cssi/SciSciGPT-SciSciNet huggingface dataset:
Square root graphical models: multivariate generalizations of univariate exponential families that permit positive dependencies

We develop Square Root Graphical Models (SQR), a novel class of parametric graphical models that provides multivariate generalizations of univariate exponential family distributions. Previous multivariate graphical models (Yang et al., 2015) did not allow positive dependencies for the exponential and Poisson generalizations. However, in many real-world datasets, variables clearly have positive dependencies. For example, the airport delay time in New York--modeled as an exponential distribution--is positively related to the delay time in Boston. With this motivation, we give an example of our model class derived from the univariate exponential distribution that allows for almost arbitrary positive and negative dependencies with only a mild condition on the parameter matrix--a condition akin to the positive definiteness of the Gaussian covariance matrix. Our Poisson generalization allows for both positive and negative dependencies without any constraints on the parameter values. We also develop parameter estimation methods using node-wise regressions with l1 regularization and likelihood approximation methods using sampling. Finally, we demonstrate our exponential generalization on a synthetic dataset and a real-world dataset of airport delay times.
Regioselective, Transition Metal-Free C–O Coupling Reactions Involving Aryne Intermediates

A new transition-metal-free synthetic method for C–O coupling between various aryl halides and alkoxides is described. This type of transformation is typically accomplished using palladium catalysts containing a specialized phosphine ligand. The reactions reported here can be performed under mild, ambient conditions using certain potassium alkoxides and a range of aryl halides, with iodide and bromide derivatives giving the best results. A likely mechanistic pathway involves the in situ generation of an aryne intermediate, and directing groups on the aryl ring inductively control regioselectivity.
Not your parents' trade politics: the Transatlantic Trade and Investment Partnership negotiations

ABSTRACTThe Transatlantic Trade and Investment Partnership (TTIP) negotiations aspire to create the world's most ambitious trade agreement between the world's two largest economies. The politics associated with TTIP are different from those associated with previous trade negotiations. Moreover, they diverge from the prevailing International Political Economy (IPE) account of trade policy. The politics of TTIP diverge from the conventional IPE account of trade politics in two particularly noteworthy ways. First, rather than being rivals, American and European business interests are allies, adopting common positions on what they want the agreement to look like. Second, opposition within both the USA and the EU comes not primarily from firms and workers fearing increased economic competition, but from less traditional trade actors – consumer and environmental groups and citizens – concerned about the erosion of valued regulations. I argue that the unusual politics is the product of two distinct, but related ...
Time to exit: Rational, behavioral, and organizational delays

Existing studies of exit delay typically focus on rational, behavioral, or organizational explanations in isolation. We integrate these different theoretical explanations, develop testable hypotheses for each, and examine them using the population of US banks between 1984 and 1997. Banks' exit behavior is not consistent with theories emphasizing the option value of avoiding reentry costs. Patterns of exit do, however, support models of rational delay under ability uncertainty. Controlling for this source of rational delay, we find evidence of delay due to behavioral bias—firms discount negative signals of profitability relative to positive signals—and organizational considerations—delay increases with the separation of ownership and control. These results demonstrate that all three sets of theories are necessary to describe exit behavior
Bridge spectra of iterated torus knots

We determine the set of all genus g bridge numbers of many iterated torus knots, listing these numbers in a sequence called the bridge spectrum. In addition, we prove a structural lemma about the decomposition of a strongly irreducible bridge surface induced by cutting along a collection of essential surfaces.
ESEEM analysis of multi-histidine Cu(II)-coordination in model complexes, peptides, and amyloid-β.

We validate the use of ESEEM to predict the number of 14N nuclei coupled to a Cu(II) ion by the use of model complexes and two small peptides with well-known Cu(II) coordination. We apply this method to gain new insight into less explored aspects of Cu(II) coordination in amyloid-β (Aβ). Aβ has two coordination modes of Cu(II) at physiological pH. A controversy has existed regarding the number of histidine residues coordinated to the Cu(II) ion in component II, which is dominant at high pH (∼8.7) values. Importantly, with an excess amount of Zn(II) ions, as is the case in brain tissues affected by Alzheimer’s disease, component II becomes the dominant coordination mode, as Zn(II) selectively substitutes component I bound to Cu(II). We confirm that component II only contains single histidine coordination, using ESEEM and set of model complexes. The ESEEM experiments carried out on systematically 15N-labeled peptides reveal that, in component II, His 13 and His 14 are more favored as equatorial ligands comp...
The Third U.S.A. Mathematical Olympiad

THE U.S.A. Mathematical Olympiad, now in its third year, has become suffi ciently well known for inquiries about it and interest in having students participate in it to be widespread. Participation is by invitation only. In 1974, invitations were sent to 155 students to take part in the Third U.S.A. Mathematical Olympiad. Most students were selected on the basis of their performance on the Annual High School Mathematics Examination. Stu dents from Michigan and Wisconsin, which conduct their own statewide mathe matics contests, were also invited, and a few students were invited on the basis of strong recommendation from school offi cials. Of these 155 invitations, 149 com plete acceptances were received. (An ac ceptance is considered complete when the student agrees to participate and the school agrees to administer the test.) The Third U.S.A. Mathematical Olym piad took place in 119 schools over the nation on 7 May, 1974. As before, it con sisted of five problems of the essay type requiring mathematical power and ingenu ity to solve. The allotted time for doing the problems was three hours. The five problems appear here ; solutions are given at the end of the article. All test papers were returned by 15 May, and graded 20 May by a committee of the mathematics faculty at Rutgers. The top thirty papers were regraded at Memphis State University. The results for all participants are shown in table 1. The eight top scorers had scores indicated to the right of the double line in the table. Final results, listing the top twenty-five scorers, were sent to the 119 schools in volved on 25 May. As in the first two Olympiads, the cor relation between scores on the Annual High School Mathematics Examination
Paraneoplastic disorders of the nervous system

Paraneoplastic neurologic disorders (PND) refer to an extensive group of syndromes that can affect any part of the nervous system by mechanisms that are mostly immune mediated (Table 1). PND are more frequent than previously considered, with an incidence that varies with tumor type. The tumors more frequently involved are small-cell lung cancer (SCLC, ~3% of patients develop PND), thymoma (15%), and the plasma cell dyscrasias associated with malignant monoclonal gammopathies (~5-15%). For solid tumors other than SCLC the incidence of PND is less than 1%. In 60% of patients, symptoms of PND precede the tumor diagnosis. The majority of these patients are first seen by neurologists who should be aware that prompt diagnosis and treatment of the tumor along with immunotherapy may stabilize or improve the PND.
Influence of Flow on Longevity of Superhydrophobic Coatings

Previous studies have demonstrated the capability of superhydrophobic surfaces to produce slip flow and drag reduction, which properties hold considerable promise for a broad range of applications. However, in order to implement such surfaces for practical utilizations, environmental factors such as water movement over the surface must be observed and understood. In this work, experiments were carried out to present a proof-of-concept study on the impact of flow on longevity of polystyrene fibrous coatings. The time-dependent hydrophobicity of a submerged coating in a pressure vessel was determined while exposing the coating to a rudimentary wall-jet flow. Rheological studies were also performed to determine the effect of the flow on drag reduction. The results show that the longevity of the surface deteriorates by increasing the flow rate. The flow appears to enhance the dissolution of air into water, which leads to a loss of drag reduction.
Rh-Catalyzed Asymmetric Hydroformylation of Functionalized 1,1-Disubstituted Olefins

The first method for the highly enantioselective rhodium-catalyzed hydroformylation of 1,1-disubstituted olefins has been developed. By employing either of the P-chirogenic phosphine ligands BenzP* and QuinoxP*, linear aldehydes with β-chirality can be prepared in a highly enantioselective fashion with good chemo- and regioselectivities.
Bayesian Approximate Kernel Regression with Variable Selection

AbstractNonlinear kernel regression models are often used in statistics and machine learning because they are more accurate than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this paper, we propose a novel framework that provides an effect size analog for each explanatory variable in Bayesian kernel regression models when the kernel is shift-invariant — for example, the Gaussian kernel. We use function analytic properties of shift-invariant reproducing kernel Hilbert spaces (RKHS) to define a linear vector space that: (i) captures nonlinear structure, and (ii) can be projected onto the original explanatory variables. This projection onto the original explanatory variables serves as an analog of effect sizes. The specific function analytic property we use is that shift-invariant kernel functions can be approximated via random Fourier bases. Based ...
Affective empathy in schizophrenia: a meta-analysis

Abstract Background Affective empathy, or the emotional response one has to the experiences or emotional states of others, contributes to relationship-maintaining behaviors and is key in fostering social connections, yet no work has synthesized the body of literature for people with schizophrenia. The aim of the present meta-analysis was to determine the extent to which those diagnosed with schizophrenia experience deficits in affective empathy. Methods A literature search was conducted of studies examining empathy. Data were analyzed using a random effects meta-analytic model with Hedges' g standardized mean difference effect size. Results Individuals with schizophrenia exhibited significant, medium deficits in affective empathy ( k = 37). Measurement type moderated the affective empathy deficit such that performance-based measures showed larger schizophrenia group deficits than self-report measures. Conclusion Consistent, significant deficits in affective empathy were found comparing people with schizophrenia to healthy controls, especially when using performance-based assessments. The medium effect suggests an important role for empathy in the realm of social cognitive research, and points to the need for further investigation of measurement techniques and associations with functional outcomes.
Microscale Thermal Biosensor: Critical Design Considerations and Optimization

Calorimetric biosensors have been used for detecting various bioprocesses such as enzyme-substrate activity, protein binding activity, DNA reactions and cell metabolism. The majority of the microcalorimeter applications were proof-of-concept in nature, but having a strong potential for development for actual clinical, scientific, or commercial need. Success of these emerging experimental methodologies will be determined by such factors as the sensitivity and speed of these analyses when compared with existing technologies. These performance metrics are fundamentally related to the thermal transport through the microsystems. In this study, we present the design and fabrication of a microcalorimeter. We also characterize the impact of flow velocity affecting the thermal time constant of the microcalorimeter, steady state response of the system, and the location of the sensor in the flow stream and provide essential guidelines for the optimization of single-stream thermopile systems. The calorimeter consists of a 100&#x03BC;m Y-shaped channel microfluidic device, which is made by sandwiching a microscope glass slide, Kapton tape cut in the form of channel and a microscope glass coverslip, and a bismuth (Bi)- antimony (Sb) thin film thermopile (Seebeck coefficient of 5.95 mV/K) integrated on the outer wall of the microscope glass coverslip. The performance of the microcalorimeter was characterized by measuring the heat released during the mixing reaction between water and ethanol. The ratio of flow rates is adjusted to change the location of the reaction zone relative to the measuring or reference junctions of the thermopile. Results indicate as the flow velocity increases the time constant to reach steady state response is decreased.
Gene Regulatory Evolution During Speciation in a Songbird

Over the last decade, tremendous progress has been made toward a comparative understanding of gene regulatory evolution. However, we know little about how gene regulation evolves in birds, and how divergent genomes interact in their hybrids. Because of the unique features of birds - female heterogamety, a highly conserved karyotype, and the slow evolution of reproductive incompatibilities - an understanding of regulatory evolution in birds is critical to a comprehensive understanding of regulatory evolution and its implications for speciation. Using a novel complement of analyses of replicated RNA-seq libraries, we demonstrate abundant divergence in brain gene expression between zebra finch (Taeniopygia guttata) subspecies. By comparing parental populations and their F1 hybrids, we also show that gene misexpression is relatively rare among brain-expressed transcripts in male birds. If this pattern is consistent across tissues and sexes, it may partially explain the slow buildup of postzygotic reproductive isolation observed in birds relative to other taxa. Although we expected that the action of genetic drift on the island-dwelling zebra finch subspecies would be manifested in a higher rate of trans regulatory divergence, we found that most divergence was in cis regulation, following a pattern commonly observed in other taxa. Thus, our study highlights both unique and shared features of avian regulatory evolution.
Message Transmission with Reverse Firewalls--Secure Communication on Corrupted Machines

Suppose Alice wishes to send a message to Bob privately over an untrusted channel. Cryptographers have developed a whole suite of tools to accomplish this task, with a wide variety of notions of security, setup assumptions, and running times. However, almost all prior work on this topic made a seemingly innocent assumption: that Alice has access to a trusted computer with a proper implementation of the protocol. The Snowden revelations show us that, in fact, powerful adversaries can and will corrupt users' machines in order to compromise their security. And, presumably accidental vulnerabilities are regularly found in popular cryptographic software, showing that users cannot even trust implementations that were created honestly. This leads to the following seemingly absurd question: "Can Alice securely send a message to Bob even if she cannot trust her own computer?!" Bellare, Paterson, and Rogaway recently studied this question. They show a strong impossibility result that in particular rules out even semantically secure public-key encryption in their model. However, Mironov and Stephens-Davidowitz recently introduced a new framework for solving such problems: reverse firewalls. A secure reverse firewall is a third party that "sits between Alice and the outside world" and modifies her sent and received messages so that even if the her machine has been corrupted, Alice's security is still guaranteed. We show how to use reverse firewalls to sidestep the impossibility result of Bellare et al., and we achieve strong security guarantees in this extreme setting. Indeed, we find a rich structure of solutions that vary in efficiency, security, and setup assumptions, in close analogy with message transmission in the classical setting. Our strongest and most important result shows a protocol that achieves interactive, concurrent CCA-secure message transmission with a reverse firewall--i.e., CCA-secure message transmission on a possibly compromised machine! Surprisingly, this protocol is quite efficient and simple, requiring only four rounds and a small constant number of public-key operations for each party. It could easily be used in practice. Behind this result is a technical composition theorem that shows how key agreement with a sufficiently secure reverse firewall can be used to construct a message-transmission protocol with its own secure reverse firewall.
Asymmetric Synthesis of β-Amino Amides by Catalytic Enantioconvergent 2-Aza-Cope Rearrangement

Dynamic kinetic resolutions of α-stereogenic-β-formyl amides in asymmetric 2-aza-Cope rearrangements are described. Chiral phosphoric acids catalyze this rare example of a non-hydrogenative DKR of a β-oxo acid derivative. The [3,3]-rearrangement occurs with high diastereo- and enantiocontrol, forming β-imino amides that can be deprotected to the primary β-amino amide or reduced to the corresponding diamine.
ASM Journals Eliminate Impact Factor Information from Journal Websites

Editor's note: This editorial is published simultaneously by the following ASM journals: Antimicrobial Agents and Chemotherapy , Applied and Environmental Microbiology , Clinical Microbiology Reviews , Infection and Immunity , Journal of Clinical Microbiology , mBio , mSphere, and mSystems . Many scientists attempt to publish their work in a journal with the highest possible journal impact factor (IF). Despite …
Bridging the Otolaryngology Peer Review Knowledge Gap A Call for a Residency Development Program

Current otolaryngology literature and future scientific direction rely heavily on a rigorous peer review process. Just as manuscripts warrant thoughtful review with constructive feedback to the authors, the same can be said for critiques written by novice peer reviewers. Formal scientific peer review training programs are lacking. Recognizing this knowledge gap, Otolaryngology–Head and Neck Surgery is excited to offer its new Resident Reviewer Development Program. All otolaryngology residents who are postgraduate year 2 and above and in excellent academic standing are eligible to participate in this mentored program, during which they will conduct 6 manuscript reviews under the direction of a seasoned reviewer in his or her subspecialty area of interest. By completing reviews alongside a mentor, participants gain the required skills to master the peer review process—a first step that often leads to journal editorial board and associate editor invitations.
Structural basis for acyl-group discrimination by human Gcn5L2

Gcn5 is a conserved acetyltransferase that regulates transcription by acetylating the N-terminal tails of histones. Motivated by recent studies identifying a chemically diverse array of lysine acyl modifications in vivo, the acyl-chain specificity of the acetyltransferase human Gcn5 (Gcn5L2) was examined. Whereas Gcn5L2 robustly catalyzes lysine acetylation, the acyltransferase activity of Gcn5L2 becomes progressively weaker with increasing acyl-chain length. To understand how Gcn5 discriminates between different acyl-CoA molecules, structures of the catalytic domain of human Gcn5L2 bound to propionyl-CoA and butyryl-CoA were determined. Although the active site of Gcn5L2 can accommodate propionyl-CoA and butyryl-CoA without major structural rearrangements, butyryl-CoA adopts a conformation incompatible with catalysis that obstructs the path of the incoming lysine residue and acts as a competitive inhibitor of Gcn5L2 versus acetyl-CoA. These structures demonstrate how Gcn5L2 discriminates between acyl-chain donors and explain why Gcn5L2 has weak activity for acyl moieties that are larger than an acetyl group.
Applications of Wireless Sensor Networks for Area Coverage in Microgrids

Area coverage is a critical issue which will have a major impact on the sensing quality over targeted regions in wireless sensor networks. This paper studies the area coverage problem (ACP) with non-penetrable obstacles in microgrids, where a sensor cannot be deployed or sensing signals cannot pass through. The target coverage region is discretized in a deployment map representation of non-penetrable obstacles. A probability detection model is utilized to represent sensors with a variable distance coverage. The ACP is formulated as a mixed integer nonlinear programming problem and an iterative process is developed to solve the problem. The microgrid at Illinois Institute of Technology (IL Tech) is used as a practical example. Both the normal and the N-1 contingency deployments of sensors are simulated for optimizing the ACP at the IL Tech microgrid.
Using feedback to improve VLSI designs

A new design optimization technique, called Constrained-Redo, uses feedback from solutions analysis to improve both the power and coverage of an existing VLSI design automation system, the DAA system. Constrained-Redo uses information from critiques of an existing solution to guide the design system to generate a new, different, and hopefully better solution. This technique is part of the feedback-directed optimization framework. The extended system has been tested on several standard microprocessor designs and has improved many of the solutions that DAA has generated.
Dissecting the influence of the collinear and flanking bars in White's effect

In White's effect equiluminant test patches placed on the black and white bars of a square-wave grating appear different in brightness. The illusion has generated intense interest because the direction of the brightness effect does not correlate with the amount of black or white border in contact with the test patch, or in its general vicinity. Therefore, unlike brightness induction effects such as simultaneous contrast, White's effect is not consistent with explanations based on contrast or assimilation that depend solely on the relative amounts of black and white surrounding the test patches. We independently manipulated the luminance of the collinear and flanking bars to investigate their influence on test patch matching luminance (brightness). The inducing grating was a 0.5c/d square-wave and test patches measured 1.0° in width and either 0.5° or 3.0° in height. Test patches measuring 0.5° in height had more extensive contact with the collinear bars and test patches measuring 3.0° in height had more extensive contact with the flanking bars. The luminance of the collinear (or flanking) bars assumed twenty values from 3.2 to 124.8cd/m(2), while the luminance of the flanking (or collinear) bars remained white (124.8cd/m(2)) or black (3.2cd/m(2)). Under these conditions the influence of the collinear and flanking bars was found to be purely in the direction of contrast. The effect was dominated by contrast from the collinear bars (which results in White's effect), however, the influence of the flanking bars was also in the contrast direction. The data elucidate the luminance relationships between the collinear and flanking bars which produce the behavior associated with White's effect as well as that associated with "the inverted White effect" which is akin to simultaneous contrast.
The Morale Effects of Pay Inequality

Relative-pay concerns have potentially broad labor market implications. In a month-long experiment with Indian manufacturing workers, we randomize whether coworkers within production units receive the same flat daily wage or differential wages according to their (baseline) productivity ranks. When coworkers’ productivity is difficult to observe, pay inequality reduces output by 0.45 standard deviations and attendance by 18 percentage points. It also lowers coworkers’ ability to cooperate in their own self-interest. However, when workers can clearly perceive that their higher-paid peers are more productive than themselves, pay disparity has no discernible effect on output, attendance, or group cohesion. These findings help inform our understanding of when pay compression is more likely to arise in the labor market
The computational nature of memory modification

Our memories contain our expectations about the world that we can retrieve to make predictions about the future. For example, most people would expect a chocolate bar to taste good, because they have previously learned to associate chocolate with pleasure. When a surprising event occurs, such as tasting an unpalatable chocolate bar, the brain therefore faces a dilemma. Should it update the existing memory and overwrite the association between chocolate and pleasure? Or should it create an additional memory? In the latter case, the brain would form a new association between chocolate and displeasure that competes with, but does not overwrite, the original one between chocolate and pleasure. Previous studies have shown that surprising events tend to create new memories unless the existing memory is briefly reactivated before the surprising event occurs. In other words, retrieving old memories makes them more malleable. Gershman et al. have now developed a computational model for how the brain decides whether to update an old memory or create a new one. The idea at the heart of the model is that the brain will attempt to infer what caused the surprising event. The reason the chocolate bar tastes unpalatable, for example, might be because it was old and had spoiled. Every time the brain infers a new possible cause for a surprising event, it will create an additional memory to store this new set of expectations. In the future we will know that spoiled chocolate bars taste bad. However, if the brain cannot infer a new cause for the surprising event – because, for example, there appears to be nothing unusual about the unpalatable chocolate bar – it will instead opt to update the existing memory. The next time we buy a chocolate bar, we will have slightly lower expectations about how good it will taste. The dilemma of whether to update an existing memory or create a new one thus boils down to the question: is the surprising event the consequence of a new cause or an old one? This theory implies that retrieving a memory nudges the brain to infer that its associated cause is once again active and, since this is an old cause, it means that the memory will be eligible for updating. Many experiments have been performed on the topic of modifying memories, but this is the first computational model that offers a unifying explanation for the results. The next step is to work out how to apply the model, which is phrased in abstract terms, to networks of neurons that are more biologically realistic.
MPI-FAUN: An MPI-Based Framework for Alternating-Updating Nonnegative Matrix Factorization

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors $\mathbf{W}$ and $\mathbf{H}$ , for the given input matrix $\mathbf{A}$ , such that $\mathbf{A}\approx \mathbf{W}\mathbf{H}$ . NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient parallel algorithms to solve the problem for big data sets. The main contribution of this work is a new, high-performance parallel computational framework for a broad class of NMF algorithms that iteratively solves alternating non-negative least squares (NLS) subproblems for $\mathbf{W}$ and $\mathbf{H}$ . It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). The framework is flexible and able to leverage a variety of NMF and NLS algorithms, including Multiplicative Update, Hierarchical Alternating Least Squares, and Block Principal Pivoting. Our implementation allows us to benchmark and compare different algorithms on massive dense and sparse data matrices of size that spans from few hundreds of millions to billions. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements. The code and the datasets used for conducting the experiments are available online.
Novel Covariance-Based Neutrality Test of Time-Series Data Reveals Asymmetries in Ecological and Economic Systems.

Systems as diverse as the interacting species in a community, alleles at a genetic locus, and companies in a market are characterized by competition (over resources, space, capital, etc) and adaptation. Neutral theory, built around the hypothesis that individual performance is independent of group membership, has found utility across the disciplines of ecology, population genetics, and economics, both because of the success of the neutral hypothesis in predicting system properties and because deviations from these predictions provide information about the underlying dynamics. However, most tests of neutrality are weak, based on static system properties such as species-abundance distributions or the number of singletons in a sample. Time-series data provide a window onto a system’s dynamics, and should furnish tests of the neutral hypothesis that are more powerful to detect deviations from neutrality and more informative about to the type of competitive asymmetry that drives the deviation. Here, we present a neutrality test for time-series data. We apply this test to several microbial time-series and financial time-series and find that most of these systems are not neutral. Our test isolates the covariance structure of neutral competition, thus facilitating further exploration of the nature of asymmetry in the covariance structure of competitive systems. Much like neutrality tests from population genetics that use relative abundance distributions have enabled researchers to scan entire genomes for genes under selection, we anticipate our time-series test will be useful for quick significance tests of neutrality across a range of ecological, economic, and sociological systems for which time-series data are available. Future work can use our test to categorize and compare the dynamic fingerprints of particular competitive asymmetries (frequency dependence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.
Flibe Blanket Concept for Transmuting Transuranic Elements and Long Lived Fission Products

A Molten salt (Flibe) fusion blanket concept has been developed to solve the disposition problems of the spent nuclear fuel and the transuranic elements. This blanket concept can achieve the top rated solution, the complete elimination of the transuranic elements and the long-lived fission products. Small driven fusion devices with low neutron wall loading and low neutron fluence can perform this function. A 344-MW fusion power from D-T plasmas for thirty years with an availability factor of 0.75 can dispose of 70,000 tons of the US inventory of spent nuclear fuel generated up to the year 2015. In addition, the utilization of this blanket concept eliminates the need for a geological repository site for these materials, which is a major advantage. This application provides an excellent opportunity to develop and to enhance the public acceptance of the fusion energy for the future. The energy from the transmutation process is utilized to produce revenue. Flibe, lithium-lead eutectic, and liquid lead are possible candidates. The liquid blankets have several features, which are suited for this application. It can operate at constant thermal power without interruption for refueling by adjusting the concentration of the transuranic elements and lithium-6. These liquids operate at low-pressure, which reduces the primary stresses in the structure material. Development and fabrication costs of solid transuranic materials are eliminated. Burnup limit of the transuranic elements due to radiation effects is eliminated. Heat is generated within the liquid, which simplifies the heat removal process without producing thermal stresses. These blanket concepts have large negative temperature coefficient with respect to the blanket reactivity, which enhances the safety performance. These liquids are chemically and thermally stable under irradiation conditions, which minimize the radioactive waste volume. The operational record of the Molten Salt Breeder Reactor with Flibe was very successful, which established the technical bases for this application. This paper provides the technical analyses and the performance of the Flibe blanket concept as an example of this class of blankets.
Photoassociative spectroscopy of ultracold argon and krypton

We report photoassociative spectroscopy performed separately on Kr84 and Ar40. The spectra were obtained as a function of laser intensity with the spectroscopy laser tuned around the ns[3/2]2→np[5/2]3 trapping transition, where n=4 for argon and n=5 for krypton. The resulting spectra are the first ever obtained for krypton on this important transition. This transition in argon was previously studied in our group. At that time, discrete features were observed in the spectrum, but their origin was not conclusively identified. We have now identified these features as artifacts from laser sidebands. We discuss the origin of these sidebands, how they imprint their presence on the spectra, and present spectra for argon with the artifacts removed. For both krypton and argon, while we observe photoassociation, we do not observe any resolved vibrational structure. We present a theoretical analysis of the expected vibrational level spacings based on calculated potential curves and show that the absence of resolved structure in the spectra of our experiments (as configured) is not surprising. Finally, we discuss the requirements of future experiments in which resolution of vibrational structure in this system might be obtained.
Route optimization for offloading congested meter fixes

The Optimized Route Capability (ORC) concept proposed by the FAA facilitates traffic managers to identify and resolve arrival flight delays caused by bottlenecks formed at arrival meter fixes when there exists imbalance between arrival fixes and runways. ORC makes use of the prediction capability of existing automation tools, monitors the traffic delays based on these predictions, and searches the best reroutes upstream of the meter fixes based on the predictions and estimated arrival schedules when delays are over a predefined threshold. Initial implementation and evaluation of the ORC concept considered only reroutes available at the time arrival congestion was first predicted. This work extends previous work by introducing an additional dimension in reroute options such that ORC can find the best time to reroute and overcome the ‘first-come-first-reroute’ phenomenon. To deal with the enlarged reroute solution space, a genetic algorithm was developed to solve this problem. Experiments were conducted using the same traffic scenario used in previous work, when an arrival rush was created for one of the four arrival meter fixes at George Bush Intercontinental Houston Airport. Results showed the new approach further improved delay savings. The suggested route changes from the new approach were on average 30 minutes later than those using other approaches, and fewer numbers of reroutes were required. Fewer numbers of reroutes reduce operational complexity and later reroutes help decision makers deal with uncertain situations.
Preference completion from partial rankings

We propose a novel and efficient algorithm for the collaborative preference completion problem, which involves jointly estimating individualized rankings for a set of entities over a shared set of items, based on a limited number of observed affinity values. Our approach exploits the observation that while preferences are often recorded as numerical scores, the predictive quantity of interest is the underlying rankings. Thus, attempts to closely match the recorded scores may lead to over-fitting and impair generalization performance. Instead, we propose an estimator that directly fits the underlying preference order, combined with nuclear norm constraints to encourage low-rank parameters. Besides (approximate) correctness of the ranking order, the proposed estimator makes no generative assumption on the numerical scores of the observations. One consequence is that the proposed estimator can fit any consistent partial ranking over a subset of the items represented as a directed acyclic graph (DAG), generalizing standard techniques that can only fit preference scores. Despite this generality, for supervision representing total or blockwise total orders, the computational complexity of our algorithm is within a log factor of the standard algorithms for nuclear norm regularization based estimates for matrix completion. We further show promising empirical results for a novel and challenging application of collaboratively ranking of the associations between brain-regions and cognitive neuroscience terms.
Adenovirus with DNA Packaging Gene Mutations Increased Virus Release

Adenoviruses (Ads) have been extensively manipulated for the development of cancer selective replication, leading to cancer cell death or oncolysis. Clinical studies using E1-modified oncolytic Ads have shown that this therapeutic platform was safe, but with limited efficacy, indicating the necessity of targeting other viral genes for manipulation. To improve the therapeutic efficacy of oncolytic Ads, we treated the entire Ad genome repeatedly with UV-light and have isolated AdUV which efficiently lyses cancer cells as reported previously (Wechman, S. L. et al. Development of an Oncolytic Adenovirus with Enhanced Spread Ability through Repeated UV Irradiation and Cancer Selection. Viruses 2016, 8, 6). In this report, we show that no mutations were observed in the early genes (E1 or E4) of AdUV while several mutations were observed within the Ad late genes which have structural or viral DNA packaging functions. This study also reported the increased release of AdUV from cancer cells. In this study, we found that AdUV inhibits tumor growth following intratumoral injection. These results indicate the potentially significant role of the viral late genes, in particular the DNA packaging genes, to enhance Ad oncolysis.
Environmental impact on young children's participation in home-based activities.

Aim To test the effect of child, family, and environmental factors on young children's participation in home-based activities. Method Caregivers of young children were recruited using convenience and snowball sampling. Participants were 395 caregivers of children (222 males, 173 females) aged from 1 month to 5 years and 11 months. Demographic items and the home section of the Young Children's Participation and Environment Measure were administered online, followed by completion of the daily activities, mobility, and social/cognitive domains of the Pediatric Evaluation of Disability Inventory Computer Adaptive Test by telephone interview. Results A structural equation model fitted the data well (comparative fit index=0.91) and explained 31.2% of the variance in perceived environmental support and 42.5% of the variance in home involvement. Functional limitations and performance had an indirect effect on young children's participation through their effect on perceived environmental support. Specifically, fewer functional limitations and higher task performance were associated with greater environmental support, which in turn predicted higher levels of home involvement. Interpretation Results suggest the importance of a young child's functional abilities and task performance on caregiver perceptions of environmental support at home, and the impact of environmental support on a child's participation in home-based activities during the early childhood period. Results warrant replication with more diverse samples to evaluate model generalizability.
The Financial Crisis and Corporate Credit Ratings

ABSTRACT: Credit ratings on many financial instruments failed to accurately portray default risk before the global financial crisis. I find no decline in the performance of corporate credit ratings during or after the crisis, indicating that the failures of ratings on financial instruments were due to conditions unique to the rating agencies' financial instruments divisions. Rather, the preponderance of tests indicate that corporate credit rating performance improves after the crisis, consistent with the rating agencies positively responding to public criticism and regulatory pressures. At the same time, I find evidence of sophisticated market participants decreasing their reliance on corporate credit ratings after the crisis. Consistent with theoretical models of reputation cyclicality, a likely explanation is that the rating agencies suffer spillover reputation damage from their failed ratings on financial instruments. My study informs regulators, practitioners, and academics about the performance of co...
Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification

In this paper, we study the problem of author identification under double-blind review setting, which is to identify potential authors given information of an anonymized paper. Different from existing approaches that rely heavily on feature engineering, we propose to use network embedding approach to address the problem, which can automatically represent nodes into lower dimensional feature vectors. However, there are two major limitations in recent studies on network embedding: (1) they are usually general-purpose embedding methods, which are independent of the specific tasks; and (2) most of these approaches can only deal with homogeneous networks, where the heterogeneity of the network is ignored. Hence, challenges faced here are two folds: (1) how to embed the network under the guidance of the author identification task, and (2) how to select the best type of information due to the heterogeneity of the network. To address the challenges, we propose a task-guided and path-augmented heterogeneous network embedding model. In our model, nodes are first embedded as vectors in latent feature space. Embeddings are then shared and jointly trained according to task-specific and network-general objectives. We extend the existing unsupervised network embedding to incorporate meta paths in heterogeneous networks, and select paths according to the specific task. The guidance from author identification task for network embedding is provided both explicitly in joint training and implicitly during meta path selection. Our experiments demonstrate that by using path-augmented network embedding with task guidance, our model can obtain significantly better accuracy at identifying the true authors comparing to existing methods.
Projections and Uncertainties About Climate Change in an Era of Minimal Climate Policies

Climate change remains one of the major international environmental challenges facing nations. Up to now, nations have to date adopted minimal policies to slow climate change. Moreover, there has been no major improvement in emissions trends as of the latest data. The current study uses the updated DICE-2016R2 model to present new projections and the impacts of alternative climate policies. It also presents a new set of estimates of the uncertainties about future climate change and compares the results with those of other integrated assessment models. The study confirms past estimates of likely rapid climate change over the next century if major climate-change policies are not taken. It suggests that it is unlikely that nations can achieve the 2°C target of international agreements, even if ambitious policies are introduced in the near term. The required carbon price needed to achieve current targets has risen over time as policies have been delayed.
Alignment of dynamic networks

Motivation Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. Results For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation http://nd.edu/∼cone/DynaMAGNA++/ . Contact tmilenko@nd.edu. Supplementary information Supplementary data are available at Bioinformatics online.
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks

Neuromorphic computing using post-CMOS technologies is gaining immense popularity due to its promising abilities to address the memory and power bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were primarily focused on device and circuit implementations of SNNs on crossbars. RESPARC advances this by proposing a complete system for SNN acceleration and its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for inner-product computation and realizes a hierarchical reconfigurable design to incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate the proposed architecture on different SNNs ranging in complexity from 2k–230k neurons and 1.2M–5.5M synapses. Simulation results on these networks show that compared to the baseline digital CMOS architecture, RESPARC achieves 500× (15×) efficiency in energy benefits at 300× (60×) higher throughput for multi-layer perceptrons (deep convolutional networks). Furthermore, RESPARC is a technology-aware architecture that maps a given SNN topology to the most optimized MCA size for the given crossbar technology.