cssi/SciSciGPT-SciSciNet huggingface dataset:
Almost politically acceptable criminal justice risk assessment

In criminal justice risk forecasting, one can prove that it is impossible to optimize accuracy and fairness at the same time. One can also prove that it is impossible optimize at once all of the usual group definitions of fairness. In the policy arena, one is left with tradeoffs about which many stakeholders will adamantly disagree. In this paper, we offer a different approach. We do not seek perfectly accurate and fair risk assessments. We seek politically acceptable risk assessments. We describe and apply to data on 300,000 offenders a machine learning approach that responds to many of the most visible charges of "racial bias." Regardless of whether such claims are true, we adjust our procedures to compensate. We begin by training the algorithm on White offenders only and computing risk with test data separately for White offenders and Black offenders. Thus, the fitted algorithm structure is exactly the same for both groups; the algorithm treats all offenders as if they are White. But because White and Black offenders can bring different predictors distributions to the white-trained algorithm, we provide additional adjustments if needed. Insofar are conventional machine learning procedures do not produce accuracy and fairness that some stakeholders require, it is possible to alter conventional practice to respond explicitly to many salient stakeholder claims even if they are unsupported by the facts. The results can be a politically acceptable risk assessment tools.
NDNTP: A Named Data Networking Time Protocol

Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring multi-producer participation, introduce new NDN design challenges. in this article, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, called Named Data Networking Time Protocol (NDNTP).
Local sensitivity analysis of the “membrane shape equation” derived from the Helfrich energy:

The Helfrich energy is commonly used to model the elastic bending energy of lipid bilayers in membrane mechanics. The governing differential equations for certain geometric characteristics of the s...
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.

We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treatment effect is roughly constant throughout. The regions are found as either the solution to a mixed integer program, or using a (fast) approximation algorithm. The result is an interpretable and tailored estimate of a causal effect for each unit.
Beam data modeling of linear accelerators (linacs) through machine learning and its potential applications in fast and robust linac commissioning and quality assurance.

Abstract Background and purpose To propose a novel machine learning-based method for reliable and accurate modeling of linac beam data applicable to the processes of linac commissioning and QA. Materials and methods We hypothesize that the beam data is a function of inherent linac features and percentage depth doses (PDDs) and profiles of different field sizes are correlated with each other. The correlation is formulated as a multivariable regression problem using a machine learning framework. Varian TrueBeam beam data sets (n = 43) acquired from multiple institutions were used to evaluate the framework. The data sets included PDDs and profiles across different energies and field sizes. A multivariate regression model was trained for prediction of beam specific PDDs and profiles of different field sizes using a 10 × 10 cm2 field as input. Results Predictions of PDDs were achieved with a mean absolute percent relative error (%RE) of 0.19–0.35% across the different beam energies investigated. The maximum mean absolute %RE was 0.93%. For profile prediction, the mean absolute %RE was 0.66–0.93% with a maximum absolute %RE of 3.76%. The largest uncertainties in the PDD and profile predictions were found at the build-up region and at the field penumbra, respectively. The prediction accuracy increased with the number of training sets up to around 20 training sets. Conclusions Through this novel machine learning-based method we have shown accurate and reproducible generation of beam data for linac commissioning for routine radiation therapy. This method has the potential to simplify the linac commissioning procedure, save time and manpower while increasing the accuracy of the commissioning process.
Selective Hydrogenation of Acrolein on a Pd/Ag(111) Single-Atom Alloy Surface

The hydrogenation of acrolein on the Ag(111) and Pd/Ag(111) single-atom alloy (SAA) surfaces was studied using temperature-programmed reaction spectroscopy. On Ag(111), the percent conversion of ac...
Bayesian Estimation of the Seroprevalence of Antibodies to SARS-CoV-2

Accurate estimations of the seroprevalence of antibodies to severe acute respiratory syndrome coronavirus 2 need to properly consider the specificity and sensitivity of the antibody tests. In addition, prior knowledge of the extent of viral infection in a population may also be important for adjusting the estimation of seroprevalence. For this purpose, we have developed a Bayesian approach that can incorporate the variabilities of specificity and sensitivity of the antibody tests, as well as the prior probability distribution of seroprevalence. We have demonstrated the utility of our approach by applying it to a recently published large-scale dataset from the US CDC, with our results providing entire probability distributions of seroprevalence instead of single-point estimates. Our Bayesian code is freely available at https://github.com/qunfengdong/AntibodyTest.
Expression Partitioning of Duplicate Genes at Single Cell Resolution in Arabidopsis Roots

Gene duplication is a key evolutionary phenomenon, prevalent in all organisms but particularly so in plants, where whole genome duplication (WGD; polyploidy) is a major force in genome evolution. Much effort has been expended in attempting to understand the evolution of duplicate genes, addressing such questions as why some paralog pairs rapidly return to single copy status whereas, in other pairs, both paralogs are retained and may diverge in expression pattern or function. The effect of a gene - its site of expression and thus the initial locus of its function - occurs at the level of a cell comprising a single cell type at a given state of the cell's development. Using Arabidopsis thaliana single cell transcriptomic data we categorized patterns of expression for 11,470 duplicate gene pairs across 36 cell clusters comprising nine cell types and their developmental states. Among these 11,470 pairs, 10,187 (88.8%) had at least one copy expressed in at least one of the 36 cell clusters. Pairs produced by WGD more often had both paralogs expressed in root cells than did pairs produced by small scale duplications. Three quarters of gene pairs expressed in the 36 cell clusters (7,608/10,187) showed extreme expression bias in at least one cluster, including 352 cases of reciprocal bias, a pattern consistent with expression subfunctionalization. More than twice as many pairs showed reciprocal expression bias between cell states than between cell types or between roots and leaves. A group of 33 gene pairs with reciprocal expression bias showed evidence of concerted divergence of gene networks in stele vs. epidermis. Pairs with both paralogs expressed without bias were less likely to have paralogs with divergent mutant phenotypes; such bias-free pairs showed evidence of preservation by maintenance of dosage balance. Overall, we found considerable evidence of shifts in gene expression following duplication, including in >80% of pairs encoding 7,653 genes expressed ubiquitously in all root cell types and states for which we inferred the polarity of change.
Speaker Representation Learning using Global Context Guided Channel and Time-Frequency Transformations

In this study, we propose the global context guided channel and time-frequency transformations to model the long-range, non-local time-frequency dependencies and channel variances in speaker representations. We use the global context information to enhance important channels and recalibrate salient time-frequency locations by computing the similarity between the global context and local features. The proposed modules, together with a popular ResNet based model, are evaluated on the VoxCeleb1 dataset, which is a large scale speaker verification corpus collected in the wild. This lightweight block can be easily incorporated into a CNN model with little additional computational costs and effectively improves the speaker verification performance compared to the baseline ResNet-LDE model and the Squeeze&Excitation block by a large margin. Detailed ablation studies are also performed to analyze various factors that may impact the performance of the proposed modules. We find that by employing the proposed L2-tf-GTFC transformation block, the Equal Error Rate decreases from 4.56% to 3.07%, a relative 32.68% reduction, and a relative 27.28% improvement in terms of the DCF score. The results indicate that our proposed global context guided transformation modules can efficiently improve the learned speaker representations by achieving time-frequency and channel-wise feature recalibration.
Trem2 Y38C mutation and loss of Trem2 impairs neuronal synapses in adult mice.

Triggering receptor expressed on myeloid cells 2 (TREM2) is expressed in the brain exclusively on microglia and genetic variants are linked to neurodegenerative diseases including Alzheimer’s disease (AD), frontotemporal dementia (FTD) and Nasu Hakola Disease (NHD). The Trem2 variant R47H, confers substantially elevated risk of developing late onset Alzheimer’s disease, while NHD-linked Trem2 variants like Y38C, are associated with development of early onset dementia with white matter pathology. However, it is not known how these Trem2 species, predisposes individuals to presenile dementia. To investigate if Trem2 Y38C or loss of Trem2 alters neuronal function we generated a novel mouse model to introduce the NHD Trem2 Y38C variant in murine Trem2 using CRISPR/Cas9 technology. Trem2Y38C/Y38C and Trem2−/− mice were assessed for Trem2 expression, differentially expressed genes, synaptic protein levels and synaptic plasticity using biochemical, electrophysiological and transcriptomic approaches. While mice harboring the Trem2 Y38C exhibited normal expression levels of TREM2, the pathological outcomes phenocopied Trem2−/− mice at 6 months. Transcriptomic analysis revealed altered expression of neuronal and oligodendrocytes/myelin genes. We observed regional decreases in synaptic protein levels, with the most affected synapses in the hippocampus. These alterations were associated with reduced synaptic plasticity. Our findings provide in vivo evidence that Trem2 Y38C disrupts normal TREM2 functions. Trem2Y38C/Y38C and Trem2−/− mice demonstrated altered gene expression, changes in microglia morphology, loss of synaptic proteins and reduced hippocampal synaptic plasticity at 6 months in absence of any pathological triggers like amyloid. This suggests TREM2 impacts neuronal functions providing molecular insights on the predisposition of individuals with TREM2 variants resulting in presenile dementia.
Interfacial thermal conductance of thiolate-protected gold nanospheres

Molecular dynamics simulations of thiolate-protected and solvated gold nanoparticles were carried out in the presence of a non-equilibrium heat flux between the solvent and the core of the particle. The interfacial thermal conductance (G) was computed for these interfaces, and the behavior of the thermal conductance was studied as a function of particle size, ligand flexibility, and ligand chain length. In all cases, thermal conductance of the ligand-protected particles was higher than the bare metal–solvent interface. A number of mechanisms for the enhanced conductance were investigated, including thiolate-driven corrugation of the metal surface, solvent ordering at the interface, solvent-ligand interpenetration, and ligand ordering relative to the particle surface. Only the smallest particles exhibited significant corrugation. All ligands permitted substantial solvent-ligand interpenetration, and ligand chain length has a significant influence on the orientational ordering of interfacial solvent. Solven...
The antebellum roots of distinctively black names

This paper explores the existence of distinctively Black names in the antebellum era. Building on recent research that documents the existence of a national naming pattern for African American male...
Duality and dynamical supersymmetry breaking in Spin(10) with a spinor

Abstract We study N = 1 supersymmetric Spin (10) chiral gauge theories with a single spinor representation and N vector representations. We present a dual description in terms of an N = 1 supersymmetric SU ( N − 5) chiral gauge theory with a symmetric tensor, one fundamental and N antifundamental representations. The Spin (10) theory with N = 0 breaks supersymmetry at strong coupling; we study how this arises at weak coupling in the dual theory, which is a spontaneously broken gauge theory. Also, we recover various known dualities, find new dual pairs and generate new examples of dynamical supersymmetry breaking.
Separating twists and the Magnus representation of the Torelli group

The Magnus representation of the Torelli subgroup of the mapping class group of a surface is a homomorphism \({r: \fancyscript{I}_{g,1} \to {\rm GL}_{2g}(\mathbb{Z}H)}\). Here H is the first homology group of the surface. This representation is not faithful; in particular, Suzuki previously described precisely when the commutator of two Dehn twists about separating curves is in ker r. Using the trace of the Magnus representation, we apply a new method of showing that two endomorphisms generate a free group to prove that the images of two positive separating multitwists under the Magnus representation either commute or generate a free group, and we characterize when each case occurs.
On the supersingular locus of the GU(2,2) Shimura variety

We describe the supersingular locus of a GU(2,2) Shimura variety at a prime inert in the corresponding quadratic imaginary field.
Stabilizing Moduli with String Cosmology

In this talk I will discuss the role of finite temperature quantum corrections in string cosmology and show that they can lead to a stabilization mechanism for the volume moduli. I will show that from the higher dimensional perspective this results from the effect of states of enhanced symmetry on the one-loop free energy. These states lead not only to stabilization, but also suggest an alternative model for cold dark matter. At late times, when the low energy effective field theory gives the appropriate description of the dynamics, the moduli will begin to slow-roll and stabilization will generically fail. However, stabilization can be recovered by considering cosmological particle production near the points of enhanced symmetry leading to the process known as moduli trapping.
PCA/LDA approach for text-independent speaker recognition

Various algorithms for text-independent speaker recognition have been developed through the decades, aiming to improve both accuracy and efficiency. This paper presents a novel PCA/LDA-based approach that is faster than traditional statistical model-based methods and achieves competitive results. First, the performance based on only PCA and only LDA is measured; then a mixed model, taking advantages of both methods, is introduced. A subset of the TIMIT corpus composed of 200 male speakers, is used for enrollment, validation and testing. The best results achieve 100%, 96% and 95% classification rate at population level 50, 100 and 200, using 39- dimensional MFCC features with delta and double delta. These results are based on 12-second text-independent speech for training and 4-second data for test. These are comparable to the conventional MFCC-GMM methods, but require significantly less time to train and operate.
Hyperbolic Multi-Monopoles with Arbitrary Mass

On a complete manifold, such as \({\mathbb{R}^{3}}\) or hyperbolic space \({\mathbb{H}^{3}}\), the limit at infinity of the norm of the Higgs field \({\Phi}\) is called the mass of the monopole. We show the existence, on \({\mathbb{H}^{3}}\), of monopoles with given magnetic charge and arbitrary mass. Previously, aside from charge one monopoles, existence was known only for monopoles with integral mass (since these arise from U(1) invariant instantons on \({\mathbb{R}^{4}}\)). The method of proof is based on Taubes’ gluing procedure, using well-separated, explicit, charge one monopoles. The analysis is carried out in a weighted Sobolev space and necessitates eliminating the possibility of point spectra.
Odd surface waves in two-dimensional incompressible fluids

We consider free surface dynamics of a two-dimensional incompressible fluid with odd viscosity. The odd viscosity is a peculiar part of the viscosity tensor which does not result in dissipation and is allowed when parity symmetry is broken. For the case of incompressible fluids, the odd viscosity manifests itself through the free surface (no stress) boundary conditions. We first find the free surface wave solutions of hydrodynamics in the linear approximation and study the dispersion of such waves. As expected, the surface waves are chiral and even exist in the absence of gravity and vanishing shear viscosity. In this limit, we derive effective nonlinear Hamiltonian equations for the surface dynamics, generalizing the linear solutions to the weakly nonlinear case. Within the small surface angle approximation, the equation of motion leads to a new class of non-linear chiral dynamics governed by what we dub the {\it chiral} Burgers equation. The chiral Burgers equation is identical to the complex Burgers equation with imaginary viscosity and an additional analyticity requirement that enforces chirality. We present several exact solutions of the chiral Burgers equation. For generic multiple pole initial conditions, the system evolves to the formation of singularities in a finite time similar to the case of an ideal fluid without odd viscosity. We also obtain a periodic solution to the chiral Burgers corresponding to the non-linear generalization of small amplitude linear waves.
A probabilistic gridded product for daily precipitation extremes over the United States

Gridded data products, for example interpolated daily measurements of precipitation from weather stations, are commonly used as a convenient substitute for direct observations because these products provide a spatially and temporally continuous and complete source of data. However, when the goal is to characterize climatological features of extreme precipitation over a spatial domain (e.g., a map of return values) at the native spatial scales of these phenomena, then gridded products may lead to incorrect conclusions because daily precipitation is a fractal field and hence any smoothing technique will dampen local extremes. To address this issue, we create a new “probabilistic” gridded product specifically designed to characterize the climatological properties of extreme precipitation by applying spatial statistical analysis to daily measurements of precipitation from the Global Historical Climatology Network over the contiguous United States. The essence of our method is to first estimate the climatology of extreme precipitation based on station data and then use a data-driven statistical approach to interpolate these estimates to a fine grid. We argue that our method yields an improved characterization of the climatology within a grid cell because the probabilistic behavior of extreme precipitation is much better behaved (i.e., smoother) than daily weather. Furthermore, the spatial smoothing innate to our approach significantly increases the signal-to-noise ratio in the estimated extreme statistics relative to an analysis without smoothing. Finally, by deriving a data-driven approach for translating extreme statistics to a spatially complete grid, the methodology outlined in this paper resolves the issue of how to properly compare station data with output from earth system models. We conclude the paper by comparing our probabilistic gridded product with a standard extreme value analysis of the Livneh gridded daily precipitation product. Our new data product is freely available on the Harvard Dataverse (https://bit.ly/2CXdnuj).
Formic acid catalyzed isomerization and adduct formation of an isoprene-derived Criegee intermediate: experiment and theory

Isoprene is the most abundant non-methane hydrocarbon emitted into the Earth's atmosphere. Ozonolysis is an important atmospheric sink for isoprene, which generates reactive carbonyl oxide species (R1R2C[double bond, length as m-dash]O+O-) known as Criegee intermediates. This study focuses on characterizing the catalyzed isomerization and adduct formation pathways for the reaction between formic acid and methyl vinyl ketone oxide (MVK-oxide), a four-carbon unsaturated Criegee intermediate generated from isoprene ozonolysis. syn-MVK-oxide undergoes intramolecular 1,4 H-atom transfer to form a substituted vinyl hydroperoxide intermediate, 2-hydroperoxybuta-1,3-diene (HPBD), which subsequently decomposes to hydroxyl and vinoxylic radical products. Here, we report direct observation of HPBD generated by formic acid catalyzed isomerization of MVK-oxide under thermal conditions (298 K, 10 torr) using multiplexed photoionization mass spectrometry. The acid catalyzed isomerization of MVK-oxide proceeds by a double hydrogen-bonded interaction followed by a concerted H-atom transfer via submerged barriers to produce HPBD and regenerate formic acid. The analogous isomerization pathway catalyzed with deuterated formic acid (D2-formic acid) enables migration of a D atom to yield partially deuterated HPBD (DPBD), which is identified by its distinct mass (m/z 87) and photoionization threshold. In addition, bimolecular reaction of MVK-oxide with D2-formic acid forms a functionalized hydroperoxide adduct, which is the dominant product channel, and is compared to a previous bimolecular reaction study with normal formic acid. Complementary high-level theoretical calculations are performed to further investigate the reaction pathways and kinetics.
Parameter Estimation in X-Ray Astronomy Revisited

The method of obtaining confidence intervals on a subset of the total number of parameters (p) of a model used for fitting X-ray spectra is to perturb the best-fitting model until, for each parameter, a range is found for which the change in the fit statistic is equal to some critical value. This critical value corresponds to the desired confidence level and is obtained from the χ2 distribution for q degrees of freedom, where q is the number of interesting parameters. With the advent of better energy-resolution detectors, such as those on board ASCA, it has become more common to fit complex models with narrow features, comparable to the instrumental energy resolution. To investigate whether this leads to significant non-Gaussian deviations between data and model, we use simulations based on ASCA data, and we show that the method is still valid in such cases. We also investigate the weak-source limit as well as the case of obtaining upper limits on equivalent widths of weak emission lines and find that, for all practical purposes, the method gives the correct confidence ranges. However, upper limits on emission-line equivalent widths may be overestimated in the extreme Poisson limit.
The impact of the charge barrier height on Germanium (Ge) detectors with amorphous-Ge contacts for light dark matter searches

Germanium (Ge) detectors with ability of measuring a single electron–hole (e–h) pair are needed in searching for light dark matter (LDM) down to the MeV scale. We investigate the feasibility of Ge detectors with amorphous-Ge (a-Ge) contacts to achieve the sensitivity of measuring a single e-h pair, which requires extremely low leakage current. Three Ge detectors with a-Ge contacts are used to study the charge barrier height for blocking electrons and holes. Using the measured bulk leakage current and the Dohler–Brodsky model, we obtain the values for charge barrier height and the density of localized energy states near the Fermi energy level for the top and bottom contacts, respectively. We predict that the bulk leakage current is extremely small and can be neglected at helium temperature ($$\sim $$4 K). Thus, Ge detectors with a-Ge contacts possess the potential to measure a single e–h pair for detecting LDM particles.
Graphene plasmonics for terahertz to mid-infrared applications

In recent years, we have seen a rapid progress in the field of graphene plasmonics, motivated by graphene’s unique electrical and optical properties, tunability, long-lived collective excitation and its extreme light confinement. Here, we review the basic properties of graphene plasmons: their energy dispersion, localization and propagation, plasmon–phonon hybridization, lifetimes and damping pathways. The application space of graphene plasmonics lies in the technologically significant, but relatively unexploited terahertz to mid-infrared regime. We discuss emerging and potential applications, such as modulators, notch filters, polarizers, mid-infrared photodetectors, and mid-infrared vibrational spectroscopy, among many others.
Neutrino Properties, Cosmology

This is a review of our present knowledge of neutrino properties and what remains to be determined. It is followed by a description of double beta decay and very high energy neutrinos. Finally, there is a description of leptogenesis. In this talk I will neglect sterile neutrinos, and thus the LSND problem.
RESONANT POST-NEWTONIAN ECCENTRICITY EXCITATION IN HIERARCHICAL THREE-BODY SYSTEMS

We study the secular, hierarchical three-body problem to first-order in a post-Newtonian expansion of general relativity (GR). We expand the first-order post-Newtonian Hamiltonian to leading-order in the ratio of the semi-major axis of the two orbits. In addition to the well-known terms that correspond to the GR precession of the inner and outer orbits, we find a new secular post-Newtonian interaction term that can affect the long-term evolution of the triple. We explore the parameter space for highly inclined and eccentric systems, where the Kozai-Lidov mechanism can produce large-amplitude oscillations in the eccentricities. The standard lore, i.e., that GR effects suppress eccentricity, is only consistent with the parts of phase space where the GR timescales are several orders of magnitude shorter than the secular Newtonian one. In other parts of phase space, however, post-Newtonian corrections combined with the three-body ones can excite eccentricities. In particular, for systems where the GR timescale is comparable to the secular Newtonian timescales, the three-body interactions give rise to a resonant-like eccentricity excitation. Furthermore, for triples with a comparable-mass inner binary, where the eccentric Kozai-Lidov mechanism is suppressed, post-Newtonian corrections can further increase the eccentricity and lead to orbital flips even when the timescale of the former is much longer than the timescale of the secular Kozai-Lidov quadrupole perturbations.
The Absence of Stars in Compact High-Velocity Clouds

We present the results of our search for faint Local Group dwarf galaxies in compact high-velocity clouds (HVCs). We used digitized Palomar Observatory Sky Survey (POSS) data to examine 1 deg2 of sky around each of ~250 northern hemisphere HVCs. The POSS images were processed to remove foreground stars and large-scale backgrounds, smoothed to enhance arcminute-sized low surface brightness features, and then compared with the original plates. Using this technique, we located 60 candidate dwarf galaxies in the ~250 deg2 that we surveyed. Follow-up observations of these candidates have revealed several distant clusters of galaxies and a number of possible Galactic cirrus clouds, but no Local Group dwarfs. It appears that many of the low surface brightness features in the sky survey data are plate flaws. The second-generation red POSS plates are sensitive down to surface brightness levels of 25-26 mag arcsec-2, and our follow-up images reached 10 σ limiting magnitudes of R = 21-23 for point sources. Given these limits, all known Local Group galaxies except four of the very diffuse, extended dwarf spheroidals located within 100 kpc of the Milky Way would have been detected had they been in our survey. Therefore, we can rule out the possibility that these HVCs are associated with normal but faint dwarf galaxies. If compact HVCs do contain stars, they must have surface brightnesses 1 mag arcsec-2 fainter than most known Local Group galaxies.
A Bayesian Cognition Approach to Improve Data Visualization

People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonstrate a Bayesian cognitive model for understanding how people interpret visualizations in light of prior beliefs and show how this model provides a guide for improving visualization evaluation. In a first study, we show how applying a Bayesian cognition model to a simple visualization scenario indicates that people's judgments are consistent with a hypothesis that they are doing approximate Bayesian inference. In a second study, we evaluate how sensitive our observations of Bayesian behavior are to different techniques for eliciting people subjective distributions, and to different datasets. We find that people don't behave consistently with Bayesian predictions for large sample size datasets, and this difference cannot be explained by elicitation technique. In a final study, we show how normative Bayesian inference can be used as an evaluation framework for visualizations, including of uncertainty.
High-spin structures as the probes of proton-neutron pairing

Rotating N = Z nuclei in the mass A = 58-80 region have been studied within the framework of isovector mean field theory. Available data is well and systematically described in the calculations. The present study supports the presence of strong isovector np pair field at low spin, which is, however, destroyed at high spin. No clear evidence for the existence of the isoscalar t = 0 np pairing has been found.
Stochastic Approximation and Newton’s Estimate of a Mixing Distribution

Many statistical problems involve mixture models and the need for computationally efficient methods to estimate the mixing distribution has increased dramatically in recent years. Newton [Sankhyā Ser. A 64 (2002) 306–322] proposed a fast recursive algorithm for estimating the mixing distribution, which we study as a special case of stochastic approximation (SA). We begin with a review of SA, some recent statistical applications, and the theory necessary for analysis of a SA algorithm, which includes Lyapunov functions and ODE stability theory. Then standard SA results are used to prove consistency of Newton’s estimate in the case of a finite mixture. We also propose a modification of Newton’s algorithm that allows for estimation of an additional unknown parameter in the model, and prove its consistency.
DRESSED QUANTUM GRAPHS WITH OPTICAL NONLINEARITIES APPROACHING THE FUNDAMENTAL LIMIT

In this paper, we dress bare quantum graphs with finite delta function potentials and calculate optical nonlinearities that are found to match the fundamental limits set by potential optimization. We show that structures whose first hyperpolarizability is near the maximum are well described by only three states, the so-called three-level Ansatz, while structures with the largest second hyperpolarizability require four states. We analyze a very large set of configurations for graphs with quasi-quadratic energy spectra and show how they exhibit better response than bare graphs through exquisite optimization of the shape of the eigenfunctions enabled by the existence of the finite potentials. We also discover an exception to the universal scaling properties of the three-level model parameters and trace it to the observation that a greater number of levels are required to satisfy the sum rules even when the three-level Ansatz is satisfied and the first hyperpolarizability is at its maximum value, as specified by potential optimization. This exception in the universal scaling properties of nonlinear optical structures at the limit is traced to the discontinuity in the gradient of the eigenfunctions at the location of the delta potential. This is the first time that dressed quantum graphs have been devised and solved for their nonlinear response, and it is the first analytical model of a confined dynamic system with a simple potential energy that achieves the fundamental limits.
Copula density estimation by finite mixture of parametric copula densities

A copula density estimation method that is based on a finite mixture of heterogeneous parametric copula densities is proposed here. More specifically, the mixture components are Clayton, Frank, Gum...
DCRoute: Speeding up Inter-Datacenter Traffic Allocation while Guaranteeing Deadlines

Datacenters provide the infrastructure for cloud computing services used by millions of users everyday. Many such services are distributed over multiple datacenters at geographically distant locations possibly in different continents. These datacenters are then connected through high speed WAN links over private or public networks. To perform data backups or data synchronization operations, many transfers take place over these networks that have to be completed before a deadline in order to provide necessary service guarantees to end users. Upon arrival of a transfer request, we would like the system to be able to decide whether such a request can be guaranteed successful delivery. If yes, it should provide us with transmission schedule in the shortest time possible. In addition, we would like to avoid packet reordering at the destination as it affects TCP performance. Previous work in this area either cannot guarantee that admitted transfers actually finish before the specified deadlines or use techniques that can result in packet reordering. In this paper, we propose DCRoute, a fast and efficient routing and traffic allocation technique that guarantees transfer completion before deadlines for admitted requests. It assigns each transfer a single path to avoid packet reordering. Through simulations, we show that DCRoute is at least 200 times faster than other traffic allocation techniques based on linear programming (LP) while admitting almost the same amount of traffic to the system.
What can the distribution of intergalactic metals tell us about the history of cosmological enrichment

I study the relationship between the spatial distribution of intergalactic metals and the masses and ejection energies of the sources that produced them. Over a wide range of models, metal enrichment is dominated by the smallest efficient sources, as the enriched volume scales roughly as E3/5 ~ M3/5 while the number density of sources goes as ~M-1. In all cases, the earliest sources have the biggest impact, because fixed comoving distances correspond to smaller physical distances at higher redshifts. This means that most of the enriched volume is found around rare peaks, and intergalactic metals are naturally highly clustered. Furthermore, this clustering is so strong as to lead to a large overlap between individual bubbles. Thus the typical radius of enriched z ~ 3 regions should be interpreted as a constraint on groupings of sources rather than the ejection radius of a typical source. Similarly, the clustering of enriched regions should be taken as a measurement of source bias rather than mass.
Computing the Chern–Schwartz–MacPherson Class of Complete Simplical Toric Varieties

Topological invariants such as characteristic classes are an important tool to aid in understanding and categorizing the structure and properties of algebraic varieties. In this note, we consider the problem of computing a particular characteristic class, the Chern–Schwartz–MacPherson class, of a complete simplicial toric variety \(X_{\Sigma }\) defined by a fan \({\Sigma }\) from the combinatorial data contained in the fan \(\Sigma \). Specifically, we give an effective combinatorial algorithm to compute the Chern–Schwartz–MacPherson class of \(X_{\Sigma }\), in the Chow ring (or rational Chow ring) of \(X_{\Sigma }\). This method is formulated by combining, and when necessary modifying, several known results from the literature and is implemented in Macaulay2 for test purposes.
Theta bases are atomic

Fock and Goncharov conjectured that the indecomposable universally positive (i.e. atomic) elements of a cluster algebra should form a basis for the algebra. This was shown to be false by Lee, Li and Zelevinsky. However, we find that the theta bases of Gross, Hacking, Keel and Kontsevich do satisfy this conjecture for a slightly modified definition of universal positivity in which one replaces the positive atlas consisting of the clusters by an enlargement we call the scattering atlas. In particular, this uniquely characterizes the theta functions.
Self-organized nonlinear gratings for ultrafast nanophotonics

As devices utilizing femtosecond-duration laser pulses become more commonplace, there is a need for next-generation nonlinear-photonics technologies that enable low-energy femtosecond pulses to be converted from one wavelength to another with high efficiency. However, designing nonlinear materials to operate with femtosecond pulses is challenging, because it is necessary to match both the phase velocities and group velocities of the light. Here, we show that femtosecond laser pulses can generate self-organized nonlinear gratings in nanophotonic waveguides, thereby providing a nonlinear optical device with both quasi-phase-matching and group-velocity matching for second-harmonic generation. We use nonlinear microscopy to uniquely characterize the self-organized nonlinear gratings and demonstrate that these waveguides enable simultaneous χ(2) and χ(3) nonlinear processes for laser-frequency-comb stabilization. Finally, we derive the equations that govern self-organized grating formation for femtosecond pulses and uncover the crucial role of group-velocity matching. In the future, nanophotonics with self-organized gratings could enable scalable, reconfigurable nonlinear photonics. Femtosecond laser pulses can generate self-organized nonlinear gratings in nanophotonic waveguides, providing both quasi-phase-matching and group-velocity matching for second-harmonic generation, and enabling simultaneous χ2 and χ3 nonlinear processes for laser-frequency-comb stabilization.
SPECTROPOLARIMETRY OF RADIO-SELECTED BROAD ABSORPTION LINE QUASARS

We report spectropolarimetry of 30 radio-selected broad absorption line (BAL) quasars with the Keck Observatory, 25 from the sample of Becker et al. Both high- and low-ionization BAL quasars are represented, with redshifts ranging from 0.5 to 2.5. The spectropolarimetric properties of radio-selected BAL quasars are very similar to those of radio-quiet BAL quasars: a sizeable fraction (20%) shows large continuum polarization (2%-10%) usually rising toward short wavelengths; emission lines are typically less polarized than the continuum; and absorption line troughs often show large polarization jumps. There are no significant correlations between polarization properties and radio properties, including those indicative of system orientation, suggesting that BAL quasars are not simply normal quasars seen from an edge-on perspective.
Zero-Shot Heterogeneous Transfer Learning from Recommender Systems to Cold-Start Search Retrieval

Many recent advances in neural information retrieval models, which predict top-K items given a query, learn directly from a large training set of (query, item) pairs. However, they are often insufficient when there are many previously unseen (query, item) combinations, often referred to as the cold start problem. Furthermore, the search system can be biased towards items that are frequently shown to a query previously, also known as the 'rich get richer' (a.k.a. feedback loop) problem. In light of these problems, we observed that most online content platforms have both a search and a recommender system that, while having heterogeneous input spaces, can be connected through their common output item space and a shared semantic representation. In this paper, we propose a new Zero-Shot Heterogeneous Transfer Learning framework that transfers learned knowledge from the recommender system component to improve the search component of a content platform. First, it learns representations of items and their natural-language features by predicting (item, item) correlation graphs derived from the recommender system as an auxiliary task. Then, the learned representations are transferred to solve the target search retrieval task, performing query-to-item prediction without having seen any (query, item) pairs in training. We conduct online and offline experiments on one of the world's largest search and recommender systems from Google, and present the results and lessons learned. We demonstrate that the proposed approach can achieve high performance on offline search retrieval tasks, and more importantly, achieved significant improvements on relevance and user interactions over the highly-optimized production system in online experiments.
Formulating turbulence closures using sparse regression with embedded form invariance

In recent years, machine-learning techniques have been leveraged to improve closures for the Reynolds-average Navier-Stokes equations. The efficacy of a sparse regression-based method that results in compact, algebraic models and ensures key physical properties such as form invariance is demonstrated. Further, success for learning accurate models from data spanning full high-fidelity sets to experimental (sparse and noisy) data is shown.
Thermophysical Modeling of NEOWISE Observations of DESTINY + Targets Phaethon and 2005 UD

Thermophysical models allow for improved constraints on the physical and thermal surface properties of asteroids beyond what can be inferred from more simple thermal modeling, provided a sufficient number of observations is available. We present thermophysical modeling results of observations from the NEOWISE mission for two near-Earth asteroids which are the targets of the DESTINY+ flyby mission: (3200) Phaethon and (155140) 2005 UD. Our model assumes a rotating, cratered, spherical surface, and employs a Monte Carlo Markov Chain to explore the multi-dimensional parameter space of the fit. We find an effective spherical diameter for Phaethon of $4.6^{+0.2}_{-0.3}~$km, a geometric albedo of $p_V=0.16\pm0.02$, and a thermal inertia $\Gamma=880$ $^{+580}_{-330}$, using five epochs of NEOWISE observations. The best model fit for (155140) 2005 UD was less well constrained due to only having two NEOWISE observation epochs, giving a diameter of $1.2\pm0.4~$km and a geometric albedo of $p_V=0.14\pm0.09$.
Greater Data Science at Baccalaureate Institutions

Donoho’s paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at le...
An Observational Study of Tidal Synchronization in Solar-Type Binary Stars in the Open Clusters M35 and M34

We present rotation periods for the solar-type primary stars in 13 close (a 5 AU) single-lined spectroscopic binaries with known orbital periods (P) and eccentricities (e). All binaries are members of the open clusters M35 (NGC 2168; ~150 Myr) and M34 (NGC 1039; ~250 Myr). The binary orbital parameters and the rotation periods of the primary stars were determined from time-series spectroscopy and time-series photometry, respectively. Knowledge of the ages, orbital periods, and eccentricities of these binaries combined with the rotation periods and masses of their primary stars makes them particularly interesting systems for studying the rates of tidal circularization and synchronization. Our sample of 13 binaries includes six with orbital periods shortward of 13 days (a 0.12 AU). The stars in these binaries orbit sufficiently close that their spins and orbits have evolved toward synchronization and circularization due to tidal interactions. We investigate the degree of tidal synchronization in each binary by comparing the angular rotation velocity of the primary stars (Ω) to the angular velocity expected if the primary star was synchronized (e = 0) or pseudosynchronized (e > 0) with the orbital motion (Ωps). Of the six closest binaries, two with circular orbits are not synchronized, one being subsynchronous and one being supersynchronous, and the primary stars in two binaries with eccentric orbits are rotating more slowly than pseudosynchronism. The remaining two binaries have reached the equilibrium state of both a circularized orbit and synchronized rotation. As a set, the six binaries present a challenging case study for tidal evolution theory, which in particular does not predict subsynchronous rotation in such close systems.
How else can we detect Fast Radio Bursts

We discuss possible electromagnetic signals accompanying Fast Radio Bursts (FRBs) that are expected in the scenario where FRBs originate in neutron star magnetospheres. For models involving Crab-like giant pulses, no appreciable contemporaneous emission is expected at other wavelengths. However, magnetar giant flares, driven by the reconfiguration of the magnetosphere, can produce both contemporaneous bursts at other wavelengths as well as afterglow-like emission. We conclude that the best chances are: (i) prompt short GRB-like emission, (ii) a contemporaneous optical flash that can reach naked eye peak luminosity (but only for a few milliseconds), and (iii) a high-energy afterglow emission. Case (i) could be tested by coordinated radio and high-energy experiments. Case (ii) could be seen in a coordinated radio-optical surveys, e.g., by the Palomar Transient Factory in a 60 s frame as a transient object of m = 15–20 mag with an expected optical detection rate of about 0.1 hr−1, an order of magnitude higher than in radio. Shallow, but large-area sky surveys such as ASAS-SN and EVRYSCOPE could also detect prompt optical flashes from the more powerful Lorimer-burst clones. The best constraints on the optical to radio power for this kind of emission could be provided by future observations with facilities like Large Synoptic Survey Telescope. Case (iii) might be seen in relatively rare cases that the relativistically ejected magnetic blob is moving along the line of sight.