cssi/SciSciGPT-SciSciNet huggingface dataset:
Fast and Deterministic Approximations for k-Cut.

In an undirected graph, a k-cut is a set of edges whose removal breaks the graph into at least k connected components. The minimum weight k-cut can be computed in n^O(k) time, but when k is treated as part of the input, computing the minimum weight k-cut is NP-Hard [Goldschmidt and Hochbaum, 1994]. For poly(m,n,k)-time algorithms, the best possible approximation factor is essentially 2 under the small set expansion hypothesis [Manurangsi, 2017]. Saran and Vazirani [1995] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed via O(k) minimum cuts, which implies a O~(km) randomized running time via the nearly linear time randomized min-cut algorithm of Karger [2000]. Nagamochi and Kamidoi [2007] showed that a (2 - 2/k)-approximately minimum weight k-cut can be computed deterministically in O(mn + n^2 log n) time. These results prompt two basic questions. The first concerns the role of randomization. Is there a deterministic algorithm for 2-approximate k-cuts matching the randomized running time of O~(km)? The second question qualitatively compares minimum cut to 2-approximate minimum k-cut. Can 2-approximate k-cuts be computed as fast as the minimum cut - in O~(m) randomized time? We give a deterministic approximation algorithm that computes (2 + eps)-minimum k-cuts in O(m log^3 n / eps^2) time, via a (1 + eps)-approximation for an LP relaxation of k-cut.
Development of a non-isothermal forging process for hollow power transmission shafts

Abstract This paper presents a new forging process for hollow power transmission shafts. The key aspect of the process is altering the flow characteristics of the material via differential heating of the tubular stock. The process is conceived in three main stages namely, partially heat the tubular stock with induction heating; upset the heated section of the tube to form a solid section; and finally shape the solid section into a flange or a conical head by further upsetting the workpiece. Parametric study was also carried out for various internal diameter to external diameter (ID/ OD) tube ratios. With the aid of finite element analysis (FEA), the feasibility of the process was evaluated based on formability, forming loads and dimensional accuracy. FEA results showed that it is feasible to produce hollow axle shafts, stepped gear shafts, and pinion gear shafts. Scaled down models of hollow axle shaft and hollow pinion gear shaft were successfully fabricated using the proposed methodology.
Predictiveness of Prior Failures Is Improved by Incorporating Trial Duration.

In recent years, there has been a proliferation of adaptive learner models that seek to predict student correctness. Improvements on earlier models have shown that separate predictors for prior successes, failures, and recent performance further improve fit while remaining interpretable. However, students who engage in “gaming” or other off-task behaviors may reduce the predictiveness of learner models that treat counts of prior performance equivalently across gaming and non-gaming student populations. The present research evaluated how sub-groups of students that varied in their potential gaming behavior were differently fit by a logistic learner model, and whether any observed differences between sub-groups could inspire the creation of new predictors that might improve model fit. Student data extracted from a college-level online learning application were clustered according to speed and accuracy using Gaussian mixture modeling. Distinct clusters were found, with similar cluster patterns detected in three separate datasets. Subsequently, each cluster was separately fit to a Performance Factors Analysis model (PFA). Significantly different parameter coefficients across clusters implied that students more likely to have been gaming benefitted less from prior failures. These differences inspired new and modified predictors that were found to improve overall model fit - an improvement that varied in magnitude across clusters. The present findings indicate that incorporating trial duration into counts of prior failures can improve the predictive power of learning models.
Contributions of White Matter Connectivity and BOLD Modulation to Cognitive Aging: A Lifespan Structure-Function Association Study

The ability to flexibly modulate brain activation to increasing cognitive challenge decreases with aging. This age-related decrease in dynamic range of function of regional gray matter may be, in part, due to age-related degradation of regional white matter tracts. Here, a lifespan sample of 171 healthy adults (aged 20-94) underwent magnetic resonance imaging (MRI) scanning including diffusion-weighted imaging (for tractography) and functional imaging (a digit n-back task). We utilized structural equation modeling to test the hypothesis that age-related decrements in white matter microstructure are associated with altered blood-oxygen-level-dependent (BOLD) modulation, and both in turn, are associated with scanner-task accuracy and executive function performance. Specified structural equation model evidenced good fit, demonstrating that increased age negatively affects n-back task accuracy and executive function performance in part due to both degraded white matter tract microstructure and reduced task-difficulty-related BOLD modulation. We further demonstrated that poorer white matter microstructure integrity was associated with weakened BOLD modulation, particularly in regions showing positive modulation effects, as opposed to negative modulation effects. This structure-function association study provides further evidence that structural connectivity influences functional activation, and the two mechanisms in tandem are predictive of cognitive performance, both during the task, and for cognition measured outside the scanner environment.
Safety of Sodium-Glucose Co-Transporter 2 Inhibitors

Abstract Sodium-glucose co-transporter 2 (SGLT2) inhibitors have a well-defined safety profile based on data obtained from numerous clinical trials, including cardiovascular outcomes trials (CVOTs) and postmarketing pharmacovigilance reporting. Adverse events including risk of genital mycotic infection and volume depletion-related events are consistent with the mechanism of action of this drug class. However, several emergent (albeit infrequent) serious safety issues have also been reported. In their respective CVOTs, the proportion of patients with reported diabetic ketoacidosis was similar in empagliflozin or canagliflozin compared with their placebo groups, but it was higher for dapagliflozin. Canagliflozin may be associated with an increased risk of bone fracture and lower limb amputation; however, data are inconclusive. There is no evidence linking SGLT2 inhibitors with an increased risk of cancer, but these agents, particularly dapagliflozin, should be used with caution in patients with hematuria or a history of bladder cancer. Postmarketing reports of acute kidney injury have occurred in patients receiving SGLT2 inhibitors, and cases identified in recent CVOTs occurred with similar frequency in SGLT2 inhibitor and placebo groups. Common adverse events associated with SGLT2 inhibitors (such as genital infections or volume depletion) are generally mild and manageable by patients or by primary care physicians, and the risk of rare events (such as ketoacidosis) can be minimized by appropriate patient selection and early recognition of symptoms. When selecting treatment, it is important that clinicians weigh the known risks of SGLT2 inhibitors against their proven benefits, including the reduction of adverse cardiovascular and renal outcomes.
Compensatory sequence variation between trans-species small RNAs and their target sites

Trans-species small regulatory RNAs (sRNAs) are delivered to host plants from diverse pathogens and parasites and can target host mRNAs. How trans-species sRNAs can be effective on diverse hosts has been unclear. Multiple species of the parasitic plant Cuscuta produce trans-species sRNAs that collectively target many host mRNAs. Confirmed target sites are nearly always in highly conserved, protein-coding regions of host mRNAs. Cuscuta trans-species sRNAs can be grouped into superfamilies that have variation in a three-nucleotide period. These variants compensate for synonymous-site variation in host mRNAs. By targeting host mRNAs at highly conserved protein-coding sites, and simultaneously expressing multiple variants to cover synonymous-site variation, Cuscuta trans-species sRNAs may be able to successfully target multiple homologous mRNAs from diverse hosts.
There is no (95, 40, 12, 20) strongly regular graph

We show that there is no $(95,40,12,20)$ strongly regular graph and, consequently, there is no $(96,45,24,18)$ strongly regular graph, no two-graph on $96$ vertices, and no partial geometry $\rm{pg}(5,9,3)$. The main idea of the result is based on the star complement technique and requires a small amount of computation.
Logomaker: beautiful sequence logos in Python.

Summary Sequence logos are visually compelling ways of illustrating the biological properties of DNA, RNA and protein sequences, yet it is currently difficult to generate and customize such logos within the Python programming environment. Here we introduce Logomaker, a Python API for creating publication-quality sequence logos. Logomaker can produce both standard and highly customized logos from either a matrix-like array of numbers or a multiple-sequence alignment. Logos are rendered as native matplotlib objects that are easy to stylize and incorporate into multi-panel figures. Availability and implementation Logomaker can be installed using the pip package manager and is compatible with both Python 2.7 and Python 3.6. Documentation is provided at http://logomaker.readthedocs.io; source code is available at http://github.com/jbkinney/logomaker.
Kinematics of Spherical Robots Rolling over 3D Terrains

Although the kinematics and dynamics of spherical robots (SRs) on flat horizontal and inclined 2D surfaces are thoroughly investigated, their rolling behavior on generic 3D terrains has remained unexplored. This paper derives the kinematics equations of the most common SR configurations rolling over 3D surfaces. First, the kinematics equations for a geometrical sphere rolling over a 3D surface are derived along with the characterization of the modeling method. Next, a brief review of current mechanical configurations of SRs is presented as well as a novel classification for SRs based on their kinematics. Then, considering the mechanical constraints of each category, the kinematics equations for each group of SRs are derived. Afterward, a path-tracking method is utilized for a desired 3D trajectory. Finally, simulations are carried out to validate the developed models and the effectiveness of the proposed control scheme.
Uncertainty quantification of tunable elastic metamaterials using polynomial chaos

Owing to their periodic and intricate configurations, metamaterials engineered for acoustic and elastic wave control inevitably suffer from manufacturing anomalies and deviate from theoretical dispersion predictions. This work exploits the Polynomial Chaos Theory to quantify the magnitude and extent of these deviations and assess their impact on the desired behavior. It is shown that uncertainties stemming from surface roughness, tolerances, and other inconsistencies in a metamaterial’s unit-cell parameters alter the targeted bandgap width, frequency range, and the confidence level with which it is guaranteed. The effect of uncertainties is projected from a Bloch-wave dispersion analysis of three distinct phononic and resonant cellular configurations and is further confirmed in the frequency response of the finite structures. The analysis concludes with a unique algorithm intended to guide the design of metamaterials in the presence of system uncertainties.
Dimension of reservoir computers

A reservoir computer is a complex dynamical system, often created by coupling nonlinear nodes in a network. The nodes are all driven by a common driving signal. In this work, three dimension estimation methods, false nearest neighbor, covariance dimension, and Kaplan-Yorke dimension, are used to estimate the dimension of the reservoir dynamical system. It is shown that the signals in the reservoir system exist on a relatively low dimensional surface. Changing the spectral radius of the reservoir network can increase the fractal dimension of the reservoir signals, leading to an increase in a testing error.
Associativity and Integrability

We provide a complete solution to the problem of extending a local Lie groupoid to a global Lie groupoid. First, we show that the classical Mal'cev's theorem, which characterizes local Lie groups that can be extended to global Lie groups, also holds in the groupoid setting. Next, we describe a construction that can be used to obtain any local Lie groupoid with integrable algebroid. Last, our main result establishes a precise relationship between the integrability of a Lie algebroid and the failure in associativity of a local integration. We give a simplicial interpretation of this result showing that the monodromy groups of a Lie algebroid manifest themselves combinatorially in a local integration, as a lack of associativity.
Invariance principles for random walks in cones

Abstract We prove invariance principles for a multidimensional random walk conditioned to stay in a cone. Our first result concerns convergence towards the Brownian meander in the cone. Furthermore, we prove functional convergence of h -transformed random walk to the corresponding h -transform of the Brownian motion. Finally, we prove an invariance principle for bridges of a random walk in a cone.
Penetrance of Parkinson's Disease in LRRK2 p.G2019S Carriers Is Modified by a Polygenic Risk Score.

Background Although the leucine-rich repeat kinase 2 p.G2019S mutation has been demonstrated to be a strong risk factor for PD, factors that contribute to penetrance among carriers, other than aging, have not been well identified. Objectives To evaluate whether a cumulative genetic risk identified in the recent genome-wide study is associated with penetrance of PD among p.G2019S mutation carriers. Methods We included p.G2019S heterozygote carriers with European ancestry in three genetic cohorts in which the mutation carriers with and without PD were selectively recruited. We also included the carriers from two data sets: one from a case-control setting without selection of mutation carriers and the other from a population sampling. Associations between polygenic risk score constructed from 89 variants reported recently and PD were tested and meta-analyzed. We also explored the interaction of age and PRS. Results After excluding eight homozygotes, 833 p.G2019S heterozygote carriers (439 PD and 394 unaffected) were analyzed. Polygenic risk score was associated with a higher penetrance of PD (odds ratio: 1.34; 95% confidence interval: [1.09, 1.64] per +1 standard deviation; P = 0.005). In addition, associations with polygenic risk score and penetrance were stronger in the younger participants (main effect: odds ratio 1.28 [1.04, 1.58] per +1 standard deviation; P = 0.022; interaction effect: odds ratio 0.78 [0.64, 0.94] per +1 standard deviation and + 10 years of age; P = 0.008). Conclusions Our results suggest that there is a genetic contribution for penetrance of PD among p.G2019S carriers. These results have important etiological consequences and potential impact on the selection of subjects for clinical trials. © 2020 International Parkinson and Movement Disorder Society.
Tripartite Vector Representations for Better Job Recommendation

Job recommendation is a crucial part of the online job recruitment business. To match the right person with the right job, a good representation of job postings is required. Such representations should ideally recommend jobs with fitting titles, aligned skill set, and reasonable commute. To address these aspects, we utilize three information graphs ( job-job, skill-skill, job-skill) from historical job data to learn a joint representation for both job titles and skills in a shared latent space. This allows us to gain a representation of job postings/ resume using both elements, which subsequently can be combined with location. In this paper, we first present how the presentation of each component is obtained, and then we discuss how these different representations are combined together into one single space to acquire the final representation. The results of comparing the proposed methodology against different base-line methods show significant improvement in terms of relevancy.
Learning from the Past: Possible Urgent Prevention and Treatment Options for Severe Acute Respiratory Infections Caused by 2019-nCoV.

With the current trajectory of the 2019-nCoV outbreak unknown, public health and medicinal measures will both be needed to contain spreading of the virus and to optimize patient outcomes. Although little is known about the virus, an examination of the genome sequence shows strong homology with its better-studied cousin, SARS-CoV. The spike protein used for host cell infection shows key nonsynonymous mutations that might hamper the efficacy of previously developed therapeutics but remains a viable target for the development of biologics and macrocyclic peptides. Other key drug targets, including RNA-dependent RNA polymerase and coronavirus main proteinase (3CLpro), share a strikingly high (>95 %) homology to SARS-CoV. Herein, we suggest four potential drug candidates (an ACE2-based peptide, remdesivir, 3CLpro-1 and a novel vinylsulfone protease inhibitor) that could be used to treat patients suffering with the 2019-nCoV. We also summarize previous efforts into drugging these targets and hope to help in the development of broad-spectrum anti-coronaviral agents for future epidemics.
Thinking outside the Box: Globalization, Labor Rights, and the Making of Preferential Trade Agreements

States can seemingly defy the dictates of globalization. In practice, although being pressured by their competitors, states rarely engage in the race to the bottom by downgrading labor rights laws that are politically costly to pursue. I argue that states’ resistance is made possible by adopting more viable policy alternatives, i.e., concluding preferential trade agreements (PTAs). PTAs can generate considerable economic gains in a less politically costly way than does reducing legal labor protection. As a result, it is expected that a pair of states is more likely to form a PTA in the face of policy pressure to lower legal labor protection. I also argue that facing such pressure, these states are more likely to include strong labor provisions in PTAs. Finally, in the face of the policy pressure, states may feel that signing a PTA is a bit less urgent when they are able to diminish practical labor protection. Applying structural equivalence technique to a new global labor rights dataset to capture the policy pressure to lower legal labor protection, I find robust evidence in support of these conjectures.
A novel approach for optimal trajectory design with multiple operation modes of propulsion system, part 1

Abstract Efficient performance of a number of engineering systems is achieved through different modes of operation - yielding systems described as “hybrid”, containing both real-valued and discrete decision variables. Prominent examples of such systems, in space applications, could be spacecraft equipped with 1) a variable- I sp , variable-thrust engine or 2) multiple engines each capable of switching on/off independently. To alleviate the challenges that arise when an indirect optimization method is used, a new framework — Composite Smooth Control (CSC) — is proposed that seeks smoothness over the entire spectrum of distinct control inputs. A salient aftermath of the application of the CSC framework is that the original multi-point boundary-value problem can be treated as a two-point boundary-value problem with smooth, differentiable control inputs; the latter is notably easier to solve, yet can be made to accurately approximate the former hybrid problem. The utility of the CSC framework is demonstrated through a multi-year, multi-revolution heliocentric fuel-optimal trajectory for a spacecraft equipped with a variable- I sp , variable-thrust engine.
A Transient-Directing-Group Strategy Enables Enantioselective Reductive Heck Hydroarylation of Alkenes

Metal-coordinating directing groups have seen extensive use in the field of transition-metal-catalyzed alkene functionalization; however, their waste-generating installation and removal steps limit the efficiency and practicality of reactions that rely on their use. Inspired by developments in asymmetric organocatalysis, where reactions rely on reversible covalent interactions between an organic substrate and a chiral mediator, we have developed a transient-directing-group approach to reductive Heck hydroarylation of alkenyl benzaldehyde substrates that proceeds under mild conditions. Highly stereoselective migratory insertion is facilitated by in situ formation of an imine from catalytic amounts of a commercially available amino acid additive. Computational studies reveal an unusual mode of enantioinduction by the remote chiral center in the transient directing group.
Interaction of nonlinearity and polarization mode dispersion

The derivation of the coupled nonlinear Schrodinger equation and the Manakov-PMD equation is reviewed. It is shown that the usual scalar nonlinear Schrodinger equation can be derived from the Manakov-PMD equation when polarization mode dispersion is negligible and the signal is initially in a single polarization state as a function of time. Applications of the Manakov-PMD equation to studies of the interaction of the Kerr nonlinearity with polarization mode dispersion are then discussed.
Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

Brain Metastases (BM) complicate 20–40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature). We propose a BM-detection framework using a single-sequence gadolinium-enhanced T1-weighted 3D MRI dataset. The framework focuses on the detection of smaller (<15 mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of an MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network (“CropNet”). Data is augmented extensively during training via a pipeline consisting of random ga mma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations. This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ∼5.4 mm and a mean volume of ∼160 mm3. For 90% BM-detection sensitivity, the framework produced on average 9.12 false-positive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of false-positives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.
Gross Worker Flows and Fluctuations in the Aggregate Labor Market

Abstract We build a three-state general equilibrium model of the aggregate labor market that features both standard labor supply forces and labor market frictions. Our model matches key features of the cyclical properties of employment, unemployment, and nonparticipation as well as those of gross worker flows across these three labor market states. Our key finding is that shocks to labor market frictions play a dominant role in accounting for labor market fluctuations. This is in contrast to the focus of the traditional RBC literature, which emphasized how employment fluctuations arise as a consequence of labor supply responses to price changes induced by TFP shocks.
Comparison of RNA isolation methods on RNA-Seq: implications for differential expression and meta-analyses.

The increasing number of transcriptomic datasets has allowed for meta-analyses, which can be valuable due to their increased statistical power. However, meta-analyses can be confounded by so-called “batch effects,” where technical variation across different batches of RNA-seq experiments can clearly produce spurious signals of differential expression and reduce our power to detect true differences. While batch effects can sometimes be accounted for, albeit with caveats, a better strategy is to understand their sources to better avoid them. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA-seq design. Based on the different chemistries of “classic” hot phenol extraction of RNA compared to common commercial RNA isolation kits, we hypothesized that specific mRNAs may be preferentially extracted depending upon method, which could masquerade as differential expression in downstream RNA-seq analyses. We tested this hypothesis using the Saccharomyces cerevisiae heat shock response as a well-validated environmental response. Comparing technical replicates that only differed in RNA isolation method, we found over one thousand transcripts that appeared “differentially” expressed when comparing hot phenol extraction with the two kits. Strikingly, transcripts with higher abundance in the phenol-extracted samples were enriched for membrane proteins, suggesting that indeed the chemistry of hot phenol extraction better solubilizes those species of mRNA. Within a self-contained experimental batch (e.g. control versus treatment), the method of RNA isolation had little effect on the ability to identify differentially expressed transcripts. However, we suggest that researchers performing meta-analyses across different experimental batches strongly consider the RNA isolation methods for each experiment.
A reaction network approach to the theory of acoustic wave turbulence

Abstract We propose a new approach to study the long time dynamics of the wave kinetic equation in the statistical description of acoustic turbulence. The approach is based on rewriting the discrete version of the wave kinetic equation in the form of a chemical reaction network, then employing techniques used to study the Global Attractor Conjecture to investigate the long time dynamics of the newly obtained chemical system. We show that the solution of the chemical system converges to an equilibrium exponentially in time. In addition, a resonance broadening modification of the acoustic wave kinetic equation is also studied with the same technique. For the near-resonance equation, if the resonance broadening frequency is larger than a threshold, the solution of the system goes to infinity as time evolves.
Shuffle algebra realizations of type A super Yangians and quantum affine superalgebras for all Cartan data

We introduce super Yangians of $$\mathfrak {gl}(V),\mathfrak {sl}(V)$$ (in the new Drinfeld realization) associated with all Dynkin diagrams. We show that all of them are isomorphic to the super Yangians introduced by Nazarov (Lett Math Phys 21(2), 123–131, 1991), by identifying them with the corresponding RTT super Yangians. However, their “positive halves” are not pairwise isomorphic, and we obtain the shuffle algebra realizations for all of those in spirit of Tsymbaliuk (PBWD bases and shuffle algebra realizations for $$U_{\varvec{v}}(L{\mathfrak {sl}}_n), U_{{\varvec{v}}_1,{\varvec{v}}_2}(L{\mathfrak {sl}}_n), U_{\varvec{v}}(L{\mathfrak {sl}}(m|n))$$ and their integral forms, preprint, arXiv:1808.09536 ). We adapt the latter to the trigonometric setup by obtaining the shuffle algebra realizations of the “positive halves” of type A quantum loop superalgebras associated with arbitrary Dynkin diagrams.
How fragile we are.

EditorialShortly after I finished delivering a keynote lecture on minor salivary gland cancers on February 23, 2020 at the Candiolo Cancer Institute in Turin, Italy, the conference chairs Drs. Giovanni Succo and Piero Nicolai announced that the conference was urgently adjourned and the rest of the program canceled. This unexpected announcement was in compliance with the Italian government’s orders to immediately end all public gatherings. Two days earlier as I set out to travel to Italy, where no cases of coronavirus infection had yet been reported, news reports were focused mostly on South Korea and Iran as hotspots of COVID-19. Out of an abundance of caution, I double-checked again before leaving for the airport and confirmed that Italy had no reported cases. Upon my arrival in Turin I was greeted by the usual warm welcome and well-known hospitality of our Italian colleagues. At the welcome reception they discussed the earlier morning report of the first five confirmed cases of COVID-19 in Lombardy region and its capital Milan. The next day, as the unplanned adjournment was announced on the first day of the 3-day conference, there were more than 120 reported cases ushering what would be the first significant outbreak in Europe. The conference chair read the Italian government emergency prohibition of public gatherings, canceling the Milan fashion week, the Venice carnival, and closing all schools and universities. But when he announced that the football (aka Soccer) game was canceled I knew that the situation was grave. As most of us know it almost takes an act of God to cancel a football game in Italy! Without delay I scrambled to get a flight back home only 24 hours after I arrived in Turin. On my way to the airport I saw on my news app that France had stopped a train of passengers from Italy and diverted it back. I was concerned about my connection in Frankfurt and ultimately getting back to USA. As I passed every step of screening and temperature checks I finally landed in Houston with a huge sigh of relief. Following instructions that were urgently sent that day, I immediately contacted our employee health at MD Anderson where I was carefully screened and cleared to go back to work.
Unsupervised Feature Enhancement for Speaker Verification

The task of making speaker verification systems robust to adverse scenarios remains a challenging and an active area of research. We developed an unsupervised feature enhancement approach in log-filter bank space with the end goal of improving speaker verification performance. We experimented with using both real speech recorded in adverse environments and degraded speech obtained by simulation to train the enhancement systems. The effectiveness of this approach was shown by testing on several real, simulated noisy, and reverberant test sets. The approach yielded significant improvements on both real and simulated sets when data augmentation was not used in speaker verification pipeline. We also experimented with training the x-vector and PLDA systems with enhanced augmented features instead of augmented features and observed better performance on real test conditions (4.2% relative improvement in minDCF on SRI).
Liability threshold modeling of case-control status and family history of disease increases association power.

Family history of disease can provide valuable information in case–control association studies, but it is currently unclear how to best combine case–control status and family history of disease. We developed an association method based on posterior mean genetic liabilities under a liability threshold model, conditional on case–control status and family history (LT-FH). Analyzing 12 diseases from the UK Biobank (average N = 350,000) we compared LT-FH to genome-wide association without using family history (GWAS) and a previous proxy-based method incorporating family history (GWAX). LT-FH was 63% (standard error (s.e.) 6%) more powerful than GWAS and 36% (s.e. 4%) more powerful than the trait-specific maximum of GWAS and GWAX, based on the number of independent genome-wide-significant loci across all diseases (for example, 690 loci for LT-FH versus 423 for GWAS); relative improvements were similar when applying BOLT-LMM to GWAS, GWAX and LT-FH phenotypes. Thus, LT-FH greatly increases association power when family history of disease is available. A new association method using both case–control status and family history (LT-FH) greatly increases association power in analyses of 12 diseases from the UK Biobank.
Incorporation of erbium ions into thin-film lithium niobate integrated photonics

As an active material with favorable linear and nonlinear optical properties, thin-film lithium niobate has demonstrated its potential in integrated photonics. Integration with rare-earth ions, which are promising candidates for quantum memories and transducers, will enrich the system with new applications in quantum information processing. Here, we investigate the optical properties at 1.5 μm wavelengths of rare-earth ions ( Er 3 +) implanted in thin-film lithium niobate waveguides and micro-ring resonators. Optical quality factors near a million after post-annealing show that ion implantation damage can be repaired. The transition linewidth and fluorescence lifetime of erbium ions are characterized. The ion-cavity coupling is observed through a Purcell enhanced fluorescence from which a Purcell factor of 3.8 ± 0.5, compared with waveguide lifetime, is extracted. This platform is compatible with top-down lithography processes and leads to a scalable path for controlling spin-photon interfaces in photonic circuits.
Test Generation for Mixed-Signal Devices Using Signal Flow Graphs

We describe a new reverse simulation approach to analog and mixed-signal circuit test generation that parallels digital test generation. We invert the analog circuit signal flow graph, reverse simulate it with good and bad machine outputs, and obtain test waveforms and component tolerances, given circuit output tolerances specified by the functional test needs of the designer. The inverted graph allows backtracing to justify analog outputs with analog input sinusoids. Mixed-signal circuits can be tested using this approach, and we present test generation results for two mixed-signal circuits and four analog circuits, one being a multiple-input, multiple-output circuit. This analog backtrace method can generate tests for second-order analog circuits and certain non-linear circuits. These cannot be handled by existing methods, which lack a fault model and a backtrace method. Our proposed method also defines the necessary tolerances on circuit structural components, in order to keep the output circuit signal within the envelope specified by the designer. This avoids the problem of overspecifying analog circuit component tolerances, and reduces cost. We prove that our parametric fault tests also detect all catastrophic faults. Unlike prior methods, ours is a structural, rather than functional, analog test generation method.
Modeling high-dimensional unit-root time series

Abstract This paper proposes a new procedure to build factor models for high-dimensional unit-root time series by postulating that a p -dimensional unit-root process is a nonsingular linear transformation of a set of unit-root processes, a set of stationary common factors that are dynamically dependent, and some idiosyncratic white noise components. For the stationary components, we assume that the factor process captures the temporal dependence, and that the idiosyncratic white noise series explains, jointly with the factors, the cross-sectional dependence. The estimation of nonsingular linear loading spaces is carried out in two steps. First, we use an eigenanalysis of a nonnegative definite matrix of the data to separate the unit-root processes from the stationary ones, and a modified method to specify the number of unit roots. We then employ another eigenanalysis and a projected principal component analysis to identify the stationary common factors and the white noise series. We propose a new procedure to specify the number of white noise series and, hence, the number of stationary common factors. We establish asymptotic properties of the proposed method for both fixed and diverging p as the sample size n increases, and use a simulation and a real example to demonstrate the performance of the proposed method in finite samples. We also compare our method with some commonly used ones in the literature regarding the forecast ability of the extracted factors, and find that the proposed method performs well in out-of-sample forecasting of a 508-dimensional PM2.5 series in Taiwan.
Design and Implementation of a Pluggable Fault-Tolerant CORBA Infrastructure

In this paper we present the design and implementation of a Pluggable Fault-Tolerant CORBA Infrastructure that provides fault tolerance for CORBA applications by utilizing the pluggable protocols framework that most CORBA ORBs provide. Our approach does not require any modification to the CORBA ORB, and requires only minimal modification to the application. Moreover, it avoids the difficulty of retrieving and assigning the ORB state by embedding the fault tolerance mechanisms into the ORB. The Pluggable Fault-Tolerant CORBA Infrastructure exhibits similar or better performance than other Fault-Tolerant CORBA systems, while providing strong replica consistency.
CHAPTER 2 – Megakaryocyte Development and Platelet Formation

Megakaryocytes are highly specialized precursor cells that produce and release platelets into the circulation. Understanding mechanisms by which megakaryocytes develop and give rise to platelets has fascinated hematologists for more than a century. Megakaryocytes are descended from pluripotent stem cells and undergo multiple DNA replications without cell divisions by the unique process of endomitosis. Upon completion of endomitosis, polyploid megakaryocytes begin a rapid cytoplasmic expansion phase characterized by the formation of an elaborate invaginated membrane system (IMS) and the accumulation of cytoplasmic proteins and granules essential for platelet function. During the final stages of development, the megakaryocyte cytoplasm undergoes a massive reorganization into beaded cytoplasmic extensions called proplatelets. This chapter focuses on the development of megakaryocytes and evaluates the proposed mechanisms and sites of platelet formation. In addition, we review the proplatelet theory of platelet biogenesis and discuss the cytoskeletal mechanics of platelet formation. Finally, we consider how insights gained from knockout (KO) animal models and human diseases have increased our understanding of megakaryocyte development and platelet formation.
Liquidity and Trading Dynamics

In this paper, we build a model where the presence of liquidity constraints tends to magnify the economy's response to aggregate shocks. We consider a decentralized model of trade, where agents may use money or credit to buy goods. When agents do not have access to credit and the real value of money balances is low, agents are more likely to be liquidity constrained. This makes them more concerned about their short-term earning prospects when making their consumption decisions and about their short-term spending opportunities when making their production decisions. This generates a coordination element in spending and production which leads to greater aggregate volatility and greater comovement across producers.
The aging of the baby boomers: Demographics and propagation of tax shocks

We study how the changing demographic composition of the US labor force has affected the response of the unemployment rate to marginal tax rate shocks. Using narratively identified tax changes as proxies for structural shocks, we establish that the responsiveness of the unemployment rates to tax changes varies significantly across age groups: the unemployment rate response of the young is nearly twice as large as that of the old. This heterogeneity is the channel through which shifts in the age composition of the labor force impact the response of the unemployment rate to tax cuts. We find that the aging of the baby boomers considerably reduces the effects of tax cuts on aggregate unemployment.
Urban Structure and Growth

Most economic activity occurs in cities. This creates a tension between local increasing returns, implied by the existence of cities, and aggregate constant returns, implied by balanced growth. To address this tension, we develop a general equilibrium theory of economic growth in an urban environment. In our theory, variation in the urban structure through the growth, birth, and death of cities is the margin that eliminates local increasing returns to yield constant returns to scale in the aggregate. We show that, consistent with the data, the theory produces a city size distribution that is well approximated by Zipf’s law, but that also displays the observed systematic underrepresentation of both very small and very large cities. Using our model, we show that the dispersion of city sizes is consistent with the dispersion of productivity shocks found in the data.
On the electron affinities of the Ca, Sc, Ti and Y atoms

Abstract For the Ca, Sc, Ti and Y atoms calculations are performed for the ground states of the neutrals and the ground and several low-lying excited states of the negative ions. Overall the computed electron affinities are in good accord with experiment. The calculations show the rapid stabilization of the 3d orbital relative to the 4p as the nuclear charge increases. The 3F° and 3D° terms are found to be close in energy in Sc− and in Y−. This confirms earlier speculation that some of the peaks in the photodetachment spectra of Y− originate from the bound excited 3F° term of Y−.
Bounding the number of nodal domains of eigenfunctions without singular points on the square

We prove Polterovich’s conjecture concerning the growth of the number of nodal domains for eigenfunctions on a unit square domain, under the assumption that the eigenfunctions do not have any singular points.
Effect of geometric sharpness on translucent material perception

When judging the optical properties of a translucent object, humans often look at sharp geometric features such as edges and thin parts. An analysis of the physics of light transport shows that these sharp geometries are necessary for scientific imaging systems to be able to accurately measure the underlying material optical properties. In this article, we examine whether human perception of translucency is likewise affected by the presence of sharp geometry, by confounding our perceptual inferences about an object's optical properties. We use physically accurate simulations to create visual stimuli of translucent materials with varying shapes and optical properties under different illuminations. We then use these stimuli in psychophysical experiments, where human observers are asked to match an image of a target object by adjusting the material parameters of a match object with different geometric sharpness, lighting, and three-dimensional geometry. We find that the level of geometric sharpness significantly affects perceived translucency by observers. These findings generalize across a few illumination conditions and object shapes. Our results suggest that the perceived translucency of an object depends on both the underlying material's optical parameters and the three-dimensional shape of the object. We also find that models based on image contrast cannot fully predict the perceptual results.
Interference Effects in the Electron Microscopy of Thin Crystal Foils

ELECTRON microscopic studies of metals thinned by electrolytic polishing and other means to a degree that they become transparent for electrons, were started by Heidenreich1 and continued by Castai...
Varying-coefficient models for dynamic networks

Abstract Dynamic networks are commonly used to model relational data that are observed over time. Statistical models for such data should capture both the temporal variation of the relational system as well as the structural dependencies within each network. As a consequence, effectively making inference on dynamic networks is a computationally challenging task, and many models are intractable even for moderately sized systems. In light of these challenges, a family of dynamic network models known as varying-coefficient exponential random graph models (VCERGMs) is proposed to characterize the evolution of network topology through smoothly varying parameters. The VCERGM provides an interpretable dynamic network model that enables the inference of temporal heterogeneity in dynamic networks. Estimation of the VCERGM is achieved via maximum pseudo-likelihood techniques, thereby providing a computationally tractable strategy for statistical inference of complex dynamic networks. Furthermore, a bootstrap hypothesis testing framework is presented for testing the temporal heterogeneity of an observed dynamic network sequence. Application to the U.S. Senate co-voting network and comprehensive simulation studies both reveal that the VCERGM provides relevant and interpretable patterns and has significant advantages over existing methods.
Real-Time Cubature Kalman Filter Parameter Estimation of Blood Pressure Response Characteristics Under Vasoactive Drugs Administration

Mathematical modeling and real-time dynamics identification of the mean arterial blood pressure (MAP) response of a patient to vasoactive drug infusion can provide a reliable tool for automated drug administration, and therefore, reduce the emergency costs and significantly benefit the patient's MAP regulation in an intensive care unit. To this end, a dynamic first-order linear time-varying model with adjustable varying parameters and a varying input delay is considered to capture the MAP response dynamics. Such a model effectively addresses the complexity and the intra- and inter-patient variability of the physiological response to vasoactive drugs. We discretize the model and augment the state vector with model parameters as unknown states of the system. A Bayesian-based multiple-model square root cubature Kalman filtering (MMSRCKF) approach is utilized for real-time estimation of the model's time-varying parameters. Since, unlike other model parameters, the input delay cannot be captured by a random-walk process, a multiple-model module with a posterior probability estimation is implemented to provide the delay identification. Validation results confirm the effectiveness of the proposed identification algorithm both in simulation scenarios and also using animal experiment data.
Logical Peering for Interdomain Networking on Testbeds

Research testbed fabrics have potential to support long-lived, evolving, interdomain experiments, including opt-in application traffic across multiple campuses and edge sites. We propose abstractions and security infrastructure to facilitate multi-domain networking, and a reusable controller toolkit (Ex-oPlex) for network service providers (NSPs) running in testbed-hosted virtual network slices. We demonstrate the idea on the ExoGENI testbed, which allows slices to interconnect and exchange traffic over peering links by mutual consent. Each ExoPlex NSP runs a peering controller that manages its interactions with its linked peers and controls the NSP's dataplane network via SDN. Our approach expresses policies for secure peering and routing in a declarative language-logical peering. The prototype uses logic rules to verify IP prefix ownership, filter and validate route advertisements, and implement user-specified policies for connectivity and path control in networks with multiple transit NSPs.