cssi/SciSciGPT-SciSciNet huggingface dataset:
Pair-cat codes: autonomous error-correction with low-order nonlinearity

We introduce a driven-dissipative two-mode bosonic system whose reservoir causes simultaneous loss of two photons in each mode and whose steady states are superpositions of pair-coherent/Barut-Girardello coherent states. We show how quantum information encoded in a steady-state subspace of this system is exponentially immune to phase drifts (cavity dephasing) in both modes. Additionally, it is possible to protect information from arbitrary photon loss in either (but not simultaneously both) of the modes by continuously monitoring the difference between the expected photon numbers of the logical states. Despite employing more resources, the two-mode scheme enjoys two advantages over its one-mode cat-qubit counterpart with regards to implementation using current circuit QED technology. First, monitoring the photon number difference can be done without turning off the currently implementable dissipative stabilizing process. Second, a lower average photon number per mode is required to enjoy a level of protection at least as good as that of the cat-codes. We discuss circuit QED proposals to stabilize the code states, perform gates, and protect against photon loss via either active syndrome measurement or an autonomous procedure. We introduce quasiprobability distributions allowing us to represent two-mode states of fixed photon number difference in a two-dimensional complex plane, instead of the full four-dimensional two-mode phase space. The two-mode codes are generalized to multiple modes in an extension of the stabilizer formalism to non-diagonalizable stabilizers. The M-mode codes can protect against either arbitrary photon losses in up to M − 1 modes or arbitrary losses and gains in any one mode.
WEIGHING GALAXY DISKS WITH THE BARYONIC TULLY–FISHER RELATION

We estimate the stellar masses of disk galaxies with two independent methods: a photometrically self-consistent color–mass-to-light ratio relation (CMLR) from population synthesis models, and the baryonic Tully–Fisher relation (BTFR) calibrated by gas-rich galaxies. These two methods give consistent results. The CMLR correctly converts distinct Tully–Fisher relations in different bands into the same BTFR. The BTFR is consistent with over nearly six decades in mass, with no hint of a change in slope over that range. The intrinsic scatter in the BTFR is negligible, implying that the IMF of disk galaxies is effectively universal. The gas-rich BTFR suggests an absolute calibration of the stellar mass scale that yields nearly constant mass-to-light ratios in the near-infrared (NIR): in Ks and at 3.6 μm. There is only modest intrinsic scatter (~0.12 dex) about these typical values. There is no discernible variation with color or other properties: the NIR luminosity is a good tracer of stellar mass.
A New Signal Model for Axion Cavity Searches from N-body Simulations

Signal estimates for direct axion dark matter (DM) searches have used the isothermal sphere halo model for the last several decades. While insightful, the isothermal model does not capture effects from a halo's infall history nor the influence of baryonic matter, which has been shown to significantly influence a halo's inner structure. The high resolution of cavity axion detectors can make use of modern cosmological structure-formation simulations, which begin from realistic initial conditions, incorporate a wide range of baryonic physics, and are capable of resolving detailed structure. This work uses a state-of-the-art cosmological N-body+Smoothed-Particle Hydrodynamics simulation to develop an improved signal model for axion cavity searches. Signal shapes from a class of galaxies encompassing the Milky Way are found to depart significantly from the isothermal sphere. A new signal model for axion detectors is proposed and projected sensitivity bounds on the Axion DM eXperiment (ADMX) data are presented.
Local Hamiltonians Whose Ground States Are Hard to Approximate

Ground states of local Hamiltonians can be generally highly entangled: any quantum circuit that generates them (even approximately) must be sufficiently deep to allow coupling (entanglement) between any pair of qubits. Until now this property was not known to be robust - the marginals of such states to a subset of the qubits containing all but a small constant fraction of them may be only locally entangled, and hence approximable by shallow quantum circuits. In this work we construct a family of 16-local Hamiltonians for which any 1-10^-8 fraction of qubits of any ground state must be highly entangled.This provides evidence that quantum entanglement is not very fragile, and perhaps our intuition about its instability is an artifact of considering local Hamiltonians which are not only local but spatially local. Formally, it provides positive evidence for two wide-open conjectures in condensed-matter physics and quantum complexity theory which are the qLDPC conjecture, positing the existence of good quantum LDPC codes, and the NLTS conjecture due to Freedman and Hastings positing the existence of local Hamiltonians in which any low-energy state is highly-entangled.Our Hamiltonian is based on applying the hypergraph product by Tillich-Zemor to the repetition code with checks from an expander graph. A key tool in our proof is a new lower bound on the vertex expansion of the output of low-depth quantum circuits, which may be of independent interest.
Comment: Demystifying Double Robustness: A Comparison of Alternative Strategies for Estimating a Population Mean from Incomplete Data

This article is an excellent introduction to doubly robust methods and we congratulate the authors for their thoroughness in bringing together the wide array of methods from different traditions that all share the property of being doubly robust. Statisticians at RAND have been making exten sive use of propensity score weighting in education (McCaffrey and Hamilton, 2007), policing and crim inal justice (Ridgeway, 2006), drug treatment evalua tion (Morral et al., 2006), and military workforce issues (Harreil, Lim, Casta?eda and Golinelli, 2004). More recently, we have been adopting doubly robust (DR) methods in these applications believing that we could achieve further bias and variance reduction. Initially, this article made us second-guess our decision. The ap parently strong performance of OLS and the authors' finding that no method outperformed OLS ran counter to our intuition and experience with propensity score weighting and DR estimators. We posited two potential explanations for this. First, we suspected that the high variance reported by the authors when using propensity score weights could result from their use of standard logistic regression. Second, stronger interaction effects in the outcome regression model might favor the DR approach.
Universality of phases in QCD and QCD-like theories

We argue that the whole or the part of the phase diagrams of QCD and QCD-like theories should be universal in the large-N c limit through the orbifold equivalence. The whole phase diagrams, including the chiral phase transitions and the BEC-BCS crossover regions, are identical between SU(N c) QCD at finite isospin chemical potential and SO(2N c) and Sp(2N c) gauge theories at finite baryon chemical potential. Outside the BEC-BCS crossover regions in these theories, the phase diagrams are also identical to that of SU(N c ) QCD at finite baryon chemical potential. We give examples of the universality in some solvable cases: (i) QCD and QCD-like theories at asymptotically high density where the controlled weak-coupling calculations are possible, (ii) chiral random matrix theories of different universality classes, which are solvable large-N (large volume) matrix models of QCD. Our results strongly suggest that the chiral phase transition and the QCD critical point at finite baryon chemical potential can be studied using sign-free theories, such as QCD at finite isospin chemical potential, in lattice simulations.
TEMPERATURE STRUCTURE AND ATMOSPHERIC CIRCULATION OF DRY TIDALLY LOCKED ROCKY EXOPLANETS

Next-generation space telescopes will observe the atmospheres of rocky planets orbiting nearby M-dwarfs. Understanding these observations will require well-developed theory in addition to numerical simulations. Here we present theoretical models for the temperature structure and atmospheric circulation of dry, tidally locked rocky exoplanets with gray radiative transfer and test them using a general circulation model (GCM). First, we develop a radiative-convective (RC) model that captures surface temperatures of slowly rotating and cool atmospheres. Second, we show that the atmospheric circulation acts as a global heat engine, which places strong constraints on large-scale wind speeds. Third, we develop an RC-subsiding model which extends our RC model to hot and thin atmospheres. We find that rocky planets develop large day-night temperature gradients at a ratio of wave-to-radiative timescales up to two orders of magnitude smaller than the value suggested by work on hot Jupiters. The small ratio is due to the heat engine inefficiency and asymmetry between updrafts and subsidence in convecting atmospheres. Fourth, we show, using GCM simulations, that rotation only has a strong effect on temperature structure if the atmosphere is hot or thin. Our models let us map out atmospheric scenarios for planets such as GJ 1132b, and show how thermal phase curves could constrain them. Measuring phase curves of short-period planets will require similar amounts of time on the James Webb Space Telescope as detecting molecules via transit spectroscopy, so future observations should pursue both techniques.
Neoclassical theory of elementary charges with spin of 1/2

We advance here our neoclassical theory of elementary charges by integrating into it the concept of spin of 1/2. The developed spinorial version of our theory has many important features identical to those of the Dirac theory such as the gyromagnetic ratio, expressions for currents including the spin current, and antimatter states. In our theory, the concepts of charge and anticharge relate naturally to their “spin” in its rest frame in two opposite directions. An important difference with the Dirac theory is that both the charge and anticharge energies are positive whereas their frequencies have opposite signs.
NEUTRINO ASTRONOMY WITH ICECUBE

IceCube is a kilometer-scale high energy neutrino telescope under construction at the South Pole, a second-generation instrument expanding the capabilities of the AMANDA telescope. The scientific portfolio of IceCube includes the detection of neutrinos from astrophysical objects such as the sources of the cosmic rays, the search for dark matter, and fundamental physics using a very large data set of atmospheric neutrinos. The design and status of IceCube are briefly reviewed, followed by a summary of results to date from AMANDA and initial IceCube results from the 2007 run, with 22 of a planned 86 strings operational. The new infill array known as Deep Core, which will extend IceCube's capabilities to energies as low as 10 GeV, is also described.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense

Abstract Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a shift that flips this paradigm upside down. Specifically, we propose a “small data for big tasks” paradigm, wherein a single artificial intelligence (AI) system is challenged to develop “common sense,” enabling it to solve a wide range of tasks with little training data. We illustrate the potential power of this new paradigm by reviewing models of common sense that synthesize recent breakthroughs in both machine and human vision. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense. When taken as a unified concept, FPICU is concerned with the questions of “why” and “how,” beyond the dominant “what” and “where” framework for understanding vision. They are invisible in terms of pixels but nevertheless drive the creation, maintenance, and development of visual scenes. We therefore coin them the “dark matter” of vision. Just as our universe cannot be understood by merely studying observable matter, we argue that vision cannot be understood without studying FPICU. We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning. In summary, we argue that the next generation of AI must embrace “dark” humanlike common sense for solving novel tasks.
General relativity from causality

We study large families of theories of interacting spin 2 particles from the point of view of causality. Although it is often stated that there is a unique Lorentz invariant effective theory of massless spin 2, namely general relativity, other theories that utilize higher derivative interactions do in fact exist. These theories are distinct from general relativity, as they permit any number of species of spin 2 particles, are described by a much larger set of parameters, and are not constrained to satisfy the equivalence principle. We consider the leading spin 2 couplings to scalars, fermions, and vectors, and systematically study signal propagation in all these other families of theories. We find that most interactions directly lead to superluminal propagation of either a spin 2 particle or a matter particle, and interactions that are subluminal generate other interactions that are superluminal. Hence, such theories of interacting multiple spin 2 species have superluminality, and by extension, acausality. This is radically different to the special case of general relativity with a single species of minimally coupled spin 2, which leads to subluminal propagation from sources satisfying the null energy condition. This pathology persists even if the spin 2 field is massive. We compare these findings to the analogous case of spin 1 theories, where higher derivative interactions can be causal. This makes the spin 2 case very special, and suggests that multiple species of spin 2 is forbidden, leading us to general relativity as essentially the unique internally consistent effective theory of spin 2.
Equivariant Semidefinite Lifts and Sum-of-Squares Hierarchies

A central question in optimization is to maximize (or minimize) a linear function over a given polytope $P$. To solve such a problem in practice one needs a concise description of the polytope $P$. In this paper we are interested in representations of $P$ using the positive semidefinite cone: a positive semidefinite lift (PSD lift) of a polytope $P$ is a representation of $P$ as the projection of an affine slice of the positive semidefinite cone $\mathbf{S}^d_+$. Such a representation allows linear optimization problems over $P$ to be written as semidefinite programs of size $d$. Such representations can be beneficial in practice when $d$ is much smaller than the number of facets of the polytope $P$. In this paper we are concerned with so-called equivariant PSD lifts (also known as symmetric PSD lifts) which respect the symmetries of the polytope $P$. We present a representation-theoretic framework to study equivariant PSD lifts of a certain class of symmetric polytopes known as orbitopes. Our main result...
Calabi-Yau threefolds with large $h^{2,1}$

We carry out a systematic analysis of Calabi-Yau threefolds that are ellipti- cally fibered with section ("EFS") and have a large Hodge number h 2,1 . EFS Calabi-Yau threefolds live in a single connected space, with regions of moduli space associated with dif- ferent topologies connected through transitions that can be understood in terms of singular Weierstrass models. We determine the complete set of such threefolds that have h 2,1 ≥ 350 by tuning coefficients in Weierstrass models over Hirzebruchsurfaces. The resulting set of Hodge numbers includes those of all known Calabi-Yau threefolds with h 2,1 ≥ 350, as well as three apparently new Calabi-Yau threefolds. We speculate that there are no other Calabi-Yau threefolds (elliptically fibered or not) with Hodge numbers that exceed this bound. We summarize the theoretical and practical obstacles to a complete enumeration of all possible EFS Calabi-Yau threefolds and fourfolds, including those with small Hodge numbers, using this approach.
Optimal design of networks of positive linear systems under stochastic uncertainty

In this paper, we study networks of positive linear systems subject to time-invariant and random uncertainties. We present linear matrix inequalities for checking the stability of the whole network of the systems around the origin with a prescribed probability and a decay rate. Based on this condition, we then give an efficient method, based on geometric programming, to find the optimal parameters of the probability distribution describing the uncertainty. We illustrate our results by studying the stability of a viral spreading process in the presence of random uncertainties.
Non-Anderson critical scaling of the Thouless conductance in 1D

Abstract We propose and investigate numerically a one-dimensional model which exhibits a non-Anderson disorder-driven transition. Such transitions have recently been attracting a great deal of attention in the context of Weyl semimetals, one-dimensional systems with long-range hopping and high-dimensional semiconductors. Our model hosts quasiparticles with the dispersion ± | k | α sign k with α 1 ∕ 2 near two points (nodes) in momentum space and includes short-range-correlated random potential which allows for scattering between the nodes and near each node. In contrast with the previously studied models in dimensions d 3 , the model considered here exhibits a critical scaling of the Thouless conductance which allows for an accurate determination of the critical properties of the non-Anderson transition, with a precision significantly exceeding the results obtained from the critical scaling of the density of states, usually simulated at such transitions. We find that in the limit of the vanishing parameter e = 2 α − 1 the correlation-length exponent ν = 2 ∕ ( 3 | e | ) at the transition is inconsistent with the prediction ν R G = 1 ∕ | e | of the perturbative renormalisation-group analysis. Our results allow for a numerical verification of the convergence of e -expansions for non-Anderson disorder-driven transitions and, in general, interacting field theories near critical dimensions.
Ultra-Intense Gamma-Rays Created Using the Texas Petawatt Laser

Abstract In a series of experiments at the Texas Petawatt Laser (TPW) in Austin, TX, we have used attenuation spectrometers, dosimeters, and a new Forward Compton Electron Spectrometer (FCES) to measure and characterize the angular distribution, flux, and energy spectrum of the X-rays and gamma rays produced by the TPW striking multi-millimeter thick gold targets. Our results represent the first such measurements at laser intensities ≥10 21 W × cm −2 and pulse durations ≤150 fs. We obtain a maximum yield of X-ray and gamma ray energy with respect to laser energy of 4% and a mean yield of 2%. We further obtain a Full Width Half Maximum (FWHM) of the gamma angular distribution of 33°. We were able to characterize the gamma-ray spectrum from 3 MeV to 50 MeV using a Forward Compton Electron Spectrometer, with an energy resolution of 10–15% and mean bremsstrahlung effective kT of ∼6 MeV. We were able to characterize the spectrum from 1 to 5 MeV using a Filter Stack (attenuation) Spectrometer, measuring a mean X-ray temperature for the spectrum from 3 to 5 MeV of 2.1 MeV, suggesting that the low-energy gammas are bremsstrahlung from secondary electrons.
Near-Sun Flux-Rope Structure of CMEs

We have used the Krall flux-rope model (Krall and St. Cyr, Astrophys. J. 2006, 657, 1740) (KFR) to fit 23 magnetic cloud (MC)-CMEs and 30 non-cloud ejecta (EJ)-CMEs in the Living With a Star (LWS) Coordinated Data Analysis Workshop (CDAW) 2011 list. The KFR-fit results shows that the CMEs associated with MCs (EJs) have been deflected closer to (away from) the solar disk center (DC), likely by both the intrinsic magnetic structures inside an active region (AR) and ambient magnetic structures (e.g. nearby ARs, coronal holes, and streamers, etc.). The mean absolute propagation latitudes and longitudes of the EJ-CMEs (18∘, 11∘) were larger than those of the MC-CMEs (11∘, 6∘) by 7∘ and 5∘, respectively. Furthermore, the KFR-fit widths showed that the MC-CMEs are wider than the EJ-CMEs. The mean fitting face-on width and edge-on width of the MC-CMEs (EJ-CMEs) were 87 (85)∘ and 70 (63)∘, respectively. The deflection away from DC and narrower angular widths of the EJ-CMEs have caused the observing spacecraft to pass over only their flanks and miss the central flux-rope structures. The results of this work support the idea that all CMEs have a flux-rope structure.
Discussion on common errors in analyzing sea level accelerations, solar trends and global warming

Errors in applying regression models and wavelet filters used to analyze geophysical signals are discussed: (1) multidecadal natural oscillations (e.g. the quasi 60-year Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO) and Pacific Decadal Oscillation (PDO)) need to be taken into account for properly quantifying anomalous accelerations in tide gauge records such as in New York City; (2) uncertainties and multicollinearity among climate forcing functions prevent a proper evaluation of the solar contribution to the 20th century global surface temperature warming using overloaded linear regression models during the 1900-2000 period alone; (3) when periodic wavelet filters, which require that a record is pre-processed with a reflection methodology, are improperly applied to decompose non-stationary solar and climatic time series, Gibbs boundary artifacts emerge yielding misleading physical interpretations. By correcting these errors and using optimized regression models that reduce multicollinearity artifacts, I found the following results: (1) the sea level in New York City is not accelerating in an alarming way, and may increase by about 350 mm from 2000 to 2100 instead of the previously projected values varying from 1130 mm to 1550 mm estimated using the methods proposed by Sallenger et al. (2012) and Boon (2012), respectively; (2) the solar activity increase during the 20th century contributed about 50% of the 0.8 K global warming observed during the 20th century instead of only 7-10% (IPCC, 2007; Benestad and Schmidt, 2009; Lean and Rind, 2009). These findings stress the importance of natural oscillations and of the sun to properly interpret climatic changes.
MAGNETIC INVERSION AS A MECHANISM FOR THE SPECTRAL TRANSITION OF BLACK HOLE BINARIES

A mechanism for the transition between low/hard, high/soft, and steep power law (SPL) spectral states in black hole X-ray binaries is proposed. The low/hard state is explained by the development of a magnetically arrested accretion disk attributable to the accumulation of a vertical magnetic field in a central bundle. This disk forms powerful jets and consists of thin spiral accretion streams of a dense optically thick plasma surrounded by a hot, magnetized, optically thin corona, which emits most of the energy in hard X-rays. State transition occurs because of the quasi-periodic or random inversion of poloidal magnetic fields in the accretion flow supplied by the secondary star. The inward advection of the inverted field results in a temporal disappearance of the central bundle caused by the annihilation of the opposed fields and restoration of the optically thick disk in the innermost region. This disk represents the high/soft state. The SPL state develops at the period of intensive field annihilation and precedes the high/soft state. The continuous supply of the inverted field leads to a new low/hard state because of the formation of another magnetically arrested disk.
ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models

Deep neural networks (DNNs) are one of the most prominent technologies of our time, as they achieve state-of-the-art performance in many machine learning tasks, including but not limited to image classification, text mining, and speech processing. However, recent research on DNNs has indicated ever-increasing concern on the robustness to adversarial examples, especially for security-critical tasks such as traffic sign identification for autonomous driving. Studies have unveiled the vulnerability of a well-trained DNN by demonstrating the ability of generating barely noticeable (to both human and machines) adversarial images that lead to misclassification. Furthermore, researchers have shown that these adversarial images are highly transferable by simply training and attacking a substitute model built upon the target model, known as a black-box attack to DNNs. Similar to the setting of training substitute models, in this paper we propose an effective black-box attack that also only has access to the input (images) and the output (confidence scores) of a targeted DNN. However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples. We use zeroth order stochastic coordinate descent along with dimension reduction, hierarchical attack and importance sampling techniques to efficiently attack black-box models. By exploiting zeroth order optimization, improved attacks to the targeted DNN can be accomplished, sparing the need for training substitute models and avoiding the loss in attack transferability. Experimental results on MNIST, CIFAR10 and ImageNet show that the proposed ZOO attack is as effective as the state-of-the-art white-box attack (e.g., Carlini and Wagner's attack) and significantly outperforms existing black-box attacks via substitute models.
Status of Matter-Gravity Couplings in the SME

Constraints on Lorentz violation in matter-gravity couplings are summarized along with existing proposals to obtain sensitivities that exceed current limits by up to 11 orders of magnitude.
Semiclassical measures on hyperbolic surfaces have full support

We show that each limiting semiclassical measure obtained from a sequence of eigenfunctions of the Laplacian on a compact hyperbolic surface is supported on the entire cosphere bundle. The key new ingredient for the proof is the fractal uncertainty principle, first formulated in “Spectral gaps, additive energy, and a fractal uncertainty principle” [Dyatlov, S. & Zahl, J. Geom. Funct. Anal., 26 (2016), 1011–1094] and proved for porous sets in “Spectral gaps without the pressure condition” [Bourgain, J. & Dyatlov, S. Ann. of Math., 187 (2018), 825–867].
Public Debt Dynamics and Intra-Year Exchange Rate Fluctuations

The public sector, in carrying out its operations, often incurs foreign currency denominated liabilities and, as such, is exposed to exchange rate fluctuations that could affect the value of public debt to GDP ratios over time. This paper shows that converting foreign currency denominated flows and stocks into local currency using the average and the end-of-period exchange rates, respectively, as envisaged in public finance manuals, gives rise to an identifiable stock-flow adjustment term—due to intra-year exchange rate fluctuations—that affects public debt accumulation. Importantly, the inclusion of this often-ignored stock-flow adjustment term is critical to accurately project public debt levels and any related indicator that could in turn inform about the risk of debt distress. Using a novel dataset covering 82 countries during 2008–19, the paper shows that this stock flow adjustment term is sizable in countries experiencing large exchange rate depreciations, namely above the 99th percentile of the full sample, reaching 1.2 percent of GDP. Interestingly, the measurement of policy-related concepts such as interest rate-growth differentials and debt stabilizing primary balances are also affected by intra-year exchange rate fluctuations, and in non-negligible ways.
Biomedical Event Extraction on Graph Edge-conditioned Attention Networks with Hierarchical Knowledge Graphs

Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via Graph Edge-conditioned Attention Networks (GEANet) and hierarchical graph representation. To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.
Multifractality distinguishes reactive from proactive cascades in postural control

Abstract Intermittency is a flexible control process entailing context-sensitive engagement with task constraints. The present work aims to situate the intermittency of dexterous behavior explicitly in multifractal modeling for non-Gaussian cascade processes. Multiscale probability density function (PDF) analysis of the center of pressure (CoP) fluctuations during quiet upright standing yields non-Gaussianity parameters lambda exhibiting task-sensitive curvilinear relationships with timescale. The present reanalysis aims for a finer-grained accounting of how non-Gaussian cascade processes might align with known, separable postural processes. It uses parallel decomposition of non-Gaussianity lambda-vs.-timescale and CoP. Orthogonal polynomials decompose lambda curvilinearity, and rambling-trembling analysis decomposes CoP into relatively more intentional rambling (displacement to new equilibrium points) and less intentional trembling sway (deviations around new equilibrium points). Modeling orthogonal polynomials of non-Gaussianity's lambda-vs.-timescale relationship allows us to differentiate linear from quadratic decay, each of which indicates scale-invariant and scale-dependent cascades, respectively. We tested whether scale-dependent and scale-invariant cascades serve different roles, that is, responding to destabilizing task demands and supporting the proactive movement to a new equilibrium point, respectively. We also tested whether these cascades appear more clearly in rambling rather than trembling sway. More generally, we test whether multifractal nonlinear correlations supports this capacity of postural control to this two-step differentiation: both into rambling vs. trembling, then into scale-dependent vs. scale-invariant cascades within rambling sway. The results supported these hypotheses. Thus, the present work aligns specific aspects of task setting with aspects of cascade dynamics and confirms multifractal foundations of the organism-task relationship.
Nested active learning for efficient model contextualization and parameterization: pathway to generating simulated populations using multi-scale computational models.

There is increasing interest in the use of mechanism-based multi-scale computational models (such as agent-based models (ABMs)) to generate simulated clinical populations in order to discover and e...
Differential Effect of Anesthesia on Visual Cortex Neurons with Diverse Population Coupling.

Abstract Cortical neurons display diverse firing patterns and synchronization properties. How anesthesia alters the firing response of different neuron groups relevant for sensory information processing is unclear. Here we investigated the graded effect of anesthesia on spontaneous and visual flash-induced spike activity of different neuron groups classified based on their spike waveform, firing rate, and population coupling (the extent neurons conform to population spikes). Single-unit activity was measured from multichannel extracellular recordings in deep layers of primary visual cortex of freely moving rats in wakefulness and at three concentrations of desflurane. Anesthesia generally decreased firing rate and increased population coupling and burstiness of neurons. Population coupling and firing rate became more correlated and the pairwise correlation between neurons became more predictable by their population coupling in anesthesia. During wakefulness, visual stimulation increased firing rate; this effect was the largest and the most prolonged in neurons that exhibited high population coupling and high firing rate. During anesthesia, the early increase in firing rate (20–150 ms post-stimulus) of these neurons was suppressed, their spike timing was delayed and split into two peaks. The late response (200–400 ms post-stimulus) of all neurons was also suppressed. We conclude that anesthesia alters the visual response of primarily high-firing highly coupled neurons, which may interfere with visual sensory processing. The increased association of population coupling and firing rate during anesthesia suggests a decrease in sensory information content.
Analysis of neural clusters due to deep brain stimulation pulses

Deep brain stimulation (DBS) is an established method for treating pathological conditions such as Parkinson’s disease, dystonia, Tourette syndrome, and essential tremor. While the precise mechanisms which underly the effectiveness of DBS are not fully understood, several theoretical studies of populations of neural oscillators stimulated by periodic pulses have suggested that this may be related to clustering, in which subpopulations of the neurons are synchronized, but the subpopulations are desynchronized with respect to each other. The details of the clustering behavior depend on the frequency and amplitude of the stimulation in a complicated way. In the present study, we investigate how the number of clusters and their stability properties, bifurcations, and basins of attraction can be understood in terms of one-dimensional maps defined on the circle. Moreover, we generalize this analysis to stimuli that consist of pulses with alternating properties, which provide additional degrees of freedom in the design of DBS stimuli. Our results illustrate how the complicated properties of clustering behavior for periodically forced neural oscillator populations can be understood in terms of a much simpler dynamical system.
Evaluating the Implementation of the SWITCH® School Wellness Intervention and Capacity-Building Process through Multiple Methods

School wellness programming is important for promoting healthy lifestyles and academic achievement in youth; however, research is needed on methods that can help schools implement and sustain such programs on their own. The purpose of this study was to investigate factors within and outside the school environment that influenced school capacity for implementation and potential sustainability of wellness programming. As part of the School Wellness Integration Targeting Child Health (SWITCH®) intervention, elementary school wellness teams (N = 30) were guided through a capacity-building process focused on promoting the adoption of healthy lifestyle behaviors in students. Data on implementation were collected through three standardized surveys and interviews (pre-mid-post) and a post-implementation interview. Indicators of organizational capacity were assessed using the School Wellness Readiness Assessment (SWRA). Paired t-tests were run to assess changes in implementation (classroom, physical education, and lunchroom settings), capacity, and stakeholder engagement over time. One-way analysis of variance (ANOVA) tests were run to examine how implementation of best practices (low, moderate, high) explained differences in capacity gains. Qualitative data were analyzed through inductive and deductive analysis, following the Consolidated Framework for Implementation Research (CFIR). Paired t-tests showed non-significant increases in school and setting-specific capacity and implementation of SWITCH best practices over time, in addition to a consistent level of engagement from key stakeholders. ANOVA results revealed non-significant associations between implementation group and gains in school capacity (F [2, 24] = 1.63; p = .21), class capacity (F [2, 24]=0.20 p = .82), lunchroom capacity (F [2, 24]=0.29; p = .78), and physical education (F [2, 24]=1.45; p = .25). Qualitative data demonstrated that factors within the outer setting (i.e., engaging community partners) facilitated programming. Inner-setting factors (i.e., relationships with administration and staff) influenced implementation. Implementation process themes (e.g., planning, adaptation of resources to meet school capacity/needs, and engaging students as leaders) were cited as key facilitators. Schools discussed factors affecting sustainability, such as school culture and knowledge of school wellness policy. The results from this implementation study document the importance of allowing schools to adapt programming to meet their local needs, and highlight the strengths of measuring multiple implementation outcomes. Increased support is needed for schools regarding the formation and improvement of wellness policies as a means to enhance sustainability over time.
Counting points on superelliptic curves in average polynomial time

We describe the practical implementation of an average polynomial-time algorithm for counting points on superelliptic curves defined over $\mathbb Q$ that is substantially faster than previous approaches. Our algorithm takes as input a superelliptic curves $y^m=f(x)$ with $m\ge 2$ and $f\in \mathbb Z[x]$ any squarefree polynomial of degree $d\ge 3$, along with a positive integer $N$. It can compute $\#X(\mathbb F_p)$ for all $p\le N$ not dividing $m\mathrm{lc}(f)\mathrm{disc}(f)$ in time $O(md^3 N\log^3 N\log\log N)$. It achieves this by computing the trace of the Cartier--Manin matrix of reductions of $X$. We can also compute the Cartier--Manin matrix itself, which determines the $p$-rank of the Jacobian of $X$ and the numerator of its zeta function modulo~$p$.
A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer’s Disease

Currently, many studies of Alzheimer’s disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.
A Characteristic Map for the Symmetric Space of Symplectic Forms Over a Finite Field

The characteristic map for the symmetric group is an isomorphism relating the representation theory of the symmetric group to symmetric functions. An analogous isomorphism is constructed for the symmetric space of symplectic forms over a finite field, with the spherical functions being sent to Macdonald polynomials with parameters $(q,q^2)$. An analogue of parabolic induction is interpreted as a certain multiplication of symmetric functions. Applications are given to Schur-positivity of skew Macdonald polynomials with parameters $(q,q^2)$ as well as combinatorial formulas for spherical function values.
Properties of equilibrium states for geodesic flows over manifolds without focal points

Abstract We prove that for closed rank 1 manifolds without focal points the equilibrium states are unique for Holder potentials satisfying the pressure gap condition. In addition, we provide a criterion for a continuous potential to satisfy the pressure gap condition. Moreover, we derive several ergodic properties of the unique equilibrium states including the equidistribution and the K-property.
A Survey of Learning Causality with Data: Problems and Methods

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from—or the same as—the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.
Introduction to the State of Federal Income Taxation: Rates, Progressivity, and Budget Processes

Many fundamental changes have occurred in our tax system in the past four years. These changes raise several important issues: Is it appropriate to decrease tax revenues in order to decrease the size of the government? What is the appropriate role of progressivity? What are the efficiency effects of recent tax cuts? How important is it to have a transparent budget process? Professor James Repetti introduces the speakers and commentators who address these and other issues in a Symposium on The State of Federal Income Taxation held at Boston College Law School on Friday April 16, 2004.
E Pluribus Unum? The Full Faith and Credit Clause and Meaningful Recognition of Out-of-State Adoptions

Parents and children whose legal relationships derive from state adoption judgments face uncertainty when they travel across state lines. State officials have denied out-of-state adoptive parents revised birth certificates, which recognize their status as legal parents in their child’s birth state, because the parents would be statutorily unable to adopt in that state. Various U.S. Courts of Appeals have disagreed as to whether, and to what extent, the Full Faith and Credit Clause in Article IV of the Constitution requires that state executive officials recognize out-of-state rights. Circuits also differ as to whether the Full Faith and Credit Clause confers an individual right for purposes of 42 U.S.C. § 1983 on parents alleging a violation of the Clause. The divergent opinions result from conflicting interpretations of the force and scope of the Full Faith and Credit Clause, distinctions between recognition and enforcement of out-ofstate rights, and the varying views of the Clause’s balance of state policy interests and federal unity imperatives. This Note argues that the language, history, and purpose of the Full Faith and Credit Clause demonstrate that the Clause requires states— including both judicial and executive officers—to give meaningful recognition to judicially established rights. It concludes that the denial of revised birth certificates to out-of-state adoptive couples violates the Full Faith and Credit Clause’s mandate to meaningfully recognize and equally enforce out-of-state judgments.
Case Dismissed: Police Discretion and Racial Differences in Dismissals of Felony Charges

Prior research has produced conflicting evidence of racial profiling during traffic stops. We instead analyze rates of case dismissal against felony arrestees by race. Superficial bias based on "unobservables" should be reduced because of the evidentiary requirements and nonnegligible costs of filing charges. Nonetheless, using data from over 58,000 US felony cases from 1990 to 1998, our probit analysis finds higher rates of dismissals for blacks for the subset of crimes that rely on police to make snap judgments. This suggests there may be more aggressive policing of blacks in these situations. Case dismissal rates are also elevated for both whites and blacks when blacks are underrepresented on local police forces.
The Death Toll from Natural Disasters: The Role of Income, Geography, and Institutions

Using a new data set on annual deaths from disasters in 73 nations from 1980 to 2002, this paper tests several hypotheses concerning natural-disaster mitigation. Though richer nations do not experience fewer natural disasters than poorer nations, richer nations do suffer less death from disaster. Economic development provides implicit insurance against nature's shocks. Democracies and nations with higher-quality institutions suffer less death from natural disaster. Because climate change is expected to increase the frequency of natural disasters such as floods, these results have implications for the incidence of global warming.
Speed Limit Policies: The Output Gap and Optimal Monetary Policy

In a standard New Keynesian model, a myopic central bank concerned with stabilizing inflation and changes in the output gap will implement a policy under discretion that replicates the optimal, timeless perspective, precommitment policy. By stabilizing output gap changes, the central bank imparts inertia into output and inflation that is absent under pure discretion. Even a fully optimizing (i.e., non-myopic) central bank operating in a discretionary policy environment achieves better social outcomes if it focuses on inflation and changes in the output gap than are achieved under inflation targeting.(This abstract was borrowed from another version of this item.)
Sham Litigation in the Pharmaceutical Sector

(2011). Sham Litigation in the Pharmaceutical Sector. European Competition Journal: Vol. 7, No. 3, pp. 455-503.
Domain specific DSS tools for knowledge-based model building

Abstract The formulation of complex planning models, such as linear programming (LP) systems, is a difficult task that enjoys little support by current decision support systems. It is hypothesized that current artificial intelligence technology is insufficient to build generalized formulation tools that would be usable by OR-naive end users. As an alternative, this paper presents a domain-specific approach to knowledge-based model formulation which combines the use of ‘syntactic’ knowledge about linear programming with ‘semantic’ guidance by knowledge specific to some application domain. As a prototype of this approach, a model formulation tool for LP-based production management is being developed at New York University.
Conditional Risk Premia in Currency Markets and Other Asset Classes

The downside risk capital asset pricing model (DR-CAPM) can price the cross section of currency returns. The market-beta differential between high and low interest rate currencies is higher conditional on bad market returns, when the market price of risk is also high, than it is conditional on good market returns. Correctly accounting for this variation is crucial for the empirical performance of the model. The DR-CAPM can jointly rationalize the cross section of equity, equity index options, commodity, sovereign bond and currency returns, thus offering a unified risk view of these asset classes. In contrast, popular models that have been developed for a specific asset class fail to jointly price other asset classes.