cssi/SciSciGPT-SciSciNet huggingface dataset:
Prediction of Aeroacoustic Resonance in Cavities of Hole-Pattern Stator Seals

A Reynolds-averaged Navier-Stokes (RANS) solver developed in-house was used to simulate grazing channel flow past single and multiple cavities. The objective of this investigation was to predict fluid instabilities in hole-pattern stator seals. The numerical results generated with the RANS solver showed good agreement with those obtained using a commercial Large Eddy Simulation (LES) code. In addition, the numerical results agreed well with experimental data. Rossiter’s formula, a popular semi-empirical model used to predict frequencies of hole-tone acoustic instabilities caused by grazing fluid flow past open cavities, was modified using the RANS solver results to allow for its application to channel flows. This was done by modifying the empirical constant κ, the ratio of vortex velocity and the freestream velocity. The dominant frequencies predicted using the Rossiter’s formula with the new κ value matched well the experimental data for hole-pattern stator seals. The RANS solver accurately captured the salient features of the flow/acoustic interaction and predicted well the dominant acoustic frequencies measured in an experimental investigation. The flow solver also provided detailed physical insight into the cavity flow instability mechanism.Copyright © 2010 by ASME
Understanding engineering educators’ pedagogical transformations through the Hero’s Journey

Many engineering education research faculty have put much effort into providing empirical evidence demonstrating the success of student-centred teaching strategies in the hopes that this evidence w...
Prediction of outliers in pain, analgesia requirement, and recovery of function after childbirth: a prospective observational cohort study.

Abstract Background Prediction models to identify parturients who experience protracted pain, prolonged opioid use, and delayed self-assessed functional recovery are currently inadequate. Methods For this study, 213 nulliparous women who planned vaginal delivery were enrolled and assessed daily until they completed three outcomes: (1) pain resolution; (2) opioid cessation; and (3) self-assessed functional recovery to predelivery level. The primary composite endpoint, ‘pain and opioid-free functional recovery' was the time required to reach all three endpoints. The subjects were divided into two categories (the worst (longest time) 20% and remaining 80%) for reaching the primary composite endpoint, and each individual component. Prediction models for prolonged recovery were constructed using multivariate logistic regression with demographic, obstetric, psychological, and health-related quality of life characteristics as candidate predictors. Results Labour induction (vs spontaneous labour onset) predicted the worst 20% for the primary composite endpoint in the final multivariate model. Labour induction and higher postpartum day 1 numerical rating score for pain were predictors for being in the worst 20% for both functional recovery and pain burden. Labour type, delivery type, Patient-Reported Outcomes Measurement Information System (PROMIS) anxiety score, RAND 36 Item Health Survey 1.0 (SF-36) physical health composite score, and postpartum breastfeeding success were predictive of delayed opioid cessation. Conclusions Labour induction and elevated numerical rating score for pain are predictive of poor recovery after childbirth. Further research is necessary to determine whether modification would benefit mothers at risk for poor recovery.
Emerging trends in numerical simulations of combustion systems

Abstract Numerical simulations have played a vital role in the design of modern combustion systems. Over the last two decades, the focus of research has been on the development of the large eddy simulation (LES) approach, which leveraged the vast increase in computing power to dramatically improve predictive accuracy. Even with the anticipated increase in supercomputing capabilities, the use of LES in design is limited by its high computational cost. Moreover, to aid decision making, such LES computations have to be augmented to estimate underlying uncertainties in simulation components. At the same time, other changes are happening across industries that build or use combustion devices. While efficiency and emissions reduction are still the primary design objectives, reducing cost of operation by optimizing maintenance and repair is becoming an important segment of the enterprise. This latter quest is aided by the digitization of combustors, which allows collection and storage of operational data from a host of sensors over a fleet of devices. Moreover, several levels of computing including low-power hardware present on board the combustion systems are becoming available. Such large data sets create unique opportunities for design and maintenance if appropriate numerical tools are made available. As LES revolutionized computing-guided design by leveraging supercomputing, a new generation of numerical approaches is needed to utilize this vast amount of data and the varied nature of computing hardware. In this article, a review of emerging computational approaches for this heterogeneous data-driven environment is provided. A case is made that new but unconventional opportunities for physics-based combustion modeling exist in this realm.
Spectral Subspace Sparsification

We introduce a new approach to spectral sparsification that approximates the quadratic form of the pseudoinverse of a graph Laplacian restricted to a subspace. We show that sparsifiers with a near-linear number of edges in the dimension of the subspace exist. Our setting generalizes that of Schur complement sparsifiers. Our approach produces sparsifiers by sampling a uniformly random spanning tree of the input graph and using that tree to guide an edge elimination procedure that contracts, deletes, and reweights edges. In the context of Schur complement sparsifiers, our approach has two benefits over prior work. First, it produces a sparsifier in almost-linear time with no runtime dependence on the desired error. We directly exploit this to compute approximate effective resistances for a small set of vertex pairs in faster time than prior work (Durfee-Kyng-Peebles-Rao-Sachdeva '17). Secondly, it yields sparsifiers that are reweighted minors of the input graph. As a result, we give a near-optimal answer to a variant of the Steiner point removal problem. A key ingredient of our algorithm is a subroutine of independent interest: a near-linear time algorithm that, given a chosen set of vertices, builds a data structure from which we can query a multiplicative approximation to the decrease in the effective resistance between two vertices after identifying all vertices in the chosen set to a single vertex with inverse polynomial additional additive error in near-constant time.
Green’s function coupled cluster formulations utilizing extended inner excitations

In this paper, we analyze new approximations of the Green’s function coupled cluster (GFCC) method where locations of poles are improved by extending the excitation level of inner auxiliary operators. These new GFCC approximations can be categorized as the GFCC-i(n, m) method, where the excitation level of the inner auxiliary operators (m) used to describe the ionization potential and electron affinity effects in the N − 1 and N + 1 particle spaces is higher than the excitation level (n) used to correlate the ground-state coupled cluster wave function for the N-electron system. Furthermore, we reveal the so-called “n + 1” rule in this category [or the GFCC-i(n, n + 1) method], which states that in order to maintain size-extensivity of the Green’s function matrix elements, the excitation level of inner auxiliary operators Xp(ω) and Yq(ω) cannot exceed n + 1. We also discuss the role of the moments of coupled cluster equations that in a natural way assures these properties. Our implementation in the present study is focused on the first approximation in this GFCC category, i.e., the GFCC-i(2,3) method. As our first practice, we use the GFCC-i(2,3) method to compute the spectral functions for the N2 and CO molecules in the inner and outer valence regimes. In comparison with the Green’s function coupled cluster singles, doubles results, the computed spectral functions from the GFCC-i(2,3) method exhibit better agreement with the experimental results and other theoretical results, particularly in terms of providing higher resolution of satellite peaks and more accurate relative positions of these satellite peaks with respect to the main peak positions.In this paper, we analyze new approximations of the Green’s function coupled cluster (GFCC) method where locations of poles are improved by extending the excitation level of inner auxiliary operators. These new GFCC approximations can be categorized as the GFCC-i(n, m) method, where the excitation level of the inner auxiliary operators (m) used to describe the ionization potential and electron affinity effects in the N − 1 and N + 1 particle spaces is higher than the excitation level (n) used to correlate the ground-state coupled cluster wave function for the N-electron system. Furthermore, we reveal the so-called “n + 1” rule in this category [or the GFCC-i(n, n + 1) method], which states that in order to maintain size-extensivity of the Green’s function matrix elements, the excitation level of inner auxiliary operators Xp(ω) and Yq(ω) cannot exceed n + 1. We also discuss the role of the moments of coupled cluster equations that in a natural way assures these properties. Our implementation in the present...
MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric

The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the L2 Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.
Large regional shortwave forcing by anthropogenic methane informed by Jovian observations

Recently, it was recognized that widely used calculations of methane radiative forcing systematically underestimated its global value by 15% by omitting its shortwave effects. We show that shortwave forcing by methane can be accurately calculated despite considerable uncertainty and large gaps in its shortwave spectroscopy. We demonstrate that the forcing is insensitive, even when confronted with much more complete methane absorption spectra extending to violet light wavelengths derived from observations of methane-rich Jovian planets. We undertake the first spatially resolved global calculations of this forcing and find that it is dependent on bright surface features and clouds. Localized annual mean forcing from preindustrial to present-day methane increases approaches +0.25 W/m2, 10 times the global annualized shortwave forcing and 43% of the total direct CH4 forcing. Shortwave forcing by anthropogenic methane is sufficiently large and accurate to warrant its inclusion in historical analyses, projections, and mitigation strategies for climate change.
GRAPH VARIOGRAM: A NOVEL TOOL TO MEASURE SPATIAL STATIONARITY

Irregularly sampling a spatially stationary random field does not yield a graph stationary signal in general. Based on this observation, we build a definition of graph stationarity based on intrinsic stationarity, a less restrictive definition of classical stationarity. We introduce the concept of graph variogram, a novel tool for measuring spatial intrinsic stationarity at local and global scales for irregularly sampled signals by selecting subgraphs of local neighborhoods. Graph variograms are extensions of variograms used for signals defined on continuous Euclidean space. Our experiments with intrinsically stationary signals sampled on a graph, demonstrate that graph variograms yield estimates with small bias of true theoretical models, while being robust to sampling variation of the space.
Large rank-based models with common noise

Abstract For large systems of Brownian particles interacting through their ranks introduced in (Banner et al., 2005), the empirical cumulative distribution function satisfies a porous medium PDE. However, when we introduce a common noise, the limit is no longer deterministic. Instead, we show that this limit is a solution of a stochastic PDE related to this porous medium PDE. This stochastic PDE is somewhat similar to the equations developed for conservation laws with rough stochastic fluxes (Lions et al., 2013).
Lexical access in the face of degraded speech: The effects of cognitive adaptation

Hearing impaired listeners use cognitive adaptations to cope with degraded input. Here, we ask if they adapt processes that normal hearing listeners use to cope with the fact that speech unfolds over time, creating brief periods of ambiguity. Normal listeners cope with this ambiguity by activating multiple lexical candidates which compete for recognition (McClelland & Elman, 1986). These competition dynamics change when processing degraded input (Brouwer & Bradlow, 2016; McMurray, Farris-Trimble, & Rigler, 2017; McQueen & Huettig, 2012), but it is unclear whether this reflects the degraded input itself or cognitive adaptation. In two visual world paradigm experiments, listeners heard different levels of degraded (noise-vocoded) speech. Experiment 1 manipulated the level of degradation either in blocks or randomly interleaved across trials. Interleaving led to processing delays beyond that of the level of degradation: we found switch-costs when degradation levels differed between trials. This suggests differences in lexical dynamics are not solely due to degradation in the input. In Experiment 2, a visual cue indicated the level of degradation before each trial. This reduced the processing delays and switch costs, suggesting participants adapted before the auditory input. These experiments support a role for central processing in dealing with degraded speech input. Hearing impaired listeners use cognitive adaptations to cope with degraded input. Here, we ask if they adapt processes that normal hearing listeners use to cope with the fact that speech unfolds over time, creating brief periods of ambiguity. Normal listeners cope with this ambiguity by activating multiple lexical candidates which compete for recognition (McClelland & Elman, 1986). These competition dynamics change when processing degraded input (Brouwer & Bradlow, 2016; McMurray, Farris-Trimble, & Rigler, 2017; McQueen & Huettig, 2012), but it is unclear whether this reflects the degraded input itself or cognitive adaptation. In two visual world paradigm experiments, listeners heard different levels of degraded (noise-vocoded) speech. Experiment 1 manipulated the level of degradation either in blocks or randomly interleaved across trials. Interleaving led to processing delays beyond that of the level of degradation: we found switch-costs when degradation levels differed between trials. This suggests diff...
Spatially heterogeneous effects of a public works program

Abstract Most research on labor market effects of the Mahatma Gandhi National Rural Employment Guarantee Scheme focuses on outcomes at the district level. This paper shows that such a focus masks substantial spatial heterogeneity: treated villages located near untreated areas see smaller increases in casual wages than treated villages located farther from untreated areas. Spatial differences in implementation or program leakages do not appear to drive this spatial heterogeneity. The effects of the program on private-sector employment display similar intra-district heterogeneity and these effects on employment are highly correlated with the effect on wages. Overall, these results suggest that worker mobility leads a district-level focus to underestimate the true effect of the program on wages. Quantifying this underestimate using two separate methods produces very similar results; the overall effect on wages appears to be approximately twice as large as district-level estimates suggest.
Traders' Dilemma : Developing Countries' Response to Trade Disputes

If trade tensions between the United States and certain trading partners escalate into a full-blown trade war, what should developing countries do? Using a global, general-equilibrium model, this paper first simulates the effects of an increase in U.S. tariffs on imports from all regions to about 30 percent (the average non-Most Favored Nation tariff currently applied to imports from Cuba and the Democratic Republic of Korea) and retaliation in kind by major trading partners—the European Union, China, Mexico, Canada, and Japan. The paper then considers four possible responses by developing countries to this trade war: (i) join the trade war; (ii) do nothing; (iii) pursue regional trade agreements (RTAs) with all regions outside the United States; and (iv) option (iii) and unilaterally liberalize tariffs on imports from the United States. The results show that joining the trade war is the worst option for developing countries (twice as bad as doing nothing), while forming RTAs with non-U.S. regions and liberalizing tariffs on U.S. imports (“turning the other cheek”) is the best. The reason is that a trade war between the United States and its major trading partners creates opportunities for developing countries to increase their exports to these markets. Liberalizing tariffs increases developing countries’ competitiveness, enabling them to capitalize on these opportunities.
Pathological and Transcriptome Changes in the ReninAAV db/db uNx Model of Advanced Diabetic Kidney Disease Exhibit Features of Human Disease:

The ReninAAV db/db uNx model of diabetic kidney disease (DKD) exhibits hallmarks of advanced human disease, including progressive elevations in albuminuria and serum creatinine, loss of glomerular filtration rate, and pathological changes. Microarray analysis of renal transcriptome changes were more similar to human DKD when compared to db/db eNOS-/- model. The model responds to treatment with arterial pressure lowering (lisinopril) or glycemic control (rosiglitazone) at early stages of disease. We hypothesized the ReninAAV db/db uNx model with advanced disease would have residual disease after treatment with lisinopril, rosiglitazone, or combination of both. To test this, ReninAAV db/db uNx mice with advanced disease were treated with lisinopril, rosiglitazone, or combination of both for 10 weeks. All treatment groups showed significant lowering of urinary albumin to creatinine ratio compared to baseline; however, only combination group exhibited lowering of serum creatinine. Treatment improved renal pathological scores compared to baseline values with residual disease evident in all treatment groups when compared to db/m controls. Gene expression analysis by TaqMan supported pathological changes with increased fibrotic and inflammatory markers. The results further validate this model of DKD in which residual disease is present when treated with agents to lower arterial pressure and glycemic control.
Audit Partner Identification and Characteristics: Evidence from U.S. Form AP Filings

SUMMARY This paper investigates the overall impact of and the information made available by the recent audit partner disclosure requirement in the U.S. After a contentious comment period, the PCAOB...
A Hybrid Beamforming Receiver with Two-Stage Analog Combining and Low-Resolution ADCs

In this paper, we propose a two-stage analog combining architecture for millimeter wave (mmWave) communications with hybrid analog&#x002F;digital beamforming and low-resolution analog-to-digital converters (ADCs). We first derive a two-stage combining solution by solving a mutual information (MI) maximization problem without a constant modulus constraint on analog combiners. With the derived solution, the proposed receiver architecture splits the analog combining into a channel gain aggregation stage followed by a spreading stage to maximize the MI by effectively managing quantization error. We show that the derived two-stage combiner achieves the optimal scaling law with respect to the number of radio frequency (RF) chains and maximizes the MI for homogeneous singular values of a MIMO channel. Then, we develop a two-stage analog combining algorithm to implement the derived solution under a constant modulus constraint for mmWave channels. Simulation results validate the algorithm performance in terms of MI.
Firms’ Markup, Cost, and Price Changes when Policymakers Permit Collusion: Does Antitrust Immunity Matter?

Abstract Airlines wanting to cooperatively set prices for their international air travel service must apply to the relevant authorities for antitrust immunity (ATI). Whether consumers, on net, benefit from a grant of ATI to partner airlines has caused much public debate. This paper investigates the impact of granting ATI to oneworld alliance members on their price, markup, and various measures of cost. The evidence suggests that implementation of the oneworld alliance without ATI did not have a statistically significant impact on the markup of products offered by the members, and there is no evidence that the subsequent grant of ATI to various members resulted in higher markups on their products. We find evidence suggesting that the grant of ATI facilitated a decrease in partner carriers’ marginal and fixed costs. Furthermore, member carriers’ price did not increase (decreased) in markets where their services do (do not) overlap, implying that consumers, on net, benefit from the grant of ATI in terms of price changes.
Bounds for Matchings in Nonabelian Groups

We give upper bounds for triples of subsets of a finite group such that the triples of elements that multiply to $1$ form a perfect matching. Our bounds are the first to give exponential savings in powers of an arbitrary finite group. Previously, Blasiak, Church, Cohn, Grochow, Naslund, Sawin, and Umans (2017) gave similar bounds in abelian groups of bounded exponent, and Petrov (2016) gave exponential bounds in certain $p$-groups.
Factor Analysis of Personality Measures

Factor analysis refers to a broad array of statistical models and techniques that have the general goal of describing underlying patterns in relational data. A common application of factor analysis is to items that assess personality, with the goal of identifying the major dimensions of human personality. These dimensions may be viewed as descriptive or causal depending upon the views of the investigator, and different numbers and types of dimensions have been proposed. Subjective decisions about the items to include in the analysis, the method of factor identification (principle components or principle factors), the number of factors, the rotation of the factors, and the interpretation of the factors all contribute to the diversity of the results obtained with different factor analyses. The goal of factor analysis is generally to simplify the complex structure of human personality, not to fully model that complexity. Thus, recent results from the application of confirmatory factor analysis to personality data that indicate that established factor models do not adequately model all the covariation among personality measures are neither surprising nor particularly relevant. Keywords: factor analysis; principle components; personality; Big Five; confirmatory factor analysis
Latent Normalizing Flows for Discrete Sequences.

Normalizing flows are a powerful class of generative models for continuous random variables, showing both strong model flexibility and the potential for non-autoregressive generation. These benefits are also desired when modeling discrete random variables such as text, but directly applying normalizing flows to discrete sequences poses significant additional challenges. We propose a VAE-based generative model which jointly learns a normalizing flow-based distribution in the latent space and a stochastic mapping to an observed discrete space. In this setting, we find that it is crucial for the flow-based distribution to be highly multimodal. To capture this property, we propose several normalizing flow architectures to maximize model flexibility. Experiments consider common discrete sequence tasks of character-level language modeling and polyphonic music generation. Our results indicate that an autoregressive flow-based model can match the performance of a comparable autoregressive baseline, and a non-autoregressive flow-based model can improve generation speed with a penalty to performance.
A question-entailment approach to question answering.

One of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is mapping new questions to formerly answered questions that are “similar”. We propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare logistic regression and deep learning methods for RQE using different kinds of datasets including textual inference, question similarity, and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources which we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.
Codes With Locality in the Rank and Subspace Metrics

We extend the notion of locality from the Hamming metric to the rank and subspace metrics. Our main contribution is to construct a class of array codes with locality constraints in the rank metric. Our motivation for constructing such codes stems from the need to design codes for efficient data recovery from correlated and/or mixed ( i.e., complete and partial) failures in distributed storage systems. Specifically, the proposed local rank-metric codes can recover locally from crisscross errors and erasures , which affect a limited number of rows and/or columns of the storage array. We also derive a Singleton-like upper bound on the minimum rank distance of (linear) codes with rank-locality constraints. Our proposed construction achieves this bound for a broad range of parameters. The construction builds upon Tamo and Barg’s method for constructing locally repairable codes with optimal minimum Hamming distance. Finally, we construct a class of constant-dimension subspace codes (also known as Grassmannian codes) with locality constraints in the subspace metric. The key idea is to show that a Grassmannian code with locality can be easily constructed from a rank-metric code with locality by using the lifting method proposed by Silva et al. We present an application of such codes for distributed storage systems, wherein nodes are connected over a network that can introduce errors and erasures.
Proceedings of the 3rd ACM workshop on Assurable and usable security configuration

It is our great pleasure to welcome you to the 3rd ACM Workshop on Assurable & Usable Security Configuration 2010 (SafeConfig '10) held in conjunction with the 17th ACM Conference on Computer and Communications Security in Chicago, IL. The workshop is focused on the unique challenges of securing a variety of configuration data maintained by enterprise networks. A typical enterprise network might have hundreds of security appliances such as firewalls, IPSec gateways, IDS/IPS, authentication servers, authorization/ RBAC servers and crypto systems. An enterprise network may also have other non-security devices such as routers, name servers, protocol gateways, etc. These must be logically integrated into a security architecture satisfying security goals at and across multiple networks. Logical integration is accomplished by consistently setting thousands of configuration variables and rules on the devices. The configuration must be constantly adapted to optimize protection and block prospective attacks. Also, the configuration must be tuned to balance security with usability. SafeConfig'10 will give researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of security configuration. The call for papers attracted 25 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 8 regular papers and 4 short papers that cover a variety of topics, including policy management, network and infrastructure configuration, and access control and system configuration. We hope that the proceedings will serve as a valuable reference for security researchers and developers.
Covered Interest Parity Deviations: Macrofinancial Determinants

Abstract This paper studies how several macrofinancial factors are associated over time with the evolution of covered interest parity (CIP) deviations in the decade after the Global Financial Crisis. Changes in a number of risk- and policy-related factors have a significant association with the evolution of CIP deviations. Key measures of FX market liquidity and intermediaries' risk-taking capacity are strongly correlated with the cross-currency basis (the deviation from CIP), and the close relationship between broad U.S. dollar strength and the basis is driven mainly by a common factor depending on other safe-haven currencies' comovements. Post-crisis monetary policies also play a role, as demonstrated by the relationship between CIP deviations, central bank balance sheets, and term premia. Risk-related factors have more explanatory power than monetary policy-related factors over the entire 2010–2018 period, but they are approximately equally influential over the period's second half. Further highlighting the role of bank regulation, we offer evidence that the year-end dynamics of the three-month dollar basis depend on financial regulations targeting global systemically important financial institutions.
Biomedical Imaging Techniques

Biomedical images are used extensively for noninvasive exploration of the internal anatomy, physiology, and metabolism of patients suffering from virtually every disease and injury known to humankind. These images are a prototypical example of “high-tech” medicine and are produced in many different ways, from receipt of X rays transmitted through the patient to detection of radio-frequency signals emitted from tissues subjected to intense magnetic fields. In the past three decades, major advances in medical imaging have occurred, including X-ray and emission-computed tomography, real-time, gray-scale and Doppler ultrasound imaging, computer and digital projection radiography, and magnetic resonance imaging. Several additional imaging techniques are under exploration, including laser optical imaging, electrical impedance tomography, infrared and microwave thermography, acoustothermometry, and electrical and magnetic source imaging. Whether these or a completely new imaging technique may emerge as a significant imaging modality remains to be seen. In the meantime, additional applications of existing medical imaging methods continue to be identified, such as the use of functional magnetic resonance and emission-computed tomography for planning and monitoring radiation treatments for cancer. Keywords: biomedical imaging; projection imaging; emission imaging; reflection imaging; digital imaging; imaging in radiation therapy; new imaging modalities; image detail
Surgical Treatment of Chronic Periprosthetic Joint Infection: Fate of Spacer Exchanges

Abstract Background Patients with periprosthetic joint infection (PJI) undergoing 2-stage exchange arthroplasty may undergo an interim spacer exchange for a variety of reasons including mechanical failure of spacer or persistence of infection. The objective of this study is to understand the risk factors and outcomes of patients who undergo spacer exchange during the course of a planned 2-stage exchange arthroplasty. Methods Our institutional database was used to identify 533 patients who underwent a 2-stage exchange arthroplasty for PJI, including 90 patients with a spacer exchange, from 2000 to 2017. A retrospective review was performed to extract relevant clinical information. Treatment outcomes included (1) progression to reimplantation and (2) treatment success as defined by a Delphi-based criterion. Both univariate and multivariate Cox regression models were performed to investigate whether spacer exchange was associated with failure. Additionally, a propensity score analysis was performed based on a 1:2 match. Results A spacer exchange was required in 16.9%. Patients who underwent spacer exchanges had a higher body mass index (P Discussion Despite an additional load of local antibiotics and repeat debridement, patients who underwent a spacer exchange demonstrated poor outcomes, including failure to undergo reimplantation and twice the failure rate. The findings of this study may need to be borne in mind when managing patients who require spacer exchange.
Expanding Media Law and Policy Education: Confronting Power, Defining Freedom, Awakening Participation

The changes brought about by the Digital Age have not triggered significant increases in political participation or meaningful reductions in longstanding social power asymmetries, which are now inc...
Investigation on the effect of cold drawing process on mechanical properties and structure of seamless steel (Ck60) tube

The cold drawing process is one of the precise metal forming techniques which have attracted attention by many researchers with respect to the given properties such as potential for improvement of mechanical properties of the material, achieving of accurate dimensional tolerances and rise of quality at final surface so that different studies are conducted on properties of various materials in the course of analysis of its effect. Using cold drawing and also exertion of special heat treatment cycle in this investigation, initially the seamless steel tube Ck60 has been produced and then effect of cold drawing has been assessed on the mechanical properties and structure of steel. The production process of seamless steel tube includes two phases of cold drawing which was followed by surface reduction levels 15.1% and 13.7% respectively. In order to analyze mechanical properties, tensile test and hardness-testing was used and metallography test was also employed to observe the structure. The given results indicate that the seamless steel tube Ck60 produced by means of this production process has ultimate tensile strength (1021 MPa), yield strength (950 MPa) and elongation (9%). Moreover, the value of hardness mean was 312 for the final tube in Vickers Hardness Test. The images taken from optical microscopy also show that the final structure of tube is uniform and as perlite ferrite.
The advantages of multiple classes for reducing overfitting from test set reuse

Excessive reuse of holdout data can lead to overfitting. However, there is little concrete evidence of significant overfitting due to holdout reuse in popular multiclass benchmarks today. Known results show that, in the worst-case, revealing the accuracy of $k$ adaptively chosen classifiers on a data set of size $n$ allows to create a classifier with bias of $\Theta(\sqrt{k/n})$ for any binary prediction problem. We show a new upper bound of $\tilde O(\max\{\sqrt{k\log(n)/(mn)},k/n\})$ on the worst-case bias that any attack can achieve in a prediction problem with $m$ classes. Moreover, we present an efficient attack that achieve a bias of $\Omega(\sqrt{k/(m^2 n)})$ and improves on previous work for the binary setting ($m=2$). We also present an inefficient attack that achieves a bias of $\tilde\Omega(k/n)$. Complementing our theoretical work, we give new practical attacks to stress-test multiclass benchmarks by aiming to create as large a bias as possible with a given number of queries. Our experiments show that the additional uncertainty of prediction with a large number of classes indeed mitigates the effect of our best attacks. Our work extends developments in understanding overfitting due to adaptive data analysis to multiclass prediction problems. It also bears out the surprising fact that multiclass prediction problems are significantly more robust to overfitting when reusing a test (or holdout) dataset. This offers an explanation as to why popular multiclass prediction benchmarks, such as ImageNet, may enjoy a longer lifespan than what intuition from literature on binary classification suggests.
Characterization of Furan- and Thiophene-Containing Bispyridyl Oligomers via Spectroscopic, Electrochemical, and TD-DFT Methods

The study of π-conjugated oligomers has garnered significant interest because of their use in organic optoelectronic devices, such as organic light-emitting diodes or organic field-effect transisto...
Management of Mandible Fracture in 150 Children Across 7 Years in a US Tertiary Care Hospital.

ImportancePediatric mandible fractures are the most common pediatric facial fracture requiring hospitalization, but data are lacking on management methods, outcomes, and complications. ObjectiveTo ...
A Low Resolution Epistasis Mapping Approach To Identify Chromosome Arm Interactions in Allohexaploid Wheat

Epistasis is an important contributor to genetic variance. In inbred populations, pairwise epistasis is present as additive by additive interactions. Testing for epistasis presents a multiple testing problem as the pairwise search space for modest numbers of markers is large. Single markers do not necessarily track functional units of interacting chromatin as well as haplotype based methods do. To harness the power of multiple markers while minimizing the number of tests conducted, we present a low resolution test for epistatic interactions across whole chromosome arms. Epistasis covariance matrices were constructed from the additive covariances of individual chromosome arms. These covariances were subsequently used to estimate an epistatic variance parameter while correcting for background additive and epistatic effects. We find significant epistasis for 2% of the interactions tested for four agronomic traits in a winter wheat breeding population. Interactions across homeologous chromosome arms were identified, but were less abundant than other chromosome arm pair interactions. The homeologous chromosome arm pair 4BL/4DL showed a strong negative relationship between additive and interaction effects that may be indicative of functional redundancy. Several chromosome arms appeared to act as hubs in an interaction network, suggesting that they may contain important regulatory factors. The differential patterns of epistasis across different traits demonstrate that detection of epistatic interactions is robust when correcting for background additive and epistatic effects in the population. The low resolution epistasis mapping method presented here identifies important epistatic interactions with a limited number of statistical tests at the cost of low precision.
Robust Perfect Adaptation in Biomolecular Reaction Networks

For control in biomolecular systems, the most basic objective of maintaining a small error in a target variable, say the expression level of some protein, is often difficult due to the presence of both large uncertainty of every type and intrinsic limitations on the controller's implementation. This paper explores the limits of biochemically plausible controller design for the problem of robust perfect adaptation (RPA), biologists' term for robust steady state tracking. It is well-known that for a large class of nonlinear systems, a system has RPA iff it has integral feedback control (IFC), which has been used extensively in real control systems to achieve RPA. However, we show that due to intrinsic physical limitations on the dynamics of chemical reaction networks (CRNs), cells cannot implement IFC directly in the concentration of a chemical species. This contrasts with electronic implementations, particularly digital, where it is trivial to implement IFC directly in a single state. Therefore, biomolecular systems have to achieve RPA by encoding the integral control variable into the network architecture of a CRN. We describe a general framework to implement RPA in CRNs and show that well-known network motifs that achieve RPA, such as (negative) integral feedback (IFB) and incoherent feedforward (IFF), are examples of such implementations. We also develop methods to designing integral feedback variables for unknown plants. This standard control notion is surprisingly nontrivial and relatively unstudied in biomolecular control. The methods developed here connect different existing fields and approaches on the problem of biomolecular control, and hold promise for systematic chemical reaction controller synthesis as well as analysis.
Identifying Gait Phases from Joint Kinematics during Walking with Switched Linear Dynamical Systems

Human-robot interaction (HRI) for gait rehabilitation would benefit from data-driven gait models that account for gait phases and gait dynamics. Here we address the current limitation in gait models driven by kinematic data, which do not model interlimb gait dynamics and have not been shown to precisely identify gait events. We used Switched Linear Dynamical Systems (SLDS) to model joint angle kinematic data from healthy individuals walking on a treadmill with normal gaits and with gaits perturbed by electrical stimulation. We compared the model-inferred gait phases to gait phases measured externally via a force plate. We found that SLDS models accounted for over 88% of the variation in each joint angle and labeled the joint kinematics with the correct gait phase with 84% precision on average. The transitions between hidden states matched measured gait events, with a median absolute difference of 25ms. To our knowledge, this is the first time that SLDS inferred gait phases have been validated by an external measure of gait, instead of against predefined gait phase durations. SLDS provide individual-specific representations of gait that incorporate both gait phases and gait dynamics. SLDS may be useful for developing control policies for HRI aimed at improving gait by allowing for changes in control to be precisely timed to different gait phases.
Guided Gaussian wave packets.

We discuss several semiclassical Gaussian wave packet approaches with emphasis on one that is not very well-known, that is, the off-center guiding approach. Off-center guiding of (thawed) Gaussian wave packets uses the same information as other Gaussian propagation schemes, that is, the full van Vleck determinant and multiple trajectories. It retains the well-known caustic smoothing property of Gaussians. The off-center guiding of Gaussians can handle hard chaos and other highly nonlinear situations, where the van Vleck propagator, for example, has over 30 000 separate branches.
Structural relationships between new carbide La14Sn(MnC6)3 and fully ordered La11(MnC6)3

Crystals of the ternary La{sub 11}(MnC{sub 6}){sub 3} and new quaternary carbide La{sub 14}Sn(MnC{sub 6}){sub 3} phases were grown from La/Ni eutectic flux and their structures were determined by means of X-ray single crystal diffraction. La{sub 11}(MnC{sub 6}){sub 3} is a new superstructure variant of La{sub 3.67}MnC{sub 6} (previously reported disordered subcell: P6{sub 3}/m; a{sub 0}=8.806 A; c{sub 0}=5.329 A, Z=2). The superstructure (R3-bar ; a={radical}3a{sub 0}=15.2649(9) A; c=3c{sub 0}=16.013(1) A, Z=6; R{sub 1}=0.022) is realized by complete ordering of the La chains within the columns of face-sharing carbon octahedra, with alternating La-La distances leading to R-centering and enlargement of the unit cell. The structure of the quaternary carbide La{sub 14}Sn(MnC{sub 6}){sub 3} (P6-bar ; a=8.756(1) A; c=10.483(2) A, Z=1; R{sub 1}=0.026) is closely related to that of La{sub 11}(MnC{sub 6}){sub 3} with part of the MnC{sub 6} units replaced by Sn atoms. The structure and precise composition of La{sub 14}Sn(MnC{sub 6}){sub 3} can be derived from that of La{sub 11}(MnC{sub 6}){sub 3} by taking into account the extent of this substitution and variation in lanthanum siting in the chain of carbon octahedra. Band structure calculations indicate both phases are metallic; the La{sub 11}(MnC{sub 6}){sub 3} phase is stabilized bymore » the ordering of La atoms which induces a pseudogap at E{sub F}. -- La{sub 11}(MnC{sub 6}){sub 3} with fully ordered superstructure and a new carbide La{sub 14}Sn(MnC{sub 6}){sub 3} were obtained from La/Ni eutectic flux. Display Omitted« less
Aberrant information transfer interferes with functional axon regeneration

Functional axon regeneration requires regenerating neurons to restore appropriate synaptic connectivity and circuit function. To model this process, we developed an assay in Caenorhabditis elegans that links axon and synapse regeneration of a single neuron to recovery of behavior. After axon injury and regeneration of the DA9 neuron, synapses reform at their pre-injury location. However, these regenerated synapses often lack key molecular components. Further, synaptic vesicles accumulate in the dendrite in response to axon injury. Dendritic vesicle release results in information misrouting that suppresses behavioral recovery. Dendritic synapse formation depends on dynein and jnk-1. But even when information transfer is corrected, axonal synapses fail to adequately transmit information. Our study reveals unexpected plasticity during functional regeneration. Regeneration of the axon is not sufficient for the reformation of correct neuronal circuits after injury. Rather, synapse reformation and function are also key variables, and manipulation of circuit reformation improves behavioral recovery.
Which way is the bookstore? A closer look at the judgments of relative directions task

We present a detailed analysis of a widely used assay in human spatial cognition, the judgments of relative direction (JRD) task. We conducted three experiments involving virtual navigation intersp...
The Two Hyperplane Conjecture

We introduce a conjecture that we call the Two Hyperplane Conjecture, saying that an isoperimetric surface that divides a convex body in half by volume is trapped between parallel hy-perplanes. The conjecture is motivated by an approach we propose to the Hots Spots Conjecture of J. Rauch using deformation and hipschitz bounds for level sets of eigenfunctions. We will relate this approach to quantitative connectivity properties of level sets of solutions to elliptic variational problems, including isoperimetric inequalities, Poincare inequalities, Harnack inequalities, and NTA (non-tangentially accessibility). This paper mostly asks questions rather than answering them, while recasting known results in a new light. Its main theme is that the level sets of least energy solutions to scalar variational problems should be as simple as possible.