cssi/SciSciGPT-SciSciNet huggingface dataset:
The reproduction number of COVID-19 and its correlation with public health interventions

Throughout the past six months, no number has dominated the public media more persistently than the reproduction number of COVID-19. This powerful but simple concept is widely used by the public media, scientists, and political decision makers to explain and justify political strategies to control the COVID-19 pandemic. Here we explore the effectiveness of political interventions using the reproduction number of COVID-19 across Europe. We propose a dynamic SEIR epidemiology model with a time-varying reproduction number, which we identify using machine learning. During the early outbreak, the basic reproduction number was 4.22 ± 1.69, with maximum values of 6.33 and 5.88 in Germany and the Netherlands. By May 10, 2020, it dropped to 0.67 ± 0.18, with minimum values of 0.37 and 0.28 in Hungary and Slovakia. We found a strong correlation between passenger air travel, driving, walking, and transit mobility and the effective reproduction number with a time delay of 17.24 ± 2.00 days. Our new dynamic SEIR model provides the flexibility to simulate various outbreak control and exit strategies to inform political decision making and identify safe solutions in the benefit of global health.
Gain Design via LMIs to Minimize the Impact of Stealthy Attacks

The goal of this paper is to design the gain matrices for estimate-based feedback to minimize the impact that falsified sensor measurements can have on the state of a stochastic linear time invariant system. Here we consider attackers that stay stealthy, by raising no alarms, to a chi-squared anomaly detector, thereby restricting the set of attack inputs within an ellipsoidal set. We design linear matrix inequalities (LMIs) to find a tight outer ellipsoidal bound on the convex set of states reachable due to the stealthy inputs (and noise). Subsequently considering the controller and estimator gains as design variables requires further linearization to maintain the LMI structure. Without a competing performance criterion, the solution of this gain design is the trivial uncoupling of the feedback loop (setting either gain to zero). Here we consider and convexify - an output constrained covariance (OCC) ||H|| 2 gain constraint on the non-attacked system. Through additional tricks to linearize the combination of these LMI constraints, we propose an iterative algorithm whose core is a combined convex optimization problem to minimize the state reachable set due to the attacker while ensuring a small enough ||H|| 2 gain during nominal operation.
Interpolation for Curves in Projective Space with Bounded Error

Given n general points p_1, p_2,..., p_n \in P^r, it is natural to ask whether there is a curve of given degree d and genus g passing through them; by counting dimensions a natural conjecture is that such a curve exists if and only if \[n \leq \left\lfloor \frac{(r + 1)d - (r - 3)(g - 1)}{r - 1}\right\rfloor.\] In the case of curves with nonspecial hyperplane section, the above conjecture was recently shown to hold with exactly three exceptions. In this paper, we prove a "bounded-error analog" for special linear series on general curves; more precisely we show that existance of such a curve subject to the stronger inequality \[n \leq \left\lfloor \frac{(r + 1)d - (r - 3)(g - 1)}{r - 1}\right\rfloor - 3.\] Note that the -3 cannot be replaced with -2 without introducing exceptions (as a canonical curve in P^3 can only pass through 9 general points, while a naive dimension count predicts 12). We also use the same technique to prove that the twist of the normal bundle N_C(-1) satisfies interpolation for curves whose degree is sufficiently large relative to their genus, and deduce from this that the number of general points contained in the hyperplane section of a general curve is at least \[\min\left(d, \frac{(r - 1)^2 d - (r - 2)^2 g - (2r^2 - 5r + 12)}{(r - 2)^2}\right).\] As explained in arXiv:1809.05980, these results play a key role in the author's proof of the Maximal Rank Conjecture.
Effective noncommutative Nevanlinna-Pick interpolation in the row ball, and applications

Abstract We provide an effective single-matrix criterion, in terms of what we call the elementary Pick matrix, for the solvability of the noncommutative Nevanlinna-Pick interpolation problem in the row ball, and provide some applications. In particular we show that the so-called “column-row property” fails for the free semigroup algebras, in stark contrast to the analogous commutative case. Additional applications of the elementary Pick matrix include a local dilation theorem for matrix row contractions and interpolating sequences in the noncommutative setting. Finally we present some numerical results related to the failure of the column-row property.
Acousto-optic modulation in lithium niobate on sapphire

We demonstrate the first acousto-optic modulators in lithium niobate films on sapphire, detailing the dependence of the piezoelectric and optomechanical coupling coefficients on the crystal orientation. This platform supports highly confined, strongly piezoelectric mechanical waves without suspensions, making it a promising candidate for broadband and efficient integrated acousto-optic devices, circuits, and systems.
Map Generation from Large Scale Incomplete and Inaccurate Data Labels

Accurately and globally mapping human infrastructure is an important and challenging task with applications in routing, regulation compliance monitoring, and natural disaster response management etc.. In this paper we present progress in developing an algorithmic pipeline and distributed compute system that automates the process of map creation using high resolution aerial images. Unlike previous studies, most of which use datasets that are available only in a few cities across the world, we utilizes publicly available imagery and map data, both of which cover the contiguous United States (CONUS). We approach the technical challenge of inaccurate and incomplete training data adopting state-of-the-art convolutional neural network architectures such as the U-Net and the CycleGAN to incrementally generate maps with increasingly more accurate and more complete labels of man-made infrastructure such as roads and houses. Since scaling the mapping task to CONUS calls for parallelization, we then adopted an asynchronous distributed stochastic parallel gradient descent training scheme to distribute the computational workload onto a cluster of GPUs with nearly linear speed-up.
Decentralized sequential active hypothesis testing and the MAC feedback capacity

We consider the problem of decentralized sequential active hypothesis testing (DSAHT), where two transmitting agents, each possessing a private message, are actively helping a third agent-and each other-to learn the message pair over a discrete memoryless multiple access channel (DM-MAC). The third agent (receiver) observes the noisy channel output, which is also available to the transmitting agents via noiseless feedback. We formulate this problem as a decentralized dynamic team, show that optimal transmission policies have a time-invariant domain, and characterize the solution through a dynamic program. Several alternative formulations are discussed involving time-homogenous cost functions and/or variable-length codes, resulting in solutions described through fixed-point, Bellman-type equations.
Individuals With Autism Have No Detectable Deficit in Neural Markers of Prediction Error When Presented With Auditory Rhythms of Varied Temporal Complexity.

The brain's ability to encode temporal patterns and predict upcoming events is critical for speech perception and other aspects of social communication. Deficits in predictive coding may contribute to difficulties with social communication and overreliance on repetitive predictable environments in individuals with autism spectrum disorder (ASD). Using a mismatch negativity (MMN) task involving rhythmic tone sequences of varying complexity, we tested the hypotheses that (1) individuals with ASD have reduced MMN response to auditory stimuli that deviate in presentation timing from expected patterns, particularly as pattern complexity increases and (2) amplitude of MMN signal is inversely correlated with level of impairment in social communication and repetitive behaviors. Electroencephalography was acquired as individuals (age 6-21 years) listened to repeated five-rhythm tones that varied in the Shannon entropy of the rhythm across three conditions (zero, medium-1 bit, and high-2 bit entropy). The majority of the tones conformed to the established rhythm (standard tones); occasionally the fourth tone was temporally shifted relative to its expected time of occurrence (deviant tones). Social communication and repetitive behaviors were measured using the Social Responsiveness Scale and Repetitive Behavior Scale-Revised. Both neurotypical controls (n = 19) and individuals with ASD (n = 21) show stepwise decreases in MMN as a function of increasing entropy. Contrary to the result forecasted by a predictive coding hypothesis, individuals with ASD do not differ from controls in these neural mechanisms of prediction error to auditory rhythms of varied temporal complexity, and there is no relationship between these signals and social communication or repetitive behavior measures. LAY SUMMARY: We tested the idea that the brain's ability to use previous experience to influence processing of sounds is weaker in individuals with autism spectrum disorder (ASD) than in neurotypical individuals. We found no difference between individuals with ASD and neurotypical controls in brain wave responses to sounds that occurred earlier than expected in either simple or complex rhythms. There was also no relationship between these brain waves and social communication or repetitive behavior scores.
Topological Descriptors for Parkinson’s Disease Classification and Regression Analysis

At present, the vast majority of human subjects with neurological disease are still diagnosed through in-person assessments and qualitative analysis of patient data. In this paper, we propose to use Topological Data Analysis (TDA) together with machine learning tools to automate the process of Parkinson’s disease classification and severity assessment. An automated, stable, and accurate method to evaluate Parkinson’s would be significant in streamlining diagnoses of patients and providing families more time for corrective measures. We propose a methodology which incorporates TDA into analyzing Parkinson’s disease postural shifts data through the representation of persistence images. Studying the topology of a system has proven to be invariant to small changes in data and has been shown to perform well in discrimination tasks. The contributions of the paper are twofold. We propose a method to 1) classify healthy patients from those afflicted by disease and 2) diagnose the severity of disease. We explore the use of the proposed method in an application involving a Parkinson’s disease dataset comprised of healthy-elderly, healthy-young and Parkinson’s disease patients. Our code is available at https://github.com/itsmeafra/Sublevel-Set-TDA.
3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials

Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow clinicians and researchers to qualitatively and quantitatively diagnose hemorrhagic stroke, guide interventions and determine inclusion criteria of patients in clinical trials. There is no currently available open source, validated tool to quickly segment hemorrhage. Using an automated pipeline and 2D and 3D deep neural networks, we show that we can quickly and accurately estimate ICH volume with high agreement with time-consuming manual segmentation. The training and validation datasets include significant heterogeneity in terms of pathology, such as the presence of intraventricular (IVH) or subdural hemorrhages (SDH) as well as variable image acquisition parameters. We show that deep neural networks trained with an appropriate anatomic context in the network receptive field, can effectively perform ICH segmentation, but those without enough context will overestimate hemorrhage along the skull and around calcifications in the ventricular system. We trained with all data from a multi-center phase II study (n = 112) achieving a best mean and median Dice coefficient of 0.914 and 0.919, a volume correlation of 0.979 and an average volume difference of 1.7 ml and root mean squared error of 4.7 ml in 500 out-of-sample scans from the corresponding multi-center phase III study. 3D networks with appropriate anatomic context outperformed both 2D and random forest models. Our results suggest that deep neural network models, when carefully developed can be incorporated into the workflow of an ICH clinical trial series to quickly and accurately segment ICH, estimate total hemorrhage volume and minimize segmentation failures. The model, weights and scripts for deployment are located at https://github.com/msharrock/deepbleed . This is the first publicly available neural network model for segmentation of ICH, the only model evaluated with the presence of both IVH and SDH and the only model validated in the workflow of a series of clinical trials.
Emulating duration and curvature of coral snake anti-predator thrashing behaviors using a soft-robotic platform

This paper presents a soft-robotic platform for exploring the ecological relevance of non-locomotory movements via animal-robot interactions. Coral snakes (genus Micrurus) and their mimics use vigorous, non-locomotory, and arrhythmic thrashing to deter predation. There is variation across snake species in the duration and curvature of anti-predator thrashes, and it is unclear how these aspects of motion interact to contribute to snake survival. In this work, soft robots composed of fiber-reinforced elastomeric enclosures (FREEs) are developed to emulate the anti-predator behaviors of three genera of snake. Curvature and duration of motion are estimated for both live snakes and robots, providing a quantitative assessment of the robots’ ability to emulate snake poses. The curvature values of the fabricated soft-robotic head, midsection, and tail segments are found to overlap with those exhibited by live snakes. Soft robot motion durations were less than or equal to those of snakes for all three genera. Additionally, combinations of segments were selected to emulate three specific snake genera with distinct anti-predatory behavior, producing curvature values that aligned well with live snake observations.
Manifold for machine learning assurance

The increasing use of machine-learning (ML) enabled systems in critical tasks fuels the quest for novel verification and validation techniques yet grounded in accepted system assurance principles. In traditional system development, model-based techniques have been widely adopted, where the central premise is that abstract models of the required system provide a sound basis for judging its implementation. We posit an analogous approach for ML systems using an ML technique that extracts from the high-dimensional training data implicitly describing the required system, a low-dimensional underlying structure–a manifold. It is then harnessed for a range of quality assurance tasks such as test adequacy measurement, test input generation, and runtime monitoring of the target ML system. The approach is built on variational autoencoder, an unsupervised method for learning a pair of mutually near-inverse functions between a given high-dimensional dataset and a low-dimensional representation. Preliminary experiments establish that the proposed manifold-based approach, for test adequacy drives diversity in test data, for test generation yields fault-revealing yet realistic test cases, and for run-time monitoring provides an independent means to assess trustability of the target system’s output. Ccs Concepts • Software and its engineering → Software testing and debugging; • Computing methodologies → Machine learning.
SimuLearn: Fast and Accurate Simulator to Support Morphing Materials Design and Workflows

Morphing materials allow us to create new modalities of interaction and fabrication by leveraging the materials? dynamic behaviors. Yet, despite the ongoing rapid growth of computational tools within this realm, current developments are bottlenecked by the lack of an effective simulation method. As a result, existing design tools must trade-off between speed and accuracy to support a real-time interactive design scenario. In response, we introduce SimuLearn, a data-driven method that combines finite element analysis and machine learning to create real-time (0.61 seconds) and truthful (97% accuracy) morphing material simulators. We use mesh-like 4D printed structures to contextualize this method and prototype design tools to exemplify the design workflows and spaces enabled by a fast and accurate simulation method. Situating this work among existing literature, we believe SimuLearn is a timely addition to the HCI CAD toolbox that can enable the proliferation of morphing materials.
Maternal transfer of environmentally relevant polybrominated diphenyl ethers (PBDEs) produces a diabetic phenotype and disrupts glucoregulatory hormones and hepatic endocannabinoids in adult mouse female offspring.

Polybrominated diphenyl ethers (PBDEs) are brominated flame retardant chemicals and environmental contaminants with endocrine-disrupting properties that are associated with diabetes and metabolic syndrome in humans. However, their diabetogenic actions are not completely characterized or understood. In this study, we investigated the effects of DE-71, a commercial penta-mixture of PBDEs, on glucoregulatory parameters in a perinatal exposure model using female C57Bl/6 mice. Results from in vivo glucose and insulin tolerance tests and ex vivo analyses revealed fasting hyperglycemia, glucose intolerance, reduced sensitivity and delayed glucose clearance after insulin challenge, decreased thermogenic brown adipose tissue mass, and exaggerated hepatic endocannabinoid tone in F1 offspring exposed to 0.1 mg/kg DE-71 relative to control. DE-71 effects on F0 dams were more limited indicating that indirect exposure to developing offspring is more detrimental. Other ex vivo glycemic correlates occurred more generally in exposed F0 and F1, i.e., reduced plasma insulin and altered glucoregulatory endocrines, exaggerated sympathoadrenal activity and reduced hepatic glutamate dehydrogenase enzymatic activity. Hepatic PBDE congener analysis indicated maternal transfer of BDE-28 and -153 to F1 at a collective level of 200 ng/g lipid, in range with maximum values detected in serum of human females. Given the persistent diabetogenic phenotype, especially pronounced in female offspring after developmental exposure to environmentally relevant levels of DE-71, additional animal studies should be conducted that further characterize PBDE-induced diabetic pathophysiology and identify critical developmental time windows of susceptibility. Longitudinal human studies should also be conducted to determine the risk of long-lasting metabolic consequences after maternal transfer of PBDEs during early-life development.
Ground state wave function overlap in superconductors and superfluids

In order to elucidate the quantum ground state structure of non-relativistic condensates, we explicitly construct the ground state wave function for multiple species of bosons, describing either superconductivity or superfluidity. Since each field $\Psi_j$ carries a phase $\theta_j$ and the Lagrangian is invariant under rotations $\theta_j \to \theta_j + \alpha_j$ for independent $\alpha_j$, one can investigate the corresponding wave function overlap between a pair of ground states $\langle G|G'\rangle$ differing by these phases. We operate in the infinite volume limit and use a particular prescription to define these states by utilizing the position space kernel and regulating the UV modes. We show that this overlap vanishes for most pairs of rotations, including $\theta_j \to \theta_j + m_j \epsilon$, where $m_j$ is the mass of each species, while it is unchanged under the transformation $\theta_j \to \theta_j + q_j \epsilon$, where $q_j$ is the charge of each species. We explain that this is consistent with the distinction between a superfluid, in which there is a non-trivial conserved number, and the superconductor, in which the electric field and conserved charge is screened, while it is compatible with a non-zero order parameter in both cases. Moreover, we find that this bulk ground state wave function overlap directly reflects the Goldstone boson structure of the effective theory and provides a useful diagnostic of its physical phase.
Urca nuclide production in Type-I X-ray bursts and implications for nuclear physics studies

The thermal structure of accreting neutron stars is affected by the presence of urca nuclei in the neutron star crust. Nuclear isobars harboring urca nuclides can be produced in the ashes of Type I X-ray bursts, but the details of their production have not yet been explored. Using the code {\tt MESA}, we investigate urca nuclide production in a one-dimensional model of Type I X-ray bursts using astrophysical conditions thought to resemble the source GS 1826-24. We find that high-mass ($A\geq55$) urca nuclei are primarily produced late in the X-ray burst, during hydrogen-burning freeze-out that corresponds to the tail of the burst light curve. The $\sim0.4$--0.6~GK temperature relevant for nucleosynthesis of these urca nuclides is much lower than the $\sim1$~GK temperature most relevant for X-ray burst light curve impacts by nuclear reaction rates involving high-mass nuclides. The latter temperature is often assumed for nuclear physics studies. Therefore, our findings alter the excitation energy range of interest in compound nuclei for nuclear physics studies of urca nuclide production. We demonstrate that for some cases this will need to be considered in planning for nuclear physics experiments. Additionally, we show that the lower temperature range for urca nuclide production explains why variations of some nuclear reaction rates in model calculations impacts the burst light curve but not local features of the burst ashes.
PET-enabled dual-energy CT: Image reconstruction and a proof-of-concept computer simulation study

Standard dual-energy computed tomography (CT) uses two different x-ray energies to obtain energy-dependent tissue attenuation information to allow quantitative material decomposition. The combined use of dual-energy CT and positron emission tomography (PET) may provide a more comprehensive characterization of disease states in cancer and other diseases. However, the integration of dual-energy CT with PET is not trivial, either requiring costly hardware upgrades or increasing radiation exposure. This paper proposes a different dual-energy CT imaging method that is enabled by PET. Instead of using a second x-ray CT scan with a different energy, this method exploits time-of-flight PET image reconstruction via the maximum likelihood attenuation and activity (MLAA) algorithm to obtain a 511 keV gamma-ray attenuation image from PET emission data. The high-energy gamma-ray attenuation image is then combined with the low-energy x-ray CT of PET/CT to provide a pair of dual-energy CT images. A major challenge with the standard MLAA reconstruction is the high noise present in the reconstructed 511 keV attenuation map, which would not compromise the PET activity reconstruction too much but may significantly affect the performance of the gamma-ray attenuation image for material decomposition. To overcome the problem, we further propose a kernel MLAA algorithm to exploit the prior information from the available x-ray CT image. We conducted a computer simulation to test the concept and algorithm for the task of material decomposition. The simulation results demonstrate that this PET-enabled dual-energy CT method is promising for quantitative material decomposition. The proposed method can be readily implemented on time-of-flight PET/CT scanners to enable simultaneous PET and dual-energy CT imaging.
Relations among characteristic classes of manifold bundles

We study relations among characteristic classes of smooth manifold bundles with highly connected fibers. For bundles with fiber the connected sum of g copies of a product of spheres Sd × Sd, where d is odd, we find numerous algebraic relations among so-called “generalized Miller–Morita–Mumford classes”. For all g > 1, we show that these infinitely many classes are algebraically generated by a finite subset. Our results contrast with the fact that there are no algebraic relations among these classes in a range of cohomological degrees that grows linearly with g, according to recent homological stability results. In the case of surface bundles (d = 1), our approach recovers some previously known results about the structure of the classical “tautological ring”, as introduced by Mumford, using only the tools of algebraic topology.
General Relativity and the Cuprates

We add a periodic potential to the simplest gravitational model of a superconductor and compute the optical conductivity. In addition to a superfluid component, we find a normal component that has Drude behavior at low frequency followed by a power law fall-off. Both the exponent and coefficient of the power law are temperature independent and agree with earlier results computed above T c. These results are in striking agreement with measurements on some cuprates. We also find a gap Δ = 4.0 T c, a rapidly decreasing scattering rate, and “missing spectral weight” at low frequency, all of which also agree with experiments.
Optimal Planning and Operation of Multi-Frequency HVac Transmission Systems

Low-frequency high-voltage alternating-current (LF-HVac) transmission scheme has been recently proposed as an alternative solution to conventional 50/60-Hz HVac and high-voltage direct-current (HVdc) schemes for bulk power transfer. This paper proposes an optimal planning and operation for loss minimization in a multi-frequency HVac transmission system. In such a system, conventional HVac and LF-HVac grids are interconnected using back-to-back (BTB) converters. The dependence of system MW losses on converter dispatch as well as the operating voltage and frequency in the LF-HVac is discussed and compared with that of HVdc transmission. Based on the results of the loss analysis, multi-objective optimization formulations for both planning and operation stages are proposed. The planning phase decides a suitable voltage level for the LF-HVac grid, while the operation phase determines the optimal operating frequency and power dispatch of BTB converters, generators, and shunt capacitors. A solution approach that effectively handles the variations of transmission line parameters with the rated voltage and operating frequency in the LF-HVac grid is proposed. The proposed solutions of the planning and operation stages are evaluated using a multi-frequency HVac system. The results show a significant loss reduction and improved voltage regulation during a 24-hour simulation.
A Fluka study of underground cosmogenic neutron production

Neutrons produced by cosmic muon interactions are important contributors to backgrounds in underground detectors when searching for rare events. Typically such neutrons can dominate the background, as they are particularly difficult to shield and detect. Since actual data is sparse and not well documented, simulation studies must be used to design shields and predict background rates. Thus validation of any simulation code is necessary to assure reliable results. This work compares in detail predictions of the FLUKA simulation code to existing data, and uses this code to report a simulation of cosmogenic backgrounds for typical detectors embedded in a water tank with liquid scintillator shielding.
Resolution of the canonical fiber metrics for a Lefschetz fibration

We consider the family of constant curvature fiber metrics for a Lefschetz fibration with regular fibers of genus greater than one. A result of Obitsu and Wolpert is refined by showing that on an appropriate resolution of the total space, constructed by iterated blow-up, this family is log-smooth, i.e., polyhomogeneous with integral powers but possible multiplicities, at the preimage of the singular fibers in terms of parameters of size comparable to the logarithm of the length of the shrinking geodesic.
A New Proof of the Existence of Embedded Surfaces with Anosov Geodesic Flow

We give a new proof of the existence of compact surfaces embedded in ℝ3 with Anosov geodesic flows. This proof starts with a noncompact model surface whose geodesic flow is shown to be Anosov using a uniformly strictly invariant cone condition. Using a sequence of explicit maps based on the standard torus embedding, we produce compact embedded surfaces that can be seen as small perturbations of the Anosov model system and hence are themselves Anosov.
Evaluating Spectral Models and the X-Ray States of Neutron Star X-Ray Transients

We analyze the X-ray spectra of the neutron star (NS) X-ray transients Aql X-1 and 4U 1608-52, obtained with RXTE during more than 20 outbursts. We test commonly used spectral models and evaluate their performance against desirability criteria, including LX ∝ T4 evolution for the multicolor disk (MCD) component and similarity to black holes (BHs) for correlated timing/spectral behavior. None of the classical models for thermal emission plus Comptonization perform well in the soft state. Instead, we devise a hybrid model: for the hard state, a single-temperature blackbody (BB) plus a broken power law (BPL), and for the soft state, two thermal components (MCD and BB) plus a constrained BPL. This model produces LX ∝ T4 tracks for both the MCD and BB, and it aligns the spectral/timing correlations of these NSs with the properties of accreting BHs. The visible BB emission area is very small (~1/16 of the NS surface), but it remains roughly constant over a wide range of LX that spans both the hard and soft states. We discuss implications of a small and constant boundary layer in terms of the presence of an innermost stable circular orbit that lies outside the NS. Finally, if the BB luminosity tracks the overall accretion rate, then we find that the Comptonization in the hard state has surprisingly high radiative efficiency compared to MCD emission in the soft state. Alternatively, if we assume that the radiative efficiency of a jet in the hard state must be less than the MCD efficiency in the soft state, while relaxing presumptions about the accretion rate, then our results may suggest substantial mass outflow in the jet.
NeTFactor, a framework for identifying transcriptional regulators of gene expression-based biomarkers

Biological and regulatory mechanisms underlying many multi-gene expression-based disease biomarkers are often not readily evident. We describe an innovative framework, NeTFactor, that combines network analyses with gene expression data to identify transcription factors (TFs) that significantly and maximally regulate such a biomarker. NeTFactor uses a computationally-inferred context-specific gene regulatory network and applies topological, statistical, and optimization methods to identify regulator TFs. Application of NeTFactor to a multi-gene expression-based asthma biomarker identified ETS translocation variant 4 (ETV4) and peroxisome proliferator-activated receptor gamma (PPARG) as the biomarker's most significant TF regulators. siRNA-based knock down of these TFs in an airway epithelial cell line model demonstrated significant reduction of cytokine expression relevant to asthma, validating NeTFactor's top-scoring findings. While PPARG has been associated with airway inflammation, ETV4 has not yet been implicated in asthma, thus indicating the possibility of novel, disease-relevant discovery by NeTFactor. We also show that NeTFactor's results are robust when the gene regulatory network and biomarker are derived from independent data. Additionally, our application of NeTFactor to a different disease biomarker identified TF regulators of interest. These results illustrate that the application of NeTFactor to multi-gene expression-based biomarkers could yield valuable insights into regulatory mechanisms and biological processes underlying disease.
Simulating High-Redshift Disk Galaxies: Applications to Long Duration Gamma-Ray Burst Hosts

The efficiency of star formation governs many observable properties of the cosmological galaxy population, yet many current models of galaxy formation largely ignore the important physics of star formation and the interstellar medium (ISM). Using hydrodynamical simulations of disk galaxies that include a treatment of the molecular ISM and star formation in molecular clouds (Robertson & Kravtsov 2008), we study the influence of star formation efficiency and molecular hydrogen abundance on the properties of high-redshift galaxy populations. In this work, we focus on a model of low-mass, star forming galaxies at 1<~z<~2 that may host long duration gamma-ray bursts (GRBs). Observations of GRB hosts have revealed a population of faint systems with star formation properties that often differ from Lyman-break galaxies (LBGs) and more luminous high-redshift field galaxies. Observed GRB sightlines are deficient in molecular hydrogen, but it is unclear to what degree this deficiency owes to intrinsic properties of the galaxy or the impact the GRB has on its environment. We find that hydrodynamical simulations of low-stellar mass systems at high-redshifts can reproduce the observed star formation rates and efficiencies of GRB host galaxies at redshifts 1<~z<~2. We show that the compact structure of low-mass high-redshift GRB hosts may lead to a molecular ISM fraction of a few tenths, well above that observed in individual GRB sightlines. However, the star formation rates of observed GRB host galaxies imply molecular gas masses of 10^8 - 10^9 M_sun similar to those produced in the simulations, and may therefore imply fairly large average H_2 fractions in their ISM.
A study of the representations supported by the orbit closure of the determinant

We show the existence of a large family of representations supported by the orbit closure of the determinant. However, the validity of our result is based on the validity of the celebrated ‘Latin square conjecture’ due to Alon and Tarsi or, more precisely, on the validity of an equivalent ‘column Latin square conjecture’ due to Huang and Rota.
Density of Complex Zeros of a System of Real Random Polynomials

We study the density of complex zeros of a system of real random SO(m+1) polynomials in m variables. We show that the density of complex zeros of this random polynomial system with real coefficients rapidly approaches the density of complex zeros in the complex coefficients case. We also show that the behavior the scaled density of complex zeros near ℝm of the system of real random polynomials is different in the m≥2 case than in the m=1 case: the density approaches infinity instead of tending linearly to zero.
BigEAR: Inferring the Ambient and Emotional Correlates from Smartphone-Based Acoustic Big Data

This paper presents a novel BigEAR big data framework that employs psychological audio processing chain (PAPC) to process smartphone-based acoustic big data collected when the user performs social conversations in naturalistic scenarios. The overarching goal of BigEAR is to identify moods of the wearer from various activities such as laughing, singing, crying, arguing, and sighing. These annotations are based on ground truth relevant for psychologists who intend to monitor/infer the social context of individuals coping with breast cancer. We pursued a case study on couples coping with breast cancer to know how the conversations affect emotional and social well being. In the state-of-the-art methods, psychologists and their team have to hear the audio recordings for making these inferences by subjective evaluations that not only are time-consuming and costly, but also demand manual data coding for thousands of audio files. The BigEAR framework automates the audio analysis. We computed the accuracy of BigEAR with respect to the ground truth obtained from a human rater. Our approach yielded overall average accuracy of 88.76% on real-world data from couples coping with breast cancer.
On the BV formulation of boundary superstring field theory

We propose a Batalin-Vilkovisky (BV) formulation of boundary superstring field theory. The superstring field action is defined in terms of a closed one-form in the space of couplings, and we compute it explicitly for exactly solvable tachyon perturbations. We also argue that the superstring field action defined in this way is the partition function on the disc, in accord with a previous proposal.
SYM description of SFT Hamiltonian in a pp-wave background

Abstract We compute string field theory Hamiltonian matrix elements and compare them with matrix elements of the dilatation operator in gauge theory. We get precise agreement between the string field theory and gauge theory computations once the correct cubic Hamiltonian matrix elements in string field theory and a particular basis of states in gauge theory are used. We proceed to compute the matrix elements of the dilatation operator to order g 2 2 in this same basis. This calculation makes a prediction for string field theory Hamiltonian matrix elements to order g 2 2 , which have not yet been computed. However, our gauge theory results precisely match the results of the recent computation by Pearson et al. of the order g 2 2 Hamiltonian matrix elements of the string bit model.
Novel applications of the dispersive optical model

A review of recent developments of the dispersive optical model (DOM) is presented. Starting from the original work of Mahaux and Sartor, several necessary steps are developed and illustrated which increase the scope of the DOM allowing its interpretation as generating an experimentally constrained functional form of the nucleon self-energy. The method could therefore be renamed as the dispersive self-energy method. The aforementioned steps include the introduction of simultaneous fits of data for chains of isotopes or isotones allowing a data-driven extrapolation for the prediction of scattering cross sections and level properties in the direction of the respective drip lines. In addition, the energy domain for data was enlarged to include results up to 200 MeV where available. An important application of this work was implemented by employing these DOM potentials to the analysis of the (\textit{d,p}) transfer reaction using the adiabatic distorted wave approximation (ADWA). We review the fully non-local DOM potential fitted to ${}^{40}$Ca where elastic-scattering data, level information, particle number, charge density and high-momentum-removal $(e,e'p)$ cross sections obtained at Jefferson Lab were included in the analysis. An important consequence of this new analysis is the finding that the spectroscopic factor for the removal of valence protons in this nucleus comes out larger by about 0.15 than the results obtained from the NIKHEF analysis of their $(e,e'p)$ data. Another important consequence of this analysis is that it can shed light on the relative importance of two-body and three-body interactions as far as their contribution to the energy of the ground state is concerned through application of the energy sum rule.
C18O depletion in starless cores in Taurus

We present here findings for C18O depletion in eight starless cores in Taurus: TMC-2, L1498, L1512, L1489, L1517B, L1521E, L1495A-S, and L1544. We compare observations of the C18O J = 2-1 transition taken with the ALMA prototype receiver on the Heinrich Hertz Submillimeter Telescope to results of radiative transfer modeling using RATRAN. We use temperature and density profiles calculated from dust continuum radiative transfer models to model the C18O emission. We present modeling of three cores, TMC-2, L1489, and L1495A-S, which have not been modeled before, and compare our results for the five cores with published models. We find that all of the cores but one, L1521E, are substantially depleted. We also find that varying the temperature profiles of these model cores has a discernable effect, and varying the central density has an even larger effect. We find no trends with depletion radius or depletion fraction with the density or temperature of these cores, suggesting that the physical structure alone is insufficient to fully constrain evolutionary state. We are able to place tighter constraints on the radius at which C18O is depleted than the absolute fraction of depletion. As the timeline of chemical depletion depends sensitively on the fraction of depletion, this difficulty in constraining depletion fraction makes comparison with other timescales, such as the free-fall timescale, very difficult.
An IDEA: an ingestion framework for data enrichment in asterixDB

Big Data today is being generated at an unprecedented rate from various sources such as sensors, applications, and devices, and it often needs to be enriched based on other reference information to support complex analytical queries. Depending on the use case, the enrichment operations can be compiled code, declarative queries, or machine learning models with different complexities. For enrichments that will be frequently used in the future, it can be advantageous to push their computation into the ingestion pipeline so that they can be stored (and queried) together with the data. In some cases, the referenced information may change over time, so the ingestion pipeline should be able to adapt to such changes to guarantee the currency and/or correctness of the enrichment results.In this paper, we present a new data ingestion framework that supports data ingestion at scale, enrichments requiring complex operations, and adaptiveness to reference data changes. We explain how this framework has been built on top of Apache AsterixDB and investigate its performance at scale under various workloads.
Interfacial friction between semiflexible polymers and crystalline surfaces

The results obtained from molecular dynamics simulations of the friction at an interface between polymer melts and weakly attractive crystalline surfaces are reported. We consider a coarse-grained bead-spring model of linear chains with adjustable intrinsic stiffness. The structure and relaxation dynamics of polymer chains near interfaces are quantified by the radius of gyration and decay of the time autocorrelation function of the first normal mode. We found that the friction coefficient at small slip velocities exhibits a distinct maximum which appears due to shear-induced alignment of semiflexible chain segments in contact with solid walls. At large slip velocities, the friction coefficient is independent of the chain stiffness. The data for the friction coefficient and shear viscosity are used to elucidate main trends in the nonlinear shear rate dependence of the slip length. The influence of chain stiffness on the relationship between the friction coefficient and the structure factor in the first fluid layer is discussed.
Triggering Collapse of the Presolar Dense Cloud Core and Injecting Short-lived Radioisotopes with a Shock Wave. III. Rotating Three-dimensional Cloud Cores

A key test of the supernova triggering and injection hypothesis for the origin of the solar system's short-lived radioisotopes is to reproduce the inferred initial abundances of these isotopes. We present here the most detailed models to date of the shock wave triggering and injection process, where shock waves with varied properties strike fully three-dimensional, rotating, dense cloud cores. The models are calculated with the FLASH adaptive mesh hydrodynamics code. Three different outcomes can result: triggered collapse leading to fragmentation into a multiple protostar system; triggered collapse leading to a single protostar embedded in a protostellar disk; or failure to undergo dynamic collapse. Shock wave material is injected into the collapsing clouds through Rayleigh-Taylor fingers, resulting in initially inhomogeneous distributions in the protostars and protostellar disks. Cloud rotation about an axis aligned with the shock propagation direction does not increase the injection efficiency appreciably, as the shock parameters were chosen to be optimal for injection even in the absence of rotation. For a shock wave from a core-collapse supernova, the dilution factors for supernova material are in the range of ∼10{sup –4} to ∼3 × 10{sup –4}, in agreement with recent laboratory estimates of the required amount of dilution formore » {sup 60}Fe and {sup 26}Al. We conclude that a type II supernova remains as a promising candidate for synthesizing the solar system's short-lived radioisotopes shortly before their injection into the presolar cloud core by the supernova's remnant shock wave.« less
Radionuclide ionization in protoplanetary disks: Calculations of decay product radiative transfer

We present simple analytic solutions for the ionization rate ζ{sub SLR} arising from the decay of short-lived radionuclides (SLRs) within protoplanetary disks. We solve the radiative transfer problem for the decay products within the disk, and thereby allow for the loss of radiation at low disk surface densities; energy loss becomes important outside R ∼> 30 AU for typical disk masses M{sub g} = 0.04 M{sub ☉}. Previous studies of chemistry/physics in these disks have neglected the impact of ionization by SLRs, and often consider only cosmic rays (CRs), because of the high CR-rate present in the interstellar medium. However, recent work suggests that the flux of CRs present in the circumstellar environment could be substantially reduced by relatively modest stellar winds, resulting in severely modulated CR ionization rates, ζ{sub CR}, equal to or substantially below that of SLRs (ζ{sub SLR} ∼< 10{sup –18} s{sup –1}). We compute the net ionizing particle fluxes and corresponding ionization rates as a function of position within the disk for a variety of disk models. The resulting expressions are especially simple for the case of vertically Gaussian disks (frequently assumed in the literature). Finally, we provide a power-law fit to the ionization rate inmore » the midplane as a function of gas disk surface density and time. Depending on location in the disk, the ionization rates by SLRs are typically in the range ζ{sub SLR} ∼ (1-10) × 10{sup –19} s{sup –1}.« less
Dispersive and effective properties of two-dimensional periodic media.

We consider transverse propagation of electromagnetic waves through a two-dimensional composite material containing a periodic rectangular array of circular cylinders. Propagation of waves is descr...