cssi/SciSciGPT-SciSciNet huggingface dataset:
Ice Loss From the Interior of Small Airless Bodies According to an Idealized Model

Ice in main belt asteroids and Near Earth Objects (NEOs) is of scientific and resource exploration interest, but small airless bodies gradually lose their ice to space by outward diffusion. Here, we quantitatively estimate the time it takes a porous airless body to lose all of its interior ice, based on an analytic solution for the interior temperature field of bodies in stable orbits. Without latent heat, the average surface temperature, which is lower than the classic effective temperature, is representative of the body interior and hence an appropriate temperature to evaluate desiccation time scales. In a spherically averaged model, an explicit analytic solution is obtained for the depth to ice as a function of time and the time to complete desiccation. Half of the ice volume is lost after 11% of this time. A bilobate structure emerges from the strong latitude dependence of desiccation rates. Cold polar regions can harbor subsurface ice, even when the body center does not. Latent heat retards ice loss, and we obtain a succinct expression for the temperature difference between the surface and the ice. In the outer main belt, nearly all bodies 10~km in size or larger should have been able to retain ice in their interiors over the age of the solar system. Each of the following factors favors the presence of ice inside NEOs: a semi-major axis in the outer belt or beyond, a mantle of very low thermal inertia, a young age, or a small and stable axis tilt.
Bounded Regret for Finitely Parameterized Multi-Armed Bandits

We consider the problem of finitely parameterized multi-armed bandits where the model of the underlying stochastic environment can be characterized based on a common unknown parameter. The true parameter is unknown to the learning agent. However, the set of possible parameters, which is finite, is known a priori. We propose an algorithm that is simple and easy to implement, which we call Finitely Parameterized Upper Confidence Bound (FP-UCB) algorithm, which uses the information about the underlying parameter set for faster learning. In particular, we show that the FP-UCB algorithm achieves a bounded regret under some structural condition on the underlying parameter set. We also show that, if the underlying parameter set does not satisfy the necessary structural condition, the FP-UCB algorithm achieves a logarithmic regret, but with a smaller preceding constant compared to the standard UCB algorithm. We also validate the superior performance of the FP-UCB algorithm through extensive numerical simulations.
VESTOIDS, PART II: THE BASALTIC NATURE AND HED METEORITE ANALOGS FOR EIGHT Vp-TYPE ASTEROIDS AND THEIR ASSOCIATIONS WITH (4) VESTA

Improving constraints on the abundance of basaltic asteroids in the main asteroid belt is necessary for better understanding the thermal and collisional environment in the early solar system, for more rigorously identifying the genetic family for (4) Vesta, for determining the effectiveness of Yarkovsky/YORP in dispersing asteroid families, and for better quantifying the population of basaltic asteroids in the outer main belt (a greater than 2.5 AU) that are likely unrelated to (4) Vesta. NIR spectral observations in this work were obtained for the Vp-type asteroids (2011) Veteraniya, (5875) Kuga, (8149) Ruff, (9147) Kourakuen, (9553) Colas, (15237) 1988 RL6, (31414) Rotaryusa, and (32940) 1995 UW4 during August and September 2014 utilizing the SpeX spectrograph at the NASA Infrared Telescope Facility (IRTF), Mauna Kea, Hawaii. Spectral band parameter (band centers, Band Area Ratios) and mineralogical analysis (pyroxene chemistry) for each average asteroid NIR reflectance spectrum suggests a howardite-eucrite-diogenite (HED) meteorite analog for each asteroid. (5875) Kuga is most closely associated with the eucrite meteorites, (31414) Rotaryusa is most closely associated with the diogenites, and the remaining other six asteroids are most closely associated with the howardite meteorites. Along with orbital locations in the inner main belt and in the vicinity of (4) Vesta, the existing evidence suggests that these eight Vp-type asteroids are also likely Vestoids.
Measuring High-Precision Astrometry with the Infrared Array Camera on the Spitzer Space Telescope

The Infrared Array Camera (IRAC) on the Spitzer Space Telescope currently offers the greatest potential for high-precision astrometry of faint mid-IR sources across arcminute-scale fields, which would be especially valuable for measuring parallaxes of cold brown dwarfs in the solar neighborhood and proper motions of obscured members of nearby star-forming regions. To more fully realize IRAC's astrometric capabilities, we have sought to minimize the largest sources of uncertainty in astrometry with its 3.6 and 4.5 $\mu$m bands. By comparing different routines that estimate stellar positions, we have found that Point Response Function (PRF) fitting with the Spitzer Science Center's Astronomical Point Source Extractor produces both the smallest systematic errors from varying intra-pixel sensitivity and the greatest precision in measurements of positions. In addition, self-calibration has been used to derive new 7$^{\rm th}$ and 8$^{\rm th}$ order distortion corrections for the 3.6 and 4.5 $\mu$m arrays of IRAC, respectively. These corrections are suitable for data throughout the mission of Spitzer when a time-dependent scale factor is applied to the corrections. To illustrate the astrometric accuracy that can be achieved by combining PRF fitting with our new distortion corrections, we have applied them to archival data for a nearby star-forming region, arriving at total astrometric errors of $\sim$20 and 70 mas at signal to noise ratios of 100 and 10, respectively.
A Diversity-Based Substation Cyber Defense Strategy Utilizing Coloring Games

Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanisms (SMs), including VPNs, firewalls, authentication, and access control mechanisms. While these mechanisms provide some level of defense, they also may contain software vulnerabilities which allow an attack to bypass their protection. Because the same SM type is often used to protect a large number of substations, a single vulnerability could enable a coordinated attack by simultaneously targeting many substations. To protect against such an attack, utilities can adopt a strategy to use a diverse set of SMs, such that the impact from a vulnerability in any SM is minimized. This paper introduces a game-theoretic graph coloring technique to determine the optimal allocation of SM diversity that minimizes the impact of security vulnerabilities to the grid. This paper demonstrates that the proposed approach provides a Nash equilibrium solution. Furthermore, the technique is demonstrated against cyber-physical models for both IEEE-14 and IEEE-118 bus systems, and compared with other non-strategic diversity allocation methods to demonstrate its effectiveness.
MODULI SPACES OF IRREGULAR SINGULAR CONNECTIONS

In the geometric version of the Langlands correspondence, irregu- lar singular point connections play the role of Galois representations with wild ramification. In this paper, we develop a geometric theory of fundamental strata to study irregular singular connections on the projective line. Funda- mental strata were originally used to classify cuspidal representations of the general linear group over a local field. In the geometric setting, fundamen- tal strata play the role of the leading term of a connection. We introduce the concept of a regular stratum, which allows us to generalize the condition that a connection has regular semisimple leading term to connections with non-integer slope. Finally, we construct a symplectic moduli space of mero- morphic connections on the projective line that contain a regular stratum at each singular point.
Optimizing the Nonlinearity and Dissipation of a SNAIL Parametric Amplifier for Dynamic Range

Quantum-limited Josephson parametric amplifiers are a key component in many precision microwave measurement setups, such as for the readout of superconducting qubits in a quantum computer. As qubit setups scale up, these amplifiers must be optimized to handle input signals of ever-larger power. The authors design a quantum-limited parametric amplifier based on an array of superconducting nonlinear asymmetric inductive elements. This ``SNAIL'' is optimized to handle large input signals without sacrificing other desirable characteristics. The method can be extended to improve all forms of parametrically induced mixing in quantum information applications.
Fokker-Planck Models for M15 Without a Central Black Hole: The Role of the Mass Function

We have developed a set of dynamically evolving Fokker-Planck models for the collapsed-core globular star cluster M15, which directly address the issue of whether a central black hole is required to fit Hubble Space Telescope (HST) observations of the stellar spatial distribution and kinematics. As in our previous work reported by Dull et al., we find that a central black hole is not needed. Using local mass-function data from HST studies, we have also inferred the global initial stellar mass function. As a consequence of extreme mass segregation, the local mass functions differ from the global mass function at every location. In addition to reproducing the observed mass functions, the models also provide good fits to the star-count and velocity-dispersion profiles, and to the millisecond pulsar accelerations. We address concerns about the large neutron star populations adopted in our previous Fokker-Planck models for M15. We find that good model fits can be obtained with as few as 1600 neutron stars; this corresponds to a retention fraction of 5% of the initial population for our best-fit initial mass function. The models contain a substantial population of massive white dwarfs, that range in mass up to 1.2M{sub sun} . The combined contributionmore » by the massive white dwarfs and neutron stars provides the gravitational potential needed to reproduce HST measurements of the central velocity-dispersion profile.« less
Limiting distribution of elliptic homogenization error with periodic diffusion and random potential

We study the limiting probability distribution of the homogenization error for second order elliptic equations in divergence form with highly oscillatory periodic conductivity coefficients and highly oscillatory stochastic potential. The effective conductivity coefficients are the same as those of the standard periodic homogenization, and the effective potential is given by the mean. We show that in the short range correlation setting, the limiting distribution of the random part of the homogenization error, as random elements in proper Hilbert spaces, is Gaussian and can be characterized by the homogenized Green's function, the homogenized solution and the statistics of the random potential. Similar results hold for random potentials that are functions of long range correlated Gaussian random fields. These results generalize previous ones in the setting with slowly varying diffusion coefficients, and the current setting with fast oscillations in the differential operator requires new methods to prove compactness of the probability distributions of the random fluctuation.
Chern–Simons theory, Vassiliev invariants, loop quantum gravity and functional integration without integration

This paper is an exposition of the relationship between Witten’s Chern–Simons functional integral and the theory of Vassiliev invariants of knots and links in three-dimensional space. We conceptualize the functional integral in terms of equivalence classes of functionals of gauge fields and we do not use measure theory. This approach makes it possible to discuss the mathematics intrinsic to the functional integral rigorously and without functional integration. Applications to loop quantum gravity are discussed.
First-passage exponents of multiple random walks

We investigate first-passage statistics of an ensemble of N noninteracting random walks on a line. Starting from a configuration in which all particles are located in the positive half-line, we study Sn(t), the probability that the nth rightmost particle remains in the positive half-line up to time t. This quantity decays algebraically, , in the long-time limit. Interestingly, there is a family of nontrivial first-passage exponents, β1 < β2 < ⋅⋅⋅ < βN − 1; the only exception is the two-particle case where β1 = 1/3. In the N → ∞ limit, however, the exponents attain a scaling form, βn(N) → β(z) with . We also demonstrate that the smallest exponent decays exponentially with N. We deduce these results from first-passage kinetics of a random walk in an N-dimensional cone and confirm them using numerical simulations. Additionally, we investigate the family of exponents that characterizes leadership statistics of multiple random walks and find that in this case, the cone provides an excellent approximation.
Pulsar Emission Physics: The First Fifty Years

Over the last fifty years since the discovery of pulsars, our understanding of where and how pulsars emit the radiation we observe has undergone significant revision. The location and mechanisms of high-energy radiation are intimately tied to the sites of particle acceleration. The evolution of emission models has paralleled the development of increasingly more sensitive telescopes, especially at high energies. I will review the history of pulsar emission modeling, from the early days of gaps at the polar caps, to outer gaps and slot gaps in the outer magnetosphere, to the present era of global magnetosphere simulations that locate most acceleration and high-energy emission in the current sheets.
Jet Quenching Beyond the Energy Loss Approach

We study the jet quenching effect in heavy ion collisions, based on medium-induced splitting functions calculated from Soft Collinear Effective Theory with Glauber Gluons. Our method is formulated in the language of DGLAP evolution equations with medium-induced splitting functions. In the small-x soft gluon approximation we analytically solve the evolution equations and find an intuitive connection to the energy loss approach. For central Pb+Pb collisions at the LHC we quantify the effect of finite-x corrections for the nuclear modification factor and compare to data.
Optimal Galaxy Shape Measurements for Weak Lensing Applications Using the Hubble Space Telescope Advanced Camera for Surveys

We present three-epoch multiband (V606, i775, and z850) measurements of galaxy shapes using the "polar shapelet" or Laguerre expansions method with the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS) data, obtained as part of the Great Observatories Origin Deep Survey (GOODS). We take advantage of the unique features of the GOODS/ACS fields to test the reliability of this relatively new method of galaxy shape measurement for weak lensing analysis and to quantify the impact of the ACS point-spread function (PSF) on HST/ACS data. We estimate the bias introduced by the sharp PSF of the ACS on shape measurement. We show that the bias in the tangential shear due to galaxy-galaxy lensing can be safely neglected provided that only well-resolved galaxies are used, while it would be comparable to the signal level (1%-3%) for cosmic shear measurements. These results should be of general utility in planning and analyzing weak lensing measurements with HST/ACS data.
Restrictions and stability of time-delayed dynamical networks

In this paper we present a criteria for the global stability of general time-delayed dynamical networks. We show that under our criteria a network's stability is invariant with respect to the removal of time delays and the addition of single type time delays. As modifying a network's delays can have a destabilizing effect on the system's dynamics, this introduces a new and stronger form of global stability, which we call intrinsic stability. To carry out this analysis we introduce a family of graph transformations that can be used to maintain or modify the spectral radius of a graph. We then introduce the notion of an implicit delay and show that by removing a network's implicit delays the result is a lower dimensional system, which we term a network restriction. We demonstrate that such restrictions can be used to obtain improved estimates of a network's global stability. The effectiveness of our approach is illustrated by applications to various classes of Cohen–Grossberg neural networks.
Irreducible characters with bounded root Artin conductor

We prove that the best possible lower bound for the Artin conductor is exponential in the degree.
Real Forms of Quantum Orthogonal Groups, q-Lorentz Groups in any Dimension

We review known real forms of the quantum orthogonal groups SOq(N). New *-conjugations are then introduced and we contruct all real forms of quantum orthogonal groups. We thus give an RTT formulation of the *-conjugations on SOq(N) that is complementary to the Uq(g) *-structure classification of Twietmeyer. In particular, we easily find and describe the real forms SOq(N-1,1) for any value of N. Quantum subspaces of the q-Minkowski space are analyzed.
Phonon transport in single-layer transition metal dichalcogenides: A first-principles study

Two-dimensional transition metal dichalcogenides (TMDCs) are finding promising electronic and optical applications due to their unique properties. In this letter, we systematically study the phonon transport and thermal conductivity of eight semiconducting single-layer TMDCs, MX2 (M = Mo, W, Zr, and Hf, X = S and Se), by using the first-principles-driven phonon Boltzmann transport equation approach. The validity of the single-mode relaxation time approximation to predict the thermal conductivity of TMDCs is assessed by comparing the results with the iterative solution of the phonon Boltzmann transport equation. We find that the phononic thermal conductivities of 2H-type TMDCs are above 50 W/mK at room temperature while the thermal conductivity values of the 1T-type TMDCs are much lower, when the size of the sample is 1 μm. A very high thermal conductivity value of 142 W/mK was found in single-layer WS2. The large atomic weight difference between W and S leads to a very large phonon bandgap which in turn forbids the scattering between acoustic and optical phonon modes and thus resulting in very long phonon relaxation time.
Pulsar timing techniques

We describe the procedure, nuances, issues, and choices involved in creating times-of-arrival, residuals and error bars from a set of radio pulsar timing data. We discuss the issue of mis-matched templates, the problem that wide-bandwidth backends introduce, possible solutions to that problem, and correcting for offsets introduced by various observing systems.
HPGe detector field calculation methods demonstrated with an educational program, GeFiCa

A review of tools and methods to calculate electrostatic potentials and fields inside high-purity germanium detectors in various configurations is given. The methods are illustrated concretely with a new educational program named GeFiCa - Germanium detector Field Calculator. Demonstrated in GeFiCa are generic numerical calculations based on the successive over-relaxation method as well as analytic ones whenever simplification is possible due to highly symmetric detector geometries. GeFiCa is written in C$$++$$ and provided as an extension to the CERN ROOT libraries widely used in the particle physics community. Calculation codes for individual detectors, provided as ROOT macros and python scripts, are distributed along with the GeFiCa core library, serving as both examples showing the usage of GeFiCa and starting points for customized calculations. They can be run without compilation in a ROOT interactive session or directly from a Linux shell. The numerical results are saved in a ROOT tree, making full use of the I/O optimization and plotting functionalities in ROOT. The speed and precision of the calculation are comparable to other commonly used packages, which qualifies GeFiCa as a scientific research tool. However, the main focus of GeFiCa is to clearly explain and demonstrate the analytic and numeric methods to solve Poisson’s equation, practical coding considerations and visualization methods, with intensive documentation and example macros. It serves as a one-stop resource for people who want to understand the operating mechanism of such a package under the hood.
Design of a Casimir-driven parametric amplifier

In this paper, we discuss a design for a MEMS parametric amplifier modulated by the Casimir force. We present the theory for such a device and show that it allows for the implementation of a very sensitive voltage measuring technique, where the amplitude of a high quality factor resonator includes a tenth power dependency on an applied DC voltage. This approach opens up a new and powerful measuring modality, applicable to other measurement types.
A recurrent neural network for classification of unevenly sampled variable stars

Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time (‘light curves’). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally due to intranight cadence choices as well as diurnal and seasonal constraints 1–5 . With nightly observations of millions of variable stars and transients from upcoming surveys 4,6 , efficient and accurate discovery and classification techniques on noisy, irregularly sampled data must be employed with minimal human-in-the-loop involvement. Machine learning for inference tasks on such data traditionally requires the laborious hand-coding of domain-specific numerical summaries of raw data (‘features’) 7 . Here, we present a novel unsupervised autoencoding recurrent neural network 8 that makes explicit use of sampling times and known heteroskedastic noise properties. When trained on optical variable star catalogues, this network produces supervised classification models that rival other best-in-class approaches. We find that autoencoded features learned in one time-domain survey perform nearly as well when applied to another survey. These networks can continue to learn from new unlabelled observations and may be used in other unsupervised tasks, such as forecasting and anomaly detection. A novel unsupervised autoencoding recurrent neural network produces state-of-the-art supervised classification models. This network can continue to learn from new unlabelled observations and may be used in other unsupervised tasks.
Glauber Dynamics for the Mean-Field Potts Model

We study Glauber dynamics for the mean-field (Curie-Weiss) Potts model with q≥3 states and show that it undergoes a critical slowdown at an inverse-temperature β s (q) strictly lower than the critical β c (q) for uniqueness of the thermodynamic limit. The dynamical critical β s (q) is the spinodal point marking the onset of metastability. We prove that when β<β s (q) the mixing time is asymptotically C(β,q)nlogn and the dynamics exhibits the cutoff phenomena, a sharp transition in mixing, with a window of order n. At β=β s (q) the dynamics no longer exhibits cutoff and its mixing obeys a power-law of order n 4/3. For β>β s (q) the mixing time is exponentially large in n. Furthermore, as β↑β s with n, the mixing time interpolates smoothly from subcritical to critical behavior, with the latter reached at a scaling window of O(n −2/3) around β s . These results form the first complete analysis of mixing around the critical dynamical temperature—including the critical power law—for a model with a first order phase transition.
Critical groups of van Lint-Schrijver cyclotomic strongly regular graphs

Abstract The critical group of a finite connected graph is an abelian group defined by the Smith normal form of its Laplacian. Let q be a power of a prime and H be a multiplicative subgroup of K = F q . By Cay ( K , H ) we denote the Cayley graph on the additive group of K with “connection” set H. A strongly regular graph of the form Cay ( K , H ) is called a cyclotomic strongly regular graph. Let l > 2 and p be primes such that p is primitive ( mod l ) . We compute the critical groups of a family of cyclotomic strongly regular graphs for which q = p ( l − 1 ) t (with t ∈ N ) and H is the unique multiplicative subgroup of order k = q − 1 l . These graphs were first discovered by van Lint and Schrijver in [24] .
QUANTUM GROUPS AND FIELD THEORY

When the symmetry of a physical theory describing a finite system is deformed by replacing its Lie group by the corresponding quantum group, the operators and state function will lie in a new algebra describing new degrees of freedom. If the symmetry of a field theory is deformed in this way, the enlarged state space will again describe additional degrees of freedom, and the energy levels will acquire fine structure. The massive particles will have a stringlike spectrum lifting the degeneracy of the point-particle theory, and the resulting theory will have a nonlocal description. Theories of this kind naturally contain two sectors with one sector lying close to the standard theory while the second sector describes particles that should be more difficult to observe.
BusTr: Predicting Bus Travel Times from Real-Time Traffic

We present BusTr, a machine-learned model for translating road traffic forecasts into predictions of bus delays, used by Google Maps to serve the majority of the world's public transit systems where no official real-time bus tracking is provided. We demonstrate that our neural sequence model improves over DeepTTE, the state-of-the-art baseline, both in performance (-30% MAPE) and training stability. We also demonstrate significant generalization gains over simpler models, evaluated on longitudinal data to cope with a constantly evolving world.
First-Principles Studies of Photoinduced Charge Transfer in Noncovalently Functionalized Carbon Nanotubes

We have studied the energetics, electronic structure, optical excitation, and electron relaxation of dinitromethane molecules (CH2N2O4) adsorbed on semiconducting carbon nanotubes (CNTs) of chiral index (n,0) (n = 7, 10, 13, 16, 19). Using first-principles density functional theory (DFT) with generalized gradient approximations and van der Waals corrections, we have calculated adsorption energies of dinitropentylpyrene, in which the dinitromethane is linked to the pyrene via an aliphatic chain, on a CNT. A 780 meV (18.0 kcal/mol) binding energy has been found, which explains why such aliphatic chain–pyrene units can be and have been used in experiments to bind functional molecules to CNTs. The calculated electronic structures show that the dinitromethane introduces a localized state inside the band gap of CNT systems of n = 10, 13, 16. and 19; such a state can trap an electron when the CNT is photoexcited. We have therefore investigated the dynamics of intraband relaxations using the reduced density matri...
Adiabatic rapid passage two-photon excitation of a Rydberg atom

We considered the two-photon adiabatic rapid passage excitation of a single atom from the ground to a Rydberg state. Three schemes were analyzed: both pump and Stokes fields chirped and pulsed, only the pump field chirped, and only the pump field pulsed and chirped while the Stokes field is continuous wave (CW). In all three cases high transfer efficiencies > 99% were achieved for the experimentally realizable Rabi frequencies and the pulse durations of the fields.
Magnifying the ATLAS Stealth Stop Splinter: Impact of Spin Correlations and Finite Widths

In this paper, we recast a “stealth stop” search in the notoriously difficult region of the stop-neutralino Simplified Model parameter space for which $$ m\left({\tilde{t}}_1\right)-m\left({\tilde{\upchi}}_1^0\right)\simeq {m}_t $$ . The properties of the final state are nearly identical for tops and stops, while the rate for stop pair production is $$ \mathcal{O} $$ (10%) of that for $$ t\overline{t} $$ . Stop searches away from this stealth region have left behind a “splinter” of open parameter space when $$ m\left({\tilde{t}}_1\right)\simeq {m}_t $$ . Removing this splinter requires surgical precision: the ATLAS constraint on stop pair production reinterpreted here treats the signal as a contaminant to the measurement of the top pair production cross section using data from $$ \sqrt{s}=7 $$ TeV and 8 TeV in a correlated way to control for some systematic errors. ATLAS fixed $$ m\left({\tilde{t}}_1\right)\simeq {m}_t\kern0.5em and\kern0.5em m\left({\tilde{\upchi}}_1^0\right)=1 $$ GeV, implying that a careful recasting of these results into the full $$ m\left({\tilde{t}}_1\right)-m\left({\tilde{\upchi}}_1^0\right) $$ plane is warranted. We find that the parameter space with $$ m\left({\tilde{\upchi}}_1^0\right)\lesssim 55 $$ GeV is excluded for $$ m\left({\tilde{t}}_1\right)\simeq {m}_t $$ — although this search does cover new parameter space, it is unable to fully pull the splinter. Along the way, we review a variety of interesting physical issues in detail: (i) when the two-body width is a good approximation; (ii) how assuming the narrow width approximation affects the total rate; (iii ) how the production rate is affected when the wrong widths are used; (iv ) what role propagating the spin correlations consistently through the multi-body decay chain plays in the limits. In addition, we provide a guide to using MadGraph for implementing the full production including finite width and spin correlation effects, and we survey a variety of pitfalls one might encounter.
Quantum Kac's Chaos

We study the notion of quantum Kac's chaos which was implicitly introduced by Spohn and explicitly formulated by Gottlieb. We prove the analogue of a result of Sznitman which gives the equivalence of Kac's chaos to 2-chaoticity and to convergence of empirical measures. Finally we give a simple, different proof of a result of Spohn which states that chaos propagates with respect to certain Hamiltonians that define the evolution of the mean field limit for interacting quantum systems.
Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data

The growing availability of genetic data sets, in combination with machine learning frameworks, offers great potential to answer long-standing questions in ecology and evolution. One such question has intrigued population geneticists, biogeographers, and conservation biologists: What factors determine intraspecific genetic diversity? This question is challenging to answer because many factors may influence genetic variation, including life history traits, historical influences, and geography, and the relative importance of these factors varies across taxonomic and geographic scales. Furthermore, interpreting the influence of numerous, potentially correlated variables is difficult with traditional statistical approaches. To address these challenges, we analysed repurposed data using machine learning and investigated predictors of genetic diversity, focusing on Nearctic amphibians as a case study. We aggregated species traits, range characteristics, and >42,000 genetic sequences for 299 species using open-access scripts and various databases. After identifying important predictors of nucleotide diversity with random forest regression, we conducted follow-up analyses to examine the roles of phylogenetic history, geography, and demographic processes on intraspecific diversity. Although life history traits were not important predictors for this data set, we found significant phylogenetic signal in genetic diversity within amphibians. We also found that salamander species at northern latitudes contained low genetic diversity. Data repurposing and machine learning provide valuable tools for detecting patterns with relevance for conservation, but concerted efforts are needed to compile meaningful data sets with greater utility for understanding global biodiversity.
On the Search for Tight Frames of Low Coherence

We introduce a projective Riesz s-kernel for the unit sphere $$\mathbb {S}^{d-1}$$ and investigate properties of N-point energy minimizing configurations for such a kernel. We show that these configurations, for s and N sufficiently large, form frames that are well-separated (have low coherence) and are nearly tight. Our results suggest an algorithm for computing well-separated tight frames which is illustrated with numerical examples.
The Role of Alcohol Consumption on Acetaminophen Induced Liver Injury: Implications from A Mathematical Model

Acetaminophen (APAP) overdose is one of the predominant causes of drug induced acute liver injury in the U.S and U.K. Clinical studies show that ingestion of alcohol may increase the risk of APAP induced liver injury. Chronic alcoholism may potentiate APAP hepatotoxicity and this increased risk of APAP toxicity is observed when APAP is ingested even shortly after alcohol is cleared from the body. However, clinical reports also suggest that acute alcohol consumption may have a protective effect against hepatotoxicity by inhibiting microsomal acetaminophen oxidation and thereby reducing N-acetyl-p-benzoquinone imine (NAPQI) production. The aim of this study is to model this dual role of alcohol to determine how the timing of alcohol ingestion affects APAP metabolism and resulting liver injury and identify mechanisms of APAP induced liver injury. The mathematical model is developed to capture condition of a patient of single time APAP overdose who may be an acute or chronic alcohol user. The analysis suggests that the risk of APAP-induced hepatotoxicity is increased if APAP is ingested shortly after alcohol is cleared from the body in chronic alcohol users. A protective effect of acute consumption of alcohol is also observed in patients with APAP overdose. For example, simultaneous ingestion of alcohol and APAP overdose or alcohol intake after or before few hours of APAP overdose may result in less APAP-induced hepatotoxicity when compared to a single time APAP overdose. The rate of hepatocyte damage in APAP overdose patients depends on trade-off between induction and inhibition of CYP enzyme.
The proximal Robbins–Monro method

The need for statistical estimation with large data sets has reinvigorated interest in iterative procedures and stochastic optimization. Stochastic approximations are at the forefront of this recent development as they yield procedures that are simple, general and fast. However, standard stochastic approximations are often numerically unstable. Deterministic optimization, in contrast, increasingly uses proximal updates to achieve numerical stability in a principled manner. A theoretical gap has thus emerged. While standard stochastic approximations are subsumed by the framework Robbins and Monro (The annals of mathematical statistics, 1951, pp. 400–407), there is no such framework for stochastic approximations with proximal updates. In this paper, we conceptualize a proximal version of the classical Robbins–Monro procedure. Our theoretical analysis demonstrates that the proposed procedure has important stability benefits over the classical Robbins–Monro procedure, while it retains the best known convergence rates. Exact implementations of the proximal Robbins–Monro procedure are challenging, but we show that approximate implementations lead to procedures that are easy to implement, and still dominate standard procedures by achieving numerical stability, practically without trade‐offs. Moreover, approximate proximal Robbins–Monro procedures can be applied even when the objective cannot be calculated analytically, and so they generalize stochastic proximal procedures currently in use.
Chemical Bonding Governs Complex Magnetism in MnPt5P.

Subtle changes in chemical bonds may result in dramatic revolutions in magnetic properties in solid-state materials. MnPt5P, a derivative of the rare-earth-free ferromagnetic MnPt5As, was discovere...
Identifying main effects and interactions among exposures using Gaussian processes

This article is motivated by the problem of studying the joint effect of different chemical exposures on human health outcomes. This is essentially a nonparametric regression problem, with interest being focused not on a black box for prediction but instead on selection of main effects and interactions. For interpretability we decompose the expected health outcome into a linear main effect, pairwise interactions and a nonlinear deviation. Our interest is in model selection for these different components, accounting for uncertainty and addressing nonidentifiability between the linear and nonparametric components of the semiparametric model. We propose a Bayesian approach to inference, placing variable selection priors on the different components, and developing a Markov chain Monte Carlo (MCMC) algorithm. A key component of our approach is the incorporation of a heredity constraint to only include interactions in the presence of main effects, effectively reducing dimensionality of the model search. We adapt a projection approach developed in the spatial statistics literature to enforce identifiability in modeling the nonparametric component using a Gaussian process. We also employ a dimension reduction strategy to sample the nonlinear random effects that aids the mixing of the MCMC algorithm. The proposed MixSelect framework is evaluated using a simulation study, and is illustrated using data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.
Data-Driven Sensor Scheduling for Remote Estimation in Wireless Networks

Sensor scheduling is a well-studied problem in signal processing and control with numerous applications. Despite its successful history, most of the related literature assumes the knowledge of the underlying probabilistic model of the sensor measurements such as the correlation structure or the entire joint probability density function. Herein, a framework for sensor scheduling for remote estimation is introduced in which the system design and the scheduling decisions are based solely on observed data. Unicast and broadcast networks and corresponding receivers are considered. In both cases, the empirical risk minimization can be posed as a difference-of-convex optimization problem, and locally optimal solutions are obtained efficiently by applying the convex–concave procedure. Our results are independent of the data's probability density function, correlation structure, and the number of sensors.
Model completeness and relative decidability

We study the implications of model completeness of a theory for the effectiveness of presentations of models of that theory. It is immediate that for a computable model $$\mathcal {A}$$ of a computably enumerable, model complete theory, the entire elementary diagram $$E(\mathcal {A})$$ must be decidable. We prove that indeed a c.e. theory T is model complete if and only if there is a uniform procedure that succeeds in deciding $$E(\mathcal {A})$$ from the atomic diagram $$\varDelta (\mathcal {A})$$ for all countable models $$\mathcal {A}$$ of T. Moreover, if every presentation of a single isomorphism type $$\mathcal {A}$$ has this property of relative decidability, then there must be a procedure with succeeds uniformly for all presentations of an expansion $$(\mathcal {A},\mathbf {a})$$ by finitely many new constants.
LAWYERS’ PERCEPTIONS OF THE FAIRNESS OF JUDICIAL ASSISTANCE TO SELF-REPRESENTED LITIGANTS

How much assistance should a trial judge provide a self-represented litigant [SRL] before the judge’s impartiality will be reasonably questioned? This question has been of continuing concern to both the bench and bar ever since the rise of the pro se litigation movement in the late 1990s, particularly in the context of “mixed” cases involving an SRL and a represented party. Case law and ethics codes provide inconsistent decisions and vague guidelines for judges, who must balance their duty to provide reasonable assistance with their duty to ensure a fair trial for all parties. This paper reports the results of a survey administered to 210 Canadian family law practitioners who were presented with 16 hypothetical scenarios involving an SRL and a represented party. Respondents indicated their views regarding the impartiality and helpfulness of the trial judge in each scenario, involving various procedural defaults by the SRL and different forms of judicial assistance or lack thereof. The results indicate that lawyers' perceptions of a judge's impartiality are affected, inter alia, by the favourability of the outcome for the SRL, and whether the assistance provided dealt with procedural or substantive matters. Future research is needed to determine whether a consensus can be established regarding perceptions of lawyers, lay persons, and judges regarding which forms of assistance are reasonable and required, permissible, or impermissible.
A Limited Defense of Clinical Placebo Deception

Placebo treatments, like sugar pills and saline injections, are effective in treating pain and perhaps a host of other conditions. To use placebos most effectively, however, doctors must mislead patients into believing that they are receiving active medications. While placebo deception is surprisingly common, its legality has rarely been tested. In November 2006, the American Medical Association (AMA) adopted a new ethics provision categorically prohibiting doctors from using placebos deceptively. In so doing, the AMA shifted the legal landscape, making it almost certain that courts will decide that placebo deception violates informed consent requirements. I argue that the AMA's new policy is overbroad, insensitive to patient preferences, and likely to have unforeseen consequences. While deception is often exploitative, placebo deception can genuinely benefit patients. Absent stronger evidence to justify a ban than we currently have, deceptive placebos should be treated as scarce medical resources--used sparingly but not categorically prohibited.
The manager as facilitator of dialogue

In this article a new role for managers is advocated to create conditions for genuine collaborative engagement in 21st Century organizations. The new role is as a facilitator of emancipatory dialogue, a discourse among parties that can lead to mutual learning, deep understanding and insight, and collaborative consciousness and action. The facilitator role is described and illustrated in the article as a means to encourage free expression and inquiry, but the article also warns about the imposition of coercive norms within the work group that might be externally imposed or even self-imposed. As managers promote an emancipatory form of dialogic engagement, conversations ensue that bring out people’s individual and collective wisdom, creativity, and dignity.
The objective and subjective economy and the presidential vote

The importance of the economy in US presidential elections is well established. Voters reward or punish incumbent party candidates based on the state of the economy. The electorate focuses particularly on economic change, not the level of the economy per se, and pays more attention to late-arriving change than earlier change. On these points there is a good amount of scholarly agreement (see e.g., Erikson and Wlezien 1996; Hibbs 1987). There is less agreement, however, on what specific indicators matter to voters. Some scholars rely on income growth, others on GDP growth, and yet others on subjective perceptions (see Abramowitz 2008; Campbell 2008; Holbrook 1996b; also see Campbell and Garand 2000). In our work, we have used the index of leading economic indicators, a composite of ten variables, including the University of Michigan's index of consumer expectations, stock prices, and eight other objective indicators.
Illiquidity and Stock Returns: Cross-Section and Time-Series Effects: A Replication

This paper replicates and extends the Amihud (2002) study that links liquidity to asset pricing. Using the current version of the CRSP dataset, we obtain essentially the same results that Amihud presents. The same methods applied to more recent data show a much weaker relation between liquidity and asset pricing. Finally, we compare the explanatory power of Amihudâ€™s illiquidity measure to that of other simple measures that use the same data for their calculation. We find that the Amihud illiquidity measure is no better than substantially simpler measures.
Unexplained gaps and Oaxaca–Blinder decompositions

Abstract We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca (1973) and Blinder (1973) and an approach involving a seemingly naive regression that includes a group indicator variable. Our analysis yields two principal findings. We show that the coefficient on a group indicator variable from an OLS regression is an attractive approach for obtaining a single measure of the unexplained gap. We also show that a commonly-used pooling decomposition systematically overstates the contribution of observable characteristics to mean outcome differences when compared to OLS regression, therefore understating unexplained differences. We then provide three empirical examples that explore the practical importance of our analytic results.
Long-Term Reversals in the Corporate Bond Market

Abstract Long-term reversals in corporate bonds are economically and statistically significant in a comprehensive sample spanning the period 1977 to 2017. Such reversals are stronger for bonds with high credit risk and more binding regulatory, capital, and funding liquidity constraints. Bond long-term reversal is not a manifestation of the equity counterpart and is mainly driven by long-term losers. A long-term reversal factor carries a sizable premium and is not explained by long-established equity and bond market factors. Thus, past returns capture investors’ ex-ante risk assessment and the degree of institutional constraints they face, so losing bonds command higher expected returns. (JEL classification: G11, G12, C13)
The impact of sustained attention on labor market outcomes: The case of Ghana

In this paper, we go beyond traditional measures of cognitive abilities (IQ) in explaining labor market and social outcomes in developing countries. We exploit a rich dataset from Ghana that provides information on demographics, labor market outcomes, and a direct measure of cognitive ability along with other test scores to construct a measure of sustained attention. Our work is therefore related to the broader literature in psychology on the importance of executive function on individual behavior and outcomes. We find that, at least for the case of Ghana, after controlling for IQ and other covariates, higher levels of sustained attention are associated with higher educational attainment and a higher likelihood of being employed in a white‐collar job.
Critical Race Empiricism: A New Means to Measure Civil Procedure

Introduction 188 I. Social Psychological Theory 196 II. An Updated Analysis of Iqbal ’s Effect on Race Discrimination Claims 201 A. Method 202 B. Results 205 Study 1: Has Iqbal Increased the Dismissal Rate for Black Plaintiffs’ Claims of Race Discrimination in the Workplace? ..... 205 Study 2: Did White and Black Judges Decide Motions to Dismiss Differently Under Conley, and Do They Decide Motions to Dismiss Differently Under Iqbal? 207 C. Discussion 210 III. Critical Race Empiricism 211 Conclusion 215