2 = 0.40, p = 0.01) in stroke survivors. This study provides novel evidence that decline in selective attention and motor accuracy together contribute to slowed braking in stroke survivors. Driving rehabilitation after stroke may benefit from the assessment and training of attentional and motor skills to improve braking during driving.
Essential normality — a unified approach in terms of local decompositions

In this paper, we define the asymptotic stable division property for submodules of the Bergman module. We show that under a mild condition, a submodule with the asymptotic stable division property is p-essentially normal for all p>n. A new technique is developed to show that certain submodules have the asymptotic stable division property. This leads to a unified proof of most known results on essential normality of submodules as well as new results. In particular, we show that an ideal defines a p-essentially normal submodule of the Bergman module, for all p>n, if its associated primary ideals are powers of prime ideals whose zero loci satisfy standard regularity conditions near the sphere.
USING COLORS TO IMPROVE PHOTOMETRIC METALLICITY ESTIMATES FOR GALAXIES

There is a well known correlation between the mass and metallicity of star-forming galaxies. Because mass is correlated with luminosity, this relation is often exploited, when spectroscopy is not available, to estimate galaxy metallicities based on single band photometry. However, we show that galaxy color is typically more effective than luminosity as a predictor of metallicity. This is a consequence of the correlation between color and the galaxy mass-to-light ratio and the recently discovered correlation between star formation rate (SFR) and residuals from the mass-metallicity relation. Using Sloan Digital Sky Survey spectroscopy of ~180, 000 nearby galaxies, we derive "LZC relations," empirical relations between metallicity (in seven common strong line diagnostics), luminosity, and color (in 10 filter pairs and four methods of photometry). We show that these relations allow photometric metallicity estimates, based on luminosity and a single optical color, that are ~50% more precise than those made based on luminosity alone; galaxy metallicity can be estimated to within ~0.05-0.1 dex of the spectroscopically derived value depending on the diagnostic used. Including color information in photometric metallicity estimates also reduces systematic biases for populations skewed toward high or low SFR environments, as we illustrate using the host galaxy of the supernova SN 2010ay. This new tool will lend more statistical power to studies of galaxy populations, such as supernova and gamma-ray burst host environments, in ongoing and future wide-field imaging surveys.
Keeping Up Appearances: Computational Modeling of Face Acts in Persuasion Oriented Discussions

The notion of face refers to the public self-image of an individual that emerges both from the individual’s own actions as well as from the interaction with others. Modeling face and understanding its state changes throughout a conversation is critical to the study of maintenance of basic human needs in and through interaction. Grounded in the politeness theory of Brown and Levinson (1978), we propose a generalized framework for modeling face acts in persuasion conversations, resulting in a reliable coding manual, an annotated corpus, and computational models. The framework reveals insights about differences in face act utilization between asymmetric roles in persuasion conversations. Using computational models, we are able to successfully identify face acts as well as predict a key conversational outcome (e.g. donation success). Finally, we model a latent representation of the conversational state to analyze the impact of predicted face acts on the probability of a positive conversational outcome and observe several correlations that corroborate previous findings.
Magnetic Flux Rope Shredding By a Hyperbolic Flux Tube: The Detrimental Effects of Magnetic Topology on Solar Eruptions

We present the analysis of an unusual failed eruption captured in high cadence and in many wavelengths during the observing campaign in support of the Very high Angular resolution Ultraviolet Telescope (VAULT2.0) sounding rocket launch. The refurbished VAULT2.0 is a Lyα (λ 1216 A) spectroheliograph launched on 2014 September 30. The campaign targeted active region NOAA AR 12172 and was closely coordinated with the Hinode and IRIS missions and several ground-based observatories (NSO/IBIS, SOLIS, and BBSO). A filament eruption accompanied by a low-level flaring event (at the GOES C-class level) occurred around the VAULT2.0 launch. No coronal mass ejection was observed. The eruption and its source region, however, were recorded by the campaign instruments in many atmospheric heights ranging from the photosphere to the corona in high cadence and spatial resolution. This is a rare occasion that enabled us to perform a comprehensive investigation on a failed eruption. We find that a rising Magnetic Flux Rope (MFR)-like structure was destroyed during its interaction with the ambient magnetic field, creating downflows of cool plasma and diffuse hot coronal structures reminiscent of "cusps." We employ magnetofrictional simulations to show that the magnetic topology of the ambient field is responsible for the destruction of the MFR. Our unique observations suggest that the magnetic topology of the corona is a key ingredient for a successful eruption.