cssi/SciSciGPT-SciSciNet huggingface dataset:
Orthogonal Polynomials and Fourier Orthogonal Series on a Cone

Orthogonal polynomials and the Fourier orthogonal series on a cone in $${{\mathbb {R}}}^{d+1}$$ are studied. It is shown that orthogonal polynomials with respect to the weight function $$(1-t)^{\gamma }(t^2-\Vert x\Vert ^2)^{\mu -\frac{1}{2}}$$ on the cone $${{\mathbb {V}}}^{d+1} = \{(x,t): \Vert x\Vert \le t \le 1\}$$ are eigenfunctions of a second order differential operator, with eigenvalues depending only on the degree of the polynomials, and the reproducing kernels of these polynomials satisfy a closed formula that has a one-dimensional characteristic. The latter leads to a convolution structure on the cone, which is then utilized to study the Fourier orthogonal series. This narrative also holds, in part, for more general classes of weight functions. Furthermore, analogous results are also established for orthogonal structure on the surface of the cone.
Functions of Gtf2i and Gtf2ird1 in the developing brain: transcription, DNA binding and long-term behavioral consequences

Gtf2ird1 and Gtf2i are two transcription factors (TFs) among the 28 genes deleted in Williams syndrome, and prior mouse models of each TF show behavioral phenotypes. Here we identify their genomic binding sites in the developing brain and test for additive effects of their mutation on transcription and behavior. GTF2IRD1 binding targets were enriched for transcriptional and chromatin regulators and mediators of ubiquitination. GTF2I targets were enriched for signal transduction proteins, including regulators of phosphorylation and WNT. Both TFs are highly enriched at promoters, strongly overlap CTCF binding and topological associating domain boundaries and moderately overlap each other, suggesting epistatic effects. Shared TF targets are enriched for reactive oxygen species-responsive genes, synaptic proteins and transcription regulators such as chromatin modifiers, including a significant number of highly constrained genes and known ASD genes. We next used single and double mutants to test whether mutating both TFs will modify transcriptional and behavioral phenotypes of single Gtf2ird1 mutants, though with the caveat that our Gtf2ird1 mutants, like others previously reported, do produce low levels of a truncated protein product. Despite little difference in DNA binding and transcriptome-wide expression, homozygous Gtf2ird1 mutation caused balance, marble burying and conditioned fear phenotypes. However, mutating Gtf2i in addition to Gtf2ird1 did not further modify transcriptomic or most behavioral phenotypes, suggesting Gtf2ird1 mutation alone was sufficient for the observed phenotypes.
Noncommutative counterparts of celebrated conjectures

In this survey, written for the proceedings of the conference K-theory in algebra, analysis and topology, Buenos-Aires, Argentina (satellite event of the ICM 2018), we give a rigorous overview of the noncommutative counterparts of some celebrated conjectures of Grothendieck, Voevodsky, Beilinson, Weil, Tate, Parshin, Kimura, Schur, and others.
Heterogeneity and Learning in Labor Markets

I develop an equilibrium matching model where heterogeneous workers and firms learn about match quality and bargain over wages. The model generalizes Jovanovic (1979) to the case of heterogeneous workers and firms. Equilibrium wage dispersion arises due to productivity differences between workers, technological differences between firms, and heterogeneity in beliefs about match quality. Under a simple CRS technology, the equilibrium wage is additively separable in worker- and firm-specific components as well as in the posterior mean of beliefs about match quality. This parallels the person and firm effects empirical specification of Abowd et al (1999) and others. The model predicts a negative correlation between estimated person and firm effects, which is consistent with most previous empirical evidence. I estimate the equilibrium wage function and test the model's empirical predictions using linked employer-employee data from the U.S. Census Bureau. I find empirical support for many of the model's predictions and estimate that dispersion in beliefs about match quality explains over 20 percent of observed earnings variation.
The Environmental Bias of Trade Policy

Author(s): Shapiro, Joseph S. | Abstract: This paper documents a new fact, then analyzes its causes and consequences: in most countries, import tariffs and non-tariff barriers are substantially lower on dirty than on clean industries, where an industry’s “dirtiness” is defined as its carbon dioxide (CO2) emissions per dollar of output. This difference in trade policy creates a global implicit subsidy to CO2 emissions in internationally traded goods and so contributes to climate change. This global implicit subsidy to CO2 emissions totals several hundred billion dollars annually. The greater protection of downstream industries, which are relatively clean, substantially accounts for this pattern. The downstream pattern can be explained by theories where industries lobby for low tariffs on their inputs but final consumers are poorly organized. A quantitative general equilibrium model suggests that if countries applied similar trade policies to clean and dirty goods, global CO2 emissions would decrease and global real income would change little
Sampling With Probability Proportional to Size

Sampling with probability to proportionate to size (PPS), as the name implies, involves sampling elements that are typically arranged into natural groupings of different sizes. Sampling PPS is an efficient design when a list of the population is not available; the elements are in clusters or natural groups that differ greatly in size; it is important to control the sample size; costs and equalizing interviewer workloads are important; the use of natural divisions is possible and supplementary information is available by the natural divisions; and when the sample must be drawn in multiple stages. This entry presents a detailed example of a PPS design, a multistage area probability design, and discusses a random digit telephone sample design that uses PPS sampling. Keywords: multistage; area probability sampling; sampling PPS; cluster sampling; nested sample design; primary sampling units; measures of size; design effect; interviewer work load
Price Discovery in Time and Space: The Course of Condominium Prices in Singapore

A random walk in time and independence in space are maintained hypotheses in traditional empirical models of housing prices. However, there is increasing evidence in the context of hedonic models that housing prices are predictable over time and space. This paper examines the price discovery process in individual dwellings by relaxing both assumptions, using a unique body of data from the Singapore private condominium market in a repeat sales framework. We develop a formal model that tests directly the hypotheses that the prices of individual dwellings follow a random walk over time and that the price of an individual dwelling is independent of the price of a neighboring dwelling. The empirical results clearly support mean reversion in housing prices and also diffusion of innovations over space. This predictability may suggest that excess returns are possible. When aggregate returns are computed from models that assume a random walk and spatial independence, we find that they are strongly autocorrelated. However, when they are calculated from models permitting mean reversion and spatial autocorrelation, predictability in investment returns is completely absent. Despite this, an extensive simulation of investor performance, over different time horizons and with different investment rules, indicates quite clearly that recognition of the spatial and autocorrelated nature of prices substantially improves investor returns. The magnitude of deviations from standard models of price dynamics are small, but their economic implications are quite large in the housing market.
Reading Lolita at Guantanamo or, This Page Cannot Be Displayed

In January 2005, a federal judge threw out the Bush administration's most prominent prosecution against obscenity, in an opinion that cast doubt on the constitutionality of every obscenity prosecution in the country. The Court of Appeals reversed that decision in December, but it did not question the judge's conclusion that the U.S. Supreme Court had undercut the foundations of obscenity law. The Court may soon have to confront, for the first time in decades, the question of whether it makes any sense to say that obscenity is not protected by the First Amendment. The answer to that question sheds light on an apparently unrelated issue: the peculiar self-deceptions that underlie the practice of the "war on terror."
Active Language I

From the user's point of view, there are mainly three ways in which a computer can appear to react to his requests: program by program, line by line, and character by character. In the earlier computers the reaction was essentially program by program unless the user was working at the console. With the advent of time-sharing, besides the program by program reaction, both line by line and character by character reactions become possible. Many languages have already been developed which are suitable for either line by line and/or character by character requests. JOSS, developed at RAND, and its offspring (such as CAL developed in Berkeley) react essentially line by line. The Culler--Fried language on the other hand reacts character by character.
Cases Relating to Chapter 2 – Approach to the Study of Criminal Evidence

Publisher Summary This chapter presents several cases related to the study of criminal evidence. These cases studies include the case of Craft versus State, Trammel versus United States, and State versus Anthony. In the first case, Rodney Craft was tried by a jury and convicted of two counts of child molestation. He was sentenced to forty years, ten in confinement and thirty on probation. The second case discusses whether an accused may invoke the privilege against adverse spousal testimony so as to exclude the voluntary testimony of his wife. The third case presents the case of David Lamar Anthony, who was charged with murdering his three children. Anthony was convicted of three counts of first-degree murder after a jury trial in Maricopa County Superior Court. Three death sentences were imposed on him.
The Information Bottleneck Problem and its Applications in Machine Learning

Inference capabilities of machine learning (ML) systems skyrocketed in recent years, now playing a pivotal role in various aspect of society. The goal in statistical learning is to use data to obtain simple algorithms for predicting a random variable $Y$ from a correlated observation $X$ . Since the dimension of $X$ is typically huge, computationally feasible solutions should summarize it into a lower-dimensional feature vector $T$ , from which $Y$ is predicted. The algorithm will successfully make the prediction if $T$ is a good proxy of $Y$ , despite the said dimensionality-reduction. A myriad of ML algorithms (mostly employing deep learning (DL)) for finding such representations $T$ based on real-world data are now available. While these methods are effective in practice, their success is hindered by the lack of a comprehensive theory to explain it. The information bottleneck (IB) theory recently emerged as a bold information-theoretic paradigm for analyzing DL systems. Adopting mutual information as the figure of merit, it suggests that the best representation $T$ should be maximally informative about $Y$ while minimizing the mutual information with $X$ . In this tutorial we survey the information-theoretic origins of this abstract principle, and its recent impact on DL. For the latter, we cover implications of the IB problem on DL theory, as well as practical algorithms inspired by it. Our goal is to provide a unified and cohesive description. A clear view of current knowledge is important for further leveraging IB and other information-theoretic ideas to study DL models.
Frequency dependence of dielectric loss in condensed matter

The dielectric response of condensed matter below microwave frequencies has been known to depart from the Debye behavior, sometimes to the point of being unrecognizable and yet the generally accepted interpretations of the departures have seldom deviated from the Debye philosophy of simple relaxation phenomena in noninteracting systems. It was recently recognized, from a synoptic view of the experimental data involving a wide range of materials, that there exists a remarkable universality of dielectric response behavior regardless of physical structure, types of bonding, chemical type, polarizing species, and geometrical configurations. This strongly suggests that there should exist a correspondingly universal mechanism of dielectric polarization in condensed matter. The present work proposes such a universal mechanism associated with the existence of some ubiquitous very-low-energy excitations in the system. These excitations exhibit an infrared-divergent-like response to transitions of the polarizing species induced by a time-varying electric field in the dielectric and give rise to the universal dielectric response.
Longevity and Life-cycle Savings*

We add health and longevity to a standard model of life-cycle saving and show that, under plausible assumptions, increases in life expectancy lead to higher savings rates at every age, even when retirement is endogenous. In a stationary population these higher savings rates are offset by increased old age dependency, but during the disequilibrium phase, when longevity is rising, the effect on aggregate savings rates can be substantial. We find empirical support for this effect using a cross-country panel of national savings rates.
Treated HIV Infection Alters Phenotype but Not HIV-Specific Function of Peripheral Blood Natural Killer Cells.

Natural killer (NK) cells are the predominant antiviral cells of the innate immune system, and may play an important role in acquisition and disease progression of HIV. While untreated HIV infection is associated with distinct alterations in the peripheral blood NK cell repertoire, less is known about how NK phenotype is altered in the setting of long-term viral suppression with antiretroviral therapy (ART), as well as how NK memory can impact functional responses. As such, we sought to identify changes in NK cell phenotype and function using high-dimensional mass cytometry to simultaneously analyze both surface and functional marker expression of peripheral blood NK cells in a cohort of ART-suppressed, HIV+ patients and HIV- healthy controls. We found that the NK cell repertoire was altered in individuals with treated HIV infection compared to healthy controls, with increased expression of markers including NKG2C and CD2, and decreased expression of CD244 and NKp30. Using co-culture assays with autologous, in vitro HIV-infected CD4 T cells, we identified a subset of NK cells with enhanced responsiveness to HIV-1-infected cells, but no differences in the magnitude of anti-HIV NK cell responses between the HIV+ and HIV- groups. In addition, by profiling of NK cell receptors on responding cells, we found similar phenotypes of HIV-responsive NK cell subsets in both groups. Lastly, we identified clusters of NK cells that are altered in individuals with treated HIV infection compared to healthy controls, but found that these clusters are distinct from those that respond to HIV in vitro. As such, we conclude that while chronic, treated HIV infection induces a reshaping of the peripheral blood NK cell repertoire, it does so in a way that does not make the repertoire more HIV-specific.
Two-Tier Signatures, Strongly Unforgeable Signatures, and Fiat-Shamir without Random Oracles.

We show how the Fiat-Shamir transform can be used to convert three-move identication protocols into two-tier signature schemes (a primitive we dene) with a proof of security that makes a standard assumption on the hash function rather than modeling it as a random oracle. The result requires security of the starting protocol against concurrent attacks. We can show that numerous protocols have the required properties and so obtain numerous ecient two-tier schemes. Our rst application is an ecient transform of any unforgeable signature scheme into a strongly unforgeable one, which uses as a tool any two-tier scheme. (This extends work of Boneh, Shen and Waters whose transform only applies to a limited class of schemes.) The second application is new one-time signature schemes that, compared to one-way function based ones of the same computational cost, have smaller key and signature sizes.
QoE Based Revenue Maximizing Dynamic Resource Allocation and Pricing for Fog-Enabled Mission-Critical IoT Applications

Fog computing is becoming a vital component for Internet of things (IoT) applications, acting as its computational engine. Mission-critical IoT applications are highly sensitive to latency, which depends on the physical location of the cloud server. Fog nodes of varying response rates are available to the cloud service provider (CSP) and it is faced with a challenge of forwarding the sequentially received IoT data to one of the fog nodes for processing. Since the arrival times and nature of requests is random, it is important to optimally classify the requests in real-time and allocate available virtual machine instances (VMIs) at the fog nodes to provide a high QoE to the users and consequently generate higher revenues for the CSP. In this paper, we use a pricing policy based on the QoE of the applications as a result of the allocation and obtain an optimal dynamic allocation rule based on the statistical information of the computational requests. The developed solution is statistically optimal, dynamic, and implementable in real-time as opposed to other static matching schemes in the literature. The performance of the proposed framework has been evaluated using simulations and the results show significant improvement as compared with benchmark schemes.
Kinetic Monte Carlo Simulation of Oxygen Diffusion in Ytterbium Disilicate

Ytterbium disilicate is of interest as a potential environmental barrier coating for aerospace applications, notably for use in next generation jet turbine engines. In such applications, the transport of oxygen and water vapor through these coatings to the ceramic substrate is undesirable if high temperature oxidation is to be avoided. In an effort to understand the diffusion process in these materials, we have performed kinetic Monte Carlo simulations of vacancy-mediated and interstitial oxygen diffusion in Ytterbium disilicate. Oxygen vacancy and interstitial site energies, vacancy and interstitial formation energies, and migration barrier energies were computed using Density Functional Theory. We have found that, in the case of vacancy-mediated diffusion, many potential diffusion paths involve large barrier energies, but some paths have barrier energies smaller than one electron volt. However, computed vacancy formation energies suggest that the intrinsic vacancy concentration is small. In the case of interstitial diffusion, migration barrier energies are typically around one electron volt, but the interstitial defect formation energies are positive, with the result that the disilicate is unlikely to exhibit experience significant oxygen permeability except at very high temperature.
On the consistency of top-k surrogate losses

The top-$k$ error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-$k$ classification. Surprisingly, it is not rigorously understood when taking the $k$-argmax of a vector is guaranteed to return the $k$-argmax of another vector, though doing so is crucial to describe Bayes optimality; we do both tasks. Then, we define top-$k$ calibration and show it is necessary and sufficient for consistency. Based on the top-$k$ calibration analysis, we propose a class of top-$k$ calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-$k$ surrogate losses are not top-$k$ calibrated and suggests no convex hinge loss is top-$k$ calibrated. On the other hand, we propose a new hinge loss which is consistent. We explore further, showing our hinge loss remains consistent under a restriction to linear functions, while cross entropy does not. Finally, we exhibit a differentiable, convex loss function which is top-$k$ calibrated for specific $k$.
Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events

Social media datasets make it possible to rapidly quantify collective attention to emerging topics and breaking news, such as crisis events. Collective attention is typically measured by aggregate counts, such as the number of posts that mention a name or hashtag. But according to rationalist models of natural language communication, the collective salience of each entity will be expressed not only in how often it is mentioned, but in the form that those mentions take. This is because natural language communication is premised on (and customized to) the expectations that speakers and writers have about how their messages will be interpreted by the intended audience. We test this idea by conducting a large-scale analysis of public online discussions of breaking news events on Facebook and Twitter, focusing on five recent crisis events. We examine how people refer to locations, focusing specifically on contextual descriptors, such as “San Juan” versus “San Juan, Puerto Rico.” Rationalist accounts of natural language communication predict that such descriptors will be unnecessary (and therefore omitted) when the named entity is expected to have high prior salience to the reader. We find that the use of contextual descriptors is indeed associated with proxies for social and informational expectations, including macro-level factors like the location's global salience and micro-level factors like audience engagement. We also find a consistent decrease in descriptor context use over the lifespan of each crisis event. These findings provide evidence about how social media users communicate with their audiences, and point towards more fine-grained models of collective attention that may help researchers and crisis response organizations to better understand public perception of unfolding crisis events.
Infinite switch simulated tempering in force (FISST)

Many proteins in cells are capable of sensing and responding to piconewton-scale forces, a regime in which conformational changes are small but significant for biological processes. In order to efficiently and effectively sample the response of these proteins to small forces, enhanced sampling techniques will be required. In this work, we derive, implement, and evaluate an efficient method to simultaneously sample the result of applying any constant pulling force within a specified range to a molecular system of interest. We start from simulated tempering in force, whereby force is added as a linear bias on a collective variable to the system's Hamiltonian, and the coefficient is taken as a continuous auxiliary degree of freedom. We derive a formula for an average collective-variable-dependent force, which depends on a set of weights learned on-the-fly throughout a simulation, that reflect the limit where force varies infinitely quickly. Simulation data can then be used to retroactively compute averages of any observable at any force within the specified range. This technique is based on recent work deriving similar equations for infinite switch simulated tempering in temperature, which showed that the infinite switch limit is the most efficient for sampling. Here, we demonstrate that our method accurately samples molecular systems at all forces within a user defined force range simultaneously and show how it can serve as an enhanced sampling tool for cases where the pulling direction destabilizes states that have low free-energy at zero-force. This method is implemented in and freely distributed with the PLUMED open-source sampling library, and hence can be readily applied to problems using a wide range of molecular dynamics software packages.
Hierarchical propagation networks for fake news detection: Investigation and exploitation

Consuming news from social media is becoming increasingly popular. However, social media also enables the wide dissemination of fake news. Because of the detrimental effects of fake news, fake news detection has attracted increasing attention. However, the performance of detecting fake news only from news content is generally limited as fake news pieces are written to mimic true news. In the real world, news pieces spread through propagation networks on social media. The news propagation networks usually involve multi-levels. In this paper, we study the challenging problem of investigating and exploiting news hierarchical propagation network on social media for fake news detection.In an attempt to understand the correlations between news propagation networks and fake news, first, we build hierarchical propagation networks for fake news and true news pieces; second, we perform a comparative analysis of the propagation network features from structural, temporal, and linguistic perspectives between fake and real news, which demonstrates the potential of utilizing these features to detect fake news; third, we show the effectiveness of these propagation network features for fake news detection. We further validate the effectiveness of these features from feature importance analysis. We conduct extensive experiments on real-world datasets and demonstrate the proposed features can significantly outperform state-of-the-art fake news detection methods by at least 1.7% with an average F1>0.84. Altogether, this work presents a data-driven view of hierarchical propagation network and fake news and paves the way towards a healthier online news ecosystem.
Mobility restrictions for the control of epidemics: When do they work?

Background Mobility restrictions—trade and travel bans, border closures and, in extreme cases, area quarantines or cordons sanitaires—are among the most widely used measures to control infectious diseases. Restrictions of this kind were important in the response to epidemics of SARS (2003), H1N1 influenza (2009), Ebola (2014) and, currently in the containment of the ongoing COVID-19 pandemic. However, they do not always work as expected. Methods To determine when mobility restrictions reduce the size of an epidemic, we use a model of disease transmission within and between economically heterogeneous locally connected communities. One community comprises a low-risk, low-density population with access to effective medical resources. The other comprises a high-risk, high-density population without access to effective medical resources. Findings Unrestricted mobility between the two risk communities increases the number of secondary cases in the low-risk community but reduces the overall epidemic size. By contrast, the imposition of a cordon sanitaire around the high-risk community reduces the number of secondary infections in the low-risk community but increases the overall epidemic size. Interpretation Mobility restrictions may not be an effective policy for controlling the spread of an infectious disease if it is assessed by the overall final epidemic size. Patterns of mobility established through the independent mobility and trade decisions of people in both communities may be sufficient to contain epidemics.
Man is to Person as Woman is to Location: Measuring Gender Bias in Named Entity Recognition

In this paper, we study the bias in named entity recognition (NER) models---specifically, the difference in the ability to recognize male and female names as PERSON entity types. We evaluate NER models on a dataset containing 139 years of U.S. census baby names and find that relatively more female names, as opposed to male names, are not recognized as PERSON entities. The result of this analysis yields a new benchmark for gender bias evaluation in named entity recognition systems. The data and code for the application of this benchmark is publicly available for researchers to use.
A Mixed Method for Time-Transient Acoustic Wave Propagation in Metamaterials

In this paper we develop a finite element method for acoustic wave propagation in Drude-type metamaterials. The governing equation is written as a symmetrizable hyperbolic system with auxiliary variables. The standard mixed finite elements and discontinuous finite elements are used for spatial discretization, and the Crank–Nicolson scheme is used for time discretization. The a priori error analysis of fully discrete scheme is carried out in details. Numerical experiments illustrating the theoretical results and metamaterial wave propagation, are included.
Geometric invariants of representations of finite groups

Abstract For a finite group τ and a field k of characteristic p dividing the order of τ, we construct a map p τ : U τ → P Y τ of varieties over k with unions of affine spaces as fibers and with ( P Y τ ) / τ p-isogenous to Proj H • ( τ , k ) . On U τ , we construct a “universal p-nilpotent operator” P Θ τ which leads to the construction of τ-equivariant coherent sheaves associated to finite dimensional kτ-modules M. These coherent sheaves are algebraic vector bundles on U τ if M has constant Jordan type. For any finite dimensional kτ-module M, these coherent sheaves are vector bundles when restricted to the complements of the generalized support varieties of M. For a linear algebraic group G , we compare these constructions for the finite group G ( F p ) to previous constructions for the infinitesimal group scheme G ( r ) . Our comparison of bundle invariants for a rational G -module M of exponential type p r upon restriction to G ( F p ) and to G ( r ) is a strengthened form of earlier comparisons of support varieties for M. Our constructions extend to semi-direct products G ⋊ τ of an infinitesimal group scheme G and a finite group τ, thus to all finite group schemes if k is algebraically closed.
Tradespace Analysis of GNSS Space Segment Architectures

Global navigation satellite systems (GNSS) provide ubiquitous, continuous, and reliable positioning, navigation, and timing information around the world. However, GNSS space segment design decisions have been based on the precursor Global Positioning System, which was designed in the 1970s. This article revisits those early design decisions in view of major technological advancements and new GNSS environmental threats. The rich tradespace between user navigation error (UNE) performance and constellation deployment costs is explored and conclusions are complemented by sensitivity analysis and association rule mining. This article finds that constellations at an orbit altitude of ∼2 earth radii can outperform existing GNSS in terms of cost, robustness, and UNE. These insights should be taken into account when designing future generations of GNSS.
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model

It is expected that human-driven vehicles and autonomous vehicles (AVs) will coexist in a mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed traffic, it is critical that the AVs can understand how humans cope with risks and make driving-related decisions. In this work, we incorporate a human decision-making model in reinforcement learning to control AVs for safe and efficient operations. Specifically, we adapt regret theory to describe a human driver's lane-changing behavior and t the personalized models to individual drivers for predicting their lane-changing decisions. The predicted decisions are incorporated in the safety regulations for reinforcement learning in training and in implementation. We then use an extended version of double deep Q-network (DDQN) to train our AV controller within the safety set. By doing so, the number of collisions in training and testing is reduced to zero, while the training accuracy is not impinged.
Learning Beam Codebooks with Neural Networks: Towards Environment-Aware mmWave MIMO

Scaling the number of antennas up is a key characteristic of current and future wireless communication systems. The hardware cost and power consumption, however, motivate large-scale MIMO systems, especially at millimeter wave (mmWave) bands, to rely on analog-only or hybrid analog/digital transceiver architectures. With these architectures, mmWave base stations normally use pre-defined beamforming codebooks for both initial access and data transmissions. Current beam codebooks, however, generally adopt single-lobe narrow beams and scan the entire angular space. This leads to high beam training overhead and loss in the achievable beamforming gains. In this paper, we propose a new machine learning framework for learning beamforming codebooks in hardware-constrained large-scale MIMO systems. More specifically, we develop a neural network architecture that accounts for the hardware constraints and learns beam codebooks that adapt to the surrounding environment and the user locations. Simulation results highlight the capability of the proposed solution in learning multi-lobe beams and reducing the codebook size, which leads to noticeable gains compared to classical codebook design approaches.
Community Detection Clustering via Gumbel Softmax

Recently, in many systems such as speech recognition and visual processing, deep learning has been widely implemented. In this research, we are exploring the possibility of using deep learning in community detection among the graph datasets. Graphs have gained growing traction in different fields, including social networks, information graphs, the recommender system, and also life sciences. In this paper, we propose a method of community detection clustering the nodes of various graph datasets. We cluster different category datasets that belong to affiliation networks, animal networks, human contact networks, human social networks, miscellaneous networks. The deep learning role in modeling the interaction between nodes in a network allows a revolution in the field of science relevant to graph network analysis. In this paper, we extend the gumbel softmax approach to graph network clustering. The experimental findings on specific graph datasets reveal that the new approach outperforms traditional clustering significantly, which strongly shows the efficacy of deep learning in graph community detection clustering. We do a series of experiments on our graph clustering algorithm, using various graph datasets: Zachary's karate club, Highland tribes, Train bombing, American Revolution, Dolphins, Zebra, Windsurfers, Les Miserables, Political books.
Testing the reliability of interpretable neural networks in geoscience using the Madden–Julian oscillation

Abstract. We test the reliability of two neural network interpretation techniques, backward optimization and layerwise relevance propagation, within geoscientific applications by applying them to a commonly studied geophysical phenomenon, the Madden–Julian oscillation. The Madden–Julian oscillation is a multi-scale pattern within the tropical atmosphere that has been extensively studied over the past decades, which makes it an ideal test case to ensure the interpretability methods can recover the current state of knowledge regarding its spatial structure. The neural networks can, indeed, reproduce the current state of knowledge and can also provide new insights into the seasonality of the Madden–Julian oscillation and its relationships with atmospheric state variables. The neural network identifies the phase of the Madden–Julian oscillation twice as accurately as a linear regression approach, which means that nonlinearities used by the neural network are important to the structure of the Madden–Julian oscillation. Interpretations of the neural network show that it accurately captures the spatial structures of the Madden–Julian oscillation, suggest that the nonlinearities of the Madden–Julian oscillation are manifested through the uniqueness of each event, and offer physically meaningful insights into its relationship with atmospheric state variables. We also use the interpretations to identify the seasonality of the Madden–Julian oscillation and find that the conventionally defined extended seasons should be shifted later by 1 month. More generally, this study suggests that neural networks can be reliably interpreted for geoscientific applications and may thereby serve as a dependable method for testing geoscientific hypotheses.
Computational complexity characterization of protecting elections from bribery

The bribery problem in election has received considerable attention in the literature, upon which various algorithmic and complexity results have been obtained. It is thus natural to ask whether we can protect an election from potential bribery. We assume that the protector can protect a voter with some cost (e.g., by isolating the voter from potential bribers). A protected voter cannot be bribed. Under this setting, we consider the following bi-level decision problem: Is it possible for the protector to protect a proper subset of voters such that no briber with a fixed budget on bribery can alter the election result? The goal of this paper is to give a full picture on the complexity of protection problems. We give an extensive study on the protection problem and provide algorithmic and complexity results. Comparing our results with that on the bribery problems, we observe that the protection problem is in general significantly harder. Indeed, it becomes \(\varSigma _2^p\)-complete even for very restricted special cases, while most bribery problems lie in NP. However, it is not necessarily the case that the protection problem is always harder. Some of the protection problems can still be solved in polynomial time, while some of them remain as hard as the bribery problem under the same setting.
Approximate selection with guarantees using proxies

Due to the falling costs of data acquisition and storage, researchers and industry analysts often want to find all instances of rare events in large datasets. For instance, scientists can cheaply capture thousands of hours of video, but are limited by the need to manually inspect long videos to identify relevant objects and events. To reduce this cost, recent work proposes to use cheap proxy models, such as image classifiers, to identify an approximate set of data points satisfying a data selection filter. Unfortunately, this recent work does not provide the statistical accuracy guarantees necessary in scientific and production settings.In this work, we introduce novel algorithms for approximate selection queries with statistical accuracy guarantees. Namely, given a limited number of exact identifications from an oracle, often a human or an expensive machine learning model, our algorithms meet a minimum precision or recall target with high probability. In contrast, existing approaches can catastrophically fail in satisfying these recall and precision targets. We show that our algorithms can improve query result quality by up to 30 x for both the precision and recall targets in both real and synthetic datasets.
Pediatric recurrent Rosai-Dorfman disease with germline heterozygous SLC29A3 and somatic MAP2K1 mutations.

Pediatric Recurrent Rosai-Dorfman Disease with germline heterozygous SLC29A3 and somatic MAP2K1 mutationsShruthi Suryaprakash, MD1, Amy George, RN2, Scott Langenburg, MD3, and Sureyya Savasan, MD2,41Department of Pediatrics, Children’s hospital of Michigan, Detroit2Division of Hematology/Oncology, Children’s hospital of Michigan, Detroit3Division of Pediatric Surgery, Children’s hospital of Michigan, Detroit4Division of Hematology/Oncology and Blood and Marrow Transplant Program, Department of Pediatrics, Children’s Hospital of Michigan, Barbara Ann Karmanos Cancer Center, Central Michigan University College of Medicine, DetroitCorrespondence: Sureyya Savasan, MD3901 Beaubien Blvd.Division of Hematology/OncologyBlood and Marrow Transplant ProgramChildren’s Hospital of MichiganDetroit, Michigan 48201E-mail: savas1s@cmich.eduPhone: 313-745-5516 Fax: 313-745-5237Text word count: 500Reference count: 16Tables and figures: 1Page number: 7Short running title: Germline and somatic mutations in recurrent Rosai-Dorfman diseaseKey words: recurrent RDD, germline SLC29A3 mutation, somatic MAP2K1 mutation, dysgammaglobulinemia, clonal T-LGL proliferation
Markov chain models of refugee migration data

The application of Markov chains to modelling refugee crises is explored, focusing on local migration of individuals at the level of cities and days. As an explicit example we apply the Markov chains migration model developed here to UNHCR data on the Burundi refugee crisis. We compare our method to a state-of-the-art `agent-based' model of Burundi refugee movements, and highlight that Markov chain approaches presented here can improve the match to data while simultaneously being more algorithmically efficient.
Map-Predictive Motion Planning in Unknown Environments

Algorithms for motion planning in unknown environments are generally limited in their ability to reason about the structure of the unobserved environment. As such, current methods generally navigate unknown environments by relying on heuristic methods to choose intermediate objectives along frontiers. We present a unified method that combines map prediction and motion planning for safe, time-efficient au-tonomous navigation of unknown environments by dynamically-constrained robots. We propose a data-driven method for predicting the map of the unobserved environment, using the robot’s observations of its surroundings as context. These map predictions are then used to plan trajectories from the robot’s position to the goal without requiring frontier selection. We applied this map-predictive motion planning strategy to randomly generated winding hallway environments, yielding substantial improvement in trajectory duration over a naive frontier pursuit method. We also experimentally demonstrate similar performance to methods using more sophisticated fron-tier selection heuristics while significantly reducing computation time.
The impact of environmental risk factors on delirium and benefits of noise and light modifications: a scoping review

Background: To explore existing literature on the association between environmental risk factors and delirium, and to investigate the effectiveness of environmental modifications on prevention or management of delirium. Methods: This is a scoping review of peer-reviewed studies in PubMed and the reference lists of reviewed articles. Observational studies reporting the effect of noise, light, and circadian rhythm on delirium and interventional studies assessing delirium in modified environments were reviewed. Results: 37 studies were included, 21 of which evaluated the impact of environment on delirium and 16 studied possible solutions to mitigate those impacts. Mixed findings of the reviewed studies yielded inconclusive results; a clearly delineated association between high noise levels, abnormal amounts of light exposure, and sleep disruption with delirium could not be established. The environmental interventions targeted reducing noise exposure, improving daytime and mitigating night-time light exposure to follow circadian rhythm, and promoting sleep. The overall evidence supporting effectiveness of environmental interventions was also of a low confidence; however, quiet-time protocols, earplugs, and bright light therapy showed a benefit for prevention or management of delirium. Conclusions: Environmental modifications are non-invasive, risk-free, and low-cost strategies that may be beneficial in preventing and managing delirium, especially when used as part of a multi-component plan. However, given the limited evidence-based conclusions, further high-quality and larger studies focusing on environmental modifications and delirium outcomes are strongly recommended.
Effects of spatially-varying substrate anchoring on instabilities and dewetting of thin nematic liquid crystal films

Partially wetting nematic liquid crystal (NLC) films on substrates are unstable to dewetting-type instabilities due to destabilizing solid/NLC interaction forces. These instabilities are modified by the nematic nature of the films, which influences the effective solid/NLC interaction. In this work, we focus on the influence of imposed substrate anchoring on the instability development. The analysis is carried out within a long-wave formulation based on the Leslie-Ericksen description of NLC films. Linear stability analysis of the resulting equations shows that some features of the instability, such as emerging wavelengths, may not be influenced by the imposed substrate anchoring. Going further into the nonlinear regime, considered via large-scale GPU-based simulations, shows however that nonlinear effects may play an important role, in particular in the case of strong substrate anchoring anisotropy. Our simulations show that instability of the film develops in two stages: the first stage involves formation of ridges that are perpendicular to the local anchoring direction; and the second involves breakup of these ridges and formation of drops, whose final distribution is influenced by the anisotropy imposed by the substrate. Finally, we show that imposing more complex substrate anisotropy patterns allows us to reach basic understanding of the influence of substrate-imposed defects in director orientation on the instability evolution.
Weighted additive spanners

A spanner of a graph G is a subgraph H that approximately preserves shortest path distances in G. Spanners are commonly applied to compress computation on metric spaces corresponding to weighted input graphs. Classic spanner constructions can seamlessly handle edge weights, so long as error is measured multiplicatively. In this work, we investigate whether one can similarly extend constructions of spanners with purely additive error to weighted graphs. These extensions are not immediate, due to a key lemma about the size of shortest path neighborhoods that fails for weighted graphs. Despite this, we recover a suitable amortized version, which lets us prove direct extensions of classic \(+2\) and \(+4\) unweighted spanners (both all-pairs and pairwise) to \(+2W\) and \(+4W\) weighted spanners, where W is the maximum edge weight. Specifically, we show that a weighted graph G contains all-pairs (pairwise) \(+2W\) and \(+4W\) weighted spanners of size \(O(n^{3/2})\) and \(O(n^{7/5})\) (\(O(np^{1/3})\) and \(O(np^{2/7})\)) respectively. For a technical reason, the \(+6\) unweighted spanner becomes a \(+8W\) weighted spanner; closing this error gap is an interesting remaining open problem. That is, we show that G contains all-pairs (pairwise) \(+8W\) weighted spanners of size \(O(n^{4/3})\) (\(O(np^{1/4})\)).
Exogenous attention generalizes perceptual learning in adults with amblyopia

Visual perceptual learning (VPL), or improved performance after practicing the same visual task, is a behavioral manifestation of the impressive neuroplasticity in the adult brain. However, its practical effectiveness is limited because improvements are often specific to the trained conditions and require significant time and effort. Thus, it is critical to understand the conditions that promote learning and its transfer. Covert spatial attention helps overcome VPL location and feature specificity in neurotypical adults, but whether it can for people with atypical visual development is unknown. Here we show that involuntary attention helps generalize learning beyond trained spatial locations in adults with amblyopia, an ideal population for investigation given their asymmetrically developed, but highly plastic, visual cortex. Our findings provide insight into the mechanisms underlying changes in neuro(a)typical brain plasticity after practice. Further, they reveal that attention can enhance the effectiveness of perceptual learning during rehabilitation of visual disorders.