cssi/SciSciGPT-SciSciNet huggingface dataset:
Pendant side-chain sterics against electrostatic forces: Influencing short-range ordering in random polyelectrolytes

The length of pendant side chains in charged, random, comb‐shaped polymers dictates the nature of their short‐range ordering. Random copolymers, and terpolymer, of 4‐vinylpyridine (4VP), styrene, and isoprene were synthesized and subsequently fully quaternized with 1‐alkylbromides having varying number of carbons on the alkyl group ranging from 2 to 8. Evaluation by wide angle X‐ray scattering revealed that dipole–dipole attraction facilitates the formation of ionomer cluster morphology in samples with two carbons on the pendant side chain, whereas for samples with four or more carbons on the pendant side chains, side‐chain sterics was dominant resulting in periodic backbone spacing. Copolymers with isoprene, having flexible backbones, favor the formation of ionomer cluster morphology while styrene copolymers having rigid backbones disfavor the formation of ionomer clusters. An “in‐line” dipole model was developed to predict the separation distance at which both ionomer cluster and backbone–backbone morphologies could coexist. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 1325–1336
Self-Teaching Networks.

We propose self-teaching networks to improve the generalization capacity of deep neural networks. The idea is to generate soft supervision labels using the output layer for training the lower layers of the network. During the network training, we seek an auxiliary loss that drives the lower layer to mimic the behavior of the output layer. The connection between the two network layers through the auxiliary loss can help the gradient flow, which works similar to the residual networks. Furthermore, the auxiliary loss also works as a regularizer, which improves the generalization capacity of the network. We evaluated the self-teaching network with deep recurrent neural networks on speech recognition tasks, where we trained the acoustic model using 30 thousand hours of data. We tested the acoustic model using data collected from 4 scenarios. We show that the self-teaching network can achieve consistent improvements and outperform existing methods such as label smoothing and confidence penalization.
Kasteleyn operators from mirror symmetry

Given a consistent bipartite graph $$\Gamma $$ in $$T^2$$ with a complex-valued edge weighting $$\mathcal {E}$$ we show the following two constructions are the same. The first is to form the Kasteleyn operator of $$(\Gamma , \mathcal {E})$$ and pass to its spectral transform, a coherent sheaf supported on a spectral curve in $$(\mathbb {C}^\times )^2$$ . The second is to form the conjugate Lagrangian $$L \subset T^* T^2$$ of $$\Gamma $$ , equip it with a brane structure prescribed by $$\mathcal {E}$$ , and pass to its mirror coherent sheaf. This lives on a stacky toric compactification of $$(\mathbb {C}^\times )^2$$ determined by the Legendrian link which lifts the zig-zag paths of $$\Gamma $$ (and to which the noncompact Lagrangian L is asymptotic). We work in the setting of the coherent–constructible correspondence, a sheaf-theoretic model of toric mirror symmetry. We also show that tensoring with line bundles on the compactification is mirror to certain Legendrian autoisotopies of the asymptotic boundary of L.
Stringent Response Governs the Oxidative Stress Resistance and Virulence of Francisella tularensis

Francisella tularensis is a Gram-negative bacterium responsible for causing tularemia in the northern hemisphere. F. tularensis has long been developed as a biological weapon due to its ability to cause severe illness upon inhalation of as few as ten organisms and, based on its potential to be used as a bioterror agent is now classified as a Tier 1 Category A select agent by the CDC. The stringent response facilitates bacterial survival under nutritionally challenging starvation conditions. The hallmark of stringent response is the accumulation of the effector molecules ppGpp and (p)ppGpp known as stress alarmones. The relA and spoT gene products generate alarmones in several Gram-negative bacterial pathogens. RelA is a ribosome-associated ppGpp synthetase that gets activated under amino acid starvation conditions whereas, SpoT is a bifunctional enzyme with both ppGpp synthetase and ppGpp hydrolase activities. Francisella encodes a monofunctional RelA and a bifunctional SpoT enzyme. Previous studies have demonstrated that stringent response under nutritional stresses increases expression of virulence-associated genes encoded on Francisella Pathogenicity Island. This study investigated how stringent response governs the oxidative stress response of F. tularensis. We demonstrate that RelA/SpoT-mediated ppGpp production alters global gene transcriptional profile of F. tularensis in the presence of oxidative stress. The lack of stringent response in relA/spoT gene deletion mutants of F. tularensis makes bacteria more susceptible to oxidants, attenuates survival in macrophages, and virulence in mice. This work is an important step forward towards understanding the complex regulatory network underlying the oxidative stress response of F. tularensis.
The continuum limit of the Kuramoto model on sparse random graphs

In this paper, we study convergence of coupled dynamical systems on convergent sequences of graphs to a continuum limit. We show that the solutions of the initial value problem for the dynamical system on a convergent graph sequence tend to that for the nonlocal diffusion equation on a unit interval, as the graph size tends to infinity. We improve our earlier results in [Arch. Ration. Mech. Anal., 21 (2014), pp. 781--803] and extend them to a larger class of graphs, which includes directed and undirected, sparse and dense, random and deterministic graphs. There are three main ingredients of our approach. First, we employ a flexible framework for incorporating random graphs into the models of interacting dynamical systems, which fits seamlessly with the derivation of the continuum limit. Next, we prove the averaging principle for approximating a dynamical system on a random graph by its deterministic (averaged) counterpart. The proof covers systems on sparse graphs and yields almost sure convergence on time intervals of order $\log n,$ where $n$ is the number of vertices. Finally, a Galerkin scheme is developed to show convergence of the averaged model to the continuum limit. The analysis of this paper covers the Kuramoto model of coupled phase oscillators on a variety of graphs including sparse Erdős-R{\' e}nyi, small-world, and power law graphs.
Oracle separations between quantum and non-interactive zero-knowledge classes

Abstract We study the relationship between problems solvable by quantum algorithms in polynomial time and those for which zero-knowledge proofs exist. In prior work, Aaronson [1] showed an oracle separation between BQP and SZK, i.e. an oracle A such that SZK A ⊈ BQP A . In this paper we give a simple extension of Aaronson's result to non-interactive zero-knowledge proofs with perfect security. This class, NIPZK, is the most restrictive zero-knowledge class. We show that even for this class we can construct an A with NIPZK A ⊈ BQP A . In building up this result, we first review Aaronson's proof for SZK and show a very simple proof for a new intermediate result, a separation between BQP and NISZK.
Gauss-Radau formulae for Jacobi and Laguerre weight functions

Explicit expressions are obtained for the weights of the Gauss-Radau quadrature formula for integration over the interval [-1, 1] relative to the Jacobi weight function (1-t)α(1+t)β, α > -1, β > -1. The nodes are known to be the eigenvalues of a symmetric tridiagonal matrix, which is also obtained explicitly. Similar results hold for Gauss-Radau quadrature over the interval [0, ∞) relative to the Laguerre weight tαe-t, α > -1.
Reversed Photoeffect in Transparent Graphene Nanocapacitors

Electronic properties of ultrathin dielectric films consistently attract much attention since they play important roles in various electronic devices, such as field effect transistors and memory el...
Regional Data in Macroeconomics: Some Advice for Practitioners

Abstract Cross-sectional or panel studies have joined time series techniques as an important element in empirical macroeconomists’ toolkit. The econometric best practices for these studies and their aggregate implications remain active topics of research. In this paper, I offer several pieces of advice for practitioners in this literature. I begin by casting regional analysis in a Rubin (1978) potential outcomes framework. This formalism clarifies three reasons why the estimated impact of a shock on a single region can differ from the aggregate effect of the shock: (i) contamination of the untreated areas through “micro” spillovers, (ii) these spillovers sum to an economically relevant magnitude, and (iii) national variables endogenously respond to national shocks but not to local shocks. I provide several examples to illustrate and discuss how economic theory can sometimes sign the spillovers and bound the difference between the regional and aggregate effects of the shock. I then turn to econometric issues including the choice of endogenous variable in a regional regression and whether or not to weight by population.
An Acute Immune Response Underlies the Benefit of Cardiac Stem-Cell Therapy

Clinical trials using adult stem cells to regenerate damaged heart tissue continue to this day1,2, despite ongoing questions of efficacy and a lack of mechanistic understanding of the underlying biological effect3. The rationale for these cell therapy trials is derived from animal studies that show a modest but reproducible improvement in cardiac function in models of cardiac ischaemic injury4,5. Here we examine the mechanistic basis for cell therapy in mice after ischaemia-reperfusion injury, and find that-although heart function is enhanced-it is not associated with the production of new cardiomyocytes. Cell therapy improved heart function through an acute sterile immune response characterized by the temporal and regional induction of CCR2+ and CX3CR1+ macrophages. Intracardiac injection of two distinct types of adult stem cells, cells killed by freezing and thawing or a chemical inducer of the innate immune response all induced a similar regional accumulation of CCR2+ and CX3CR1+ macrophages, and provided functional rejuvenation to the heart after ischaemia-reperfusion injury. This selective macrophage response altered the activity of cardiac fibroblasts, reduced the extracellular matrix content in the border zone and enhanced the mechanical properties of the injured area. The functional benefit of cardiac cell therapy is thus due to an acute inflammatory-based wound-healing response that rejuvenates the infarcted area of the heart.
Concordance between clinician, supervisor and observer ratings of therapeutic competence in CBT and treatment as usual: does clinician competence or supervisor session observation improve agreement?

Background Lowering the cost of assessing clinicians' competence could promote the scalability of evidence-based treatments such as cognitive behavioral therapy (CBT). Aims This study examined the concordance between clinicians', supervisors' and independent observers' session-specific ratings of clinician competence in school-based CBT and treatment as usual (TAU). It also investigated the association between clinician competence and supervisory session observation and rater agreement. Method Fifty-nine school-based clinicians (90% female, 73% Caucasian) were randomly assigned to implement TAU or modular CBT for youth anxiety. Clinicians rated their confidence after each therapy session (n = 1898), and supervisors rated clinicians' competence after each supervision session (n = 613). Independent observers rated clinicians' competence from audio recordings (n = 395). Results Patterns of rater discrepancies differed between the TAU and CBT groups. Correlations with independent raters were low across groups. Clinician competence and session observation were associated with higher agreement among TAU, but not CBT, supervisors and clinicians. Conclusions These results support the gold standard practice of obtaining independent ratings of adherence and competence in implementation contexts. Further development of measures and/or rater training methods for clinicians and supervisors is needed.
Sensitive Detection of Protein Binding to the Plasma Membrane with Dual-Color Z-Scan Fluorescence

Abstract Delicate and transitory protein engagement at the plasma membrane (PM) is crucial to a broad range of cellular functions, including cell motility, signal transduction, and virus replication. Here, we describe a dual-color (DC) extension of the fluorescence z-scan technique, which has proven successful for quantification of peripheral membrane protein binding to the PM in living cells. We demonstrate that the coexpression of a second, distinctly colored fluorescent protein provides a soluble reference species that delineates the extent of the cell cytoplasm and lowers the detection threshold of z-scan PM-binding measurements by an order of magnitude. DC z-scan generates an intensity profile for each detection channel that contains information on the axial distribution of the peripheral membrane and reference protein. Fit models for DC z-scan are developed and verified using simple model systems. Next, we apply the quantitative DC z-scan technique to investigate the binding of two peripheral membrane protein systems for which previous z-scan studies failed to detect binding: human immunodeficiency virus type 1 (HIV-1) matrix (MA) protein and lipidation-deficient mutants of the fibroblast growth factor receptor substrate 2α. Our findings show that these mutations severely disrupt PM association of fibroblast growth factor receptor substrate 2α but do not eliminate it. We further detected binding of HIV-1 MA to the PM using DC z-scan. Interestingly, our data indicate that HIV-1 MA binds cooperatively to the PM with a dissociation coefficient of Kd ∼16 μM and Hill coefficient of n ∼2.
Sexual antagonism leads to a mosaic of X-autosome conflict.

Males and females have different optimal values for some traits, such as body size. When the same genes control these traits in both sexes, selection pushes in opposite directions in males and females. Alleles at autosomal loci spend equal amounts of time in males and females, suggesting that the sexually antagonistic selective forces may approximately balance between the opposing optima. Frank and Crespi noted that alleles on the X chromosome spend twice as much time in diploid females as in haploid males. That distinction between the sexes may tend to favor X-linked genes that push more strongly toward the female optimum than the male optimum. The female bias of X-linked genes opposes the intermediate optimum of autosomal genes, potentially creating a difference between the direction of selection on traits favored by X chromosomes and autosomes. Patten has recently argued that explicit genetic assumptions about dominance and the relative magnitude of allelic effects may lead X-linked genes to favor the male rather than the female optimum, contradicting Frank and Crespi. This article combines the insights of those prior analyses into a new, more general theory. We find some parameter combinations for X-linked loci that favor a female bias and other parameter combinations that favor a male bias. We conclude that the X likely contains a mosaic pattern of loci that differ with autosomes over sexually antagonistic traits. The overall tendency for a female or male bias on the X depends on prior assumptions about the distribution of key parameters across X-linked loci. Those parameters include the dominance coefficient and the way in which ploidy influences the magnitude of allelic effects.
SqORAM: Read-Optimized Sequential Write-Only Oblivious RAM

Oblivious RAM protocols (ORAMs) allow a client to access data from an untrusted storage device without revealing the access patterns. Typically, the ORAM adversary can observe both read and write accesses. Write-only ORAMs target a more practical, {\em multi-snapshot adversary} only monitoring client writes -- typical for plausible deniability and censorship-resilient systems. This allows write-only ORAMs to achieve significantly-better asymptotic performance. However, these apparent gains do not materialize in real deployments primarily due to the random data placement strategies used to break correlations between logical and physical namespaces, a required property for write access privacy. Random access performs poorly on both rotational disks and SSDs (often increasing wear significantly, and interfering with wear-leveling mechanisms). In this work, we introduce SqORAM, a new locality-preserving write-only ORAM that preserves write access privacy without requiring random data access. Data blocks close to each other in the logical domain land in close proximity on the physical media. Importantly, SqORAM maintains this data locality property over time, significantly increasing read throughput. A full Linux kernel-level implementation of SqORAM is 100x faster than non locality-preserving solutions for standard workloads and is 60-100% faster than the state-of-the-art for typical file system workloads.
Is attention the missing link? Coviewing and preschoolers’ comprehension of educational media

Abstract Coviewing is a commonly recommended practice, but little is known about how coviewing impacts children's educational media viewing experience. We investigated how coviewing impacts attention and comprehension of educational media, as well as the role of baseline vocabulary in understanding these associations. Eighty-three preschoolers viewed two videos on an eye-tracker – one with an adult coviewer and the other without. Children's baseline vocabulary, attention, and comprehension were assessed. Results indicated that coviewing benefited visual attention. Neither coviewing condition nor attention, however, predicted children's comprehension. Instead, comprehension was predicted by age, vocabulary, and an interaction between coviewing condition, vocabulary, and attention. The interaction revealed that comprehension was stronger in the coviewing condition than the noninteractive condition only when children also had stronger visual attention to the program and larger vocabularies. Results suggest that coviewing benefits attention, but that both attention and child language are integrally tied to whether coviewing predicts comprehension.
Optimal Voltage Regulation of Unbalanced Distribution Networks with Coordination of OLTC and PV Generation

Photovoltaic (PV) smart inverters can regulate voltage in distribution systems by modulating reactive power of PV systems. In this paper, an optimization framework for optimal coordination of reactive power injection of smart inverters and tap operations of voltage regulators for multi-phase unbalanced distribution systems is proposed. Optimization objectives are minimization of voltage deviations and tap operations. A novel linearization method convexifies the problem and speeds up the solution. The proposed method is validated against conventional rule-based autonomous voltage regulation (AVR) on the highly-unbalanced IEEE 37 bus test system. Simulation results show that the proposed method estimates feeder voltage accurately, voltage deviation reductions are significant, over-voltage problems are mitigated, and voltage imbalance is reduced.
A step forward from high-entropy ceramics to compositionally complex ceramics: a new perspective

High-entropy ceramics (HECs) have quickly gained attention since 2015. To date, nearly all work has focused on five-component, equimolar compositions. This perspective article briefly reviews different families of HECs and selected properties. Following a couple of our most recent studies, we propose a step forward to expand HECs to compositionally complex ceramics (CCCs) to include medium-entropy and non-equimolar compositions. Using defective fluorite and ordered pyrochlore oxides as two primary examples, we further consider the complexities of aliovalent cations and anion vacancies as well as ordered structures with two cation sublattices. Better thermally insulating yet stiff CCCs have been found in non-equimolar compositions with optimal amounts of oxygen vacancies and in ordered pyrochlores with substantial size disorder. It is demonstrated that medium-entropy ceramics can prevail over their high-entropy counterparts. The diversifying classes of CCCs provide even more possibilities than HECs to tailor the composition, defects, disorder/order, and, consequently, various properties.
Model for disordered proteins with strongly sequence-dependent liquid phase behavior

Phase separation of intrinsically disordered proteins is important for the formation of membraneless organelles or biomolecular condensates, which play key roles in the regulation of biochemical processes within cells. In this work, we investigated the phase separation of different sequences of a coarse-grained model for intrinsically disordered proteins and discovered a surprisingly rich phase behavior. We studied both the fraction of total hydrophobic parts and the distribution of hydrophobic parts. Not surprisingly, sequences with larger hydrophobic fractions showed conventional liquid-liquid phase separation. The location of the critical point was systematically influenced by the terminal beads of the sequence due to changes in interfacial composition and tension. For sequences with lower hydrophobicity, we observed not only conventional liquid-liquid phase separation but also re-entrant phase behavior in which the liquid phase density decreases at lower temperatures. For some sequences, we observed the formation of open phases consisting of aggregates, rather than a normal liquid. These aggregates had overall lower densities than the conventional liquid phases and exhibited complex geometries with large interconnected string-like or membrane-like clusters. Our findings suggest that minor alterations in the ordering of residues may lead to large changes in the phase behavior of the protein, a fact of significant potential relevance for biology.
High-Dimensional Interactions Detection with Sparse Principal Hessian Matrix

In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional problems, detecting interactions is challenging due to combinatorial complexity and limited data information. We consider detecting interactions by exploring their connections with the principal Hessian matrix. Specifically, we propose a one-step synthetic approach for estimating the principal Hessian matrix by a penalized M-estimator. An alternating direction method of multipliers (ADMM) is proposed to efficiently solve the encountered regularized optimization problem. Based on the sparse estimator, we detect the interactions by identifying its nonzero components. Our method directly targets at the interactions, and it requires no structural assumption on the hierarchy of the interaction effects. We show that our estimator is theoretically valid, computationally efficient, and practically useful for detecting the interactions in a broad spectrum of scenarios.
Blood and tissue biomarker analysis in dogs with osteosarcoma treated with palliative radiation and intra-tumoral autologous natural killer cell transfer

We have previously reported radiation-induced sensitization of canine osteosarcoma (OSA) to natural killer (NK) therapy, including results from a first-in-dog clinical trial. Here, we report correlative analyses of blood and tissue specimens for signals of immune activation in trial subjects. Among 10 dogs treated with palliative radiotherapy (RT) and intra-tumoral adoptive NK transfer, we performed ELISA on serum cytokines, flow cytometry for immune phenotype of PBMCs, and PCR on tumor tissue for immune-related gene expression. We then queried The Cancer Genome Atlas (TCGA) to evaluate the association of cytotoxic/immune-related gene expression with human sarcoma survival. Updated survival analysis revealed five 6-month survivors, including one dog who lived 17.9 months. Using feeder line co-culture for NK expansion, we observed maximal activation of dog NK cells on day 17-19 post isolation with near 100% expression of granzyme B and NKp46 and high cytotoxic function in the injected NK product. Among dogs on trial, we observed a trend for higher baseline serum IL-6 to predict worse lung metastasis-free and overall survival (P = 0.08). PCR analysis revealed low absolute gene expression of CD3, CD8, and NKG2D in untreated OSA. Among treated dogs, there was marked heterogeneity in the expression of immune-related genes pre- and post-treatment, but increases in CD3 and CD8 gene expression were higher among dogs that lived > 6 months compared to those who did not. Analysis of the TCGA confirmed significant differences in survival among human sarcoma patients with high and low expression of genes associated with greater immune activation and cytotoxicity (CD3e, CD8a, IFN-γ, perforin, and CD122/IL-2 receptor beta). Updated results from a first-in-dog clinical trial of palliative RT and autologous NK cell immunotherapy for OSA illustrate the translational relevance of companion dogs for novel cancer therapies. Similar to human studies, analyses of immune markers from canine serum, PBMCs, and tumor tissue are feasible and provide insight into potential biomarkers of response and resistance.
Electron Transport Through 2D Waveguide Using QTBM

Simulation of Electron Transport through two dimensional(2D) waveguide using Quantum Transport Boundary Method (QTBM) is done. Specifically, as an example the results of modeling L-shaped contact for a rectangular waveguide are presented. 2D-QTBM approach can be used in any scenario where the vertical axis of the device structure is significantly large and therefore the 2D approximation would be considered valid. This method elegantly generalizes to arbitrary shaped waveguides.
How developmental neuroscience can help address the problem of child poverty

Nearly 1 in 5 children in the United States lives in a household whose income is below the official federal poverty line, and more than 40% of children live in poor or near-poor households. Research on the effects of poverty on children's development has been a focus of study for many decades and is now increasing as we accumulate more evidence about the implications of poverty. The American Academy of Pediatrics recently added "Poverty and Child Health" to its Agenda for Children to recognize what has now been established as broad and enduring effects of poverty on child development. A recent addition to the field has been the application of neuroscience-based methods. Various techniques including neuroimaging, neuroendocrinology, cognitive psychophysiology, and epigenetics are beginning to document ways in which early experiences of living in poverty affect infant brain development. We discuss whether there are truly worthwhile reasons for adding neuroscience and related biological methods to study child poverty, and how might these perspectives help guide developmentally based and targeted interventions and policies for these children and their families.
Learning from Power Sector Reform Experiences: The Case of Vietnam

Vietnam's power sector has developed rapidly since the 1990s to become a top performer among developing countries. This success has occurred mostly under a state-owned utility, Electricity Vietnam. Select market-oriented reforms to date have also had some positive impact. By the late 1990s, the Government realized the need to gradually introduce competition to ensure long-term sustainability without jeopardizing security of supply for the fast-growing economy. Vietnam's 2004 Electricity Law has provided the framework to develop a competitive power market, unbundle Electricity Vietnam, set prices that better reflect costs, promote private investment, and establish a regulatory authority. Today, state-owned entities continue to dominate the sector. Whereas the power market is partially competitive, improved operational efficiency and financial performance of generators in this market has contributed to keeping generation costs relatively low. Plans are broadly on track for further extensive reforms, including a clean energy transition. Lessons include that state-centric institutions can develop the power sector with top-level government commitment, highly-qualified staff, and consensus among sector institutions. Gradual reforms offer an opportunity to learn by doing; yet, the sequence of reforms matters. Introducing market mechanisms ahead of other elements may limit the market effectiveness and even make subsequent reform steps more difficult.
Integral Quadratic Constraints: Exact Convergence Rates and Worst-Case Trajectories

We consider a linear time-invariant system in discrete time where the state and input signals satisfy a set of integral quadratic constraints (IQCs). Analogous to the autonomous linear systems case, we define a new notion of spectral radius that exactly characterizes stability of this system. In particular, (i) when the spectral radius is less than one, we show that the system is asymptotically stable for all trajectories that satisfy the IQCs, and (ii) when the spectral radius is equal to one, we construct an unstable trajectory that satisfies the IQCs. Furthermore, we connect our new definition of the spectral radius to the existing literature on IQCs.
The dark matter interpretation of the 3.5-keV line is inconsistent with blank-sky observations.

Observations of nearby galaxies and galaxy clusters have reported an unexpected x-ray emission line around 3.5 kilo–electron volts (keV). Proposals to explain this line include decaying dark matter—in particular, that the decay of sterile neutrinos with a mass around 7 keV could match the available data. If this interpretation is correct, the 3.5-keV line should also be emitted by dark matter in the halo of the Milky Way. We used more than 30 megaseconds of XMM-Newton (X-ray Multi-Mirror Mission) blank-sky observations to test this hypothesis, finding no evidence of the 3.5-keV line emission from the Milky Way halo. We set an upper limit on the decay rate of dark matter in this mass range, which is inconsistent with the possibility that the 3.5-keV line originates from dark matter decay.
Reliable THz Communications for Outdoor based Applications- Use Cases and Methods

Future (beyond 5G) wireless networks will demand high throughput and low latency and would benefit from greenfield, contiguous, and wider bandwidth, all of which THz spectrum can provide. Although THz has been envisioned to be deployed in an indoor setting, with proper enforcement and planning, we can draw a limited number of use cases for outdoor THz communication. THz can provide high capacity and ultra-high throughput but at the cost of high path loss and sensitivity to device orientation/mobility. We identify scenarios where the use of the THz spectrum for an outdoor setting is justified and their critical operating parameters. We further categorize the applications based on the relative mobility between the access point (AP) and user equipment (UE). We present an approach for deploying THz on an outdoor framework by presenting preliminary technical parameter analysis for scenarios, like wireless backhaul, high-speed kiosks, and the aerial base station (ABS). Our preliminary analysis shows that the application for each of these scenarios is limited based on multiple parameters, such as distance, device mobility, device orientation, user geometry, antenna gain, and environment settings' which requires separate consideration and optimization.
The Power of Self-Reducibility: Selectivity, Information, and Approximation

This chapter provides a hands-on tutorial on the important technique known as self-reducibility. Through a series of “Challenge Problems” that are theorems that the reader will—after being given definitions and tools—try to prove, the tutorial will ask the reader not to read proofs that use self-reducibility, but rather to discover proofs that use self-reducibility. In particular, the chapter will seek to guide the reader to the discovery of proofs of four interesting theorems—whose focus areas range from selectivity to information to approximation—from the literature, whose proofs draw on self-reducibility.
Environmental policies and political feasibility: Eco-labels versus emission taxes

Abstract This paper examines the economic and political implications of two market-based policies, eco-certifications and emission taxes. We evaluate each policy’s effects on the environment, investment in clean technology, and social welfare under imperfect competition. We find that eco-certification reduces total damage to the environment, increases consumer benefits, and is socially desirable. However, polluting firms will never voluntarily accept the socially optimal eco-standard, leading to suboptimal certification programs. Unless the marginal damage to the environment from emissions is sufficiently low and demand is sufficiently large, environmental damage occurring under voluntary eco-certification is higher in comparison to alternative policies. We examine the welfare impacts of each policy to identify social preferences. Using realized market benefits to construct policy preferences, we show conditions under which the socially optimal environmental policy is unlikely to be politically feasible. Our results explain the popularity and suboptimal qualities of eco-certification programs.
Artifact-free whole-slide imaging with structured illumination microscopy and Bayesian image reconstruction

Background Structured illumination microscopy (SIM) is a method that can be used to image biological samples and can achieve both optical sectioning and super-resolution effects. Optimization of the imaging set-up and data-processing methods results in high-quality images without artifacts due to mosaicking or due to the use of SIM methods. Reconstruction methods based on Bayesian estimation can be used to produce images with a resolution beyond that dictated by the optical system. Findings Five complete datasets are presented including large panoramic SIM images of human tissues in pathophysiological conditions. Cancers of the prostate, skin, ovary, and breast, as well as tuberculosis of the lung, were imaged using SIM. The samples are available commercially and are standard histological preparations stained with hematoxylin-eosin. Conclusion The use of fluorescence microscopy is increasing in histopathology. There is a need for methods that reduce artifacts caused by the use of image-stitching methods or optical sectioning methods such as SIM. Stitched SIM images produce results that may be useful for intraoperative histology. Releasing high-quality, full-slide images and related data will aid researchers in furthering the field of fluorescent histopathology.
Unit-Root Tests Are Useful for Selecting Forecasting Models

We study the usefulness of unit-root tests as diagnostic tools for selecting forecasting models. Difference-stationary and trend-stationary models of economic and financial time series often imply very different predictions, so deciding which model to use is tremendously important for applied forecasters. We consider three strategies: Always difference the data, never difference, or use a unit-root pretest. We characterize the predictive loss of these strategies for the canonical AR(1) process with trend, focusing on the effects of sample size, forecast horizon, and degree of persistence. We show that pretesting routinely improves forecast accuracy relative to forecasts from models in differences, and we give conditions under which pretesting is likely to improve forecast accuracy relative to forecasts from models in levels.
9 – MONEY AND BANKING

Publisher Summary Money is anything generally accepted in exchange for goods and services, and in payment of debt. Many items have served as money. Money's value is derived from the faith people have in its purchasing power. The money supply measures the buying power of the economy. Several money supply definitions are used to determine spending capacity. Interest rates are based on opportunity cost. People with a positive time preference must be compensated for delayed spending. Modern banks operate on the fractional reserve system. Required reserves are kept on hand, with remaining funds being used to make loans. Banks create money when they make loans. A new deposit in the banking system expands into many loans through the money multiplier process. This chapter explores the federal system. The Federal Reserve System was invented to stabilize the banking industry. It allows for a flexible money supply. The Fed uses open market operations, reserve requirement policy, and the discount to change money and credit supplies.
Who counts in evaluating the effects of air pollution policies on households? Non-market valuation in the presence of dependencies

Abstract Individuals who are likely to realize the largest benefits from improvements in air quality often depend on other members of their households to make time or monetary contributions to their care. The presence of these dependency relationships among household members poses challenges for benefit estimation since it is unlikely that the conditions necessary for recovering the underlying individual preferences from household choices are satisfied in this setting. We propose a conceptual framework that highlights the role of these dependencies in the choice models used to estimate the willingness to pay for environmental quality improvements. We design a complementary stated preference survey that describes hypothetical dependency relationships for household members of different ages to test the implications of our conceptual model. Respondents' stated choices take into account the proposed care-giving responsibilities for young children and teenagers but not for older adults.
Foreign Bank Penetration, Resource Allocation and Economic Growth: Evidence from Emerging Economies

This paper examines the implications of foreign bank penetration on economic growth from the perspective of resource allocation in host countries. We use aggregate banking data, constructed from bank-level balance sheets and income statement information covering more than 1200 banks in the 35 emerging economies of Asia, Latin America and Eastern and Central Europe for the period from 1996 to 2003. By applying the pooled OLS and fixed-effects models, we present consistent evidence that the effect of gross fixed capital formation on output growth is higher in an economy with a more pronounced level of foreign bank penetration relative to an economy with a lower level of foreign bank penetration. This finding suggests that foreign banks play an important role in allocating capital in a more productive way, thus leading to a higher economic growth rate. One of the main policy implications of our findings in this paper is that foreign banks may serve as a channel in enhancing economic integration of emerging economies with advanced economies that are the home countries of foreign banks.
Complete Lorentz transformation of a charge-current density

It is generally assumed in the literature that a Lorentz transformation on a neutral current loop results in a moving current loop with a nonvanishing charge distribution and an electric dipole mom...
Design of the Adaptive Feedback Loop in Parameter-Perturbation Adaptive Controls

Abstract Prior analyses of parameter-perturbation adaptive control systems have demonstrated that the idea will work, but have not investigated the design of the adaptive loop. They require that the perturbations be small and slow and that the adaptive feedback be slow, but do not address quantitatively the questions of how small is small enough and how slow is slow enough. These are the questions dealt with in this paper.
Dynamic Inventory Control with Stockout Substitution and Demand Learning

We consider an inventory control problem with multiple products and stockout substitution. The firm knows neither the primary demand distribution for each product nor the customers’ substitution pr...
Learning Over Dirty Data Without Cleaning

Real-world datasets are dirty and contain many errors, such as violations of integrity constraints and entity duplicates. Learning over dirty databases may result in inaccurate models. Data scientists spend most of their time on preparing and repairing data errors to create clean databases for learning. Moreover, as the information required to repair these errors is not often available, there may be numerous possible clean versions for a dirty database. We propose Dirty Learn, DLearn, a novel learning system that learns directly over dirty databases effectively and efficiently without any preprocessing. DLearn leverages database constraints to learn accurate relational models over inconsistent and heterogeneous data. Its learned models represent patterns over all possible clean versions of the data in a usable form. Our empirical study indicates that DLearn learns accurate models over large real-world databases efficiently.
Efficient construction of directed hopsets and parallel approximate shortest paths

The approximate single-source shortest-path problem is as follows: given a graph with nonnegative edge weights and a designated source vertex s, return estimates of the distances from s to each other vertex such that the estimate falls between the true distance and (1+є) times the distance. This paper provides the first nearly work-efficient parallel algorithm with sublinear span (also called depth) for the approximate shortest-path problem on directed graphs. Specifically, for constant є and polynomially-bounded edge weights, our algorithm has work O(m) and span n 1/2+o(1). Several algorithms were previously known for the case of undirected graphs, but none of the techniques seem to translate to the directed setting. The main technical contribution is the first nearly linear-work algorithm for constructing hopsets on directed graphs. A (β,є)-hopset is a set of weighted edges (sometimes called shortcuts) which, when added to the graph, admit β-hop paths with weight no more than (1+є) times the true shortest-path distances. There is a simple sequential algorithm that takes as input a directed graph and produces a linear-cardinality hopset with β=O(√n), but its running time is quite high—specifically O(m√n). Our algorithm is the first more efficient algorithm that produces a directed hopset with similar characteristics. Specifically, our sequential algorithm runs in O(m) time and constructs a hopset with O(n) edges and β = n 1/2+o(1). A parallel version of the algorithm has work O(m) and span n 1/2+o(1).
CNN-Generated Images Are Surprisingly Easy to Spot… for Now

In this work we ask whether it is possible to create a "universal" detector for telling apart real images from these generated by a CNN, regardless of architecture or dataset used. To test this, we collect a dataset consisting of fake images generated by 11 different CNN-based image generator models, chosen to span the space of commonly used architectures today (ProGAN, StyleGAN, BigGAN, CycleGAN, StarGAN, GauGAN, DeepFakes, cascaded refinement networks, implicit maximum likelihood estimation, second-order attention super-resolution, seeing-in-the-dark). We demonstrate that, with careful pre- and post-processing and data augmentation, a standard image classifier trained on only one specific CNN generator (ProGAN) is able to generalize surprisingly well to unseen architectures, datasets, and training methods (including the just released StyleGAN2). Our findings suggest the intriguing possibility that today's CNN-generated images share some common systematic flaws, preventing them from achieving realistic image synthesis.
PhraseCut: Language-Based Image Segmentation in the Wild

We consider the problem of segmenting image regions given a natural language phrase, and study it on a novel dataset of 77,262 images and 345,486 phrase-region pairs. Our dataset is collected on top of the Visual Genome dataset and uses the existing annotations to generate a challenging set of referring phrases for which the corresponding regions are manually annotated. Phrases in our dataset correspond to multiple regions and describe a large number of object and stuff categories as well as their attributes such as color, shape, parts, and relationships with other entities in the image. Our experiments show that the scale and diversity of concepts in our dataset poses significant challenges to the existing state-of-the-art. We systematically handle the long-tail nature of these concepts and present a modular approach to combine category, attribute, and relationship cues that outperforms existing approaches.
A new proof of scattering below the ground state for the non-radial focusing NLS

We revisit the scattering result of Duyckaerts, Holmer, and Roudenko for the non-radial $\dot H^{1/2}$-critical focusing NLS. By proving an interaction Morawetz inequality, we give a simple proof of scattering below the ground state in dimensions $d\geq 3$ that avoids the use of concentration compactness.
Choice Set Optimization Under Discrete Choice Models of Group Decisions

The way that people make choices or exhibit preferences can be strongly affected by the set of available alternatives, often called the choice set. Furthermore, there are usually heterogeneous preferences, either at an individual level within small groups or within sub-populations of large groups. Given the availability of choice data, there are now many models that capture this behavior in order to make effective predictions--however, there is little work in understanding how directly changing the choice set can be used to influence the preferences of a collection of decision-makers. Here, we use discrete choice modeling to develop an optimization framework of such interventions for several problems of group influence, namely maximizing agreement or disagreement and promoting a particular choice. We show that these problems are NP-hard in general, but imposing restrictions reveals a fundamental boundary: promoting a choice can be easier than encouraging consensus or sowing discord. We design approximation algorithms for the hard problems and show that they work well on real-world choice data.