cssi/SciSciGPT-SciSciNet huggingface dataset:
The Neumann problem for higher order elliptic equations with symmetric coefficients

In this paper we establish well posedness of the Neumann problem with boundary data in $$L^2$$ or the Sobolev space $$\dot{W}^2_{-1}$$ , in the half space, for linear elliptic differential operators with coefficients that are constant in the vertical direction and in addition are self adjoint. This generalizes the well known well posedness result of the second order case and is based on a higher order and one sided version of the classic Rellich identity, and is the first known well posedness result for an elliptic divergence form higher order operator with rough variable coefficients and boundary data in a Lebesgue or Sobolev space.
THE MEDIAL STABILISED KNEE: THE “POST-CAM” REPLACEMENT – OPPOSES

Introduction: Although volumetric wear reduction has been demonstrated in knee simulator studies, there is no long-term in-vivo evidence supporting wear reduction and durability with uni-directional rotating platform PS design. This design was introduced to reduced spin-out and provide greater range of motion. This is the first long-term report of this implant, a prospective study investigating clinical and radiographic survivorship with 10 years follow-up. Material and Methods: Between January 2000 to March 2001, 118 consecutive patients (141 knees) received cemented RP TKRs. All patients were followed prospectively using clinical and radiographic criteria as defined by the Knee Society. At minimum nine years follow-up 20 patients were deceased, 11 were lost to follow-up and two refused to participate in the study, leaving 85 patients (100 knees) for final analysis. Results: Good to excellent results were achieved in 95% of patients. There were no cases of malalignment, spinout, aseptic loosening or osteolysis. The mean ROM improved from 111.2 ± 15.2 degrees to 119 ± 3.8 degrees. The mean WOMAC score was 30 ± 14, KSS scores improved from an average of 48 to 96. Sporting activities such as golf, tennis and walking was 29%, 12% and 32%, respectively. Anterior knee pain was present in 15% of cases. The incidence of asymptomatic crepitation and painful crepitation requiring scar excision was 10% and 4% respectively. During this period we had one case of infection and one revision for fracture. Kaplan-Meier survivorship at 10 years for mechanical failure and failure for all failures was 100% and 95.7%, respectively. Discussion and Conclusion: Ten-year follow-up of RP-PS design demonstrates excellent clinical and radiographic results with no failures for mechanical reasons. There were no spin-out and average ROM was 119 ± 3.8 degrees.
Differential Privacy in the Wild: A Tutorial on Current Practices & Open Challenges

Differential privacy has emerged as an important standard for privacy preserving computation over databases containing sensitive information about individuals. Research on differential privacy spanning a number of research areas, including theory, security, database, networks, machine learning, and statistics, over the last decade has resulted in a variety of privacy preserving algorithms for a number of analysis tasks. Despite maturing research efforts, the adoption of differential privacy by practitioners in industry, academia, or government agencies has so far been rare. Hence, in this tutorial, we will first describe the foundations of differentially private algorithm design that cover the state of the art in private computation on tabular data. In the second half of the tutorial we will highlight real world applications on complex data types, and identify research challenges in applying differential privacy to real world applications.
A mechanism for oscillatory instability in viscoelastic cross-slot flow

Interior stagnation-point flows of viscoelastic liquids arise in a wide variety of applications including extensional viscometry, polymer processing and microfluidics. Experimentally, these flows have long been known to exhibit instabilities, but the mechanisms underlying them have not previously been elucidated. We computationally demonstrate the existence of a supercritical oscillatory instability of low-Reynolds-number viscoelastic flow in a two-dimensional cross-slot geometry. The fluctuations are closely associated with the ‘birefringent strand’ of highly stretched polymer chains associated with the outflow from the stagnation point at high Weissenberg number. Additionally, we describe the mechanism of instability, which arises from the coupling of flow with extensional stresses and their steep gradients in the stagnation-point region.
The Impact of Management Alumni Affiliation and Persuasion Tactics on Auditors' Internal Control Judgments

ABSTRACT: Concerns over “revolving door” practices of companies hiring directly from their external auditor led to a Sarbanes-Oxley Act provision mandating a one-year cooling-off period before such hires can occur. Yet little is known as to whether these alumni affiliations, still prevalent today, actually impair audit quality. Drawing on Social Identity Theory, we conduct an experiment to examine whether auditors experience heightened identification with an alumni-affiliated client manager and, if so, how this perceived relationship affects their professional skepticism in response to a management persuasion attempt. As predicted, absent the use of a management persuasion tactic, auditors identify more with an alumni-affiliated manager than a non-alumnus with equal professional experience, and this perceived social bond enhances the manager's influence. However, the use of a common persuasion tactic, while effective at influencing auditor judgment when used by an unaffiliated manager, “backfires” when us...
A Novel Hybrid HDP-LDA Model for Sentiment Analysis.

Sentiment analysis studies the public opinions towards an entity, and it is an important research area in data mining. Recently, a lot of sentiment analysis models have been proposed, including supervised and unsupervised approaches. However, the role of supervised models has been undermined by the phenomenon of big data, and the unsupervised ones are drawing more and more attention. But, most current unsupervised methods are based on Latent Dirichlet Allocation (LDA), and they need to specify the number of aspects in advance, making them subjective. In addition, these methods treat factual words and opinioned words the same, and assume that one sentence contains only one aspect, all of which make the existing unsupervised methods unsatisfactory. To solve these problems, this paper proposes a novel hybrid Hierarchical Dirichlet Process-Latent Dirichlet Allocation (HDP-LDA) model. This model can automatically determine the number of aspects, distinguish factual words from opinioned words, and further effectively extracts the aspect specific sentiment words. Experiment result shows that our model can clearly capture the aspects people mentioned and the specific sentiment words they use in each aspect, improving the performance of sentiment analysis efficiently. At last, we compared our model with the influential topic models, namely, JST, AUSM and Maxine-LDA, on the online restaurant review, and found our model performs very well.
Conceptual design for the NSTX central instrumentation and control system

The design and construction phase for the national spherical torus experiment (NSTX) is under way at the Princeton Plasma Physics Laboratory (PPPL). Operation is scheduled to begin on April 30, 1999. This paper describes the conceptual design for the NSTX central instrumentation and control (I&C) system. Major elements of the central I&C system include the process control system, plasma control system, network system, data acquisition system, and synchronization system to support the NSTX experimental device.
NONLINEAR MODEL-MATCHING DESIGN OF SERVOMECHANISMS

Abstract This paper examines the design and realization of nonlinear multivariable servomechanisms utilizing higher-order spectral information about the plant. Specifically, the simultaneous design for specified output responses and reasonable control signals is addressed. For a desired output behavior and a given plant, a systematic model-matching procedure is employed which exploits the higher-order concepts contained in the multidimensional transfer function representations of the Volterra kernels of the plant and its compensation. The method applies to the design of non linear output feedback dynamics and to higher-order feedforward excitation of the closed-loop system. A recursive algorithm for realization of the higher-order kernel transforms obtained in the design is presented in detail. The potential improvement in performance is demonstrated in two applications, in which the design goals are partial feedback linearization with associated decoupling
Oil price shocks and American depositary receipt stock returns

Abstract In this paper we examine the impact of oil price shocks on twelve countries American Depositary Receipt (ADR) returns using monthly data from 1999.01 to 2014.12. The results show that oil price shocks have a positive and statistically significant impact on ADR return in all twelve countries. These results are robust to the inclusion of other explanatory variables such as oil price volatility and the spillover of the United States stock market. Further analysis shows that this effect is stronger in the post financial crisis time period compared to the pre-financial crisis time period.
Immunohistochemical Evidence for the Vascular Origin of Primary Adrenal Pseudocysts

Pseudocysts are the most common nonfunctioning cystic adrenal lesions associated with symptoms. We have studied tissue from two patients with large adrenal pseudocysts in whom no antecedent cause could be documented. Immunohistochemical examination of these formaldehyde-fixed paraffin-embedded tissues with antibodies directed against the major proteins of the basement membrane (laminin and type IV collagen) revealed intense linear staining surrounding the cystic spaces and at the compressed adrenal cortical/pseudocyst interface. These findings lead us to conclude that these lesions are vascular in nature and, therefore, closely related to the previously recognized adrenal cysts of endothelial origin.
Decitabine Priming Enhances Mucin 1 Inhibition Mediated Disruption of Redox Homeostasis in Cutaneous T-Cell Lymphoma

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous neoplasm and patients with relapsed/refractory disease exhibit resistance to standard therapies. We have previously demonstrated that the MUC1-C oncoprotein plays a critical role in protection from oxidative stress in CTCL cells. Targeting of MUC1-C with a pharmacologic inhibitor, GO-203, was associated with apoptosis in CTCL. However, disease responses were incomplete underscoring the need for combinatorial strategies that could exploit the vulnerability of CTCL cells to oxidative signals. Cell lines, primary samples and xenograft models of CTCL were used to assess synergy of GO-203 with decitabine, a hypomethylating agent. Present studies demonstrate that exposure of CTCL cells to decitabine in combination with GO-203, increased the generation of reactive oxygen species (ROS) levels and decreased levels of scavenger molecules, NADP, NADPH, glutathione (GSH), and TIGAR, critical to intracellular redox homeostasis. Dual exposure to GO-203 and decitabine resulted in marked down-regulation of DNA methyl transferases demonstrating significant synergy of these agents in inducing global and gene specific hypomethylation. Accordingly, treatment with decitabine and GO-203 upregulated the ROS generating enzymes, NADPH oxidase 4 (Nox4) and Dual oxidase 2 (Duox2) potentially due to their effect on epigenomic regulation of these proteins. In concert with these findings, exposure to decitabine and GO-203 resulted in heightened apoptotic death in CTCL cell lines, patient derived primary samples and in a murine xenograft model. These findings indicate that decitabine intensifies MUC1-C inhibition induced redox imbalance and provides a novel combination of targeted and epigenetic agents for patients with CTCL.
A neural algorithm for a fundamental computing problem.

Similarity search—for example, identifying similar images in a database or similar documents on the web—is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem.
Navigating the Data Lake with DATAMARAN: Automatically Extracting Structure from Log Datasets

Organizations routinely accumulate semi-structured log datasets generated as the output of code; these datasets remain unused and uninterpreted, and occupy wasted space---this phenomenon has been colloquially referred to as "data lake'' problem. One approach to leverage these semi-structured datasets is to convert them into a structured relational format, following which they can be analyzed in conjunction with other datasets. We present DATAMARAN, an tool that extracts structure from semi-structured log datasets with no human supervision. DATAMARAN automatically identifies field and record endpoints, separates the structured parts from the unstructured noise or formatting, and can tease apart multiple structures from within a dataset, in order to efficiently extract structured relational datasets from semi-structured log datasets, at scale with high accuracy. Compared to other unsupervised log dataset extraction tools developed in prior work, DATAMARAN does not require the record boundaries to be known beforehand, making it much more applicable to the noisy log files that are ubiquitous in data lakes. DATAMARAN can successfully extract structured information from all datasets used in prior work, and can achieve 95% extraction accuracy on automatically collected log datasets from GitHub---a substantial 66% increase of accuracy compared to unsupervised schemes from prior work. Our user study further demonstrates that the extraction results of DATAMARAN are closer to the desired structure than competing algorithms.
T/Key: Second-Factor Authentication From Secure Hash Chains

Time-based one-time password (TOTP) systems in use today require storing secrets on both the client and the server. As a result, an attack on the server can expose all second factors for all users in the system. We present T/Key, a time-based one-time password system that requires no secrets on the server. Our work modernizes the classic S/Key system and addresses the challenges in making such a system secure and practical. At the heart of our construction is a new lower bound analyzing the hardness of inverting hash chains composed of independent random functions, which formalizes the security of this widely used primitive. Additionally, we develop a near-optimal algorithm for quickly generating the required elements in a hash chain with little memory on the client. We report on our implementation of T/Key as an Android application. T/Key can be used as a replacement for current TOTP systems, and it remains secure in the event of a server-side compromise. The cost, as with S/Key, is that one-time passwords are longer than the standard six characters used in TOTP.
Behavior-Based Pricing in Marketing Channels

With behavior-based pricing BBP, firms use customers' purchase history data to price discriminate between past and new customers. Prior research has examined BBP in a non-channel setting. In this paper, we investigate BBP in a channel setting in which manufacturers sell to customers through exclusive retailers. We examine how channel members' adoption of BBP affects wholesale and retail prices, profits, consumer surplus, and social welfare. We find that BBP decreases channel members' profits when retailers use BBP and manufacturers use uniform pricing. However, BBP increases channel members' profits when manufacturers and retailers use BBP. In addition, BBP by retailers alone increases consumer surplus, whereas BBP by manufacturers and retailers decreases consumer surplus. When manufacturers also use BBP, BBP decreases social welfare to a greater degree than when only retailers use BBP. Furthermore, when manufacturers cannot use BBP, their profits are higher with long-term wholesale price contracts. When manufacturers can use BBP, short-term wholesale price contracts yield higher profits for manufacturers and retailers. The online appendix is available at https://doi.org/10.1287/mksc.2017.1070 .
Toroidal regularization of the guiding center Lagrangian

In the Lagrangian theory of guiding center motion, an effective magnetic field B*=B+(m/e)v∥∇× b appears prominently in the equations of motion. Because the parallel component of this field can vanish, there is a range of parallel velocities where the Lagrangian guiding center equations of motion are either ill-defined or very badly behaved. Moreover, the velocity dependence of B* greatly complicates the identification of canonical variables and therefore the formulation of symplectic integrators for guiding center dynamics. This letter introduces a simple coordinate transformation that alleviates both these problems simultaneously. In the new coordinates, the Liouville volume element is equal to the toroidal contravariant component of the magnetic field. Consequently, the large-velocity singularity is completely eliminated. Moreover, passing from the new coordinate system to canonical coordinates is extremely simple, even if the magnetic field is devoid of flux surfaces. We demonstrate the utility of this...
I, The Supreme : Politically Related Themes in the Novel

I, The Supreme is a novel that intrigues the reader on many levels. This paper concentrates on the novel’s treatment of both independence era and mid-twentieth century politics, and on the author’s approach to political analysis and the explanation of political events.
Bank loan renegotiation and credit default swaps

Abstract Using Roberts (2015) loan-level data from 2000 to 2011, we find that the inception of CDS trading on reference firms’ debt is associated with a decreased number and lower probability of amendments, restatements, and rollovers to existing lenders of bank loans. Reference firms are also less likely to terminate loans prematurely or refinance with different lenders after the inception of CDS trading and tend to exhibit longer loan maturities. Our evidence is consistent with the empty creditor problem arising from CDS trading and the resulting decrease in the negotiation power of borrowers. Our research contributes to understanding how financial innovations alter bank-lending relationships.
Demonstration of a Sensitive Method to Measure Nuclear-Spin-Dependent Parity Violation

Nuclear-spin-dependent parity violation (NSD-PV) effects in atoms and molecules arise from Z^{0} boson exchange between electrons and the nucleus, and from the magnetic interaction between electrons and the parity-violating nuclear anapole moment. We demonstrate measurements of NSD-PV that use an enhancement of the effect in diatomic molecules, here using the test system ^{138}Ba^{19}F. Our sensitivity surpasses that of any previous atomic parity violation measurement. We show that systematic errors can be suppressed to at least the level of the present statistical sensitivity. We measure the matrix element W of the NSD-PV interaction with total uncertainty δW/(2π)<0.7 Hz, for each of two configurations where W must have different signs. This sensitivity would be sufficient to measure NSD-PV effects of the size anticipated across a wide range of nuclei including ^{137}Ba in ^{137}BaF, where |W|/(2π)≈5 Hz is expected.
Fast access to columnar, hierarchically nested data via code transformation

Big Data query systems represent data in a columnar format for fast, selective access, and in some cases (e.g. Apache Drill), perform calculations directly on the columnar data without row materialization, avoiding runtime costs. However, many analysis procedures cannot be easily or efficiently expressed as SQL. In High Energy Physics, the majority of data processing requires nested loops with complex dependencies. When faced with tasks like these, the conventional approach is to convert the columnar data back into an object form, usually with a performance price. This paper describes a new technique to transform procedural code so that it operates on hierarchically nested, columnar data natively, without row materialization. It can be viewed as a compiler pass on the typed abstract syntax tree, rewriting references to objects as columnar array lookups. We will also present performance comparisons between transformed code and conventional object-oriented code in a High Energy Physics context.
Implementing Arrow-Debreu equilibria by trading infinitely-lived securities

We show that Arrow-Debreu equilibria with countably additive prices in infinite-time economy under uncertainty can be implemented by trading infinitely-lived securities in complete sequential markets under two different portfolio feasibility constraints: wealth constraint, and essentially bounded portfolios. Sequential equilibria with no price bubbles implement Arrow-Debreu equilibria, while those with price bubbles implement Arrow-Debreu equilibria with transfers. Transfers are equal to price bubbles on initial portfolio holdings. Price bubbles arise in sequential equilibrium under the wealth constraint if some securities are in zero supply or negative prices are permitted, but cannot arise with essentially bounded portfolios.
Demographics and Automation

We argue theoretically and document empirically that aging leads to greater (industrial) automation, and in particular, to more intensive use and development of robots. Using US data, we document that robots substitute for middle-aged workers (those between the ages of 36 and 55). We then show that demographic changeâcorresponding to an increasing ratio of older to middle-aged workersâis associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across US commuting zones. We also provide evidence of more rapid development of automation technologies in coun- tries undergoing greater demographic change. Our directed technological change model further predicts that the induced adoption of automation technology should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation. Both of these predictions receive support from country-industry variation in the adoption of robots. Our model also implies that the productivity implications of aging are ambiguous when technology responds to demographic change, but we should expect produc- tivity to increase and labor share to decline relatively in industries that are most amenable to automation, and this is indeed the pattern we find in the data.
Civil Litigation and the Opioid Epidemic: The Role of Courts in a National Health Crisis.

The devastating impact of the national opioid epidemic has given rise to hundreds of lawsuits. This article details the extremely broad range of legal claims, compares the opioid cases to other public health litigation efforts, including tobacco, and describes the special mechanism — a multidistrict litigation — through which more than 700 opioid-related cases have been consolidated thus far, with settlement almost certain to follow.
Advances in the conceptualization and measurement of religion and spirituality: Implications for physical and mental health research.

Empirical studies have identified significant links between religion and spirituality and health. The reasons for these associations, however, are unclear. Typically, religion and spirituality have been measured by global indices (e.g., frequency of church attendance, self-rated religiousness and spirituality) that do not specify how or why religion and spirituality affect health. The authors highlight recent advances in the delineation of religion and spirituality concepts and measures theoretically and functionally connected to health. They also point to areas for areas for growth in religion and spirituality conceptualization and measurement. Through measures of religion and spirituality more conceptually related to physical and mental health (e.g., closeness to God, religious orientation and motivation, religious support, religious struggle), psychologists are discovering more about the distinctive contributions of religiousness and spirituality to health and well-being.
Extracellular Sodium Dependence of the Conduction Velocity-Calcium Relationship: Evidence of Ephaptic Self-Attenuation

Introduction: We previously demonstrated that perfusate sodium and potassium composition can modulate cardiac conduction velocity (CV) consistent with theoretical predictions of ephaptic coupling (EpC). EpC depends on the volume and ionic currents in intercalated disc microdomains like the perinexus. We suggested that perinexal width (WP) correlates with changes in extracellular calcium - [Ca2+]o. Here, we explicitly test the hypothesis that increasing [Ca2+]o reduces WP and increases CV. Mathematical models of EpC also predict that reducing WP can reduce sodium driving force and CV by self-attenuation. Therefore, we further hypothesized that reducing WP and extracellular sodium - [Na+]o - will reduce CV consistent with ephaptic self-attenuation.Methods and Results: Transmission electron microscopy revealed that increasing [Ca2+]o (1 to 3.4mM) significantly decreased WP. Optically mapping WT (100% Cx43) mouse hearts demonstrated that increasing [Ca2+]o increases transverse CV (CVT) during normonatremia (147.3mM), but slows CVT during hyponatremia (120mM). Additionally, CV in HZ (∼50% Cx43) hearts was more sensitive to changes in [Ca2+]o relative to WT during normonatremia. During hyponatremia, CV slowed in both WT and HZ hearts to the same extent. Importantly, neither [Ca2+]o nor [Na+]o altered Cx43 expression or phosphorylation determined by Western blotting, or gap junctional resistance determined by electrical impedance spectroscopy of myocardium.Conclusions: Narrowing WP, by increasing [Ca2+]o, increases CV consistent with enhanced ephaptic coupling between myocytes. Interestingly, during hyponatremia, reducing WP slowed CV, consistent with theoretical predictions of ephaptic self-attenuation. This study suggests that serum ion concentrations may be an important determinant of cardiac disease expression.
Convex risk minimization and conditional probability estimation

This paper proves, in very general settings, that convex risk minimization is a procedure to select a unique conditional probability model determined by the classification problem. Unlike most previous work, we give results that are general enough to include cases in which no minimum exists, as occurs typically, for instance, with standard boosting algorithms. Concretely, we first show that any sequence of predictors minimizing convex risk over the source distribution will converge to this unique model when the class of predictors is linear (but potentially of infinite dimension). Secondly, we show the same result holds for \emph{empirical} risk minimization whenever this class of predictors is finite dimensional, where the essential technical contribution is a norm-free generalization bound.
SDCNet: Smoothed dense-convolution network for restoring low-dose cerebral CT perfusion

With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests. In this paper, we embrace the deep Convolutional Neural Networks (CNN) based approaches and introduce Smoothed Dense-Convolution Neural Network (SDCNet) to recover high-dose quality CTP images from low-dose ones. SDCNet is composed of sub-network blocks cascaded by skip-connections to infer the noise (differentials) from paired low/high-dose CT scans. SDCNet can effectively remove the noise in real low-dose CT scans and enhance the quality of medical images. We evaluate the proposed architecture on thousands of CT perfusion frames for both reconstructed image denoising and perfusion map quantification including cerebral blood flow (CBF) and cerebral blood volume (CBV). SDCNet achieves high performance in both visual and quantitative results with promising computational efficiency, comparing favorably with state-of-the-art approaches.
Random matrices: overcrowding estimates for the spectrum

Abstract We address overcrowding estimates for the singular values of random iid matrices, as well as for the eigenvalues of random Wigner matrices. We show evidence of long range separation under arbitrary perturbation even in matrices of discrete entry distributions. In many cases our method yields nearly optimal bounds.
Diffusion and consensus on weakly connected directed graphs

Abstract Let G be a weakly connected directed graph with asymmetric graph Laplacian L . Consensus and diffusion are dual dynamical processes defined on G by x ˙ = − L x for consensus and p ˙ = − p L for diffusion. In this survey, we consider both these processes as well their discrete time analogues. We define a basis of row vectors { γ ¯ i } i = 1 k of the left null-space of L and a basis of column vectors { γ i } i = 1 k of the right null-space of L in terms of the partition of G into strongly connected components. This allows for complete characterization of the asymptotic behavior of both diffusion and consensus — discrete and continuous — in terms of these eigenvectors. As an application of these ideas, we present a treatment of the pagerank algorithm that is dual to the usual one. We further show that the teleporting (see below) feature usually included in the algorithm is not strictly necessary. Together with [1] , this is a complete and self-contained overview of the asymptotics of consensus and diffusion on digraphs. Many of the ideas presented here can be found scattered in the literature, though mostly outside mainstream mathematics and not always with complete proofs. This paper seeks to remedy this by providing a compact and accessible survey.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data

With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e.g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs). However, it becomes increasingly challenging to model such data, due to high density of the monitoring data, heterogeneous data types and the requirement for interpretable models. Integration of these high-density monitoring data with the discrete clinical events (including diagnosis, medications, labs) is challenging but potentially rewarding since richness and granularity in such multimodal data increase the possibilities for accurate detection of complex problems and predicting outcomes (e.g., length of stay and mortality). We propose Recurrent Attentive and Intensive Model (RAIM) for jointly analyzing continuous monitoring data and discrete clinical events. RAIM introduces an efficient attention mechanism for continuous monitoring data (e.g., ECG), which is guided by discrete clinical events (e.g, medication usage). We apply RAIM in predicting physiological decompensation and length of stay in those critically ill patients at ICU. With evaluations on MIMIC-III Waveform Database Matched Subset, we obtain an AUC-ROC score of $90.18%$ for predicting decompensation and an accuracy of $86.82%$ for forecasting length of stay with our final model, which outperforms our six baseline models.
A two-stage performance assessment of utility-scale wind farms in Texas using data envelopment analysis and Tobit models

Abstract Wind power becomes one of the most promising energy sources in the electricity generation sector in Texas over the past decade by declining levelized cost of wind energy. However, recent studies show that the wind farms in Texas are relatively less productive. Hence, this study aims to find out reasons of inefficiencies by constructing a two-stage performance assessment of wind farms in Texas. In the first stage of analysis, comprehensive input- and output-oriented Data Envelopment Analysis (DEA) models are applied to evaluate productive efficiencies of the 95 large utility-scale wind farms by using pre-determined three input and two output variables. The sensitivity analysis is provided for the robustness of the DEA models with different combinations of input and output variables of the original model. The slack analysis and projection data are obtained for inefficient wind farms to find out optimal input-output variables. Tobit regression models are conducted for the second stage of the analysis to investigate the reasons of inefficiencies. DEA results indicate that half of the wind farms were operated efficiently in Texas during 2016. 13 wind farms were performed at the most productive scale size, ten wind farms should reduce their operational size to improve production efficiency, and 72 wind farms have the notable potential to increase their production efficiency by expanding operational sizes with modern wind turbine technologies. The sensitivity analysis shows the importance of each input-output variables. Tobit regression models indicate that elevation of the site, rotor diameter, hub height, and brand of the turbine have significant contributions to the relative efficiency scores of the wind farms, and the age of turbine has a negative impact on the productive efficiency of the wind farms.
Embedding Logical Queries on Knowledge Graphs

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict "em what drugs are likely to target proteins involved with both diseases X and Y?" -- a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries -- a flexible but tractable subset of first-order logic -- on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.
Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes

Alzheimer’s disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer’s spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.
Hunchback is counter-repressed to regulate even-skipped stripe 2 expression in Drosophila embryos

Hunchback is a bifunctional transcription factor that can activate and repress gene expression in Drosophila development. We investigated the regulatory DNA sequence features that control Hunchback function by perturbing enhancers for one of its target genes, even-skipped (eve). While Hunchback directly represses the eve stripe 3+7 enhancer, we found that in the eve stripe 2+7 enhancer, Hunchback repression is prevented by nearby sequences-this phenomenon is called counter-repression. We also found evidence that Caudal binding sites are responsible for counter-repression, and that this interaction may be a conserved feature of eve stripe 2 enhancers. Our results alter the textbook view of eve stripe 2 regulation wherein Hb is described as a direct activator. Instead, to generate stripe 2, Hunchback repression must be counteracted. We discuss how counter-repression may influence eve stripe 2 regulation and evolution.
Does Student Loan Debt Contribute to Racial Wealth Gaps? A Decomposition Analysis

There is evidence of a large and growing student debt burden over the last decade. Previous research has shown that the presence of student debt jeopardized the short‐term financial wealth of U.S. households during the Great Recession. We examine the effects of student loan use on the wealth of U.S. households post‐recession, using recent data from the 2013 and 2016 Survey of Consumer Finances. We find that mean 2016 wealth for households with no outstanding student debt is more than four times higher than households with student debt. We find that living in a household at the 15th, 30th, 50th, 70th, and 85th percentile of the wealth distribution with student debt is associated with an 80%, 49%, 37%, 35%, and 36% wealth loss compared with a similar household with no student debt. Our decomposition results suggest that student loan use can explain between 3% and 7% of the Black‐White wealth gap across the wealth distribution but is insignificant in explaining the Hispanic‐White wealth gap.
Strategic responses to shocks: Comparative adjustment costs, transaction costs, and opportunity costs

Research Summary Shocks, whether they derive from shifts in demand, supply, regulation, or innovation, can create the need for competitive repositioning by industry participants when they disrupt established sources of competitive advantage. Such situations can therefore create a canonical strategic problem: whether, where, and how to (re‐)position following an industry shock. In this paper, we explore the role of comparative adjustment costs in determining competitive advantage in dynamic environments. In so doing, we synthesize contributions from Penrose, Porter, and Williamson to conceptualize the relationship between adjustment costs and related concepts such as resources/capabilities, dynamic capabilities, transaction costs, and opportunity costs. Managerial Summary Whether, where, and how should leaders reposition their firms in response to industry shocks? This paper develops a framework to guide leaders charged with making these decisions. The framework emphasizes that firms facing an industry shock must: (a) assess their firm's cost and time to move to each new position, and compare that cost and time to rivals' costs and time to move to those same positions; (b) compare the cost of delaying repositioning (such as forfeiting first mover advantage) to the profits from remaining in its original position, and (c) consider that, in order to speed repositioning, the efficient choice may be to temporarily accept certain hazards from outsourcing, and later integrate to eliminate those hazards. We illustrate the framework using an example from the smartphone industry.
Symmetric and Baroclinic Instability in Dense Shelf Overflows

AbstractIn this study, we revisit the problem of rotating dense overflow dynamics by performing nonhydrostatic numerical simulations, resolving submesoscale variability. Thermohaline stratification...
Temporal stability of urinary cadmium in samples collected several years apart in a population of older persons.

Abstract Objectives There is growing evidence that urine cadmium is a temporally stable biomarker indicative of long-term cadmium exposure; however questions remain with regard to generalizability to older persons, the impact of changes in smoking behavior, and the degree of temporal stability when repeat sample collection spans years instead of weeks or months. Methods Using archived samples from cohorts of older men (Osteoporotic Fractures in Men (MrOS-US)) and women (Study of Osteoporotic Fractures (SOF)) (mean age = 80 at study visit 2), we analyzed two morning urine samples each from 39 men and 18 women with a diverse self-reported smoking history. For MrOS, samples were collected approximately 6 years apart, and 4 years apart for SOF. Intra-class correlations were computed to assess temporal stability, and adjusted for age and body mass index. Results The median creatinine-adjusted urinary cadmium levels (0.39 μg/g for men, 0.89 μg/g for women) were similar to levels expected for these age/sex groups in the US according to the National Health and Nutrition Examination Survey. The overall intra-class correlation was high (ICC = 0.85; 95% CI: 0.76–0.91) and similar between cohorts (MrOS: ICC = 0.74; 95% CI: 0.58–0.86; SOF: ICC = 0.81; 95% CI: 0.59–0.93), but slightly lower among those who stopped smoking between visits of sample collection (ICC = 0.64; 95% CI: 0.31–0.87) or among former smokers who quit prior to the first sample collection (ICC = 0.68; 95% CI: 0.25–0.93). Conclusions We report good-to-excellent reproducibility of urine cadmium using morning urine samples collected 4–6 years apart from older men and women, but slightly lower correlations among those with a history of smoking. Single measures of urine cadmium are a reliable biomarker in older men and women.
An Experience Is a Knowledge Representation.

Computational agents use knowledge representations to reason about the data world they occupy. A theory of consciousness, Integrated Information Theory, suggests beings that are conscious use experiences to reason about the world they occupy. Herein, the question is considered: Is an experience a knowledge representation?
Spatial Release From Informational Masking: Evidence From Functional Near Infrared Spectroscopy:

Informational masking (IM) can greatly reduce speech intelligibility, but the neural mechanisms underlying IM are not understood. Binaural differences between target and masker can improve speech perception. In general, improvement in masked speech intelligibility due to provision of spatial cues is called spatial release from masking. Here, we focused on an aspect of spatial release from masking, specifically, the role of spatial attention. We hypothesized that in a situation with IM background sound (a) attention to speech recruits lateral frontal cortex (LFCx) and (b) LFCx activity varies with direction of spatial attention. Using functional near infrared spectroscopy, we assessed LFCx activity bilaterally in normal-hearing listeners. In Experiment 1, two talkers were simultaneously presented. Listeners either attended to the target talker (speech task) or they listened passively to an unintelligible, scrambled version of the acoustic mixture (control task). Target and masker differed in pitch and interaural time difference (ITD). Relative to the passive control, LFCx activity increased during attentive listening. Experiment 2 measured how LFCx activity varied with ITD, by testing listeners on the speech task in Experiment 1, except that talkers either were spatially separated by ITD or colocated. Results show that directing of auditory attention activates LFCx bilaterally. Moreover, right LFCx is recruited more strongly in the spatially separated as compared with colocated configurations. Findings hint that LFCx function contributes to spatial release from masking in situations with IM.
International law and political philosophy: Uncovering new linkages

Despite a common agenda of normative analysis of the international order, philosophical work on international political morality and international law and legal scholarship have, until recently, worked at a distance from one another. The mutual suspicion can be traced to different aims and methodologies, including a divide between work on matters of deep structure, on the one hand, and practical institutional analysis and prescription, on the other. Yet international law is a key part of the normative practices of states, has a direct effect on state behavior, and, as a methodological matter, can contribute to good theorizing on matters of international ethics. Recently, philosophical work has demonstrated a greater engagement with the moral aspects of international law. One strand of scholarship has treated the rules of international law as a proper subject for philosophical inquiry. Another has used international legal rules to support moral arguments about aspects of the international order. Future dialogue and cooperation would benefit both fields, in particular on the challenges to global cooperation from nationalism and on strategies for allocating responsibilities among global actors for rectifying global harms.
3D Ultrasound Imaging of Residual Limbs With Camera-Based Motion Compensation

Ultrasound is a cost-effective, readily available, and non-ionizing modality for musculoskeletal imaging. Though some research groups have pursued methods that involve submerging the transducer and imaged body segment into a water bath, many limitations remain in regards to acquiring an unloaded volumetric image of an entire human limb in a fast, safe, and adequately accurate manner. A 3D dataset of a limb is useful in several rehabilitative applications including biomechanical modeling of soft tissue, prosthetic socket design, monitoring muscle condition and disease progression, bone health, and orthopedic surgery. This paper builds on previous work from our group and presents the design, prototyping, and preliminary testing of a novel multi-modal imaging system for rapidly acquiring volumetric ultrasound imagery of human limbs, with a particular focus on residual limbs for improved prosthesis design. Our system employs a mechanized water tank setup to scan a limb with a clinical ultrasound transducer and 3D optical imagery to track motion during a scan. The iterative closest point algorithm is utilized to compensate for motion and stitch the images into a final dataset. The results show preliminary 2D and 3D imaging of both a tissue-mimicking phantom and residual limbs. A volumetric error compares the ultrasound image data obtained to a previous MRI method. The results indicate potential for future clinical implementation. Concepts presented in this paper could reasonably transfer to other imaging applications such as acoustic tomography, where motion artifact may distort image reconstruction.