cssi/SciSciGPT-SciSciNet huggingface dataset:
A unifying framework for generalizations of the Eneström–Kakeya theorem

The classical Enestrom-Kakeya theorem establishes upper and lower bounds on the zeros of a polynomial with positive coefficients that are explicit functions of those coefficients. We establish a unifying framework that incorporates this theorem and several similar ones as special cases, while generating new theorems of a similar type. These establish zero inclusion and exclusion regions consisting of a single disk or the union of several disks in the complex plane. Our framework is built on two basic tools, namely a generalization of an observation by Cauchy, and a family of polynomial multipliers. Its approach is transparent and reduces algebraic manipulations to a minimum.
Asymptotics of Polynomials Orthogonal on a Cross with a Jacobi-Type Weight

We investigate asymptotic behavior of polynomials $$ Q_n(z) $$ satisfying non-Hermitian orthogonality relations $$\begin{aligned} \int _\Delta s^kQ_n(s)\rho (s){\mathrm {d}}s =0, \quad k\in \{0,\ldots ,n-1\}, \end{aligned}$$where $$ \Delta := [-\,a,a]\cup [-\,{\mathrm {i}}b,{\mathrm {i}}b] $$, $$ a,b>0 $$, and $$ \rho (s) $$ is a Jacobi-type weight.
RiboFlow, RiboR and RiboPy: an ecosystem for analyzing ribosome profiling data at read length resolution.

SUMMARY Ribosome occupancy measurements enable protein abundance estimation and infer mechanisms of translation. Recent studies have revealed that sequence read lengths in ribosome profiling data are highly variable and carry critical information. Consequently, data analyses require the computation and storage of multiple metrics for a wide range of ribosome footprint lengths. We developed a software ecosystem including a new efficient binary file format named 'ribo'. Ribo files store all essential data grouped by ribosome footprint lengths. Users can assemble ribo files using our RiboFlow pipeline that processes raw ribosomal profiling sequencing data. RiboFlow is highly portable and customizable across a large number of computational environments with built-in capabilities for parallelization. We also developed interfaces for writing and reading ribo files in the R (RiboR) and Python (RiboPy) environments. Using RiboR and RiboPy, users can efficiently access ribosome profiling quality control metrics, generate essential plots and carry out analyses. Altogether, these components create a software ecosystem for researchers to study translation through ribosome profiling. AVAILABILITY AND IMPLEMENTATION For a quickstart, please see https://ribosomeprofiling.github.io. Source code, installation instructions and links to documentation are available on GitHub: https://github.com/ribosomeprofiling. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Analyticity of Steklov eigenvalues of nearly circular and nearly spherical domains

We consider the Dirichlet-to-Neumann operator (DNO) on nearly circular and nearly spherical domains in two and three dimensions, respectively. Treating such domains as perturbations of the ball, we prove the analyticity of the DNO with respect to the domain perturbation parameter. Consequently, the Steklov eigenvalues are also shown to be analytic in the domain perturbation parameter. To obtain these results, we use the strategy of Nicholls and Nigam (J Comput Phys 194(1):278–303, 2004. https://doi.org/10.1016/j.jcp.2003.09.006); we transform the equation on the perturbed domain to a ball and geometrically bound the Neumann expansion of the transformed Dirichlet-to-Neumann operator.
HOMOLOGICAL STABILITY FOR UNLINKED CIRCLES IN A 3-MANIFOLD

We prove a homological stability theorem for unlinked circles in $3$-manifolds and give an application to certain groups of diffeomorphisms of 3-manifolds.
Neanderthal-Denisovan ancestors interbred with a distantly related hominin

Previous research has shown that modern Eurasians interbred with their Neanderthal and Denisovan predecessors. We show here that hundreds of thousands of years earlier, the ancestors of Neanderthals and Denisovans interbred with their own Eurasian predecessors-members of a "superarchaic" population that separated from other humans about 2 million years ago. The superarchaic population was large, with an effective size between 20 and 50 thousand individuals. We confirm previous findings that (i) Denisovans also interbred with superarchaics, (ii) Neanderthals and Denisovans separated early in the middle Pleistocene, (iii) their ancestors endured a bottleneck of population size, and (iv) the Neanderthal population was large at first but then declined in size. We provide qualified support for the view that (v) Neanderthals interbred with the ancestors of modern humans.
Distributionally robust chance constrained programming with generative adversarial networks (GANs)

This paper presents a novel deep learning based data-driven optimization method. A novel generative adversarial network (GAN) based data-driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way without a priori approximation or assumption. Since GAN utilizes deep neural networks, complicated data distributions and modes can be learned, and it can model uncertainty efficiently and accurately. Distributionally robust chance constrained programming takes into consideration ambiguous probability distributions of uncertain parameters. To tackle the computational challenges, sample average approximation method is adopted, and the required data samples are generated by GAN in an end-to-end way through the differentiable networks. The proposed framework is then applied to supply chain optimization under demand uncertainty. The applicability of the proposed approach is illustrated through a county-level case study of a spatially explicit biofuel supply chain in Illinois.
Statistical Distances and Their Role in Robustness

Statistical distances, divergences, and similar quantities have a large history and play a fundamental role in statistics, machine learning and associated scientific disciplines. However, within the statistical literature, this extensive role has too often been played out behind the scenes, with other aspects of the statistical problems being viewed as more central, more interesting, or more important. The behind the scenes role of statistical distances shows up in estimation, where we often use estimators based on minimizing a distance, explicitly or implicitly, but rarely studying how the properties of a distance determine the properties of the estimators. Distances are also prominent in goodness-of-fit, but the usual question we ask is “how powerful is this method against a set of interesting alternatives” not “what aspect of the distance between the hypothetical model and the alternative are we measuring?”
Metal nanoclusters supported on metal oxide thin films: bridging the materials gap

Characterization and reactivity studies were performed on model catalysts comprised of metal clusters supported on metal oxide thin films. The thin films are prepared by vaporizing the parent metal onto a refractory metal substrate in an O2 environment. The oxide films are sufficiently conductive via defects and tunneling to the substrate that the use of charged particle spectroscopies does not lead to any adverse charging effects. Numerous characterization techniques demonstrated that both spectroscopically and chemically these thin films are comparable to the analogous bulk metal oxides. Model supported catalysts were subsequently prepared by vapor‐depositing catalytically‐interesting metals onto these thin film oxide supports. This deposition method realizes tight control over cluster size and, therefore, represents an ideal approach to studying size‐dependent chemical and physical properties. Reactivity studies established the validity of the supported systems as models of conventional catalysts. Furthermore, the use of these model catalysts provides a bridge between fundamental studies of single crystal reactivities and applied studies of high‐surface‐area catalyst activities.
The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller

In this paper, a neural network predictive controller (NNPC) is proposed to control a buck converter. Conventional controllers such as proportional-integral (PI) or proportional-integral-derivative (PID) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller in the start-up, load change, or reference change is not optimal since the system model changes by changing the operating point. The neural network predictive controller optimally controls the buck converter by following the concept of the traditional model predictive controller. The advantage of the NNPC is that the neural network system identification decreases the inaccuracy of the system model with inaccurate parameters. A NNPC with a well-trained neural network can perform as an optimal controller for the buck converter. To compare the effectiveness of the traditional buck converter and the NNPC, the simulation results are provided.
Learning to Code: Coded Caching via Deep Reinforcement Learning

We consider a system comprising a file library and a network with a server and multiple users equipped with cache memories. The system operates in two phases: a prefetching phase, where users load their caches with parts of contents from the library, and a delivery phase, where users request files from the library and the server needs to send the uncached parts of the requested files to the users. For the case where the users’ caches are arbitrarily loaded, we propose an algorithm based on deep reinforcement learning to minimize the delay of delivering requested contents to the users in the delivery phase. Simulation results demonstrate that our proposed deep reinforcement learning agent learns a coded delivery strategy for sending the requests to the users, which slightly outperforms the state-of-the-art performance in terms of delivery delay, while drastically reducing the computational complexity.
Optimal Crossover Designs for Generalized Linear Models

We identify locally D-optimal crossover designs for generalized linear models. We use generalized estimating equations to estimate the model parameters along with their variances. To capture the dependency among the observations coming from the same subject, we propose six different correlation structures. We identify the optimal allocations of units for different sequences of treatments. For two-treatment crossover designs, we show via simulations that the optimal allocations are reasonably robust to different choices of the correlation structures. We discuss a real example of multiple-treatment crossover experiments using Latin square designs. Using a simulation study, we show that a two-stage design with our locally D-optimal design at the second stage is more efficient than the uniform design, especially when the responses from the same subject are correlated.
Propensity Score Analysis With Latent Covariates: Measurement Error Bias Correction Using the Covariate’s Posterior Mean, aka the Inclusive Factor Score

We address measurement error bias in propensity score (PS) analysis due to covariates that are latent variables. In the setting where latent covariate X is measured via multiple error-prone items W...
DOT: Gene-set analysis by combining decorrelated association statistics

Historically, the majority of statistical association methods have been designed assuming availability of SNP-level information. However, modern genetic and sequencing data present new challenges to access and sharing of genotype-phenotype datasets, including cost of management, difficulties in consolidation of records across research groups, etc. These issues make methods based on SNP-level summary statistics particularly appealing. The most common form of combining statistics is a sum of SNP-level squared scores, possibly weighted, as in burden tests for rare variants. The overall significance of the resulting statistic is evaluated using its distribution under the null hypothesis. Here, we demonstrate that this basic approach can be substantially improved by decorrelating scores prior to their addition, resulting in remarkable power gains in situations that are most commonly encountered in practice; namely, under heterogeneity of effect sizes and diversity between pairwise LD. In these situations, the power of the traditional test, based on the added squared scores, quickly reaches a ceiling, as the number of variants increases. Thus, the traditional approach does not benefit from information potentially contained in any additional SNPs, while our decorrelation by orthogonal transformation (DOT) method yields steady gain in power. We present theoretical and computational analyses of both approaches, and reveal causes behind sometimes dramatic difference in their respective powers. We showcase DOT by analyzing breast cancer and cleft lip data, in which our method strengthened levels of previously reported associations and implied the possibility of multiple new alleles that jointly confer disease risk.
Informal Financial Networks: Theory and Evidence

We develop a model of informal financial networks and present corroborating evidence by studying the role of property brokers in the U.S. commercial real estate market. Our model demonstrates that service intermediaries, who do not themselves supply loans, can facilitate their clients' access to finance through informal relationships with lenders. Empirically we find that, controlling for endogenous broker selection, hiring a broker strikingly increases the probability of obtaining bank finance. Our results demonstrate that even in the United States, with its well-developed capital markets, informal networks play an important role in controlling access to finance. Copyright 2003, Oxford University Press.
Are Engel Curve Estimates of CPI Bias Biased

Recent literature has advanced the use of Engel curves to estimate overall consumer price index (CPI) bias. The author shows that this methodology is sensitive to the modeling of household demography. Existing estimates of CPI bias do not account for the changing effect of household size on the budget shares, which can lead to omitted variable bias. Because the effect of household size on demand changes over time, this effect partially explains the drift in Engel curves attributed to CPI bias. The author's estimates of the changing effect for household size alters the annual rate of CPI bias every year from 1888 to 1935 by at least 25 percent and usually more than 50 percent. These findings caution against the use of Engel curves to correct CPI bias in the past.
9 – Community Structure

Publisher Summary Communities are composed of the species that occupy a site. Identification of patterns in community structure has been a major goal of ecological research. Ecologists have developed a variety of nontaxonomic approaches in describing community structure, which provides different types of information to meet different objectives. The diversity of approaches has hindered the comparison of communities that have been described in different terms. However, distinct geographic patterns can be seen in community structure, and some community types characterize particular habitat conditions. A number of factors determine community composition, distribution, and dynamics. This chapter focuses on approaches to describing community structure, and on biogeographic patterns and underlying factors, which contribute to community structure. Species diversity has two components: richness and evenness. Richness is the number of species in community, whereas evenness is a measure of relative abundances. These two components can be represented by rank–abundance curves and by diversity indices. The study briefly explains the approaches describing communities, patterns of community structure, and determinants of community structure.
Intended college attendance: Evidence from an experiment on college returns and costs

Abstract We conduct an information experiment about college returns and costs embedded within a representative survey of US household heads. Baseline perceptions of college costs and benefits are substantially biased, with larger biases among lower-income and non-college households. Respondents are randomly exposed to objective information about average college “returns” or costs. We find a significant impact of the “returns” experiment, persisting in a follow-up survey two months later: intended college attendance expectations increase by about 0.2 of the standard deviation in the baseline likelihood, and gaps by household income or parents’ education decline by 20–30%. We find no impact of the cost information treatment. Further analysis supports the information’s salience, as opposed to information-based updating, as the main channel through which the returns intervention impacts intentions.
Formation of novel D-ring and E-ring isoprostane-like compounds (D4/E4-neuroprostanes) in vivo from docosahexaenoic acid.

Oxidant stress and peroxidation of lipids have been implicated in the pathogenesis of a variety of human diseases’-7. A highly accurate marker of oxidative stressin vivois the quantification of isoprostanes8’9. Isoprostanes (IsoPs) are prostaglandin (PG)-like compounds generated from the free radical catalyzed peroxidation of arachidonic acid. The generation of these compounds proceeds through bicyclic endoperoxide PGH2-like intermediates which are subsequently reduced to PGF2-like compounds (F2-IsoPs)10or undergo rearrangement to PGD2and PGE2-like compounds (D2/E2- IsoPs)’1and thromboxane-like compounds (isothromboxanes)12.In vivo, arachidonic acid is primarily present esterified to phospholipids. Thus, IsoPs are formed esterifiedin situand are subsequently released preformed13. In addition to being reliable markers of oxidative stressin vivo, several of the IsoPs have been shown to be capable of exerting potent biological activity; therefore, these compounds may mediate some of the pathophysiological sequelae of oxidant stress“.
Long-Term Nutrient Enrichment of an Oligotroph-Dominated Wetland Increases Bacterial Diversity in Bulk Soils and Plant Rhizospheres

In nutrient-limited conditions, plants rely on rhizosphere microbial members to facilitate nutrient acquisition, and in return, plants provide carbon resources to these root-associated microorganisms. However, atmospheric nutrient deposition can affect plant-microbe relationships by changing soil bacterial composition and by reducing cooperation between microbial taxa and plants. To examine how long-term nutrient addition shapes rhizosphere community composition, we compared traits associated with bacterial (fast-growing copiotrophs, slow-growing oligotrophs) and plant (C3 forb, C4 grass) communities residing in a nutrient-poor wetland ecosystem. Results revealed that oligotrophic taxa dominated soil bacterial communities and that fertilization increased the presence of oligotrophs in bulk and rhizosphere communities. Additionally, bacterial species diversity was greatest in fertilized soils, particularly in bulk soils. Nutrient enrichment (fertilized versus unfertilized) and plant association (bulk versus rhizosphere) determined bacterial community composition; bacterial community structure associated with plant functional group (grass versus forb) was similar within treatments but differed between fertilization treatments. The core forb microbiome consisted of 602 unique taxa, and the core grass microbiome consisted of 372 unique taxa. Forb rhizospheres were enriched in potentially disease-suppressive bacterial taxa, and grass rhizospheres were enriched in bacterial taxa associated with complex carbon decomposition. Results from this study demonstrate that fertilization serves as a strong environmental filter on the soil microbiome, which leads to distinct rhizosphere communities and can shift plant effects on the rhizosphere microbiome. These taxonomic shifts within plant rhizospheres could have implications for plant health and ecosystem functions associated with carbon and nitrogen cycling.IMPORTANCE Over the last century, humans have substantially altered nitrogen and phosphorus cycling. Use of synthetic fertilizer and burning of fossil fuels and biomass have increased nitrogen and phosphorus deposition, which results in unintended fertilization of historically low-nutrient ecosystems. With increased nutrient availability, plant biodiversity is expected to decline, and the abundance of copiotrophic taxa is anticipated to increase in bacterial communities. Here, we address how bacterial communities associated with different plant functional types (forb, grass) shift due to long-term nutrient enrichment. Unlike other studies, results revealed an increase in bacterial diversity, particularly of oligotrophic bacteria in fertilized plots. We observed that nutrient addition strongly determines forb and grass rhizosphere composition, which could indicate different metabolic preferences in the bacterial communities. This study highlights how long-term fertilization of oligotroph-dominated wetlands could alter diversity and metabolism of rhizosphere bacterial communities in unexpected ways.
The Relationship between Effective Molarity and Affinity Governs Rate Enhancements in Tethered Kinase–Substrate Reactions

Scaffold proteins are thought to accelerate protein phosphorylation reactions by tethering kinases and substrates together, but there is little quantitative data on their functional effects. To assess the contribution of tethering to kinase reactivity, we compared intramolecular and intermolecular kinase reactions in a minimal model system. We found that tethering can enhance reaction rates in a flexible tethered kinase system and that the magnitude of the effect is sensitive to the structure of the tether. The largest effective molarity we obtained was ∼0.08 μM, which is much lower than the effects observed in small molecule model systems and other tethered protein reactions. We further demonstrated that the tethered intramolecular reaction only makes a significant contribution to the observed rates when the scaffolded complex assembles at concentrations below the effective molarity. These findings provide a quantitative framework that can be applied to understand endogenous protein scaffolds and engineer synthetic networks.
The UV Upturn in Elliptical Galaxies

The hot stellar component in elliptical galaxies offers clues to both stellar evolution and galaxy evolution. Current observations suggest that extreme horizontal branch (EHB) stars dominate the far-UV emission from galaxies with the strongest "UV upturns," while post asymptotic giant branch (PAGB) stars are probably significant contributors for weaker galaxies. Spectra near the Lyman limit indicate that a rather narrow range of temperature (and hence EHB star mass) is required. However, other arguments suggest that most of the helium-burning stars in elliptical galaxies are in the red clump. The HB star mass distribution therefore appears to be strongly bimodal. Such bimodality is qualitatively reproduced by two radically different stellar population models, (those of Lee and Bressan et al., 1994), both of which require that the galaxies be very old. However, the Galactic open cluster NGC 6791 also contains EHB stars and exhibits strong bimodality, indicating that old age may not necessarily be a requirement for the UV upturn phenomenon.
Deep Learning: Computational Aspects

In this article we review computational aspects of Deep Learning (DL). Deep learning uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high-dimensional input-output models. Training a deep learning architecture is computationally intensive, and efficient linear algebra libraries is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive data sets.
Reassessing dust’s role in forming the CMB

The notion that dust might have formed the cosmic microwave background (CMB) has been strongly refuted on the strength of four decades of observation and analysis, in favour of recombination at a redshift $$z\sim 1080$$ . But tension with the data is growing in several other areas, including measurements of the Hubble constant H(z) and the BAO scale, which directly or indirectly impact the physics at the surface of last scattering (LSS). The $$R_\mathrm{h}=ct$$ universe resolves at least some of this tension. We show in this paper that—if the BAO scale is in fact equal to the acoustic horizon—the redshift of the LSS in this cosmology is $$z_\mathrm{cmb} \sim 16$$ , placing it within the era of Pop III star formation, prior to the epoch of reionization at $$15\gtrsim z \gtrsim 6$$ . Quite remarkably, the measured values of $$z_\mathrm{cmb}$$ and $$H_0\equiv H(0)$$ in this model are sufficient to argue that the CMB temperature today ought to be $$\sim 3$$ K, so $$H_0$$ and the baryon-to-photon ratio are not independent free parameters. This scenario might have resulted from rethermalization of the CMB photons by dust, presumably supplied to the interstellar medium by the ejecta of Pop III stars. Dust rethermalization may therefore yet resurface as a relevant ingredient in the $$R_\mathrm{h}=ct$$ universe. Upcoming high-sensitivity instruments should be able to readily distinguish between the recombination and dust scenarios by either (i) detecting recombination lines at $$z\sim 1080$$ or (ii) establishing a robust frequency-dependent variation of the CMB power spectrum at the level of $$\sim $$ 2–4% across the sampled frequency range.
Connecting Coil-to-Globule Transitions to Full Phase Diagrams for Intrinsically Disordered Proteins

Phase separation is thought to underlie spatial and temporal organization that is required for controlling biochemical reactions in cells. Multivalence of interaction motifs, also known as stickers, is a defining feature of proteins that drive phase separation. Intrinsically disordered proteins with stickers uniformly distributed along the linear sequence can serve as scaffold molecules that drive phase separation. The sequence-intrinsic contributions of disordered proteins to phase separation can be discerned by computing or measuring sequence-specific phase diagrams. These help to delineate the combinations of protein concentration and a suitable control parameter, such as temperature, that support phase separation. Here, we present an approach that combines detailed simulations with a numerical adaptation of an analytical Gaussian cluster theory to enable the calculation of sequence-specific phase diagrams. Our approach leverages the known equivalence between the driving forces for single-chain collapse in dilute solutions and the driving forces for phase separation in concentrated solutions. We demonstrate the application of the theory-aided computations through calculation of phase diagrams for a set of archetypal intrinsically disordered low-complexity domains. We also leverage theories to compute sequence-specific percolation lines and thereby provide a thermodynamic framework for hardening transitions that have been observed for many biomolecular condensates.
ON THE BETTI NUMBERS OF SPRINGER FIBERS FOR CLASSICAL TYPES

For a Weyl group W of classical type, we present a formula to calculate the restriction of (graded) Springer representations of W to a maximal parabolic subgroup W′ where the types of W and W′ are in the same series. As a result, we obtain recursive formulas for the Betti numbers of Springer fibers for classical types.
Scarlatiniform Rash Caused by Mycoplasma pneumoniae

Mycoplasma pneumoniae is a significant cause of acute respiratory disease in children and young adults. We describe a case of a 15-year-old boy who presented with an unusual scarlatiniform rash in the setting of a positive Mycoplasma pneumonia IgM but negative anti-streptolysin antibody. While mycoplasma infections with cutaneous manifestations such as scarlatiniform exanthema have been described in textbooks, there are no reports in the primary literature. We wish to highlight the recognition of alternate presentations of mycoplasma, and briefly discuss the role of molecular mimicry in its pathogenesis.
Learning Optimal Distributionally Robust Individualized Treatment Rules.

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy m...
Does Census Hiring Stimulate Jobs Growth

Governments perform national, labor-intensive censuses on a regular schedule. Censuses represent many of the largest peacetime expansions and contractions in federal hiring. The predetermined occurrence and scale of the census offers an economic experiment in the effects of temporary government hiring. This paper describes the construction of a data series on census hiring in the United States since 1950 and also collects available data on census employment in England and Wales, Canada, Korea, and Japan. Regressing total employment changes on census hiring yields coefficients extremely close to 1, indicating that there is no spillover from census hiring to the rest of the economy. Using census hiring and occurrence as instruments for government hiring in the US, Canada, and Korea, I estimate the effect of federal hiring on overall employment. Different samples yield varying jobs multipliers, with point estimates varying from -0.01 to 1.48. Including Korean and Canadian data yields lower multipliers, while including pre-1990 US data yields higher multipliers. In no specification can I reject the hypothesis that the job multiplier equals 1. In all specifications, standard errors are large enough that I can reject neither Keynesian nor crowd-out effects.
Power-Constrained Trajectory optimization for Wireless UAV Relays with Random Requests

This paper studies the adaptive trajectory design of a rotary-wing UAV serving as a relay between ground nodes dispersed in a circular cell and a central base station. Assuming the ground nodes generate uplink data transmissions randomly according to a Poisson process, we seek to minimize the expected average communication delay to service the data transmission requests, subject to an average power constraint on the mobility of the UAV. The problem is cast as a semi-Markov decision process, and it is shown that the policy exhibits a two-scale structure, which can be efficiently optimized: in the outer decision, upon starting a communication phase, and given its current radius, the UAV selects a target end radius position so as to optimally balance a trade-off between average long-term communication delay and power consumption; in the inner decision, the UAV selects its trajectory between the start radius and the selected end radius, so as to greedily minimize the delay and energy consumption to serve the current request. Numerical evaluations show that, during waiting phases, the UAV circles at some optimal radius at the most energy efficient speed, until a new request is received. Lastly, the expected average communication delay and power consumption of the optimal policy is compared to that of static and mobile heuristic schemes, demonstrating a reduction in latency by over 50% and 20%, respectively.
Pattern-Based Analysis of Time Series: Estimation

While Internet of Things (IoT) devices and sensors create continuous streams of information, Big Data infrastructures are deemed to handle the influx of data in realtime. One type of such a continuous stream of information is time series data. Due to the richness of information in time series and inadequacy of summary statistics to encapsulate structures and patterns in such data, development of new approaches to learn time series is of interest. In this paper, we propose a novel method, called pattern tree, to learn patterns in the times-series using a binary-structured tree. While a pattern tree can be used for many purposes such as lossless compression, prediction and anomaly detection, in this paper we focus on its application in time series estimation and forecasting. In comparison to other methods, our proposed pattern tree method improves the mean squared error of estimation.
From Navier-Stokes to Maxwell via Einstein

We revisit the cutoff surface formulation of fluid-gravity duality in the context of the classical double copy. The spacetimes in this fluid-gravity duality are algebraically special, with Petrov type II when the spacetime is four dimensional. We find two special classes of fluids whose dual spacetimes exhibit higher algebraic speciality: constant vorticity flows have type D gravity duals, while potential flows map to type N spacetimes. Using the Weyl version of the classical double copy, we construct associated single-copy gauge fields for both cases, finding that constant vorticity fluids map to a solenoid gauge field. Additionally we find the scalar in a potential flow fluid maps to the zeroth copy scalar.
Intelligent Time-Varying Metasurface Transceiver for Index Modulation in 6G Wireless Networks

Index modulation (IM) is one of the candidate technologies for the upcoming sixth-generation (6G) wireless communications networks. In this letter, we propose a space-time-modulated reconfigurable intelligent metasurface (RI-MTS) that is configured to implement various frequency-domain IM techniques in a multiple-input–multiple-output (MIMO) array configuration. Unlike prior works that mostly analyze the signal theory of general RI-MTS IM, we present novel electromagnetics-compliant designs of specific IMs such as subcarrier index modulation (SIM) and MIMO orthogonal frequency-domain modulation IM (MIMO-OFDM-IM). Our full-wave electromagnetic simulations and analytical computations establish the programmable ability of these transceivers to vary the reflection phase and generate frequency harmonics for IM. Our experiments for the bit error rate show that RI-MTS-based SIM and MIMO-OFDM-IM are lower than the conventional MIMO-OFDM.
Why Do Corporations Decide to Do Good

In his highly influential essay, Milton Friedman argued that companies should never engage in “social responsibility,” defined as going beyond regulatory compliance and normal business practices to...
PKIS deep dive yields a chemical starting point for dark kinases and a cell active BRSK2 inhibitor.

The Published Kinase Inhibitor Set (PKIS) is a publicly-available chemogenomic library distributed to more than 300 laboratories by GlaxoSmithKline (GSK) between 2011 and 2015 and by SGC-UNC from 2015 to 2017. Screening this library of well-annotated, published kinase inhibitors has yielded a plethora of data in diverse therapeutic and scientific areas, funded applications, publications, and provided impactful pre-clinical results. GW296115 is a compound that was included in PKIS based on its promising selectivity following profiling against 260 human kinases. Herein we present more comprehensive profiling data for 403 wild type human kinases and follow-up enzymatic screening results for GW296115. This more thorough investigation of GW296115 has confirmed it as a potent inhibitor of kinases including BRSK1 and BRSK2 that were identified in the original panel of 260 kinases as well as surfaced other kinases that it potently inhibits. Based on these new kinome-wide screening results, we report that GW296115 is an inhibitor of several members of the Illuminating the Druggable Genome (IDG) list of understudied dark kinases. Specifically, our results establish GW296115 as a potent lead chemical tool that inhibits six IDG kinases with IC50 values less than 100 nM. Focused studies establish that GW296115 is cell active, and directly engages BRSK2. Further evaluation showed that GW296115 downregulates BRSK2-driven phosphorylation and downstream signaling. Therefore, we present GW296115 as a cell-active chemical tool that can be used to interrogate the poorly characterized function(s) of BRSK2.
Efficient Graph Construction For Image Representation

Graphs are useful to interpret widely used image processing methods, e.g., bilateral filtering, or to develop new ones, e.g., kernel based techniques. However, simple graph constructions are often used, where edge weight and connectivity depend on a few parameters. In particular, the sparsity of the graph is determined by the choice of a window size. As an alternative, we extend and adapt to images recently introduced non negative kernel regression (NNK) graph construction. In NNK graphs sparsity adapts to intrinsic data properties. Moreover, while previous work considered NNK graphs in generic settings, here we develop novel algorithms that take advantage of image properties, so that the NNK approach can scale to large images. Our experiments show that sparse NNK graphs achieve improved energy compaction and denoising performance when compared to using graphs directly derived from the bilateral filter.
Nonlinear eigenvalue problems for coupled Helmholtz equations modeling gradient-index graphene waveguides

Abstract We discuss a quartic eigenvalue problem arising in the context of an optical waveguiding problem involving atomically thick 2D materials. The waveguide configuration we consider consists of a gradient-index (spatially dependent) dielectric equipped with conducting interior interfaces. This leads to a quartic eigenvalue problem with mixed transverse electric and transverse magnetic modes, and strongly coupled electric and magnetic fields. We derive a weak formulation of the quartic eigenvalue problem and introduce a numerical solver based on a quadratification approach in which the quartic eigenvalue problem is transformed to a spectrally equivalent companion problem. We verify our numerical framework against analytical solutions for prototypical geometries. As a practical example, we demonstrate how an improved quality factor (defined by the ratio of the real and the imaginary part of the computed eigenvalues) can be obtained for a family of gradient-index host materials with internal conducting interfaces. We outline how this result lays the groundwork for solving related shape optimization problems.
Efficient Regularized Field Map Estimation in 3D MRI

Magnetic field inhomogeneity estimation is important in some types of magnetic resonance imaging (MRI), including field-corrected reconstruction for fast MRI with long readout times, and chemical shift based water-fat imaging. Regularized field map estimation methods that account for phase wrapping and noise involve nonconvex cost functions that require iterative algorithms. Most existing minimization techniques were computationally or memory intensive for 3D datasets, and are designed for single-coil MRI. This article considers 3D MRI with optional consideration of coil sensitivity, and addresses the multi-echo field map estimation and water-fat imaging problem. Our efficient algorithm uses a preconditioned nonlinear conjugate gradient method based on an incomplete Cholesky factorization of the Hessian of the cost function, along with a monotonic line search. Numerical experiments show the computational advantage of the proposed algorithm over state-of-the-art methods with similar memory requirements.
Non-Negative Matrix Factorization via Low-Rank Stochastic Manifold Optimization

Several real-world applications, notably in non-negative matrix factorization, graph-based clustering, and machine learning, require solving a convex optimization problem over the set of stochastic and doubly stochastic matrices. A common feature of these problems is that the optimal solution is generally a low-rank matrix. This paper suggests reformulating the problem by taking advantage of the low-rank factorization X = UVT and develops a Riemannian optimization framework for solving optimization problems on the set of low-rank stochastic and doubly stochastic matrices. In particular, this paper introduces and studies the geometry of the low-rank stochastic multinomial and the doubly stochastic manifold in order to derive first-order optimization algorithms. Being carefully designed and of lower dimension than the original problem, the proposed Riemannian optimization framework presents a clear complexity advantage. The claim is attested through numerical experiments on real-world and synthetic data for Non-negative Matrix Factorization (NFM) applications. The proposed algorithm is shown to outperform, in terms of running time, state-of-the-art methods for NFM.
Preconditioning of Caenorhabditis elegans to anoxic insult by inactivation of cholinergic, GABAergic and muscle activity.

For most metazoans, oxygen deprivation leads to cell dysfunction and if severe, death. Sublethal stress prior to a hypoxic or anoxic insult ("preconditioning") can protect cells from subsequent oxygen deprivation. The molecular mechanisms by which sublethal stress can buffer against a subsequent toxic insult and the role of the nervous system in the response are not well understood. We studied the role of neuronal activity preconditioning to oxygen deprivation in Caenorhabditis elegans. Animals expressing the histamine gated chloride channels (HisCl1) in select cell populations were used to temporally and spatially inactivate the nervous system or tissue prior to an anoxic insult. We find that inactivation of the nervous system for 3 h prior to the insult confers resistance to a 48-h anoxic insult in 4th-stage larval animals. Experiments show that this resistance can be attributed to loss of activity in cholinergic and GABAergic neurons as well as in body wall muscles. These observations indicate that the nervous system activity can mediate the organism's response to anoxia.
Coefficients for higher order Hochschild cohomology

When studying deformations of an $A$-module $M$, Laudal and Yau showed that one can consider 1-cocycles in the Hochschild cohomology of $A$ with coefficients in the bi-module $End_k(M).$ With this in mind, the use of higher order Hochschild (co)homology, presented by Pirashvili and Anderson, to study deformations seems only natural though the current definition allows only symmetric bi-module coefficients. In this paper we present an extended definition for higher order Hochschild cohomology which allows multi-module coefficients (when the simplicial sets $X_{\bullet}$ are accommodating) which agrees with the current definition. Furthermore we determine the types of modules that can be used as coefficients for the Hochschild cochain complexes based on the simplicial sets they are associated to.
A Straight and Narrow Ionized Filament

We report the discovery of an extremely narrow, extremely linear, ionized filament. The filament is ~25 long and has an Hα surface brightness of ~0.5 R. It is at high galactic latitude, stretching from (l, b) = (1408°, 390) to (1380, 377). The filament is approximately Y-shaped. The vertical segment of the Y is ~12 long and ~20'' wide, and the widest separation of the two diagonal segments is ~5'. We discuss four possible origins for this feature: (1) an extremely low density, nearby jet; (2) an unusually linear filament associated with some large-scale nearby nebula, perhaps even the Local Bubble; (3) an ionized trail left by mechanical input from a star or compact object moving through the interstellar medium (ISM); or (4) an ionized trail left by photoionization ("Fossil Stromgren Trail") from a star or compact object. We favor this last hypothesis, and derive some of the basic properties for an ionized trail. Regardless of whether this latter hypothesis applies to this specific filament, the basic properties of such a trail, its length, width, and brightness, are interesting, predictable, and should be observable behind some white dwarfs. If the filament is a photoionized trail, then the source should be closer than a few hundred parsecs, with a measurable proper motion and a luminosity of hydrogen ionizing photons of 1044 ergs s-1. We have searched for such sources in line with the filament and find one candidate, the X-ray source RX J094247.2+700238. If the M dwarf binary star Gl 360 is the optical counterpart of the X-ray source, as has been thought, then its proper motion eliminates it as a candidate, and we have no other potential candidate to leave a trail. We note the similarity of this structure to long, narrow features ("canals") observed as depolarizing regions against the Galactic synchrotron background and find that this emission filament may also be detectable as a region of Faraday depolarization. We suggest future tests for ascertaining the origin of this filament and discuss how this structure might be useful to constrain the thermal and velocity structure of the nearby ISM.
Symmetry breaking with opposite stability between bifurcated asymmetric solitons in parity-time-symmetric potentials

We report a new type of symmetry-breaking bifurcation of solitons in optical systems with parity-time-symmetric potentials. In this bifurcation, the two bifurcated branches of asymmetric solitons exhibit opposite stability, which contrasts all previous symmetry-breaking bifurcations in conservative and non-conservative systems. We show that this novel symmetry-breaking bifurcation can be exploited to achieve unidirectional propagation of high-intensity light beams in parity-time-symmetric potentials.
Representations of the Kauffman bracket skein algebra I: invariants and miraculous cancellations

We study finite-dimensional representations of the Kauffman bracket skein algebra of a surface S. In particular, we construct invariants of such irreducible representations when the underlying parameter $$q={{\mathrm {e}}}^{2\pi {{\mathrm {i}}}\hbar }$$ is a root of unity. The main one of these invariants is a point in the character variety consisting of group homomorphisms from the fundamental group $$\pi _1(S)$$ to $${{\mathrm {SL}}}_2(\mathbb {C})$$ , or in a twisted version of this character variety. The proof relies on certain miraculous cancellations that occur for the quantum trace homomorphism constructed by the authors. These miraculous cancellations also play a fundamental role in subsequent work of the authors, where novel examples of representations of the skein algebra are constructed.
Age Determination for 346 Nearby Stars in the Herschel DEBRIS Survey

DEBRIS is a flux-limited survey of nearby stars (spectral types A-M) for evidence of debris disks with the Herschel Space Observatory. One goal of the survey is to determine disk incidence as a function of various stellar parameters. Understanding debris disk evolution depends on knowledge of the precise age of stars around which these debris disks are found. However, finding ages for field stars is notoriously difficult. Furthermore, in an unbiased sample like DEBRIS, one is working with stars across many spectral types. This requires a multi-method approach to age determination. In this paper, we outline several methods of age determination broken down by spectral type, including some strengths and limitations of each method. In total, we were able to calculate ages for 263 of 274 F-, G-, and K-type stars, and all 83 A-type stars in the DEBRIS sample.