cssi/SciSciGPT-SciSciNet huggingface dataset:
Uncertainty in the design stage of two‐stage Bayesian propensity score analysis

The two-stage process of propensity score analysis (PSA) includes a design stage where propensity scores (PSs) are estimated and implemented to approximate a randomized experiment and an analysis stage where treatment effects are estimated conditional on the design. This article considers how uncertainty associated with the design stage impacts estimation of causal effects in the analysis stage. Such design uncertainty can derive from the fact that the PS itself is an estimated quantity, but also from other features of the design stage tied to choice of PS implementation. This article offers a procedure for obtaining the posterior distribution of causal effects after marginalizing over a distribution of design-stage outputs, lending a degree of formality to Bayesian methods for PSA that have gained attention in recent literature. Formulation of a probability distribution for the design-stage output depends on how the PS is implemented in the design stage, and propagation of uncertainty into causal estimates depends on how the treatment effect is estimated in the analysis stage. We explore these differences within a sample of commonly used PS implementations (quantile stratification, nearest-neighbor matching, caliper matching, inverse probability of treatment weighting, and doubly robust estimation) and investigate in a simulation study the impact of statistician choice in PS model and implementation on the degree of between- and within-design variability in the estimated treatment effect. The methods are then deployed in an investigation of the association between levels of fine particulate air pollution and elevated exposure to emissions from coal-fired power plants.
Burr, Lomax, Pareto, and Logistic Distributions from Ultrasound Speckle.

After 100 years of theoretical treatment of speckle patterns from coherent illumination, there remain some open questions about the nature of ultrasound speckle from soft vascularized tissues. A re...
Ensemble observability of Bloch equations with unknown population density

Abstract We introduce in the paper a novel observability problem for a large population (in the limit, a continuum ensemble) of nonholonomic control systems with unknown population density. We address the problem by focussing on a prototype, namely, the ensemble of Bloch equations which is known for its use of describing the evolution of the bulk magnetization of a collective of non-interacting nuclear spins in a static field modulated by a radio frequency (rf) field. The dynamics of the equations are structurally identical, but show variations in Larmor dispersion and rf inhomogeneity. We assume that the initial state of any individual system (i.e., individual Bloch equation) is unknown and, moreover, the population density of these individual systems is also unknown. Furthermore, we assume that at any time, there is only one scalar measurement output at our disposal. The measurement output integrates a certain observation function, common to all individual systems, over the continuum ensemble. The observability problem we pose in the paper is thus the following: Whether one is able to use the common control input (i.e., the rf field) and the single measurement output to estimate both the initial states of the individual systems and the population density? Amongst other things, we establish a sufficient condition for the ensemble system to be observable: We show that if the common observation function is any harmonic homogeneous polynomial of positive degree, then the ensemble system is observable. The proof of the result utilizes tools from representation theory of Lie algebras. Although the results established in the paper are for the specific ensemble of Bloch equations, the approach developed along the analysis can be generalized to investigate observability of other ensemble systems with single, integrated measurement outputs.
Neutrino anomalies without oscillations

I review explanations for the three neutrino anomalies (solar, atmospheric and LSND) which go beyond the ‘conventional’ neutrino oscillations induced by mass-mixing. Several of these require non-zero neutrino masses as well.
ExpBERT: Representation Engineering with Natural Language Explanations

Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to "interpret" these explanations with respect to the input sentence, producing explanation-guided representations of the input. Across three relation extraction tasks, our method, ExpBERT, matches a BERT baseline but with 3--20x less labeled data and improves on the baseline by 3--10 F1 points with the same amount of labeled data.
Predicting Performance for Natural Language Processing Tasks

Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting. In this work, we attempt to explore the possibility of gaining plausible judgments of how well an NLP model can perform under an experimental setting, without actually training or testing the model. To do so, we build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input. Experimenting on~9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures, outperforming reasonable baselines as well as human experts. %we represent experimental settings using an array of features. Going further, we outline how our predictor can be used to find a small subset of representative experiments that should be run in order to obtain plausible predictions for all other experimental settings. (Code, data and logs are publicly available at https://github.com/xiamengzhou/NLPerf.)
Multi-trait Genome-Wide Analyses of the Brain Imaging Phenotypes in UK Biobank

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.
Data Amplification: Instance-Optimal Property Estimation

The best-known and most commonly used distribution-property estimation technique uses a plug-in estimator, with empirical frequency replacing the underlying distribution. We present novel linear-time-computable estimators that significantly "amplify" the effective amount of data available. For a large variety of distribution properties including four of the most popular ones and for every underlying distribution, they achieve the accuracy that the empirical-frequency plug-in estimators would attain using a logarithmic-factor more samples. Specifically, for Shannon entropy and a very broad class of properties including $\ell_1$-distance, the new estimators use $n$ samples to achieve the accuracy attained by the empirical estimators with $n\log n$ samples. For support-size and coverage, the new estimators use $n$ samples to achieve the performance of empirical frequency with sample size $n$ times the logarithm of the property value. Significantly strengthening the traditional min-max formulation, these results hold not only for the worst distributions, but for each and every underlying distribution. Furthermore, the logarithmic amplification factors are optimal. Experiments on a wide variety of distributions show that the new estimators outperform the previous state-of-the-art estimators designed for each specific property.
Fairness-Aware Explainable Recommendation over Knowledge Graphs

There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems. For example, explainable recommendation systems may suffer from both explanation bias and performance disparity. We show that inactive users may be more susceptible to receiving unsatisfactory recommendations due to their insufficient training data, and that their recommendations may be biased by the training records of active users due to the nature of collaborative filtering, which leads to unfair treatment by the system. In this paper, we analyze different groups of users according to their level of activity, and find that bias exists in recommendation performance between different groups. Empirically, we find that such performance gap is caused by the disparity of data distribution, specifically the knowledge graph path distribution in this work. We propose a fairness constrained approach via heuristic re-ranking to mitigate this unfairness problem in the context of explainable recommendation over knowledge graphs. We experiment on several real-world datasets with state-of-the-art knowledge graph-based explainable recommendation algorithms. The promising results show that our algorithm is not only able to provide high-quality explainable recommendations, but also reduces the recommendation unfairness in several aspects.
Algorithms for heavy-tailed statistics: regression, covariance estimation, and beyond

We study polynomial-time algorithms for linear regression and covariance estimation in the absence of strong (Gaussian) assumptions on the underlying distributions of samples, making assumptions instead about only finitely-many moments. We focus on how many samples are required to perform estimation and regression with high accuracy and exponentially-good success probability in the face of heavy-tailed data. For covariance estimation, linear regression, and several other problems in high-dimensional statistics, estimators have recently been constructed whose sample complexities and rates of statistical error match what is possible when the underlying distribution is Gaussian, but known algorithms for these estimators require exponential time. We narrow the gap between the Gaussian and heavy-tailed settings for polynomial-time estimators with: (a) a polynomial-time estimator which takes n samples from a d-dimensional random vector X with covariance Σ and produces Σ such that in spectral norm ||Σ − Σ ||2 ≤ O(d 3/4/√n) w.p. 1−2−d where the information-theoretically optimal error bound is O(√d/n), while previous approaches to polynomial-time algorithms were stuck at O(d/√n) and (b) a polynomial-time algorithm which takes n samples (X i ,Y i ) where Y i = ⟨ u,X i ⟩ + i where both X and have a constant number of bounded moments and produces u such that the loss ||u − u||2 ≤ O(d/n) w.p. 1−2−d for any n ≥ d 3/2 log(d). This (information-theoretically optimal) error is achieved by inefficient algorithms for any n ≫ d, while previous approaches to polynomial-time algorithms suffer loss Ω(d 2/n) and require n ≫ d 2. Our algorithms make crucial use of degree-8 sum-of-squares semidefinite programs. Both apply to any X which has constantly-many certifiably hypercontractive moments. We offer preliminary evidence that improving on these rates of error in polynomial time is not possible in the median of means framework our algorithms employ. Our work introduces new techniques to high-probability estimation, and suggests numerous new algorithmic questions in the following vein: when is it computationally feasible to do statistics in high dimensions with Gaussian-style errors when data is far from Gaussian?
Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data. While effective, feature extractors like BERT may be prohibitively large for some deployment scenarios. We explore weight pruning for BERT and ask: how does compression during pre-training affect transfer learning? We find that pruning affects transfer learning in three broad regimes. Low levels of pruning (30-40%) do not affect pre-training loss or transfer to downstream tasks at all. Medium levels of pruning increase the pre-training loss and prevent useful pre-training information from being transferred to downstream tasks. High levels of pruning additionally prevent models from fitting downstream datasets, leading to further degradation. Finally, we observe that fine-tuning BERT on a specific task does not improve its prunability. We conclude that BERT can be pruned once during pre-training rather than separately for each task without affecting performance.
Population structure across scales facilitates coexistence and spatial heterogeneity of antibiotic-resistant infections

Antibiotic-resistant infections are a growing threat to human health, but basic features of the eco-evolutionary dynamics remain unexplained. Most prominently, there is no clear mechanism for the long-term coexistence of both drug-sensitive and resistant strains at intermediate levels, a ubiquitous pattern seen in surveillance data. Here we show that accounting for structured or spatially-heterogeneous host populations and variability in antibiotic consumption can lead to persistent coexistence over a wide range of treatment coverages, drug efficacies, costs of resistance, and mixing patterns. Moreover, this mechanism can explain other puzzling spatiotemporal features of drug-resistance epidemiology that have received less attention, such as large differences in the prevalence of resistance between geographical regions with similar antibiotic consumption or that neighbor one another. We find that the same amount of antibiotic use can lead to very different levels of resistance depending on how treatment is distributed in a transmission network. We also identify parameter regimes in which population structure alone cannot support coexistence, suggesting the need for other mechanisms to explain the epidemiology of antibiotic resistance. Our analysis identifies key features of host population structure that can be used to assess resistance risk and highlights the need to include spatial or demographic heterogeneity in models to guide resistance management.
A suspension cell‐based interaction platform for interrogation of membrane proteins

The majority of clinically approved therapeutics target membrane proteins (MPs), highlighting the need for tools to study this important category of proteins. To overcome limitations with recombinant MP expression, whole cell screening techniques have been developed that present MPs in their native conformations. Whereas many such platforms utilize adherent cells, here we introduce a novel suspension cell-based platform termed “biofloating” that enables quantitative analysis of interactions between proteins displayed on yeast and MPs expressed on mammalian cells, without need for genetic fusions. We characterize and optimize biofloating and illustrate its sensitivity advantage compared to an adherent cell-based platform (biopanning). We further demonstrate the utility of suspension cell-based approaches by iterating rounds of magnetic-activated cell sorting (MACS) selections against MP-expressing mammalian cells to enrich for a specific binder within a yeast-displayed antibody library. Overall, biofloating represents a promising new technology that can be readily integrated into protein discovery and development workflows.
Exchangeable Input Representations for Reinforcement Learning.

Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an efficient representation space that is invariant under changes to input ordering. We show that our proposed representation results in an input space that is a factor of $m!$ smaller for inputs of $m$ objects. We also show that our method is able to represent inputs over variable numbers of objects. Our experiments demonstrate improvements in sample efficiency for policy gradient methods on a variety of tasks. We show that our representation allows us to solve problems that are otherwise intractable when using naive approaches.
Automorphisms of the shift: Lyapunov exponents, entropy, and the dimension representation

Let be a shift of finite type and its corresponding automorphism group. Associated to are certain Lyapunov exponents , which describe asymptotic behavior of the sequence of coding ranges of . We give lower bounds on in terms of the spectral radius of the corresponding action of on the dimension group associated to . We also give lower bounds on the topological entropy in terms of a distinguished part of the spectrum of the action of on the dimension group, but show that, in general, is not bounded below by the logarithm of the spectral radius of the action of on the dimension group.
Online Mingling: Supporting Ad Hoc, Private Conversations at Virtual Conferences

Even though today’s videoconferencing systems are often very useful, these systems do not provide support for one of the most important aspects of in-person meetings: the ad hoc, private conversations that happen before, after, and during the breaks of scheduled events–the proverbial hallway conversations. Here we describe our design of a simple system, called Minglr, which supports this kind of interaction by facilitating the matching of conversational partners. We describe two studies of this system’s use at two virtual conferences with over 450 total participants. Our results provide evidence for the usefulness of this capability, showing that, for example, 81% of people who used the system successfully thought that future virtual conferences should include a tool with similar functionality. We believe that similar functionality is likely to be widely implemented in many videoconferencing systems and to increase the feasibility and desirability of many kinds of remote work and socializing.
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

Today's proliferation of powerful facial recognition systems poses a real threat to personal privacy. As this http URL demonstrated, anyone can canvas the Internet for data and train highly accurate facial recognition models of individuals without their knowledge. We need tools to protect ourselves from potential misuses of unauthorized facial recognition systems. Unfortunately, no practical or effective solutions exist. In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models. Fawkes achieves this by helping users add imperceptible pixel-level changes (we call them "cloaks") to their own photos before releasing them. When used to train facial recognition models, these "cloaked" images produce functional models that consistently cause normal images of the user to be misidentified. We experimentally demonstrate that Fawkes provides 95+% protection against user recognition regardless of how trackers train their models. Even when clean, uncloaked images are "leaked" to the tracker and used for training, Fawkes can still maintain an 80+% protection success rate. We achieve 100% success in experiments against today's state-of-the-art facial recognition services. Finally, we show that Fawkes is robust against a variety of countermeasures that try to detect or disrupt image cloaks.
The Influence of Stellar Phosphorus on Our Understanding of Exoplanets and Astrobiology

When searching for exoplanets and ultimately considering their habitability, it is necessary to consider the planet's composition, geophysical processes, and geochemical cycles in order to constrain the bioessential elements available to life. Determining the elemental ratios for exoplanetary ecosystems is not yet possible, but we generally assume that planets have compositions similar to those of their host stars. Therefore, using the Hypatia Catalog of high-resolution stellar abundances for nearby stars, we compare the C, N, Si, and P abundance ratios of main sequence stars with those in average marine plankton, Earth's crust, as well as bulk silicate Earth and Mars. We find that, in general, plankton, Earth, and Mars are N-poor and P-rich compared with nearby stars. However, the dearth of P abundance data, which exists for only ~1% of all stars and 1% of exoplanet hosts, makes it difficult to deduce clear trends in the stellar data, let alone the role of P in the evolution of an exoplanet. Our Sun has relatively high P and Earth biology requires a small, but finite, amount of P. On rocky planets that form around host stars with substantially less P, the strong partitioning of P into the core could rule out the potential for surface P and, consequently, for life on that planet's surface. Therefore, we urge the stellar abundance community to make P observations a priority in future studies and telescope designs.
Geometries with mismatched branes

We study Randall-Sundrum two brane setups with mismatched brane tensions. For the vacuum solutions, boundary conditions demand that the induced metric on each of the branes is either de Sitter, Anti-de Sitter, or Minkowski. For incompatible boundary conditions, the bulk metric is necessarily time-dependent. This introduces a new class of time-dependent solutions with the potential to address cosmological issues and provide alternatives to conventional inflationary (or contracting) scenarios. We take a first step in this paper toward such solutions. One important finding is that the resulting solutions can be very succinctly described in terms of an effective action involving only the induced metric on either one of the branes and the radion field. But the full geometry cannot necessarily be simply described with a single coordinate patch. We concentrate here on the time- dependent solutions but argue that supplemented with a brane stabilization mechanism one can potentially construct interesting cosmological models this way. This is true both with and without a brane stabilization mechanism.
Tunable 670-nm DBR lasers

Single frequency visible wavelength lasers are of interest for spectroscopic and optical memory applications.
30 Day Readmissions After Transcatheter Aortic Valve Replacement

Background: As the number of centers performing Transcatheter Aortic Valve Replacement (TAVR) continue to proliferate, it has become increasingly important to minimize costs associated with readmissions following this procedure, which requires up-to-date data on 30 day readmissions. Methods: Using the 2016 National Readmissions Database (NRD), we used ICD-10 codes to identify patients who had TAVR procedure and separated them into two groups based on whether they had tricuspid vs bicuspid aortic valve. Baseline characteristics of patients as well as incidence and causes of 30-day readmissions were identified. Results: 16700 index admissions were identified and the overall 30 day readmission rate among all patients was 13.6% (2284 patients). Out of these readmissions, there were 2276 (99.6%) tricuspid aortic valve patients and 8 (3.5%) bicuspid aortic valve patients. The most common cause of readmission in tricuspid valve patients was heart failure (17.6%). This was followed by arrhythmias (11.2%), sepsis (6.7%), gastrointestinal bleed (4.6%), and renal failure (2.2%). Other cardiac causes of readmission in this group were complication of cardiovascular device (2.1%), myocardial infarction (1.8%), aortic or peripheral aneurysms (1%), and pericarditis (0.9%). In the bicuspid aortic valve group, chest pain and arrhythmias were the major causes of readmission. Independent predictors of 30 day readmission were acute kidney injury (OR 1.98; 95% CI, 1.73-2.26), chronic kidney injury (OR 1.53; 95% CI, 1.38-1.70), and Elixhauser comorbidity score more than 4 (OR 1.75; 95% CI, 1.32-2.33). Conclusion: 30 day readmissions after TAVR in 2016 NRD was 13.6%, which is reduced compared to large studies from prior years, which showed readmissions rate of 17.9% (Kolte et al) and 16% (Danielsen et al). Renal failure and Elixhauser comorbidity score are risk factors associated with readmissions. Heart failure and arrhythmia are significant cardiac causes of readmission as well as potential areas requiring improvement and careful monitoring.
Residual Based Sampling for Online Low Rank Approximation

We propose online algorithms for Column Subset Selection (CSS) and Principal Component Analysis (PCA), two methods that are widely employed for data analysis, summarization, and visualization. Given a data matrix A that is revealed one column at a time, the online CSS problems asks to keep a small set of columns, S, that best approximates the space spanned by the columns of A. As each column arrives, the algorithm must irrevocably decide whether to add it to S, or to ignore it. In the online PCA problem, the goal is to output a projection of each column to a low dimensional subspace. In other words, the algorithm must provide an embedding for each column as it arrives, which cannot be changed as new columns arrive.While both of these problems have been studied in the online setting, only additive approximations were known prior to our work. The core of our approach is an adaptive sampling technique that gives a practical and efficient algorithm for both of these problems. We prove that by sampling columns using their "residual norm" (i.e. their norm orthogonal to directions sampled so far), we end up with a significantly better dependence between the number of columns sampled, and the desired error in the approximation.We further show how to combine our algorithm "in series" with prior algorithms. In particular, using the results of Boutsidis et al. [5] and Frieze et al. [15] that have additive guarantees, we show how to improve the bounds on the error of our algorithm.
NeuralExplorer: State Space Exploration of Closed Loop Control Systems Using Neural Networks

In this paper, we propose a framework for performing state space exploration of closed loop control systems. Our approach involves approximating sensitivity and a newly introduced notion of inverse sensitivity by a neural network. We show how the approximation of sensitivity and inverse sensitivity can be used for computing estimates of the reachable set. We then outline algorithms for performing state space exploration by generating trajectories that reach a neighborhood. We demonstrate the effectiveness of our approach by applying it not only to standard linear and nonlinear dynamical systems, but also to nonlinear hybrid systems and also neural network based feedback control systems.
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads

Specialized accelerators such as GPUs, TPUs, FPGAs, and custom ASICs have been increasingly deployed to train deep learning models. These accelerators exhibit heterogeneous performance behavior across model architectures. Existing schedulers for clusters of accelerators, which are used to arbitrate these expensive training resources across many users, have shown how to optimize for various multi-job, multi-user objectives, like fairness and makespan. Unfortunately, existing schedulers largely do not consider performance heterogeneity. In this paper, we propose Gavel, a heterogeneity-aware scheduler that systematically generalizes a wide range of existing scheduling policies. Gavel expresses these policies as optimization problems, making it easy to optimize for objectives in a heterogeneity-aware way, while also being cognizant of performance optimizations like space sharing. Gavel then uses a round-based scheduling mechanism to ensure jobs receive their ideal allocation given the target scheduling policy. Gavel’s heterogeneity-aware policies allow a heterogeneous cluster to sustain higher input load, and improve end objectives such as average job completion time and makespan by up to 3.5\times× compared to heterogeneity-agnostic policies.
Two-front solutions of the SQG equation and its generalizations

The generalized surface quasi-geostrophic (GSQG) equations are transport equations for an active scalar that depend on a parameter $0<\alpha \le 2$. Special cases are the two-dimensional incompressible Euler equations ($\alpha = 2$) and the surface quasi-geostrophic (SQG) equations ($\alpha = 1$). We derive contour-dynamics equations for a class of two-front solutions of the GSQG equations when the fronts are a graph. Scalar reductions of these equations include ones that describe a single front in the presence of a rigid, flat boundary. We use the contour dynamics equations to determine the linearized stability of the GSQG shear flows that correspond to two flat fronts. We also prove local-in-time existence and uniqueness for large, smooth solutions of the two-front equations in the parameter regime $1<\alpha\le 2$, and small, smooth solutions in the parameter regime $0<\alpha\le 1$.
Instrument-Free Protein Microarray Fabrication for Accurate Affinity Measurements

Protein microarrays have gained popularity as an attractive tool for various fields, including drug and biomarker development, and diagnostics. Thus, multiplexed binding affinity measurements in microarray format has become crucial. The preparation of microarray-based protein assays relies on precise dispensing of probe solutions to achieve efficient immobilization onto an active surface. The prohibitively high cost of equipment and the need for trained personnel to operate high complexity robotic spotters for microarray fabrication are significant detriments for researchers, especially for small laboratories with limited resources. Here, we present a low-cost, instrument-free dispensing technique by which users who are familiar with micropipetting can manually create multiplexed protein assays that show improved capture efficiency and noise level in comparison to that of the robotically spotted assays. In this study, we compare the efficiency of manually and robotically dispensed α-lactalbumin probe spots by analyzing the binding kinetics obtained from the interaction with anti-α-lactalbumin antibodies, using the interferometric reflectance imaging sensor platform. We show that the protein arrays prepared by micropipette manual spotting meet and exceed the performance of those prepared by state-of-the-art robotic spotters. These instrument-free protein assays have a higher binding signal (~4-fold improvement) and a ~3-fold better signal-to-noise ratio (SNR) in binding curves, when compared to the data acquired by averaging 75 robotic spots corresponding to the same effective sensor surface area. We demonstrate the potential of determining antigen-antibody binding coefficients in a 24-multiplexed chip format with less than 5% measurement error.
Fundamental limits of hot carrier injection from metal in nanoplasmonics

Evolution of the nonequilibrium carriers excited in the process of decay of surface plasmon polaritons in metal is described for each step, from the carrier generation to their extraction from the metal. The relative importance of various carrier generating mechanism is discussed. It is shown that both carrier generation and their decay are inherently quantum processes as for realistic illumination conditions no more than a single SPP per nanoparticle exists at a given time. As a result, the distribution of non-equilibrium carriers cannot be described by a single temperature. It is also shown that the originally excited carriers that have not undergone a single electron-electron scattering event, are practically the only ones that contribute to the injection. The role of the momentum conservation in the carrier extraction is discussed and it is shown that if all the momentum conservation rules are relaxed, it is the density of states in the semiconductor/dielectric that determines the ultimate injection efficiency. A set of recommendations aimed at improving the efficiency of plasmonic-assisted photodetection and (to a lesser degree) photocatalysis is made in the end.
Evaluation of Attack Vectors and Risks in Automobiles and Road Infrastructure

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.
Laboratory coded-aperture imaging experiments: radial hole coded masks and depth-sensitive CZT detectors

The proposed black-hole finder mission EXIST will consist of multiple wide-field hard X-ray coded-aperture telescopes. The high science goals set for the mission require innovations in telescope design. In particular, wide energy band coverage and fine angular resolution require relatively thick coded masks and thick detectors compared to their pixel size, which may introduce mask self-collimation and depth-induced image blurring with conventional design approaches. Previously we proposed relatively simple solutions to these potential problems: radial hole for mask selfcollimation and cathode depth sensing detector for image blurring. We have now performed laboratory experiments to explore the potential of these two techniques. The experimental results show that the radial hole mask greatly alleviates mask self-collimation and a ~1 mm resolution depth-sensitive detector scheme can be relatively easily achieved for the large scale required for EXIST.
AN EMERGING WOLF–RAYET MASSIVE STAR CLUSTER IN NGC 4449

We present a panchromatic investigation of the partially embedded, emerging massive cluster Source 26 (=S26) in NGC 4449 with optical spectra obtained at Apache Point Observatory and archival Hubble, Spitzer, and Herschel 5 Space Telescope images. First identified as a radio continuum source with a thermal component due to ionized material, the massive cluster S26 also exhibits optical Wolf–Rayet (WR) emission lines that reveal a large evolved massive star population. We find that S26 is host to ∼240 massive stars, of which ∼18 are WR stars; the relative populations are roughly consistent with other observed massive star-forming clusters and galaxies. We construct SEDs over two spatial scales (∼100 and ∼300 pc) that clearly exhibit warm dust and polycyclic aromatic hydrocarbon (PAH) emission. The best fit dust and grain models reveal that both the intensity of the exciting radiation and PAH grain destruction increase toward the cluster center. Given that the timescale of evacuation is important for the future dynamical evolution of the cluster, it is important to determine whether O-type and WR stars can evacuate the material gradually before supernova do so on a much faster timescale. With a minimum age of ≈ 3 Myr, it is clear that S26 has not yet fully evacuated its natal material, which indicates that unevolved O-type stars alone do not provide sufficient feedback to remove the gas and dust. We hypothesize that the feedback of WR stars in this cluster may be necessary for clearing the material from the gravitational potential of the cluster. We find S26 is similar to emission line clusters observed in the Antennae galaxies and may be considered a younger analog to 30 Doradus in the LMC.
Isometry types of frame bundles

We consider the orthonormal frame bundle F(M) of a Riemannian manifold M. A construction of Sasaki defines a canonical Riemannian metric on F(M). We prove that for two closed Riemannian n-manifolds M and N, the frame bundles F(M) and F(N) are isometric if and only if M and N are isometric, except possibly in dimensions 3, 4, and 8. This answers a question of Benson Farb except in dimensions 3, 4, and 8.
Influence of charge order on the ground states of TMTTF molecular salts

(TMTTF) 2 AsF 6 and (TMTTF) 2 SbF 6 are both known to undergo a charge ordering phase transition, though their ground states are different. The ground state of the first is Spin-Peierls, and the second is an antiferromagnet. We study the effect of pressure on the ground states and the charge-ordering using 13 C NMR spectroscopy. The experiments demonstrate that the the CO and SP order parameters are repulsive, and consequently the AF state is stabilized when the CO order parameter is large, as it is for (TMTTF) 2 SbF 6 . An extension of the well-known temperature/pressure phase diagram is proposed.
LARGE SCALE GEOMETRY OF COMMUTATOR SUBGROUPS

Let G be a finitely presented group, and Gits commutator sub- group. Let C be the Cayley graph of Gwith all commutators in G as genera- tors. Then C is large scale simply connected. Furthermore, if G is a torsion-free nonelementary word-hyperbolic group, C is one-ended. Hence (in this case), the asymptotic dimension of C is at least 2.
Neutralizing Self-Selection Bias in Sampling for Sortition

Sortition is a political system in which decisions are made by panels of randomly selected citizens. The process for selecting a sortition panel is traditionally thought of as uniform sampling without replacement, which has strong fairness properties. In practice, however, sampling without replacement is not possible since only a fraction of agents is willing to participate in a panel when invited, and different demographic groups participate at different rates. In order to still produce panels whose composition resembles that of the population, we develop a sampling algorithm that restores close-to-equal representation probabilities for all agents while satisfying meaningful demographic quotas. As part of its input, our algorithm requires probabilities indicating how likely each volunteer in the pool was to participate. Since these participation probabilities are not directly observable, we show how to learn them, and demonstrate our approach using data on a real sortition panel combined with information on the general population in the form of publicly available survey data.
AN EFFICIENT COARSE GRID PROJECTION METHOD FOR QUASIGEOSTROPHIC MODELS OF LARGE-SCALE OCEAN CIRCULATION

This paper puts forth a coarse grid projection (CGP) multiscale method to accelerate computations of quasigeostrophic (QG) models for large scale ocean circulation. These models require solving an elliptic sub-problem at each time step, which takes the bulk of the computational time. The method we propose here is a modular approach that facilitates data transfer with simple interpolations and uses black-box solvers for solving the elliptic sub-problem and potential vorticity equations in the QG flow solvers. After solving the elliptic sub-problem on a coarsened grid, an interpolation scheme is used to obtain the fine data for subsequent time stepping on the full grid. The potential vorticity field is then updated on the fine grid with savings in computational time due to the reduced number of grid points for the elliptic solver. The method is applied to both single layer barotropic and two-layer stratified QG ocean models for mid-latitude oceanic basins in the beta plane, which are standard prototypes of more realistic ocean dynamics. The method is found to accelerate these computations while retaining the same level of accuracy in the fine-resolution field. A linear acceleration rate is obtained for all the cases we consider due to the efficient linear-cost fast Fourier transform based elliptic solver used. We expect the speed-up of the CGP method to increase dramatically for versions of the method that use other, suboptimal, elliptic solvers, which are generally quadratic cost. It is also demonstrated that numerical oscillations due to lower grid resolutions, in which the Munk scales are not resolved adequately, are effectively eliminated with CGP method.
Giant voltage manipulation of MgO-based magnetic tunnel junctions via localized anisotropic strain: A potential pathway to ultra-energy-efficient memory technology

Voltage control of magnetization via strain in piezoelectric/magnetostrictive systems is a promising mechanism to implement energy-efficient straintronic memory devices. Here, we demonstrate giant voltage manipulation of MgO magnetic tunnel junctions (MTJ) on a Pb(Mg1/3Nb2/3)0.7Ti0.3O3 piezoelectric substrate with (001) orientation. It is found that the magnetic easy axis, switching field, and the tunnel magnetoresistance (TMR) of the MTJ can be efficiently controlled by strain from the underlying piezoelectric layer upon the application of a gate voltage. Repeatable voltage controlled MTJ toggling between high/low-resistance states is demonstrated. More importantly, instead of relying on the intrinsic anisotropy of the piezoelectric substrate to generate the required strain, we utilize anisotropic strain produced using a local gating scheme, which is scalable and amenable to practical memory applications. Additionally, the adoption of crystalline MgO-based MTJ on piezoelectric layer lends itself to high TMR in the strain-mediated MRAM devices.
Vortex production in a first-order phase transition at finite temperature

Abstract We simulate the production of vortices in a first-order phase transition at finite temperature. The transition is carried out by randomly nucleating critical bubbles and the effects of thermal fluctuations (which could be relevant for vortex production) are represented by randomly nucleating subcritical bubbles. Our results show that the presence of subcritical bubbles suppresses vortices with clear and prominent profiles, though net number of vortices is consistent with theoretical estimates. We also determine the typical speed of vortices arising due to randomness associated with the phase transition to be about 0.5.
The Brill---Noether rank of a tropical curve

We construct a space classifying divisor classes of a fixed degree on all tropical curves of a fixed combinatorial type and show that the function taking a divisor class to its rank is upper semicontinuous. We extend the definition of the Brill---Noether rank of a metric graph to tropical curves and use the upper semicontinuity of the rank function on divisors to show that the Brill---Noether rank varies upper semicontinuously in families of tropical curves. Furthermore, we present a specialization lemma relating the Brill---Noether rank of a tropical curve with the dimension of the Brill---Noether locus of an algebraic curve.
Comparative Study of Nonproportionality and Electronic Band Structures Features in Scintillator Materials

The origin of nonproportionality in scintillator materials has been a long standing problem for more than four decades. In this manuscript, we show that, with the help of first principle modeling, the parameterization of the nonproportionality for several systems, with respect to their band structure curvature suggests a correlation between carrier effective mass and energy response. We attribute this correlation to the case where free electrons and holes are the major energy carriers. Excitonic scintillators do not show such a definitive trend. This model suggests a potential high-throughput approach for discovering novel proportional scintillators in the former class of materials.
An exploration of the role of principal inertia components in information theory

The principal inertia components of the joint distribution of two random variables X and Y are inherently connected to how an observation of Y is statistically related to a hidden variable X. In this paper, we explore this connection within an information theoretic framework. We show that, under certain symmetry conditions, the principal inertia components play an important role in estimating one-bit functions of X, namely f(X), given an observation of Y. In particular, the principal inertia components bear an interpretation as filter coefficients in the linear transformation of p f(X)|X into p f(X)|Y . This interpretation naturally leads to the conjecture that the mutual information between f(X) and Y is maximized when all the principal inertia components have equal value. We also study the role of the principal inertia components in the Markov chain B → X → Y → B, where B and B are binary random variables. We illustrate our results for the setting where X and Y are binary strings and Y is the result of sending X through an additive noise binary channel.
Resting State Functional Connectivity Is Decreased Globally Across the C9orf72 Mutation Spectrum.

A repeat expansion mutation in the C9orf72 gene causes amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), or symptoms of both, and has been associated with gray and white matter changes in brain MRI scans. We used graph theory to examine the network properties of brain function at rest in a population of mixed-phenotype C9orf72 mutation carriers (C9+). Twenty-five C9+ subjects (pre-symptomatic, or diagnosed with ALS, behavioral variant FTD (bvFTD), or both ALS and FTD) and twenty-six healthy controls underwent resting state fMRI. When comparing all C9+ subjects with healthy controls, both global and connection-specific decreases in resting state connectivity were observed, with no substantial reorganization of network hubs. However, when analyzing subgroups of the symptomatic C9+ patients, those with bvFTD (with and without comorbid ALS) show remarkable reorganization of hubs compared to patients with ALS alone (without bvFTD), indicating that subcortical regions become more connected in the network relative to other regions. Additionally, network connectivity measures of the right hippocampus and bilateral thalami increased with increasing scores on the Frontal Behavioral Inventory, indicative of worsening behavioral impairment. These results indicate that while C9orf72 mutation carriers across the ALS-FTD spectrum have global decreased resting state brain connectivity, phenotype-specific effects can also be observed at more local network levels.