cssi/SciSciGPT-SciSciNet huggingface dataset:
A Rotational Pressure-Correction Scheme for Incompressible Two-Phase Flows with Open Boundaries

Two-phase outflows refer to situations where the interface formed between two immiscible incompressible fluids passes through open portions of the domain boundary. We present several new forms of open boundary conditions for two-phase outflow simulations within the phase field framework, as well as a rotational pressure correction based algorithm for numerically treating these open boundary conditions. Our algorithm gives rise to linear algebraic systems for the velocity and the pressure that involve only constant and time-independent coefficient matrices after discretization, despite the variable density and variable viscosity of the two-phase mixture. By comparing simulation results with theory and the experimental data, we show that the method produces physically accurate results. We also present numerical experiments to demonstrate the long-term stability of the method in situations where large density contrast, large viscosity contrast, and backflows occur at the two-phase open boundaries.
Premise Selection for Theorem Proving by Deep Graph Embedding

We propose a deep learning-based approach to the problem of premise selection: selecting mathematical statements relevant for proving a given conjecture. We represent a higher-order logic formula as a graph that is invariant to variable renaming but still fully preserves syntactic and semantic information. We then embed the graph into a vector via a novel embedding method that preserves the information of edge ordering. Our approach achieves state-of-the-art results on the HolStep dataset, improving the classification accuracy from 83% to 90.3%.
Fast k -nearest neighbour search via prioritized DCI

Most exact methods for k-nearest neighbour search suffer from the curse of dimensionality; that is, their query times exhibit exponential dependence on either the ambient or the intrinsic dimensionality. Dynamic Continuous Indexing (DCI) (Li & Malik, 2016) offers a promising way of circumventing the curse and successfully reduces the dependence of query time on intrinsic dimensionality from exponential to sublinear. In this paper, we propose a variant of DCI, which we call Prioritized DCI, and show a remarkable improvement in the dependence of query time on intrinsic dimensionality. In particular, a linear increase in intrinsic dimensionality, or equivalently, an exponential increase in the number of points near a query, can be mostly counteracted with just a linear increase in space. We also demonstrate empirically that Prioritized DCI significantly outperforms prior methods. In particular, relative to Locality-Sensitive Hashing (LSH), Prioritized DCI reduces the number of distance evaluations by a factor of 14 to 116 and the memory consumption by a factor of 21.
Some good-filtration subgroups of simple algebraic groups

Abstract Let G be a connected and reductive algebraic group over an algebraically closed field of characteristic p > 0 . An interesting class of representations of G consists of those G -modules having a good filtration — i.e. a filtration whose layers are the induced highest weight modules obtained as the space of global sections of G -linearized line bundles on the flag variety of G . Let H ⊂ G be a connected and reductive subgroup of G . One says that ( G , H ) is a Donkin pair , or that H is a good filtration subgroup of G , if whenever the G -module V has a good filtration, the H -module r e s H G V has a good filtration. In this paper, we show when G is a “classical group” that the optimal S L 2 -subgroups of G are good filtration subgroups. We also consider the cases of subsystem subgroups in all types and determine some primes for which they are good filtration subgroups.
Counterexamples for Cohen–Macaulayness of lattice ideals

AbstractLet ℒ⊂Zn be a lattice, I is the corresponding lattice ideal, and J is the toric ideal arising from the saturation of ℒ. We produce infinitely many examples, in every codimension, of pairs I, J where one of these ideals is Cohen–Macaulay but the other is not.
The eigenvalues of the graphs D(4,q)

Abstract The graphs D ( k , q ) have connected components C D ( k , q ) giving the best known bounds on extremal problems with forbidden even cycles, and are denser than the well-known graphs of Lubotzky, Phillips, Sarnak [14] and Margulis [15] , [16] . Despite this, little is known about the spectrum and expansion properties of these graphs. In this paper we find the spectrum for k = 4 , the smallest open case. For each prime power q, the graph D ( 4 , q ) is q-regular graph on 2 q 4 vertices, all of whose eigenvalues other than ±q are bounded in absolute value by 2 q . Accordingly, these graphs are good expanders, in fact very close to Ramanujan .
The number of self-conjugate core partitions

A conjecture on the monotonicity of t-core partitions in an article of Stanton [Dennis Stanton, Open positivity conjectures for integer partitions, Trends Math. 2 (1999) 19–25] has been the catalyst for much recent research on t-core partitions. We conjecture Stanton-like monotonicity results comparing self-conjugate (t+2)- and t-core partitions of n. We obtain partial results toward these conjectures for values of t that are large with respect to n, and an application to the block theory of the symmetric and alternating groups. To this end we prove formulas for the number of self-conjugate t-core partitions of n as a function of the number of self-conjugate partitions of smaller n. Additionally, we discuss the positivity of self-conjugate 6-core partitions and introduce areas for future research in representation theory, asymptotic analysis, unimodality, and numerical identities and inequalities.
Automatically Generating a Large, Culture-Specific Blocklist for China

Internet censorship measurements rely on lists of websites to be tested, or "block lists" that are curated by third parties. Unfortunately, many of these lists are not public, and those that are tend to focus on a small group of topics, leaving other types of sites and services untested. To increase and diversify the set of sites on existing block lists, we use natural language processing and search engines to automatically discover a much wider range of websites that are censored in China. Using these techniques, we create a list of 1125 websites outside the Alexa Top 1,000 that cover Chinese politics, minority human rights organizations, oppressed religions, and more. Importantly, $\textit{none of the sites we discover are present on the current largest block list}$. The list that we develop not only vastly expands the set of sites that current Internet measurement tools can test, but it also deepens our understanding of the nature of content that is censored in China. We have released both this new block list and the code for generating it.
Uncertain for a century: quantum mechanics and the dilemma of interpretation

Quantum mechanics, the physical theory describing the microworld, is one of science's greatest triumphs. Remarkably, however, after more than 100 years it is still unclear what quantum mechanics means in terms of basic philosophical questions about the nature of reality. While there are many interpretations of the mathematical machinery of quantum physics, there remain no experimental means to distinguish between most of them. In this contribution, I wish to consider the ways in which the enduring lack of an agreed-upon interpretation of quantum physics influences a number of critical philosophical debates about physics and reality. I briefly review two problems affected by quantum interpretations: the meaning of the term universe and the nature of consciousness.
The Maslov and Morse indices for Schrödinger operators on [0,1]

Abstract Assuming a symmetric potential and separated self-adjoint boundary conditions, we relate the Maslov and Morse indices for Schrodinger operators on [ 0 , 1 ] . We find that the Morse index can be computed in terms of the Maslov index and two associated matrix eigenvalue problems. This provides an efficient way to compute the Morse index for such operators.
Best constants for a family of Carleson sequences

Abstract We consider a general family of Carleson sequences associated with dyadic A 2 weights and find sharp — or, in one case, simply best known — upper and lower bounds for their Carleson norms in terms of the A 2 -characteristic of the weight. The results obtained make precise and significantly generalize earlier estimates by Wittwer, Vasyunin, Beznosova, and others. We also record several corollaries, one of which is a range of new characterizations of dyadic A 2 . Particular emphasis is placed on the relationship between sharp constants and optimizing sequences of weights; in most cases explicit optimizers are constructed. Our main estimates arise as consequences of the exact expressions, or explicit bounds, for the Bellman functions for the problem, and the paper contains a measure of Bellman-function innovation.
On Scaling Limits of Power Law Shot-Noise Fields

This article studies the scaling limit of a class of shot-noise fields defined on an independently marked stationary Poisson point process and with a power law response function. Under appropriate conditions, it is shown that the shot-noise field can be scaled suitably to have a α-stable limit, intensity of the underlying point process goes to infinity. It is also shown that the finite dimensional distributions of the limiting random field have i.i.d. stable random components. We hence propose to call this limte the α- stable white noise field. Analogous results are also obtained for the extremal shot-noise field which converges to a Fr\'{e}chet white noise field. Finally, these results are applied to the analysis of wireless networks.
Analyzing distributed Join-Idle-Queue: A fluid limit approach

In the context of load balancing, Lu et al. [2] introduced the distributed Join-Idle-Queue algorithm, where a group of dispatchers distribute jobs to a cluster of parallel servers. Each dispatcher maintains a queue of recently idle servers; when a job arrives to a dispatcher, it sends it to a server on its queue, or to a random server if the queue is empty. In turn, when a server becomes idle, it requests to be placed on the queue of a randomly chosen dispatcher.
Differentiable learning of logical rules for knowledge base reasoning

We study the problem of learning probabilistic first-order logical rules for knowledge base reasoning. This learning problem is difficult because it requires learning the parameters in a continuous space as well as the structure in a discrete space. We propose a framework, Neural Logic Programming, that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model. This approach is inspired by a recently-developed differentiable logic called TensorLog [5], where inference tasks can be compiled into sequences of differentiable operations. We design a neural controller system that learns to compose these operations. Empirically, our method outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.
Extracting Domain Invariant Features by Unsupervised Learning for Robust Automatic Speech Recognition

The performance of automatic speech recognition (ASR) systems can be significantly compromised by previously unseen conditions, which is typically due to a mismatch between training and testing distributions. In this paper, we address robustness by studying domain invariant features, such that domain information becomes transparent to ASR systems, resolving the mismatch problem. Specifically, we investigate a recent model, called the Factorized Hierarchical Variational Autoencoder (FHVAE). FHVAEs learn to factorize sequence-level and segment-level attributes into different latent variables without supervision. We argue that the set of latent variables that contain segment -level information is our desired domain invariant feature for ASR. Experiments are conducted on Aurora-4 and CHiME-4, which demonstrate 41 % and 27% absolute word error rate reductions respectively on mismatched domains.
Combinatorial Heegaard Floer homology and sign assignments

Abstract We provide an integral lift of the combinatorial definition of Heegaard Floer homology for nice diagrams, and show that the proof of independence using convenient diagrams adapts to this setting.
A monotonicity formula for mean curvature flow with surgery

Abstract We prove a monotonicity formula for mean curvature flow with surgery. This formula differs from Huisken's monotonicity formula by an extra term involving the mean curvature. As a consequence, we show that a surgically modified flow which is sufficiently close to a smooth flow in the sense of geometric measure theory is, in fact, free of surgeries. This result is used in the analysis of mean curvature flow with surgery in Riemannian three-manifolds (cf. [5] ).
Band-passes and long virtual knot concordance

Every classical knot is band-pass equivalent to the unknot or the trefoil. The band-pass class of a knot is a concordance invariant. Every ribbon knot, for example, is band-pass equivalent to the unknot. Here we introduce the long virtual knot concordance group &#x1d4b1;&#x1d49e;. It is shown that for every concordance class [K] ∈&#x1d4b1;&#x1d49e;, there is a J ∈ [K] that is not band-pass equivalent to K and an L ∈ [K] that is not band-pass equivalent to either the long unknot or any long trefoil. This is accomplished by proving that v2,1 + v2,2(mod 2) is a band-pass invariant but not a concordance invariant of long virtual knots, where v2,1 and v2,2 generate the degree two Polyak group for long virtual knots.
Trust to Pay? Tax Morale and Trust in Africa

Although low tax morale hits developing countries hardest, little is known about its determinants in those countries. This paper examines the impact of trust in public institutions and the neighborhood on individual tax morale in four African countries: Algeria, Ghana, Morocco, and Nigeria. First, the paper provides theoretical foundations of such a relationship. Further, the paper uses the World Value Survey to estimate the effects of trust in public institutions and the neighborhood on individual tax morale. The identification strategy employs the instrumental variables method and relies on historical data on the slave trade and the literature on the cultural heritage of trust. The paper finds that trust in public institutions and the neighborhood are largely associated with tax morale in the African countries under consideration. The findings are robust to an alternative identification strategy, additional controls, and a falsification test.
From Precision Medicine to Precision Health: A Full Angle from Diagnosis to Treatment and Prevention

Health Intelligence, a term that encompasses a broad range of techniques and methods from artificial intelligence and data science that provide better insights and improved decision making about individuals’ health and well-being, is increasingly used in today’s medicine and healthcare services. Here we discuss some applications of precision medicine and health, innovative approaches that utilize health intelligence to improve diagnosing people’s illnesses and making decisions about different treatment and prevention options in a timely manner.
The lasting effects of growing up in a military-connected home: a qualitative study of college-aged American military kids

Being reared as a military kid is a unique experience shared by millions of children all over the world. This study seeks to understand the experiences shared by military kids and how those experie...
Automatic segmentation of intravital fluorescence microscopy images by K-means clustering of FLIM phasors

Fluorescence lifetime imaging microscopy (FLIM) provides additional contrast for fluorophores with overlapping emission spectra. The phasor approach to FLIM greatly reduces the complexity of FLIM analysis and enables a useful image segmentation technique by selecting adjacent phasor points and labeling their corresponding pixels with different colors. This phasor labeling process, however, is empirical and could lead to biased results. In this Letter, we present a novel and unbiased approach to automate the phasor labeling process using an unsupervised machine learning technique, i.e., K-means clustering. In addition, we provide an open-source, user-friendly program that enables users to easily employ the proposed approach. We demonstrate successful image segmentation on 2D and 3D FLIM images of fixed cells and living animals acquired with two different FLIM systems. Finally, we evaluate how different parameters affect the segmentation result and provide a guideline for users to achieve optimal performance.
Inosine RNA modifications are enriched at the codon wobble position in mouse oocytes and eggs

Mammalian oocytes and eggs are transcriptionally quiescent and depend on post-transcriptional mechanisms for proper maturation. Post-transcriptional mRNA modifications comprise an important regulatory mechanism that can alter protein and miRNA recognition sites, splicing, stability, secondary structure, and protein coding. We discovered that fully grown mouse germinal vesicle oocytes and metaphase II eggs display abundant inosine mRNA modifications compared to growing oocytes from postnatal day 12 oocytes. These inosines were enriched in mRNA protein coding regions (CDS) and specifically located at the third codon base, or wobble position. Inosines, observed at lower frequencies in CDS of somatic tissues, were similarly enriched at the codon wobble position. In oocytes and eggs, inosine modifications lead primarily to synonymous changes in mRNA transcripts. Inosines may ultimately affect maternal mRNA stability by changing codon usage, thereby altering translational efficiency and translationally coupled mRNA degradation. These important observations advance our understanding of post-transcriptional mechanisms contributing to mammalian oocyte maturation.
Estimating the Frequency of a Clustered Signal.

We consider the problem of locating a signal whose frequencies are "off grid" and clustered in a narrow band. Given noisy sample access to a function $g(t)$ with Fourier spectrum in a narrow range $[f_0 - \Delta, f_0 + \Delta]$, how accurately is it possible to identify $f_0$? We present generic conditions on $g$ that allow for efficient, accurate estimates of the frequency. We then show bounds on these conditions for $k$-Fourier-sparse signals that imply recovery of $f_0$ to within $\Delta + \tilde{O}(k^3)$ from samples on $[-1, 1]$. This improves upon the best previous bound of $O\big( \Delta + \tilde{O}(k^5) \big)^{1.5}$. We also show that no algorithm can do better than $\Delta + \tilde{O}(k^2)$. In the process we provide a new $\tilde{O}(k^3)$ bound on the ratio between the maximum and average value of continuous $k$-Fourier-sparse signals, which has independent application.
Smart disclosure: promise and perils

The move to smart disclosure promises to revitalize disclosure mandates and save them from a fate of ignored verbiage. But by making disclosure relevant and effective, this shift to smart disclosure also raises several concerns. Specifically, simple disclosures like genetically modified food disclosures, restaurant hygiene grades, annual percentage rate disclosures, etc., can result in market distortions and inefficiencies as: (1) consumers might draw false inferences from the disclosure; and (2) disclosing one dimension will elevate this dimension relative to other dimensions, and thus distort demand for the product and even alter the product itself. Relatedly, System 1 disclosures, like graphic cigarette labels, might influence behavior by triggering an emotional response rather than through informed deliberation, thus abandoning traditional justifications for disclosure mandates. In light of these concerns, it is more difficult to view disclosure mandates as minimally paternalistic. Government, by tweaking disclosure design, wields substantial power over markets and consumers.
Heart analytics: Analytical modeling of cardiovascular care

Cardiovascular diseases (CVDs) are the leading cause of death and disability both in the United States and worldwide. Despite high morbidity, mortality, and cost in the US and global healthcare systems, cardiovascular care has been under-studied in the healthcare operations management literature. In this paper, we identify research opportunities for healthcare operations management scholars to aid in improving cardiovascular care. We focus on three burdensome conditions, including (1) coronary artery disease, (2) stroke, and (3) heart failure, which, collectively, lead to the vast majority of CVD-caused mortality and disability. For each condition, we characterize a typical patient’s journey in receiving cardiovascular care, elucidating key challenges in improving care and outlining research questions for healthcare operations management scholars. We close with a reference to new research opportunities that emerge as artificial intelligence is likely to transform much of cardiovascular care.
Congruences for modular forms and generalized Frobenius partitions

The partition function is known to exhibit beautiful congruences that are often proved using the theory of modular forms. In this paper, we study the extent to which these congruence results apply to the generalized Frobenius partitions defined by Andrews (Mem Am Math Soc 49(301):iv+44, 1984). In particular, we prove that there are infinitely many congruences for $$c\phi _k(n)$$ modulo $$\ell ,$$ where $$\gcd (\ell ,6k)=1,$$ and we also prove results on the parity of $$c\phi _k(n).$$ Along the way, we prove results regarding the parity of coefficients of weakly holomorphic modular forms which generalize work of Ono (J Reine Angew Math 472:1–15, 1996).
Atomistic simulations of tungsten nanotubes under uniform tensile loading

Metallic nanotubes (NTs) have gained much attention in recent years due to their exciting potential to be just as strong or even stronger than their heavier counterparts, nanowires (NWs), with the same outer radius. Unlike NWs, NTs have inner wall diameter and wall thickness parameters that can be engineered to provide advantage in structural materials design. In this work, molecular dynamics is used to quantify the combined effects of NT specific dimensions, outer radius and wall thickness, on the tensile strength of single crystalline tungsten NTs at room temperature. Uniaxial tensile simulations are carried out for three different crystallographic orientations along the NT axis—two known as brittle orientations and one as ductile orientation. For these three orientations, the strength of NTs can be made higher than NWs, for the same outer radius, as the wall thickness decreases. The calculations indicate that even for the brittle orientations, NTs can be engineered to be more ductile by tuning the outer radius and the wall thickness.
Decomposing Generalization: Models of Generic, Habitual, and Episodic Statements

We present a novel semantic framework for modeling linguistic expressions of generalization---generic, habitual, and episodic statements---as combinations of simple, real-valued referential properties of predicates and their arguments. We use this framework to construct a dataset covering the entirety of the Universal Dependencies English Web Treebank. We use this dataset to probe the efficacy of type-level and token-level information---including hand-engineered features and static (GloVe) and contextual (ELMo) word embeddings---for predicting expressions of generalization.
Using SOS for Optimal Semialgebraic Representation of Sets: Finding Minimal Representations of Limit Cycles, Chaotic Attractors and Unions

In this paper we show that Sum-of-Squares optimization can be used to find optimal semialgebraic representations of sets. These sets may be explicitly defined, as in the case of discrete points or unions of sets; or implicitly defined, as in the case of attractors of nonlinear systems. We define optimality in the sense of minimum volume, while satisfying constraints that can include set containment, convexity, or Lyapunov stability conditions. Our admittedly heuristic approach to volume minimization is based on the use of a determinant-like objective function. We provide numerical examples for the Lorenz attractor and the Van der Pol limit cycle.
Facing the Quadrilemma: Taylor Rules, Intervention Policy and Capital Controls in Large Emerging Markets

Abstract This paper investigates extended Taylor rules and foreign exchange intervention functions in large Emerging Markets (EM), measuring the extent to which policies are designed to stabilize output, inflation, exchange rates and accumulate international reserves. We focus on two large emerging markets - India and Brazil. We also consider the impact of greater capital account openness and which rules dominate when policy conflicts arise. We find that output stabilization is a dominant characteristic of interest rate policy in India, as is inflation targeting in Brazil. Both countries actively use intervention policy to achieve exchange rate stabilization and, at times, stabilizing reserves around a target level tied to observable economic fundamentals. Large unpredicted intervention purchases (sales) accommodate low (high) interest rates, suggesting that external operations are subordinate to domestic policy objectives. We extend the work to Chile and China for purposes of comparison. Chile’s policy functions are similar to Brazil, while China pursues policies that substantially diverge from other EMs.
A comparison of two- and three-dimensional single-mode reshocked Richtmyer-Meshkov instability growth

Abstract The growth dynamics of two- and three-dimensional single-mode reshocked Richtmyer–Meshkov instability are compared systematically using data from high-resolution implicit large-eddy simulations of a model of the Mach 1.3 air(acetone) and sulfur hexafluoride (Jacobs and Krivets, 2005) shock tube experiment. The vorticity deposition by the incident shock and the dynamics of interface evolution are examined quantitatively and qualitatively. The perturbation amplitudes from the two- and three-dimensional simulations are compared to the experimental data and to the predictions of several nonlinear instability growth models. It is shown that the perturbation amplitudes from the two- and three-dimensional simulations with matching initial Richtmyer velocity are in excellent agreement with the experimental data. In addition, the dynamics of reshock (not considered in the experiment) are described in detail, and the post-reshock mixing layer amplitude growth rate is compared to the predictions of several reshock models. It is shown that using two-dimensional simulations to understand three-dimensional dynamics is valid only at early-to-intermediate times before reshock; at intermediate-to-late times after reshock the three-dimensional growth is generally larger than the corresponding two-dimensional growth. The reshock dynamics are also different between two and three dimensions. The quantitative results, together with visualizations of the flow field, were also used to contrast the difference between two- and three-dimensional vorticity and enstrophy dynamics.
Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems

Introduction Health inequality measurements are vital in understanding disease patterns to identify high-risk patients and implementing effective intervention programs in treating and managing sexually transmitted diseases. Our study seeks to measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. Methods Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study. Results Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). Conclusion The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.
Single-Server Single-Message Online Private Information Retrieval with Side Information

In many practical settings, the user needs to retrieve information messages from a server in a periodic manner, over multiple rounds of communication. The messages are retrieved one at a time and the identity of future requests are not known to the server. In this paper, we focus on the private information retrieval protocols that ensure that the identities of all the messages retrieved from the server are protected. This scenario can occur in practical settings such as periodic content download from text and multimedia repositories. We refer to this problem of minimizing the rate of data download as online private information retrieval problem.Following the previous line of work by Kadhe et al. we assume that the user knows a subset of M messages in the database as side information. The identities of these M messages are initially unknown to the server. Focusing on scalar-linear settings, we characterize the per-round capacity, i.e., the maximum achievable download rate at each round. In particular, we show that for the setting with K messages stored at the server, the per-round capacity of the scalar-linear setting is C 1 = (M + 1)/K for round i = 1 and C i = (2i −1(M + 1))/KM for round i ≥ 2, provided that K/(M + 1) is a power of 2. The key idea≥of our achievability scheme is to combine the data downloaded during the current round and the previous rounds with the original side information messages and use the resulting data as side information for the subsequent rounds.
Analysis of Data Harvesting by Unmanned Aerial Vehicles

This paper explores an emerging wireless architecture based on unmanned aerial vehicles (UAVs), i.e., drones. We consider a network where UAVs at fixed altitude harvest data from Internet-of-Things (IoT) devices on the ground. Each UAV serves IoT devices within its coverage area. In such a system, the UAVs’ motion activates IoT uplink transmissions and so the motion triggers the interference field and determines the network performance. To analyze the performance, we propose a stochastic geometry model. The coverage area of each UAV, referred to as the activation window, is modeled for simplicity as a rectangle where at most one IoT device is scheduled to transmit at any given time. In this setting, we analyze the signal-to-interference and data rate from two typical perspectives, namely from a typical UAV’s and from a typical IoT device’s points of view.
Transfer Learning Using Ensemble Neural Networks for Organic Solar Cell Screening

Organic Solar Cells are a promising technology for solving the clean energy crisis in the world. However, generating candidate chemical compounds for solar cells is a time-consuming process requiring thousands of hours of laboratory analysis. For a solar cell, the most important property is the power conversion efficiency which is dependent on the highest occupied molecular orbitals (HOMO) values of the donor molecules. Recently, machine learning techniques have proved to be very useful in building predictive models for HOMO values of donor structures of Organic Photovoltaic Cells (OPVs). Since experimental datasets are limited in size, current machine learning models are trained on data derived from calculations based on density functional theory (DFT). Molecular line notations such as SMILES or InChI are popular input representations for describing the molecular structure of donor molecules. The two types of line representations encode different information, such as SMILES defines the bond types while InChi defines protonation. In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets. Harvard CEP dataset contains molecular structures and properties for 2.3 million candidate donor structures for OPV while HOPV contains DFT-computed and experimental values of 350 and 243 molecules respectively. Our results demonstrate significant performance improvement from the use of transfer learning and leveraging both molecular representations.
Public Health Surveillance

Public health surveillance seeks to assess and track the health of the public, define public health priorities, evaluate programs, and stimulate research. It includes case surveillance focuses on individuals, or sometimes groups of individuals, to identify individuals with certain diseases and take action to stop spread of disease. Statistical surveillance, on the other hand, focuses on populations to identify differentials and trends that can inform public health policymaking, including the allocation of resources. Surveillance systems rely on a diversity of data sources. Surveillance of infectious disease relies primarily on the system of notifiable diseases put in place in the nineteenth century. Statistical surveillance systems use data from vital statistics, population-based sample surveys, registries, and health care records. The introduction and growth of syndromic surveillance following 9/11 has focused on a statistical approach to detecting bioterrorist attacks. Despite many accomplishments, the value of syndromic surveillance for detecting bioterrorist attacks has not yet been demonstrated. Ultimately, the most important contribution of syndromic surveillance systems to public health may be for natural disease outbreaks, such as seasonal and pandemic influenza, and for other public health purposes beyond simple detection. Keywords: public health; disease surveillance; syndromic surveillance; emerging diseases; pandemic influenza; bioterrorism
Graphene epsilon-near-zero plasmonic crystals

Plasmonic crystals are a class of optical metamaterials that consist of engineered structures at the sub-wavelength scale. They exhibit optical properties that are not found under normal circumstances in nature, such as negative-refractive-index and epsilon-near-zero (ENZ) behavior. Graphene-based plasmonic crystals present linear, elliptical, or hyperbolic dispersion relations that exhibit ENZ behavior, normal or negative-index diffraction. The optical properties can be dynamically tuned by controlling the operating frequency and the doping level of graphene. We propose a construction approach to expand the frequency range of the ENZ behavior. We demonstrate how the combination of a host material with an optical Lorentzian response in combination with a graphene conductivity that follows a Drude model leads to an ENZ condition spanning a large frequency range.
Perturbation of Linear Forms of Singular Vectors Under Gaussian Noise

Let \(A \in \mathbb{R}^{m\times n}\) be a matrix of rank r with singular value decomposition (SVD) A = ∑k = 1 r σ k (u k ⊗ v k ), where {σ k , k = 1, …, r} are singular values of A (arranged in a non-increasing order) and \(u_{k} \in \mathbb{R}^{m},v_{k} \in \mathbb{R}^{n},k = 1,\ldots,r\) are the corresponding left and right orthonormal singular vectors. Let \(\tilde{A} = A + X\) be a noisy observation of A, where \(X \in \mathbb{R}^{m\times n}\) is a random matrix with i.i.d. Gaussian entries, \(X_{ij} \sim \mathcal{N}(0,\tau ^{2}),\) and consider its SVD \(\tilde{A} =\sum _{ k=1}^{m\wedge n}\tilde{\sigma }_{k}(\tilde{u}_{k} \otimes \tilde{ v}_{k})\) with singular values \(\tilde{\sigma }_{1} \geq \ldots \geq \tilde{\sigma }_{m\wedge n}\) and singular vectors \(\tilde{u}_{k},\tilde{v}_{k},k = 1,\ldots,m \wedge n.\) The goal of this paper is to develop sharp concentration bounds for linear forms \(\langle \tilde{u}_{k},x\rangle,x \in \mathbb{R}^{m}\) and \(\langle \tilde{v}_{k},y\rangle,y \in \mathbb{R}^{n}\) of the perturbed (empirical) singular vectors in the case when the singular values of A are distinct and, more generally, concentration bounds for bilinear forms of projection operators associated with SVD. In particular, the results imply upper bounds of the order \(O{\biggl (\sqrt{\frac{\log (m+n)} {m\vee n}} \biggr )}\) (holding with a high probability) on $$\displaystyle{\max _{1\leq i\leq m}\big\vert \big \big \vert \ \ \mathrm{and}\ \ \max _{ 1\leq j\leq n}\big\vert \big \big \vert,}$$ where b k are properly chosen constants characterizing the bias of empirical singular vectors \(\tilde{u}_{k},\tilde{v}_{k}\) and {e i m , i = 1, …, m}, {e j n , j = 1, …, n} are the canonical bases of \(\mathbb{R}^{m}, \mathbb{R}^{n},\) respectively.
Tangent developable surfaces and the equations defining algebraic curves

This is an introduction, aimed at a general mathematical audience, to recent work of Aprodu, Farkas, Papadima, Raicu and Weyman. These authors established a long-standing folk conjecture concerning the equations defining the tangent developable surface of a rational normal curve. This in turn led to a new proof of a fundamental theorem of Voisin on the syzygies of a general canonical curve. The present note, which is the write-up of a talk given by the second author at the Current Events seminar at the 2019 JMM, surveys this circle of ideas.
Braiding Majorana fermions and creating quantum logic gates with vortices on a periodic pinning structure

We show how vortices that support Majorana fermions when placed on a periodic pinning array can be used for vortex exchange and independent braiding by performing a series of specific moves with a probe tip. Using these braiding operations, we demonstrate realizations of a Hadamard and a cnot gate. We specifically consider the first matching field at which there is one vortex per pinning site, and we show that there are two basic dynamic operations, move and exchange, from which basic braiding operations can be constructed in order to create specific logic gates. The periodic pinning array permits both control of the world lines of the vortices and freedom for vortex manipulation using a set of specific moves of the probe during which the probe tip strength and height remain unchanged. We measure the robustness of the different moves against thermal effects and show that the three different operations produce distinct force signatures on the moving tip.
SURREAL: Subgraph Robust Representation Learning

The success of graph embeddings or nodrepresentation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying node neighborhoods. However, many existing network representation learning methods generate embeddings that are still not effective enough, or lead to unstable representations due to random processes (e.g., random walks to generate context) and thus, cannot generalize to multi-graph problems. In this paper, we propose SURREAL, a novel, stable graph embedding algorithmic framework that leverages “spatio-electric” (SE) subgraphs: it learns graph representations using the analogy of graphs with electrical circuits. It preserves both local and global connectivity patterns, and addresses the issue of high-degree nodes that may incidentally connect a pair of nodes in a graph. Further, it exploits the strength of weak ties and meta-data that have been neglected by baselines. The experiments show that SURREAL outperforms state-of-the-art techniques by up to 37% (6% on average) on different multi-label classification problems. Further, in contrast to baseline methods, SURREAL, being deterministic, is stable and thus can generalize to single and multi-graph tasks.
Egalitarian Provision of Necessary Medical Treatment

Considerations of autonomy and independence, properly understood, support strictly egalitarian provision of necessary medical treatment. If the financially better-off can purchase access to necessary medical treatments that the financially less well-off cannot purchase without help, then their discretionary power to give or to withhold monetary gifts indirectly gives them the power to make life-and-death or sickness-and-health decisions for others. To prevent private citizens from having this objectionable form of power, government must ensure that citizens’ finances do not affect their access to medical treatments that significantly prolong life, relieve suffering, or cure or mitigate disabilities. Government should ensure this even if doing so involves leveling down and even if it is unclear whether egalitarian provision of necessary treatment would provide better care to the poor than a less egalitarian health care system would.
ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents

Identifying the topic (domain) of each user's utterance in open-domain conversational systems is a crucial step for all subsequent language understanding and response tasks. In particular, for complex domains, an utterance is often routed to a single component responsible for that domain. Thus, correctly mapping a user utterance to the right domain is critical. This is a challenging task: users could mention entities like actors, singers or locations to implicitly indicate the domain, which requires extensive domain knowledge to interpret. To address this problem, we introduce ConCET: a Concurrent Entity-aware conversational Topic classifier, which incorporates entity type information together with the utterance content features. Specifically, ConCET utilizes entity information to enrich the utterance representation, combining character, word, and entity type embeddings into a single representation. However, for rich domains with millions of available entities, unrealistic amounts of labeled training data would be required. To complement our model, we propose a simple and effective method for generating synthetic training data, to augment the typically limited amounts of labeled training data, using commonly available knowledge bases as to generate additional labeled utterances. We extensively evaluate ConCET and our proposed training method first on an openly available human-human conversational dataset called Self-Dialogue, to calibrate our approach against previous state-of-the-art methods; second, we evaluate ConCET on a large dataset of human-machine conversations with real users, collected as part of the Amazon Alexa Prize. Our results show that ConCET significantly improves topic classification performance on both datasets, reaching 8-10% improvements compared to state-of-the-art deep learning methods. We complement our quantitative results with detailed analysis of system performance, which could be used for further improvements of conversational agents.
Corals in the city: cultivating ocean life in the Anthropocene

Ocean life is often portrayed as antithetical to life in the city. Drawing on interviews with coral hobbyists and aquarists, my article focuses on the emergence of the coral aquarium hobby within t...
Separate and Attend in Personal Email Search

In personal email search, user queries often impose different requirements on different aspects of the retrieved emails. For example, the query "my recent flight to the US" requires emails to be ranked based on both textual contents and recency of the email documents, while other queries such as "medical history" do not impose any constraints on the recency of the email. Recent deep learning-to-rank models for personal email search often directly concatenate dense numerical features (e.g., document age) with embedded sparse features (e.g., n-gram embeddings). In this paper, we first show with a set of experiments on synthetic datasets that direct concatenation of dense and sparse features does not lead to the optimal search performance of deep neural ranking models. To effectively incorporate both sparse and dense email features into personal email search ranking, we propose a novel neural model, SepAttn. SepAttn first builds two separate neural models to learn from sparse and dense features respectively, and then applies an attention mechanism at the prediction level to derive the final prediction from these two models. We conduct a comprehensive set of experiments on a large-scale email search dataset, and demonstrate that our SepAttn model consistently improves the search quality over the baseline models.
Venus: A Thick Basal Magma Ocean May Exist Today

Basal magma oceans develop in Earth and Venus after accretion as their mantles solidify from the middle outwards. Fractional crystallization of the basal mantle is buffered by the core and radiogen...
Minimal Fractal Manifold and the Dual Nature of Dark Matter

As of today, the nature of Dark Matter (DM) remains controversial. Broadly speaking, there are two major schools of thought on the topic. While the first school considers DM to be an extrapolation of particle physics, the second one asserts that DM is a signature of gravitational phenomena. The basic tenet of the second school of thought is that DM can be explained away either through revisions of General Relativity (GR), through its inherent nonlinearity or by deploying non-traditional interpretations of GR. The goal of this brief note is to point out that DM and gravitational physics may no longer be mutually exclusive, but complementary manifestations of the same reality. Elaborating from the minimal fractality of spacetime above the Fermi scale - along with its quaternion formulation - we suggest that DM can replicate the contribution of gravitational nonlinearities on galactic scales.
Response of neuroglia to hypoxia-induced oxidative stress using enzymatically crosslinked hydrogels

Three-dimensional cultures have exciting potential to mimic aspects of healthy and diseased brain tissue to examine the role of physiological conditions on neural biomarkers, as well as disease onset and progression. Hypoxia is associated with oxidative stress, mitochondrial damage, and inflammation, key processes potentially involved in Alzheimer's and multiple sclerosis. We describe the use of an enzymatically-crosslinkable gelatin hydrogel system within a microfluidic device to explore the effects of hypoxia-induced oxidative stress on rat neuroglia, human astrocyte reactivity, and myelin production. This versatile platform offers new possibilities for drug discovery and modeling disease progression.
Trellis-Coded Non-Orthogonal Multiple Access

In this letter, we propose a trellis-coded non-orthogonal multiple access (NOMA) scheme. The signals for different users are produced by trellis coded modulation (TCM) and then superimposed on different power levels. By interpreting the encoding process via the tensor product of trellises, we introduce a joint detection method based on the Viterbi algorithm. Then, we determine the optimal power allocation between the two users by maximizing the free distance of the tensor product trellis. Finally, we manifest that the trellis-coded NOMA outperforms the uncoded NOMA at high signal-to-noise ratio (SNR).