cssi/SciSciGPT-SciSciNet huggingface dataset:
Tight Limits on Nonlocality from Nontrivial Communication Complexity; a.k.a. Reliable Computation with Asymmetric Gate Noise

It has long been known that the existence of certain superquantum nonlocal correlations would cause communication complexity to collapse. The absurdity of a world in which any function could be evaluated by two players with a constant amount of communication in turn provides a tantalizing way to distinguish quantum mechanics from incorrect theories of physics; the statement “communication complexity is nontrivial” has even been conjectured to be a concise information-theoretic axiom for characterizing quantum mechanics. We directly address the viability of that perspective with two results. First, we exhibit a nonlocal game such that communication complexity collapses in any physical theory whose maximal winning probability exceeds the quantum value. Second, we consider the venerable CHSH game that initiated this line of inquiry. In that case, the quantum value is about 0.85 but it is known that a winning probability of approximately 0.91 would collapse communication complexity. We show that the 0.91 result is the best possible using a large class of proof strategies, suggesting that the communication complexity axiom is insufficient for characterizing CHSH correlations. Both results build on new insights about reliable classical computation. The first exploits our formalization of an equivalence between amplification and reliable computation, while the second follows from a rigorous determination of the threshold for reliable computation with formulas of noise-free XOR gates and noisy AND gates.
Zinc: Multidimensional Effects on Living Organisms

Zinc is a redox-inert trace element that is second only to iron in abundance in biological systems. In cells, zinc is typically buffered and bound to metalloproteins, but it may also exist in a labile or chelatable (free ion) form. Zinc plays a critical role in prokaryotes and eukaryotes, ranging from structural to catalytic to replication to demise. This review discusses the influential properties of zinc on various mechanisms of bacterial proliferation and synergistic action as an antimicrobial element. We also touch upon the significance of zinc among eukaryotic cells and how it may modulate their survival and death through its inhibitory or modulatory effect on certain receptors, enzymes, and signaling proteins. A brief discussion on zinc chelators is also presented, and chelating agents may be used with or against zinc to affect therapeutics against human diseases. Overall, the multidimensional effects of zinc in cells attest to the growing number of scientific research that reveal the consequential prominence of this remarkable transition metal in human health and disease.
GWAS reveal a role for the central nervous system in regulating weight and weight change in response to exercise

Body size and weight show considerable variation both within and between species. This variation is controlled in part by genetics, but also strongly influenced by environmental factors including diet and the level of activity experienced by the individual. Due to the increasing obesity epidemic in much of the world, there is considerable interest in the genetic factors that control body weight and how weight changes in response to exercise treatments. Here, we address this question in the Drosophila model system, utilizing 38 strains of the Drosophila Genetics Reference Panel. We use GWAS to identify the molecular pathways that control weight and weight changes in response to exercise. We find that there is a complex set of molecular pathways controlling weight, with many genes linked to the central nervous system (CNS). The CNS also plays a role in the weight change with exercise, in particular, signaling from the CNS. Additional analyses revealed that weight in Drosophila is driven by two factors, animal size, and body composition, as the amount of fat mass versus lean mass impacts the density. Thus, while the CNS appears to be important for weight and exercise-induced weight change, signaling pathways are particularly important for determining how exercise impacts weight.
The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems

Automated decision-making systems implemented in public life are typically standardized. One algorithmic decision-making system can replace thousands of human deciders. Each of the humans so replaced had her own decision-making criteria: some good, some bad, and some arbitrary. Is such arbitrariness of moral concern? We argue that an isolated arbitrary decision need not morally wrong the individual whom it misclassifies. However, if the same algorithms are applied across a public sphere, such as hiring or lending, a person could be excluded from a large number of opportunities. This harm persists even when the automated decision-making systems are "fair" on standard metrics of fairness. We argue that such arbitrariness at scale is morally problematic and propose technically informed solutions that can lessen the impact of algorithms at scale and so mitigate or avoid the moral harms we identify.
Common Pharmaceutical Price and Cost Controls in the United Kingdom, France, and Germany: Lessons for the United States:

To identify pharmaceutical spending-control options for the United States, we analyzed the policies of the United Kingdom, France, and Germany, which encourage drugmakers to undertake innovations that improve health while controlling spending. Their main strategies today include: using legislation to set default rules that increase the insurer's bargaining position, employing health technology assessment that measures cost-effectiveness or comparative effectiveness and caps the purchase or reimbursement price, setting a single maximum price for similar drugs (reference group pricing), capping prices near prices in other European countries (external reference pricing), prohibiting price increases, contracting to obtain discounts as sales volume rises, procuring drugs through competitive bids, and requiring manufacturers to pay rebates when spending exceeds a global budget. Each strategy addresses a distinct cause of high spending and supports overall goals. Most recent US reform proposals recommend incremental changes that would not address the major sources of high and increasing pharmaceutical prices. However, some US reform proposals resemble certain European strategies and could bring more significant change. US policymakers should consider adopting each of the strategies employed in these countries.
A Global-in-time Domain Decomposition Method for the Coupled Nonlinear Stokes and Darcy Flows

We study a decoupling iterative algorithm based on domain decomposition for the time-dependent nonlinear Stokes–Darcy model, in which different time steps can be used in the flow region and in the porous medium. The coupled system is formulated as a space-time interface problem based on the interface condition for mass conservation. The nonlinear interface problem is then solved by a nested iteration approach which involves, at each Newton iteration, the solution of a linearized interface problem and, at each Krylov iteration, parallel solution of time-dependent linearized Stokes and Darcy problems. Consequently, local discretizations in time (and in space) can be used to efficiently handle multiphysics systems of coupled equations evolving at different temporal scales. Numerical results with nonconforming time grids are presented to illustrate the performance of the proposed method.
Combinatorial Resultants in the Algebraic Rigidity Matroid

Motivated by a rigidity-theoretic perspective on the Localization Problem in 2D, we develop an algorithm for computing circuit polynomials in the algebraic rigidity matroid associated to the Cayley-Menger ideal for $n$ points in 2D. We introduce combinatorial resultants, a new operation on graphs that captures properties of the Sylvester resultant of two polynomials in the algebraic rigidity matroid. We show that every rigidity circuit has a construction tree from $K_4$ graphs based on this operation. Our algorithm performs an algebraic elimination guided by the construction tree, and uses classical resultants, factorization and ideal membership. To demonstrate its effectiveness, we implemented our algorithm in Mathematica: it took less than 15 seconds on an example where a Groebner Basis calculation took 5 days and 6 hrs.
Forecasting with Multiple Seasonality

Several modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. In particular, modeling time series with multiple seasonality is a challenging task with relatively few discussions. In this paper, we propose a two-stage method for predicting time series with multi-seasonality, which does not require predetermined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average model to multi-seasonality scenarios. In the second stage, we utilize an appropriate criterion for lag order selection. Simulation and empirical studies show the excellent predictive performance of our method, especially when compared with a recently popular ‘Facebook Prophet’ model for time series.
A critical analysis of least-squares tensor hypercontraction applied to MP3

The least-squares tensor hypercontraction (LS-THC) approach is a promising method of reducing the high polynomial scaling of wavefunction methods, for example, those based on many-body perturbation theory or coupled cluster. Here, we focus on LS-THC-MP3 and identify four variants with differing errors and efficiency characteristics. The performance of LS-THC-MP3 is analyzed for regular test systems with up to 40 first-row atoms. We also analyze the size-extensivity/size-consistency and grid- and basis set dependence of LS-THC-MP3. Overall, the errors observed are favorably small in comparison with standard density fitting, and a more streamlined method of generating grids via pruning is suggested. A practical crossover (the point at which LS-THC-MP3 is cheaper than the canonical method) is achieved around 240 correlated electrons. Despite several drawbacks of LS-THC that have been identified: an initial non-linearity of error when increasing system size, poor description of angular correlation, and a potentially large increase in error with the basis set size, the results show that LS-THC has significant potential for practical application to MP3 and other wavefunction methods.
Dynamics of entanglement entropy of interacting fermions in a 1D driven harmonic trap

Following up on a recent analysis of two cold atoms in a time-dependent harmonic trap in one dimension, we explore the entanglement entropy of two and three fermions in the same situation when driven through a parametric resonance. We find that the presence of such a resonance in the two-particle system leaves a clear imprint on the entanglement entropy. We show how the signal is modified by attractive and repulsive contact interactions, and how it remains present for the three-particle system. Additionaly, we extend the work of recent experiments to demonstrate how restricting observation to a limited subsystem gives rise to locally thermal behavior.
Structural variant evolution after telomere crisis

Telomere crisis contributes to cancer genome evolution, yet only a subset of cancers display breakage-fusion-bridge (BFB) cycles and chromothripsis, hallmarks of experimental telomere crisis identified in previous studies. We examine the spectrum of structural variants (SVs) instigated by natural telomere crisis. Eight spontaneous post-crisis clones did not show prominent patterns of BFB cycles or chromothripsis. Their crisis-induced genome rearrangements varied from infrequent simple SVs to more frequent and complex SVs. In contrast, BFB cycles and chromothripsis occurred in MRC5 fibroblast clones that escaped telomere crisis after CRISPR-controlled telomerase activation. This system revealed convergent evolutionary lineages altering one allele of chromosome 12p, where a short telomere likely predisposed to fusion. Remarkably, the 12p chromothripsis and BFB events were stabilized by independent fusions to chromosome 21. The data establish that telomere crisis can generate a wide spectrum of SVs implying that a lack of BFB patterns and chromothripsis in cancer genomes does not indicate absence of past telomere crisis.
Taxing the Taxpayers: An Empirical Investigation of the Drivers of Baseline Changes in U.S. Federal Government Technology Programs

Problem definition: The U.S. federal government makes significant investments in technology programs to deliver essential services to the public. The execution of a program is monitored against a b...
Natural Graph Wavelet Packet Dictionaries

We introduce a set of novel multiscale basis transforms for signals on graphs that utilize their “dual” domains by incorporating the “natural” distances between graph Laplacian eigenvectors, rather than simply using the eigenvalue ordering. These basis dictionaries can be seen as generalizations of the classical Shannon wavelet packet dictionary to arbitrary graphs, and do not rely on the frequency interpretation of Laplacian eigenvalues. We describe the algorithms (involving either vector rotations or orthogonalizations) to construct these basis dictionaries, use them to efficiently approximate graph signals through the best basis search, and demonstrate the strengths of these basis dictionaries for graph signals measured on sunflower graphs and street networks.
Cation Dynamics in Hybrid Halide Perovskites

Hybrid halide perovskite semiconductors exhibit complex, dynamical disorder while also harboring properties ideal for optoelectronic applications that include photovoltaics. However, these material...
Best-Arm Identification in Correlated Multi-Armed Bandits

In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability $1-\delta $ for some $\delta >0$ , the arm with the highest mean reward in minimum possible samples from the set of arms $\mathcal {K}$ . Most existing best-arm identification algorithms and analyses operate under the assumption that the rewards corresponding to different arms are independent of each other. We propose a novel correlated bandit framework that captures domain knowledge about correlation between arms in the form of upper bounds on expected conditional reward of an arm, given a reward realization from another arm. Our proposed algorithm C-LUCB, which generalizes the LUCB algorithm utilizes this partial knowledge of correlations to sharply reduce the sample complexity of best-arm identification. More interestingly, we show that the total samples obtained by C-LUCB are of the form $\mathrm {O}\left({\sum _{k \in \mathcal {C}} \log \left({\frac {1}{\delta }}\right)}\right)$ as opposed to the typical $\mathrm {O}\left({\sum _{k \in \mathcal {K}} \log \left({\frac {1}{\delta }}\right)}\right)$ samples required in the independent reward setting. The improvement comes, as the $\mathrm {O}(\log (1/\delta))$ term is summed only for the set of competitive arms $\mathcal {C}$ , which is a subset of the original set of arms $\mathcal {K}$ . The size of the set $\mathcal {C}$ , depending on the problem setting, can be as small as 2, and hence using C-LUCB in the correlated bandits setting can lead to significant performance improvements. Our theoretical findings are supported by experiments on the Movielens and Goodreads recommendation datasets.
Racial and Ethnic Inequities in Mortality During Hospitalization for Traumatic Brain Injury: A Call to Action.

The health disparities which drive inequities in health outcomes have long plagued our already worn healthcare system and are often dismissed as being a result of social determinants of health. Herein, we explore the nature of these inequities by comparing outcomes for racial and ethnic minorities patients suffering from traumatic brain injury (TBI). We retrospectively reviewed all patients enrolled in the Trauma One Database at the Oregon Health & Science University Hospital from 2006 to October 2017 with an abbreviated injury scale (AIS) scale for the head or neck greater than 2. Racial and ethnic minority patients were defined as non-White or Hispanic. A total of 6,352 patients were included in our analysis with 1,504 in the racial and ethnic minority cohort vs. 4,848 in the non-minority cohort. A propensity score (PS) model was generated to account for differences in baseline characteristics between these cohorts to generate 1,500 matched pairs. The adjusted hazard ratio for in-hospital mortality for minority patients was 2.21 (95% Confidence Interval (CI) 1.43-3.41, p<0.001) using injury type, probability of survival, and operative status as covariates. Overall, this study is the first to specifically look at racial and ethnic disparities in the field of neurosurgical trauma. This research has demonstrated significant inequities in the mortality of TBI patients based on race and ethnicity and indicates a substantive need to reshape the current healthcare system and advocate for safer and more supportive pre-hospital social systems to prevent these life-threatening sequelae.
High-Power Backward-Wave Oscillator Using Folded Waveguide With Distributed Power Extraction Operating at an Exceptional Point

The concept of exceptional point of degeneracy (EPD) is used to conceive an exceptional synchronization regime that is able to enhance the level of output power and power conversion efficiency for backward-wave oscillators (BWOs) operating at millimeter-wave and terahertz frequencies. Standard BWOs operating at such high frequency ranges typically generate output power not exceeding tens of watts with very poor power conversion efficiency in the order of 1%. The novel concept of exceptional synchronization for the BWO based on a folded waveguide is implemented by engineering distributed gain and power extraction along the slow-wave waveguide. The distributed power extraction along the folded waveguide is useful to satisfy the necessary conditions to have an EPD at the synchronization point. Particle-in-cell (PIC) simulation results show that BWO operating at an EPD regime is capable of generating output power exceeding 3 kW with conversion efficiency of exceeding 20% at frequency of 88.5 GHz.
Potential output purity of completely positive maps

The notion of potential output purity of a completely positive map is introduced as a generalization of the regularized output purity. An upper bound is derived for this quantity, and for several classes of maps (including CQ (classical-quantum), QC (quantum-classical), Hadamard channels), it is shown that potential purity does not exceed the standard output purity. As an application, the potential purity is used to bound the logarithmic Sobolev constant of a product of depolarizing channel semigroups.
On Energy-Based Models with Overparametrized Shallow Neural Networks

Energy-based models (EBMs) are a simple yet powerful framework for generative modeling. They are based on a trainable energy function which defines an associated Gibbs measure, and they can be trained and sampled from via well-established statistical tools, such as MCMC. Neural networks may be used as energy function approximators, providing both a rich class of expressive models as well as a flexible device to incorporate data structure. In this work we focus on shallow neural networks. Building from the incipient theory of overparametrized neural networks, we show that models trained in the so-called “active” regime provide a statistical advantage over their associated “lazy” or kernel regime, leading to improved adaptivity to hidden low-dimensional structure in the data distribution, as already observed in supervised learning. Our study covers both maximum likelihood and Stein Discrepancy estimators, and we validate our theoretical results with numerical experiments on synthetic data.
Beyond Fowler–Nordheim model: harmonic generation from metallic nano-structures

Metallic structures interacting with electromagnetic fields are known to exhibit properties similar to those found in atoms and molecules, such as multi-photon and tunnel ionization. Developing this similarity beyond the electron emission current, we generalize the well-known Fowler–Nordheim model, and predict a heretofore unrecognized source of nonlinear optical response from nano-structures exposed to illumination with intense optical pulses. We find that the harmonic generation by these structures is non-perturbative in the sense that higher harmonic orders can be almost as strong as the lower orders. At the same time, the predicted spectrum is distinct from the usual high-harmonic generation in that it does not exhibit a sharp cutoff.
Prior Forecasting Accuracy and Investor Reaction to Management Earnings Forecasts

We examine the properties of firms’ forecasting records and whether the accuracy of their prior earnings forecasts affects investor response to their subsequent forecasts. Within the context of a Bayesian model of investor learning, we find that the stock price response to management forecast news is increasing in prior forecast accuracy and also in the length of a firm’s forecasting record. Further, we document that investors are more responsive to extreme good and bad news forecasts when a firm has an established forecasting record. Overall, these results suggest that a firm’s prior forecasting behavior allows it to establish a forecasting reputation. JEL Descriptors G19, G39, D89, M40 * Corresponding Author: ahutton@bc.edu; 617 552-1951 phone; 617 552-6345 fax; Fulton Hall 520, 140 Commonwealth Ave. Chestnut Hill, MA 02467-3808 *We thank Ray Ball, Steve Baginski, Linda Bamber, Ken French, Charles Hsu, Michael Kimbrough, S.P. Kothari, Rafael La Porta, Franco Wong, Eric Yeung, Valentina Zamora, an anonymous referee, and workshop participants at Boston College, Boston University, Carnegie Mellon University, Emory University, INSEAD, University of Georgia, MIT Sloan School of Management, Tuck School of Business, George Washington University, FEA Conference at the University of North Carolina, Chapel Hill and the 2006 American Accounting Association annual meetings for helpful comments. Finally, we thank Robert Burnham for excellent research assistance.
Near-Optimal Entrywise Anomaly Detection for Low-Rank Matrices with Sub-Exponential Noise

We study the problem of identifying anomalies in a low-rank matrix observed with sub-exponential noise, motivated by applications in retail and inventory management. State of the art approaches to anomaly detection in low-rank matrices apparently fall short, since they require that non-anomalous entries be observed with vanishingly small noise (which is not the case in our problem, and indeed in many applications). So motivated, we propose a conceptually simple entrywise approach to anomaly detection in low-rank matrices. Our approach accommodates a general class of probabilistic anomaly models. We extend recent work on entrywise error guarantees for matrix completion, establishing such guarantees for sub-exponential matrices, where in addition to missing entries, a fraction of entries are corrupted by (an also unknown) anomaly model. Viewing the anomaly detection as a classification task, to the best of our knowledge, we are the first to achieve the min-max optimal detection rate (up to log factors). Using data from a massive consumer goods retailer, we show that our approach provides significant improvements over incumbent approaches to anomaly detection.
Acoustic Investigation of Jet Mixing Noise in Dual Stream Nozzles

In an earlier study, a prediction model for jet noise in dual stream jets was proposed that is founded on velocity scaling laws in single stream jets and similarity features of the mean velocity and turbulent kinetic energy in dual stream flows. The model forms a composite spectrum from four component single-stream jets each believed to represent noise-generation from a distinct region in the actual flow. While the methodology worked effectively at conditions considered earlier, recent examination of acoustic data at some unconventional conditions indicate that further improvements are necessary in order to expand the range of applicability of the model. The present work demonstrates how these predictions compare with experimental data gathered by NASA and industry for the purpose of examining the aerodynamic and acoustic performance of such nozzles for a wide range of core and fan stream conditions. Of particular interest are jets with inverted velocity and temperature profiles and the appearance of a second spectral peak at small aft angles to the jet under such conditions. It is shown that a four-component spectrum succeeds in modeling the second peak when the aft angle refraction effects are properly incorporated into the model. A tradeoff of noise emission takes place between two turbulent regions identified as transition and fully mixed regions as the fan stream velocity exceeds that of the core stream. The effect of nozzle discharge coefficients will also be discussed.
Centralizing data to unlock whole-cell models

Despite substantial potential to transform bioscience, medicine, and bioengineering, whole-cell models remain elusive. One of the biggest challenges to whole-cell models is assembling the large and diverse array of data needed to model an entire cell. Thanks to rapid advances in experimentation, much of the necessary data is becoming available. Furthermore, investigators are increasingly sharing their data due to growing recognition of the importance of research that is transparent and reproducible to others. However, the scattered organization of this data continues to hamper modeling. Toward more predictive models, we highlight the challenges to assembling the data needed for whole-cell modeling and outline how we can overcome these challenges by working together to build a central data warehouse.
Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach

We propose to measure fine-grained domain relevance– the degree that a term is relevant to a broad (e.g., computer science) or narrow (e.g., deep learning) domain. Such measurement is crucial for many downstream tasks in natural language processing. To handle long-tail terms, we build a core-anchored semantic graph, which uses core terms with rich description information to bridge the vast remaining fringe terms semantically. To support a fine-grained domain without relying on a matching corpus for supervision, we develop hierarchical core-fringe learning, which learns core and fringe terms jointly in a semi-supervised manner contextualized in the hierarchy of the domain. To reduce expensive human efforts, we employ automatic annotation and hierarchical positive-unlabeled learning. Our approach applies to big or small domains, covers head or tail terms, and requires little human effort. Extensive experiments demonstrate that our methods outperform strong baselines and even surpass professional human performance.
Theory of chirped photonic crystals in biological broadband reflectors

Photonic crystals with slowly varying lattice period are responsible for broadband light reflectance in many biological contexts, ranging from the coatings of shiny beetles to the eye shine of butterflies. We utilize a quantum scattering analogy to obtain an approximate but closed-form expression for the reflectance of these adiabatically chirped photonic crystals (ACPCs). This expression allows us to devise a method for designing ACPCs with tailored reflectance spectra and obtain an estimate for the minimal number of layers required to exceed a given reflectance threshold. Comparison against the number of layers found in ACPCs throughout nature gives a quantitative measure of the optimality of chirped biological reflectors. Together, these results elucidate the design of chirped reflectors in nature and their possible application to future optical technologies.
The Inducible lac Operator-Repressor System Is Functional in Zebrafish Cells.

Background Zebrafish are a foundational model organism for studying the spatio-temporal activity of genes and their regulatory sequences. A variety of approaches are currently available for editing genes and modifying gene expression in zebrafish, including RNAi, Cre/lox, and CRISPR-Cas9. However, the lac operator-repressor system, an E. coli lac operon component which has been adapted for use in many other species and is a valuable, flexible tool for inducible modulation of gene expression studies, has not been previously tested in zebrafish. Results Here we demonstrate that the lac operator-repressor system robustly decreases expression of firefly luciferase in cultured zebrafish fibroblast cells. Our work establishes the lac operator-repressor system as a promising tool for the manipulation of gene expression in whole zebrafish. Conclusion Our results lay the groundwork for the development of lac-based reporter assays in zebrafish, and adds to the tools available for investigating dynamic gene expression in embryogenesis. We believe this work will catalyze the development of new reporter assay systems to investigate uncharacterized regulatory elements and their cell-type specific activities.
Atmospheric General Circulation and its Low Frequency Variance: Radiative Influences

The possible effects of radiation on the evolution of the atmosphere on time scales ranging from about a week to about 90 days are examined with reference to the available observational and modeling studies. The clear-sky and cloud radiative processes are shown to exert significant vertical, latitudinal, and longitudinal gradients in the diabatic heating within the troposphere and the stratosphere. The meridional heating gradient, which drives the general circulation, is altered significantly by clouds. The major conclusion of the study is that the observed negative anomalies in the outgoing IR radiation following intense warm episodes of tropicl sea-surface temperature (El Nino) are indeed anomalies in the cloud-radiative forcing.
A Two-stage Stochastic Programming DSO Framework for Comprehensive Market Participation of DER Aggregators under Uncertainty

In this paper, a distribution system operator (DSO) framework is proposed for comprehensive retail and wholesale markets participation of distributed energy resource (DER) aggregators under uncertainty based on two-stage stochastic programming. Different kinds of DER aggregators including energy storage aggregators (ESAGs), demand response aggregators (DRAGs), electric vehicle (EV) aggregating charging stations (EVCSs), dispatchable distributed generation (DDG) aggregators (DDGAGs), and renewable energy aggregators (REAGs) are modeled. Distribution network operation constraints are considered using a linearized power flow. The problem is modeled using mixed-integer linear programming (MILP) which can be solved by using commercial solvers. Case studies are conducted to investigate the performance of the proposed DSO framework.
The dynamics of two coupled van der Pol oscillators with delay coupling

We investigate the dynamics of a system of twovan der Pol oscillators with delayed velocity coupling.We use the method of averaging to reduce the problem to the studyof a slow-flow in three dimensions.We study the steady state solutions of this slow-flow, with specialattention given to the bifurcations accompanying their change innumber and stability. We compare these stability results with numericalintegration of the original equations and show that the two sets of resultsare in excellent agreement under certain parameter restrictions.Our interest in this system is due to its relevance to coupled laseroscillators.
Field patterns without blow up

Field patterns, first proposed by the authors in Milton and Mattei (2017 Proc. R. Soc. A 473 20160819), are a new type of wave propagating along orderly patterns of characteristic lines which arise in specific space-time microstructures whose geometry in one spatial dimension plus time is somehow commensurate with the slope of the characteristic lines. In particular, in Milton and Mattei (2017 Proc. R. Soc. A 473 20160819) the authors propose two examples of space-time geometries in which field patterns occur: they are two-phase microstructures in which rectangular space-time inclusions of one material are embedded in another material. After a sufficiently long interval of time, field patterns have local periodicity both in time and space. This allows one to focus only on solving the problem on the discrete network on which a field pattern lives and to define a suitable transfer matrix that, given the solution at a certain time, provides the solution after one time period. For the aforementioned microstructures, many of the eigenvalues of this -symmetric transfer matrix have unit norm and hence the corresponding eigenvectors correspond to propagating modes. However, there are also modes that blow up exponentially with time coupled with modes that decrease exponentially with time. The question arises as to whether there are space-time microstructures such that the transfer matrix only has eigenvalues on the unit circle, so that there are no growing modes (modes that blow-up)? The answer is found here, where we see that certain space-time checkerboards have the property that all the modes are propagating modes, within a certain range of the material parameters. Interestingly, when there is no blow-up, the waves generated by an instantaneous disturbance at a point look like shocks with a wake of oscillatory waves, whose amplitude, very remarkably, does not tend to zero away from the wave front.
Information-theoretic limits of two-dimensional optical recording channels

During the past five years, advances in the information-theoretic analysis of "one-dimensional (1D)" recording channels have clarified the limits on linear densities that can be achieved by track-oriented magnetic and optical storage technologies. Channel architectures incorporating powerful codes, such as turbo codes and low-density parity-check codes, have been shown to achieve performance very close to the information-theoretic limits. As 1D track-oriented data storage technologies reach maturity, there is increasing interest in "two-dimensional (2D)" recording technologies, such as two-dimensional optical storage (TwoDOS) and holographic storage. This paper provides an overview of some recently developed techniques for determining analytical bounds and simulation-based estimates for achievable densities of such 2D recording channels, as well as some recently proposed signal processing and coding methods that can move system performance closer to the information-theoretic limits.© (2007) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Influence Dynamics Among Narratives: A Case Study of the Venezuelan Presidential Crisis

It is widely understood that diffusion of and simultaneous interactions between narratives—defined here as persistent point-of-view messaging—significantly contributes to the shaping of political discourse and public opinion. In this work, we propose a methodology based on Multi-Variate Hawkes Processes and our newly-introduced Process Influence Measures for quantifying and assessing how such narratives influence (Granger-cause) each other. Such an approach may aid social scientists enhance their understanding of socio-geopolitical phenomena as they manifest themselves and evolve in the realm of social media. In order to show its merits, we apply our methodology on Twitter narratives during the 2019 Venezuelan presidential crisis. Our analysis indicates a nuanced, evolving influence structure between 8 distinct narratives, part of which could be explained by landmark historical events.
Laguerre expansions on conic domains

We study the Fourier orthogonal expansions with respect to the Laguerre type weight functions on the conic surface of revolution and the domain bounded by such a surface. The main results include a closed form formula for the reproducing kernels of the orthogonal projection operator and a pseudo convolution structure on the conic domain; the latter is shown to be bounded in an appropriate $$L^p$$ space and used to study mean convergence of the Cesaro means of the Laguerre expansions on conic domains.
Natural killer cell immunosuppressive function requires CXCR3-dependent redistribution within lymphoid tissues

Natural killer (NK) cell suppression of T cells is a key determinant of viral pathogenesis and vaccine efficacy. This process involves perforin-dependent elimination of activated CD4 T cells during the first three days of infection. Although this mechanism requires cell-cell contact, NK cells and T cells typically reside in different compartments of lymphoid tissues at steady state. Here, we showed that NK-cell suppression of T cells is associated with transient accumulation of NK cells within T cell-rich sites of the spleen during lymphocytic choriomeningitis virus infection. The chemokine receptor CXCR3 was required for this relocation and suppression of antiviral T cells. Accordingly, NK-cell migration was mediated by type I interferon (IFN)-dependent promotion of CXCR3 ligand expression. In contrast, adenoviral vectors that weakly induced type I IFN and did not stimulate NK-cell inhibition of T cells also did not promote measurable redistribution of NK cells to T-cell zones. Exogenous IFN rescued NK-cell migration during adenoviral vector immunization. Thus, type I IFN and CXCR3 were critical for properly positioning NK cells to constrain antiviral T-cell responses. Development of strategies to curtail migration of NK cells between lymphoid compartments may enhance vaccine-elicited immune responses.
History Modulates Early Sensory Processing of Salient Distractors.

To find important objects, we must focus on our goals, ignore distractions, and take our changing environment into account. This is formalized in models of visual search whereby goal-driven, stimulus-driven, and history-driven factors are integrated into a priority map that guides attention. Stimulus history robustly influences where attention is allocated even when the physical stimulus is the same: when a salient distractor is repeated over time, it captures attention less effectively. A key open question is how we come to ignore salient distractors when they are repeated. Goal-driven accounts propose that we use an active, expectation-driven mechanism to attenuate the distractor signal (e.g., predictive coding), whereas stimulus-driven accounts propose that the distractor signal is attenuated because of passive changes to neural activity and inter-item competition (e.g., adaptation). To test these competing accounts, we measured item-specific fMRI responses in human visual cortex during a visual search task where trial history was manipulated (colors unpredictably switched or were repeated). Consistent with a stimulus-driven account of history-based distractor suppression, we found that repeated singleton distractors were suppressed starting in V1, and distractor suppression did not increase in later visual areas. In contrast, we observed signatures of goal-driven target enhancement that were absent in V1, increased across visual areas, and were not modulated by stimulus history. Our data suggest that stimulus history does not alter goal-driven expectations, but rather modulates canonically stimulus-driven sensory responses to contribute to a temporally integrated representation of priority. SIGNIFICANCE STATEMENT Visual search refers to our ability to find what we are looking for in a cluttered visual world (e.g., finding your keys). To perform visual search, we must integrate information about our goals (e.g., “find the red keychain”), the environment (e.g., salient items capture your attention), and changes to the environment (i.e., stimulus history). Although stimulus history impacts behavior, the neural mechanisms that mediate history-driven effects remain debated. Here, we leveraged fMRI and multivariate analysis techniques to measure history-driven changes to the neural representation of items during visual search. We found that stimulus history influenced the representation of a salient “pop-out” distractor starting in V1, suggesting that stimulus history operates via modulations of early sensory processing rather than goal-driven expectations.
Early Shifts of Brain Metabolism By Caloric Restriction Preserve White Matter Integrity and Long-term Memory in Aging Mice

Preservation of brain integrity with age is highly associated with lifespan determination. Caloric restriction (CR) has been shown to increase longevity and healthspan in various species; however, its effects on preserving living brain functions in aging remain largely unexplored. In the study, we used multimodal, non-invasive neuroimaging (PET/MRI/MRS) to determine in vivo brain glucose metabolism, energy metabolites, and white matter structural integrity in young and old mice fed with either control or 40% CR diet. In addition, we determined the animals’ memory and learning ability with behavioral assessments. Blood glucose, blood ketone bodies, and body weight were also measured. We found distinct patterns between normal aging and CR aging on brain functions - normal aging showed reductions in brain glucose metabolism, white matter integrity, and long-term memory, resembling human brain aging. CR aging, in contrast, displayed an early shift from glucose to ketone bodies metabolism, which was associated with preservations of brain energy production, white matter integrity, and long-term memory in aging mice. Among all the mice, we found a positive correlation between blood glucose level and body weight, but an inverse association between blood glucose level and lifespan. Our findings suggest that CR could slow down brain aging, in part due to the early shift of energy metabolism caused by lower caloric intake, and we were able to identify the age-dependent effects of CR non-invasively using neuroimaging. These results provide a rationale for CR-induced sustenance of brain health with extended longevity.
Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based.

We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem. In previous LWE-based FHE schemes, multiplication is a complicated and expensive step involving “relinearization”. In this work, we propose a new technique for building FHE schemes that we call the approximate eigenvector method. In our scheme, for the most part, homomorphic addition and multiplication are just matrix addition and multiplication. This makes our scheme both asymptotically faster and (we believe) easier to understand. In previous schemes, the homomorphic evaluator needs to obtain the user’s “evaluation key”, which consists of a chain of encrypted secret keys. Our scheme has no evaluation key. The evaluator can do homomorphic operations without knowing the user’s public key at all, except for some basic parameters. This fact helps us construct the first identity-based FHE scheme. Using similar techniques, we show how to compile a recent attribute-based encryption scheme for circuits by Gorbunov et al. into an attribute-based FHE scheme that permits data encrypted under the same index to be processed homomorphically.
Well-posedness in weighted spaces for the generalized Hartree equation with p < 2

We investigate the well-posedness in the generalized Hartree equation iut + Δu + (|x|−(N−γ) ∗|u|p)|u|p−2u = 0, x ∈ ℝN, 0 < γ < N, for low powers of nonlinearity, p < 2. We establish the local well-...
The international spillover effects of US monetary policy uncertainty

Abstract An extensive literature studies the international transmission of US monetary policy surprises (shifts in expected path of the policy rate). In this paper we show that changes in uncertainty around the expected path constitute an important additional dimension of spillover effects to global bond yields. In advanced countries, it is the term premium component of yields that responds to uncertainty. We find that this can be explained by an international portfolio balance mechanism. In contrast, for emerging countries it is the expected component of yields that reacts to uncertainty. This can be rationalized from a flight to safety channel. We find heterogeneity in the country-level response to uncertainty only in emerging countries and it is driven by the degree of financial openness. Finally, equity markets in both advanced and emerging countries also respond to US monetary policy uncertainty.
How to Sell Hard Information

The seller of an asset has the option to buy hard information about the value of the asset from an intermediary. The seller can then disclose the acquired information before selling the asset in a competitive market. We study how the intermediary designs and sells hard information to robustly maximize her revenue across all equilibria. Even though the intermediary could use an accurate test that reveals the asset's value, we show that robust revenue maximization leads to a noisy test with a continuum of possible scores that are distributed exponentially. In addition, the intermediary always charges the seller for disclosing the test score to the market, but not necessarily for running the test. This enables the intermediary to robustly appropriate a significant share of the surplus resulting from the asset sale even though the information generated by the test provides no social value.
Polynomial Approximations of Conditional Expectations in Scalar Gaussian Channels

We consider a channel $Y=X+N$ where $X$ is a random variable satisfying $\mathbb{E}[\vert X\vert] and $N$ is an independent standard normal random variable. We show that the minimum mean-square estimator of $X$ from $Y$ , which is given by the conditional expectation $\mathbb{E}[X\vert Y]$ , is a polynomial in $Y$ if and only if it is linear or constant; these two cases correspond to $X$ being Gaussian or a constant, respectively. We also prove that the higher-order derivatives of $y\rightarrow \mathbb{E}[X\vert Y=y]$ are expressible as multivariate polynomials in the functions $y\rightarrow \mathbb{E}[(X-\mathbb{E}[X\vert Y])^{k}-\vert Y=y]$ for $k\in \mathbb{N}$ . These expressions yield bounds on the 2-norm of the derivatives of the conditional expectation. These bounds imply that, if $X$ has a compactly-supported density that is even and decreasing on the positive half-line, then the error in approximating the conditional expectation $\mathbb{E}[X\vert Y]$ by polynomials in $Y$ of degree at most $n$ decays faster than any polynomial in $n$ .
Posterior Contraction Rate of Sparse Latent Feature Models with Application to Proteomics

The Indian buffet process (IBP) and phylogenetic Indian buffet process (pIBP) can be used as prior models to infer latent features in a data set. The theoretical properties of these models are under-explored, however, especially in high dimensional settings. In this paper, we show that under mild sparsity condition, the posterior distribution of the latent feature matrix, generated via IBP or pIBP priors, converges to the true latent feature matrix asymptotically. We derive the posterior convergence rate, referred to as the contraction rate. We show that the convergence holds even when the dimensionality of the latent feature matrix increases with the sample size, therefore making the posterior inference valid in high dimensional setting. We demonstrate the theoretical results using computer simulation, in which the parallel-tempering Markov chain Monte Carlo method is applied to overcome computational hurdles. The practical utility of the derived properties is demonstrated by inferring the latent features in a reverse phase protein arrays (RPPA) dataset under the IBP prior model. Software and dataset reported in the manuscript are provided at this http URL.
Partial factorizations of products of binomial coefficients

Let G¯n =∏k=0nn k , the product of the elements of the nth row of Pascal’s triangle. This paper studies the partial factorizations of G¯n given by the product G(n,x) of all prime factors p of G¯n h...