cssi/SciSciGPT-SciSciNet huggingface dataset:
The Inhomogeneous Wave Equation with $L^p$ Data

We prove existence and uniqueness of $L^2$ solutions to the inhomogeneous wave equation on $\mathbb{R}^{n-1}\times\mathbb{R}$ under the assumption that the inhomogeneous data lies in $L^p(\mathbb{R}^n)$ for $p=2n/(n+4)$. We also require the Fourier transform of the inhomogeneous data to vanish on an infinite cone where the solution could become singular. Subsequently, we show sharpness of the exponent $p$. This extends work of Michael Goldberg, in which similar Fourier-analytic techniques were used to study the inhomogeneous Helmholtz equation.
Opponent regulation of action performance and timing by striatonigral and striatopallidal pathways

The basal ganglia have been implicated in action selection and timing, but the relative contributions of the striatonigral (direct) and striatopallidal (indirect) pathways to these functions remain unclear. We investigated the effects of optogenetic stimulation of D1+ (direct) and A2A+ (indirect) neurons in the ventrolateral striatum in head-fixed mice on a fixed time reinforcement schedule. Direct pathway stimulation initiates licking, whereas indirect pathway stimulation suppresses licking and results in rebound licking after stimulation. Moreover, direct and indirect pathways also play distinct roles in timing. Direct pathway stimulation produced a resetting of the internal timing process, whereas indirect pathway stimulation transiently paused timing, and proportionally delayed the next bout of licking. Our results provide evidence for the continuous and opposing contributions of the direct and indirect pathways in the production and timing of reward-guided behavior.
The discovery of the binary pulsar

Exactly 20 years ago today, on December 8, 1973, I was at the Arecibo Observatory in Puerto Rico recording in my notebook the confirming observation of the first pulsar discovered by the search which formed the basis for my Ph.D. thesis. As excited as I am sure I was at that point in time, I certainly had no idea of what lay in store for me in the months ahead, a path which would ultimately lead me here today. I would like to take you along on a scientific adventure, a story of intense preparation, long hours, serendipity, and a certain level of compulsive behavior that tries to make sense out of everything that one observes. The remarkable and unexpected result of this detective story was a discovery which is still yielding fascinating scientific results to this day, nearly 20 years later, as Professor Taylor will describe for you in his lecture. I hope that by sharing this story with you, you will be able to join me in reliving the challenges and excitement of this adventure, and that we will all be rewarded with some personal insights as to the process of scientific discovery and the nature of science as a human endeavor.
Chapter 7 – Membrane Sugars

In addition to lipids and proteins a third major component exists primarily on the outer surface of the cell plasma membrane. This component is a collection of very water-soluble carbohydrates (sugars) (general carbohydrate references [1–4]). The word carbohydrate is a contraction of “hydrated carbon”. Cn(H2O)n is the empirical formulation of most sugars. For example, the most common sugar, glucose (Fig. 7.1), is C6H12O6 or C6 (H2O)6, and is thus a “hydrated carbon”.
Lesch–Nyhan Syndrome

Lesch–Nyhan syndrome is a dramatic X-linked disease caused by the deficient activity of the purine-salvage enzyme hypoxanthine–guanine phosphoribosyltransferase (HPRT), which results in hyperuricemia and gout-like manifestations along with neurological and behavioral abnormalities.
15 – The history of nuclear energy

Publisher Summary This chapter discusses the history of nuclear energy. A series of investigations in atomic and nuclear physics in the period 1879–1939 led to the discovery of fission. New knowledge was developed about particles, rays, radioactivity, and the structures of the atom and the nucleus. The existence of fission suggested that a chain reaction involving neutrons was possible, and that the process had military significance. The development of uranium isotope separation methods, of nuclear reactors for plutonium production, and of weapons technology culminated in the use of atomic bombs to end the war. In the post-war period, emphasis was placed on maintenance of nuclear protection and on peaceful applications of nuclear processes under the U.S. Atomic Energy Commission. Four reactors concepts—the pressurized water, boiling water, fast breeder, and gas-cooled—evolved through work by national laboratories and industry. The first two concepts were brought to commercial status in the 1960s. Support for nuclear power has waned since the early days, but sustained safe and economical operation may restore public confidence.
The "Wall Street Walk" and Shareholder Activism: Exit as a Form of Voice

We examine whether a large shareholder can alleviate conflicts of interest between managers and shareholders through the credible threat of exit on the basis of private information. In our model, the threat of exit often reduces agency costs, but additional private information need not enhance the effectiveness of the mechanism. Moreover, the threat of exit can produce quite different effects depending on whether the agency problem involves desirable or undesirable actions from shareholders’ perspective. Our results are consistent with empirical findings on the interaction between managers and minority large shareholders and have further empirical implications. (JEL D53, D82, G10, G30, G34) The role of active large shareholders in improving corporate performance has been discussed extensively in the last two decades. Although large shareholders (including pension funds, mutual funds, hedge funds, and other investors) hold a substantial and increasing fraction of shares in public companies in the United States, most large shareholders play a limited role in overt forms of shareholder activism such as takeovers, proxy fights, strategic voting, shareholders’ proposals, etc. One likely reason for this is that active shareholders only realize a relatively small fraction of the benefits from their monitoring while bearing the full cost, which can be substantial. In other words, we have a classic “free rider” problem. In addition, legal barriers, agency problems affecting the incentives of the large shareholder, and the fact that many large shareholders are committed to being passive and not investing resources to monitor their portfolio firms
CHAPTER SEVEN – Mathematical Optimization Techniques for Mass Integration

Chapter 6, Combining Mass-Integration Strategies, presented a graphical approach to the development of combined mass-integration strategies. This chapter presents mathematical optimization techniques for mass integration including mass-exchange interception and rerouting of targeted species throughout the process. Since the interception devices in this chapter are limited to mass-exchange operations, the species interception network or waste-interception network becomes a mass-exchange network. Other separation networks (e.g., heat-induced separation and membrane systems) can be similarly included in the interception network. The emphasis in this chapter is on targeting pollution at the heart of the plant rather than dealing with pollutants in terminal waste streams. The chapter also provides a mathematical framework for tackling pollution as a multimedia (multiphase) problem.
Stock Grants as a Commitment Device

Abstract A large and increasing fraction of the value of executives’ compensation is accounted for by security grants. However, in most models of executive compensation, the optimal allocation can be implemented through a sequence of state-contingent cash payments. Security awards are redundant. In this paper we develop a dynamic model of managerial compensation where neither the firm nor the manager can commit to long-term contracts. We show that, in this environment, if stock grants are not used, then the optimal contract collapses to a series of short term contracts. When stock grants are used, however, nonlinear intertemporal schemes can be implemented to achieve better risk-sharing and higher firm value.
Global Health with Justice: Controlling the Floodgates of the Upstream Determinants of Health through Evidence-Based Law

This article introduces a special issue on the legal determinants of health, following the publication of the Lancet-O'Neill Institute of Georgetown University Commission's report on the subject. We contextualize legal determinants as a significant and vital aspect of the social determinants of health, explain the work of the Lancet-O'Neill Commission and outline where consequent research will usefully be directed. We also introduce the papers that follow in the special issue, which together set out in greater detail the work of the Commission and critically engage with different aspects of the report and the application of its findings and recommendations.
11 – Polar Coordinates

Publisher Summary This chapter discusses polar coordinates. The polar coordinates system is the most important way of representing points in a plane. In rectangular coordinates , the graph of the equation y = f(x) or, more generally, f(x, y) = 0 is defined as the set of points (x, y) whose coordinates satisfy the equation. In polar coordinates, however, each point in the plane has an infinite number of representations. The chapter further presents graphs of some general curves in polar coordinates and illustrates the points of intersection of graphs of polar equations with the help of equations. The chapter further explains the derivation of a method for calculating the area of a region whose boundary is given in polar coordinates.
The Government as Venture Capitalist: The Long-Run Impact of the SBIR Program

Government programs that finance small firms have attracted little empirical attention. This article examines the largest U.S. initiative, the Small Business Innovation Research program. Using a unique database, this article shows that SBIR awardees grew significantly faster than matched firms over a decade and were more likely to attract venture financing. The superior performance of awardees was confined to firms in regions with substantial venture capital activity and was particularly pronounced in high-technology industries. Multiple awards did not increase performance. These results suggest that awards played out an important role in certifying firm quality, but also that distortions of the award process do occur.
9 – Experimental Methods

Publisher Summary In this chapter test procedures are provided to determine the properties of both the precursor form as well as the final ionomer. Most important among these properties are equivalent weight (EW) and melt flow (MF). Infrared spectra are very useful for many purposes: to determine equivalent weight (EW), to follow the chemical reactions of the functional groups, and to check the completeness of hydrolysis are just a few examples of how they can be utilized. Polymer precursors, sulfonyl fluoride, and carboxylic ester, can be hydrolyzed using solutions of sodium or potassium hydroxide. The solution can be aqueous or a mixture of an organic solvent, such as dimethyl sulfoxide (DMSO) and water to increase the rate of hydrolysis. The sulfonyl fluoride precursor form can undergo reactions with many reagents. In discussing ion exchange equilibrium, it is frequently necessary to express the concentration of ions in the polymer phase, requiring somewhat arbitrary assumptions of the volume of electrolyte occupying the polymer phase. Titration is used to calibrate other analytical methods. Its major limitation is the difficulty of drying the polymer in the proton form to a known water content value. A number of titrations of samples of different EWs can form the basis of a calibration curve for the faster infra red analysis of the precursor resin. Next to EW of an ionomer the melt flow (MF) of precursor polymer is an important characteristic of an ionomer. It can be conveniently be determined by a device called a melt indexer.
Solving a continuous multifacility location problem by DC algorithms

The paper presents a new approach to solve multifacility location problems, which is based on mixed integer programming and algorithms for minimizing differences of convex (DC) functions. The main ...
Deformation and burst of a liquid droplet with viscous surface moduli in a linear flow field

We investigate conditions for the breakup of a droplet with viscous surface moduli, under the assumption of weak flow and negligible Marangoni forces. The viscous interface is treated as a homogenous fluid obeying the Boussinesq--Scriven constitutive law. We observed that the presence of surface shear viscosity stabilizes the droplet, while that of surface dilational viscosity destabilizes it. The destabilizing effect of surface dilational viscosity appears similar to surfactant convection effects, while the stabilizing impact of surface shear viscosity appears similar to surfactant dilution.
Effectiveness of stay-in-place-orders during COVID-19 pandemic: Evidence from US border counties

Recent studies on US counties, with varying effect sizes, show that stay-in-place-orders (SIPOs) are associated with a decline in new COVID-19 cases. Our estimation approach relies on county-pairs across state-borders where one state has SIPO whereas the other state does not, controls for matched county-pair fixed effects and day of observation fixed-effects. The county-pair sample from southern, mid-western, and mountain region states (from March 1, 2020 to April 25, 2020) shows that daily COVID-19 incidence case growth rate is 1.994 percentage points lower for counties in SIPO states relative to those bordering in non-SIPO states. Specifically, we find SIPO reduced daily growth rates by 1.97, 2.14, 2.03, and 2.27 percentage points after 1 to 5 days, 6 to 10 days, 11 to 15 days, and 16 to 20 days, respectively. Our effect sizes are much smaller than in the previous studies with the caveat that states in the northeast and on the west coast could not be included in the border county-pair specification. We find limited evidence of heterogeneous effects in counties with a higher population density, percentage of black or Hispanic residents, proportion of population over 65 years, and social association rates in a county. Nor do we find evidence of meaningful differences in effects of SIPO by county Gini index, unemployment, or GDP. The results of this study could further inform policymakers in making decisions on SIPO extensions or lifting of such orders.
Accounting for model uncertainty in multiple imputation under complex sampling

Multiple imputation provides an effective way to handle missing data. When several possible models are under consideration for the data, the multiple imputation is typically performed under a single-best model selected from the candidate models. This single model selection approach ignores the uncertainty associated with the model selection and so leads to underestimation of the variance of multiple imputation estimator. In this paper, we propose a new multiple imputation procedure incorporating model uncertainty in the final inference. The proposed method incorporates possible candidate models for the data into the imputation procedure using the idea of Bayesian Model Averaging (BMA). The proposed method is directly applicable to handling item nonresponse in survey sampling. Asymptotic properties of the proposed method are investigated. A limited simulation study confirms that our model averaging approach provides better estimation performance than the single model selection approach.
Lookahead-Bounded Q-learning

We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably convergent variant of Q-learning that seeks to improve the performance of standard Q-learning in stochastic environments through the use of ``lookahead'' upper and lower bounds. To do this, LBQL employs previously collected experience and each iteration's state-action values as dual feasible penalties to construct a sequence of sampled information relaxation problems. The solutions to these problems provide estimated upper and lower bounds on the optimal value, which we track via stochastic approximation. These quantities are then used to constrain the iterates to stay within the bounds at every iteration. Numerical experiments on benchmark problems show that LBQL exhibits faster convergence and more robustness to hyperparameters when compared to standard Q-learning and several related techniques. Our approach is particularly appealing in problems that require expensive simulations or real-world interactions.
DeepMatch: Balancing Deep Covariate Representations for Causal Inference Using Adversarial Training

We study optimal covariate balance for causal inferences from observational data when rich covariates and complex relationships necessitate flexible modeling with neural networks. Standard approaches such as propensity weighting and matching/balancing fail in such settings due to miscalibrated propensity nets and inappropriate covariate representations, respectively. We propose a new method based on adversarial training of a weighting and a discriminator network that effectively addresses this methodological gap. This is demonstrated through new theoretical characterizations of the method as well as empirical results using both fully connected architectures to learn complex relationships and convolutional architectures to handle image confounders, showing how this new method can enable strong causal analyses in these challenging settings.
A Near-Linear Time Approximation Scheme for Geometric Transportation with Arbitrary Supplies and Spread.

The geometric transportation problem takes as input a set of points P in d-dimensional Euclidean space and a supply function μ : P → ℝ. The goal is to find a transportation map, a non-negative assignment τ : P × P → ℝ_{≥ 0} to pairs of points, so the total assignment leaving each point is equal to its supply, i.e., ∑_{r ∈ P} τ(q, r) - ∑_{p ∈ P} τ(p, q) = μ(q) for all points q ∈ P. The goal is to minimize the weighted sum of Euclidean distances for the pairs, ∑_{(p, q) ∈ P × P} τ(p, q) ⋅ ||q - p||₂. We describe the first algorithm for this problem that returns, with high probability, a (1 + e)-approximation to the optimal transportation map in O(n poly(1 / e) polylog n) time. In contrast to the previous best algorithms for this problem, our near-linear running time bound is independent of the spread of P and the magnitude of its real-valued supplies.
On the difference of partial theta functions

Sums of the form $$\sum _{n\ge 0} (-1)^nq^{(n-1)n/2} x^n $$ are called partial theta functions. In his lost notebook, Ramanujan recorded many identities for those functions. In 2003, Warnaar found an elegant formula for a sum of two partial theta functions. Subsequently, Andrews and Warnaar established a similar result for the product of two partial theta functions. In this note, I discuss the relation between the Andrews–Warnaar identity and the (1986) product formula due to Gasper and Rahman. I employ nonterminating extension of Sears–Carlitz transformation for $$ _3\phi _2$$ to provide a new elegant proof for a companion identity for the difference of two partial theta series. This difference formula first appeared in the work of Schilling–Warnaar (Conjugate Bailey pairs from configuration sums and fractional-level string functions to Bailey’s lemma. Contemporary mathematics, American Mathematical Society, Providence, vol 297, pp 227–255, 2002). Finally, I show that Schilling–Warnnar (2002) and Warnaar (Proc Lond Math Soc 87:363–395, 2003) formulas are, in fact, equivalent.
Universal inference

We propose a general method for constructing confidence sets and hypothesis tests that have finite-sample guarantees without regularity conditions. We refer to such procedures as “universal.” The method is very simple and is based on a modified version of the usual likelihood-ratio statistic that we call “the split likelihood-ratio test” (split LRT) statistic. The (limiting) null distribution of the classical likelihood-ratio statistic is often intractable when used to test composite null hypotheses in irregular statistical models. Our method is especially appealing for statistical inference in these complex setups. The method we suggest works for any parametric model and also for some nonparametric models, as long as computing a maximum-likelihood estimator (MLE) is feasible under the null. Canonical examples arise in mixture modeling and shape-constrained inference, for which constructing tests and confidence sets has been notoriously difficult. We also develop various extensions of our basic methods. We show that in settings when computing the MLE is hard, for the purpose of constructing valid tests and intervals, it is sufficient to upper bound the maximum likelihood. We investigate some conditions under which our methods yield valid inferences under model misspecification. Further, the split LRT can be used with profile likelihoods to deal with nuisance parameters, and it can also be run sequentially to yield anytime-valid P values and confidence sequences. Finally, when combined with the method of sieves, it can be used to perform model selection with nested model classes.
Tools for visualizing and analyzing Fourier space sampling in Cryo-EM.

Abstract A complete understanding of how an orientation distribution contributes to a cryo-EM reconstruction remains lacking. It is necessary to begin critically assessing the set of views to gain an understanding of its effect on experimental reconstructions. Toward that end, we recently suggested that the type of orientation distribution may alter resolution measures in a systematic manner. We introduced the sampling compensation factor (SCF), which incorporates how the collection geometry might change the spectral signal-to-noise ratio (SSNR), irrespective of the other experimental aspects. We show here that knowledge of the sampling restricted to spherical surfaces of sufficiently large radii in Fourier space is equivalent to knowledge of the set of projection views. Moreover, the SCF geometrical factor may be calculated from one such surface. To aid cryo-EM practitioners, we developed a graphical user interface (GUI) tool that evaluates experimental orientation distributions. The GUI returns plots of projection directions, sampling constrained to the surface of a sphere, the SCF value, the fraction of the empty region of Fourier space, and a histogram of the sampling values over the points on a sphere. Finally, a fixed tilt angle may be incorporated to determine how tilting the grid during collection may improve the distribution of views and Fourier space sampling. We advocate this simple conception of sampling and the use of such tools as a complement to the distribution of views to capture the different aspects of the effect of projection directions on cryo-EM reconstructions.
AccQOC: accelerating quantum optimal control based pulse generation

In the last decades, we have witnessed the rapid growth of Quantum Computing. In the current Noisy Intermediate-Scale Quantum (NISQ) era, the capability of a quantum machine is limited by the decoherence time, gate fidelity and the number of Qubits. Current quantum computing applications are far from the real “quantum supremacy” due to the fragile physical Qubits, which can only be entangled for a few microseconds. Recent works use quantum optimal control to reduce the latency of quantum circuits, thereby effectively increasing quantum volume. However, the key challenge of this technique is the large overhead due to long compilation time. In this paper, we propose AccQOC, a comprehensive static/dynamic hybrid workflow to transform gate groups (equivalent to matrices) to pulses using QOC (Quantum Optimal Control) with a reasonable compilation time budget. AccQOC is composed of static pre-compilation and accelerated dynamic compilation. After the quantum program is mapped to the quantum circuit with our heuristic mapping algorithm considering crosstalk, we leverage static pre-compilation to generate pulses for the frequently used groups to eliminate the dynamic compilation time for them. The pulse is generated using QOC with binary search to determine the latency. For a new program, we use the same policy to generate groups, thus avoid incurring overhead for the “covered” groups. The dynamic compilation deals with “un-covered” groups with accelerated pulse generation. The key insight is that the pulse of a group can be generated faster based on the generated pulse of a similar group. We propose to reduce the compilation time by generating an ordered sequence of groups in which the sum of similarity among consecutive groups in the sequence is minimized. We can find the sequence by constructing a similarity graph – a complete graph in which each vertex is a gate group and the weight of an edge is the similarity between the two groups it connects, then construct a Minimum Spanning Tree (MST) for SG. With the methodology of AccQOC, we reached a balanced point of compilation time and overall latency. The results show that accelerated compilation based on MST achieves $9.88\times$ compilation speedup compared to the standard compilation of each group while maintaining an average $2.43\times$ latency reduction compared with gate-based compilation.
Risk-Averse Explore-Then-Commit Algorithms for Finite-Time Bandits

In this paper, we study multi-armed bandit problems in an explore-then-commit setting. In our proposed explore-then-commit setting, the goal is to identify the best arm after a pure experimentation (exploration) phase and exploit it once or for a given finite number of times. We identify that although the arm with the highest expected reward is the most desirable objective for infinite exploitations, it is not necessarily the one that is most probable to have the highest reward in a single or finite-time exploitations. Alternatively, we advocate the idea of risk-aversion where the objective is to compete against the arm with the best risk-return trade-off. We propose two algorithms whose objectives are to select the arm that is most probable to reward the most. Using a new notion of finite-time exploitation regret, we find an upper bound of order ln (1/(ϵ)) for the minimum number of experiments before commitment, to guarantee upper bound ϵ for regret. As compared to existing risk-averse bandit algorithms, our algorithms do not rely on hyper-parameters, resulting in a more robust behavior, which is verified by numerical evaluations.
Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

Abstract We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation ( r = 0.70 , p
Instabilities driven by frontal polymerization in thermosetting polymers and composites

Abstract Frontal Polymerization (FP) was recently demonstrated as a faster, more energy-efficient way to manufacture fiber-reinforced thermosetting-polymer-matrix composites. FP uses heat from the exothermic reaction of the solution of monomer and initiator to generate a self-propagating polymerization front. In most cases, the polymerization front propagates in a steady fashion. However, under some conditions, the front experiences instabilities, which do affect the quality of the manufactured composite part. In this work, we use a coupled thermo-chemical model and an adaptive nonlinear finite element solver to simulate FP-driven instabilities in dicyclopentadiene (DCPD) and in carbon-fiber DCPD-matrix composites. With the aid of 1-D transient simulations, we investigate how the initial temperature and the carbon fiber volume fraction affect the amplitude and wavelength of the thermal instabilities. We also extract the range of processing conditions for which the instabilities are predicted to appear. The last part of this work investigates the effect of convective heat loss on the FP-driven instabilities in both neat resin and composite cases.
Nearly-incompressible transverse isotropy (NITI) of cornea elasticity: model and experiments with acoustic micro-tapping OCE.

The cornea provides the largest refractive power for the human visual system. Its stiffness, along with intraocular pressure (IOP), are linked to several pathologies, including keratoconus and glaucoma. Although mechanical tests can quantify corneal elasticity ex vivo, they cannot be used clinically. Dynamic optical coherence elastography (OCE), which launches and tracks shear waves to estimate stiffness, provides an attractive non-contact probe of corneal elasticity. To date, however, OCE studies report corneal moduli around tens of kPa, orders-of-magnitude less than those (few MPa) obtained by tensile/inflation testing. This large discrepancy impedes OCE’s clinical adoption. Based on corneal microstructure, we introduce and fully characterize a nearly-incompressible transversely isotropic (NITI) model depicting corneal biomechanics. We show that the cornea must be described by at least two shear moduli, contrary to current single-modulus models, decoupling tensile and shear responses. We measure both as a function of IOP in ex vivo porcine cornea, obtaining values consistent with both tensile and shear tests. At pressures above 30 mmHg, the model begins to fail, consistent with non-linear changes in cornea at high IOP.
Gravin-associated kinase signaling networks coordinate γ-tubulin organization at mitotic spindle poles

Mitogenic signals that regulate cell division often proceed through multienzyme assemblies within defined intracellular compartments. The anchoring protein Gravin restricts the action of mitotic kinases and cell-cycle effectors to defined mitotic structures. In this report we discover that genetic deletion of Gravin disrupts proper accumulation and asymmetric distribution of γ-tubulin during mitosis. We utilize a new precision pharmacology tool, Local Kinase Inhibition, to inhibit the Gravin binding partner polo-like kinase 1 at spindle poles. Using a combination of gene-editing approaches, quantitative imaging, and biochemical assays, we provide evidence that disruption of local polo-like kinase 1 signaling underlies the γ-tubulin distribution defects observed with Gravin loss. Our study uncovers a new role for Gravin in coordinating γ-tubulin recruitment during mitosis and illuminates the mechanism by which signaling enzymes regulate this process at a distinct subcellular location.
DERauth: A Battery-Based Authentication Scheme for Distributed Energy Resources

Over the past decades, power systems have experienced drastic transformations in order to address the growth in energy demand, reduce carbon emissions, and enhance power quality and energy efficiency. This shift to the smart grid concept involves, among others, the utilization of distributed energy resources (DERs) such as rooftop solar panels and storage systems, contributing towards grid decentralization while improving control over power generation. In order to seamlessly integrate DERs into power systems, embedded devices are used to support the communication and control functions of DERs. As a result, vulnerabilities of such components can be ported to the industrial environment. Insecure control networks and protocols further exacerbate the problem. Towards reducing the attack surface, we present an authentication scheme for DERs, DERauth, which leverages the inherent entropy of the DER battery energy storage system (BESS) as a root-of-trust. The DER authentication is achieved using a challenge-reply mechanism that relies on the corresponding DER's BESS state-of-charge (SoC) and voltage measurements. A dynamically updating process ensures that the BESS state is up-to-date. We evaluate our proof-of-concept in a prototype development that uses lithium-ion (li-ion) batteries for the BESS. The robustness of our design is assessed against modeling attacks performed by neural networks.
Dynamic intermolecular interactions through hydrogen bonding of water promote heat conduction in hydrogels

Heat-conducting polymers provide a new opportunity to tackle thermal management challenges in advanced technologies such as wearable electronics and soft robotics. One strategy to enhance heat conduction in amorphous polymers has been tuning their intermolecular interactions. These intermolecular forces are often static in nature as the participating molecules are anchored on the polymer chains. In this work, using hydrogel as a model system, we demonstrate how dynamic intermolecular forces, which break and re-form constantly, can also enhance thermal transport. Utilizing calorimetric and spectroscopic measurements, we show that this arises from the hydrogen bonds formed between water and nearby polymer chains, which enhances the inter-chain heat transfer efficiency. This mechanism may potentially allow the design of heat-conducting polymers with self-healing or adaptability functionalities.
Information Theoretic Limits for Phase Retrieval With Subsampled Haar Sensing Matrices

We study information theoretic limits of recovering an unknown ${n}$ dimensional, complex signal vector ${x}_{\star} $ with unit norm from ${m}$ magnitude-only measurements of the form ${y}_{i} = |({A}{x}_{\star} )_{i}|^{2}, \; {i} = 1,2 {\dots }, {m}$ , where ${A}$ is the sensing matrix. This is known as the Phase Retrieval problem and models practical imaging systems where measuring the phase of the observations is difficult. Since in a number of applications, the sensing matrix has orthogonal columns, we model the sensing matrix as a subsampled Haar matrix formed by picking ${n}$ columns of a uniformly random ${m} \times {m}$ unitary matrix. We study this problem in the high dimensional asymptotic regime, where ${m},{n} \rightarrow \infty $ , while ${m}/{n} \rightarrow \delta $ with $\delta $ being a fixed number, and show that if ${m} , then any estimator is asymptotically orthogonal to the true signal vector ${x}_{\star} $ . This lower bound is sharp since when ${m} > (2+{o}_{n}(1)) \cdot {n} $ , estimators that achieve a non trivial asymptotic correlation with the signal vector are known from previous works.
Prevalence of Asymptomatic SARS-CoV-2 Infection in Children and Adults in Marion County, Indiana.

BACKGROUND AND OBJECTIVES: Two community studies outside the US showed asymptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in adults, but not in children <10 years of age. In this study, we assessed the prevalence of asymptomatic SARS-CoV-2 infection in children and adults in Marion County, Indiana. METHODS: Individuals living in Marion County with no symptoms of coronavirus 2019 disease (COVID-19) within seven days of enrollment were eligible for this cross-sectional household study. Study kits were delivered to the participant's residence for self-swabbing, picked up by the study team, and tested by polymerase chain reaction (PCR) for SAR-CoV-2 infection. RESULTS: Five hundred eleven nasal swabs were collected from 119 children and 392 adults ≥18 years of age. One participant (seven years of age) tested positive, for an overall study prevalence of 0.2% (95% CI 0, 0.6%). The participant had no known contact with a person with SARS-CoV-2 infection, and five family members tested negative for infection. The child and family members all tested negative for infection 10 and 20 days after the first test, and none developed symptoms of COVID-19 for 20 days after testing. CONCLUSIONS: Asymptomatic SARS-CoV-2 infection can occur in children <10 years with no known COVID-19 exposure. Large cohort studies should be conducted to determine prevalence of asymptomatic infection and risk of transmission from asymptomatic infection in children and adults over time.
Siamese Neural Networks for EEG-based Brain-computer Interfaces

Motivated by the inconceivable capability of human brain in simultaneously processing multi-modal signals and its real-time feedback to the outer world events, there has been a surge of interest in establishing a communication bridge between the human brain and a computer, which are referred to as Brain-computer Interfaces (BCI). To this aim, monitoring the electrical activity of brain through Electroencephalogram (EEG) has emerged as the prime choice for BCI systems. To discover the underlying and specific features of brain signals for different mental tasks, a considerable number of research works are developed based on statistical and data-driven techniques. However, a major bottleneck in development of practical and commercial BCI systems is their limited performance when the number of mental tasks for classification is increased. In this work, we propose a new EEG processing and feature extraction paradigm based on Siamese neural networks, which can be conveniently merged and scaled up for multi-class problems. The idea of Siamese networks is to train a double-input neural network based on a contrastive loss-function, which provides the capability of verifying if two input EEG trials are from the same class or not. In this work, a Siamese architecture, which is developed based on Convolutional Neural Networks (CNN) and provides a binary output on the similarity of two inputs, is combined with One vs. Rest (OVR) and One vs. One (OVO) techniques to scale up for multi-class problems. The efficacy of this architecture is evaluated on a 4-class Motor Imagery (MI) dataset from BCI Competition IV 2a and the results suggest a promising performance compared to its counterparts.
Distributed Degenerate Band Edge Oscillator

We propose a new class of oscillators by engineering the dispersion of two-coupled periodic waveguides to exhibit a degenerate band edge (DBE). The DBE is an exceptional point of degeneracy (EPD) of order four, i.e., representing the coalescence of four eigenmodes of a waveguide system without loss and gain. We present a distributed DBE oscillator realized in periodic coupled transmission lines with a unique mode selection scheme that leads to a stable single-frequency oscillation, even in the presence of load variation. The DBE oscillator potentially leads to a boost of the efficiency and performance of radio frequency (RF) sources, due to the unique features associated with the EPD concept. This class of oscillators is promising for improving discrete-distributed coherent sources and can be extended to radiating structures to achieve a new class of active integrated antenna arrays.
Semantic Parsing with Combinatory Categorial Grammars

Semantic parsers map natural language sentences to formal representations of their underlying meaning. Building accurate semantic parsers without prohibitive engineering costs is a long-standing, open research problem.The tutorial will describe general principles for building semantic parsers. The presentation will be divided into two main parts: learning and modeling. In the learning part, we will describe a unified approach for learning Combinatory Categorial Grammar (CCG) semantic parsers, that induces both a CCG lexicon and the parameters of a parsing model. The approach learns from data with labeled meaning representations, as well as from more easily gathered weak supervision. It also enables grounded learning where the semantic parser is used in an interactive environment, for example to read and execute instructions. The modeling section will include best practices for grammar design and choice of semantic representation. We will motivate our use of lambda calculus as a language for building and representing meaning with examples from several domains.The ideas we will discuss are widely applicable. The semantic modeling approach, while implemented in lambda calculus, could be applied to many other formal languages. Similarly, the algorithms for inducing CCG focus on tasks that are formalism independent, learning the meaning of words and estimating parsing parameters. No prior knowledge of CCG is required. The tutorial will be backed by implementation and experiments in the University of Washington Semantic Parsing Framework (UW SPF, http://yoavartzi.com/spf).
A Truncation Error Analysis of Third‐Order MUSCL Scheme for Nonlinear Conservation Laws

This paper is a rebuttal to the claim found in the literature that the MUSCL scheme cannot be third-order accurate for nonlinear conservation laws. We provide a rigorous proof for third-order accuracy of the MUSCL scheme based on a careful and detailed truncation error analysis. Throughout the analysis, the distinction between the cell average and the point value will be strictly made for the numerical solution as well as for the target operator. It is shown that the average of the solutions reconstructed at a face by Van Leer's kappa-scheme recovers a cubic solution exactly with kappa = 1/3, the same is true for the average of the nonlinear fluxes evaluated by the reconstructed solutions, and a dissipation term is already sufficiently small with a third-order truncation error. Finally, noting that the target spatial operator is a cell-averaged flux derivative, we prove that the leading truncation error of the MUSCL finite-volume scheme is third-order with kappa = 1/3. The importance of the diffusion scheme is also discussed: third-order accuracy will be lost when the third-order MUSLC scheme is used with a wrong fourth-order diffusion scheme for convection-diffusion problems. Third-order accuracy is verified by thorough numerical experiments for both steady and unsteady problems. This paper is intended to serve as a reference to clarify confusions about third-order accuracy of the MUSCL scheme, as a guide to correctly analyze and verify the MUSCL scheme for nonlinear equations, and eventually as the basis for clarifying third-order unstructured-grid schemes in a subsequent paper.
No-regret Exploration in Contextual Reinforcement Learning

We consider the recently proposed reinforcement learning (RL) framework of Contextual Markov Decision Processes (CMDP), where the agent interacts with a (potentially adversarial) sequence of episodic tabular MDPs. In addition, a context vector determining the MDP parameters is available to the agent at the start of each episode, thereby allowing it to learn a context-dependent near-optimal policy. In this paper, we propose a no-regret online RL algorithm in the setting where the MDP parameters are obtained from the context using generalized linear mappings (GLMs). We propose and analyze optimistic and randomized exploration methods which make (time and space) efficient online updates. The GLM based model subsumes previous work in this area and also improves previous known bounds in the special case where the contextual mapping is linear. In addition, we demonstrate a generic template to derive confidence sets using an online learning oracle and give a lower bound for the setting.
Ultra-dense low data rate (UDLD) communication in the THz

In the future, with the advent of the Internet of Things (IoT), wireless sensors, and multiple 5G applications yet to be developed, an indoor room might be filled with 1000s of devices. These devices will have different Quality of Service (QoS) demands and resource constraints, such as mobility, hardware, and efficiency requirements. The THz band has a massive greenfield spectrum and is envisioned to cater to these dense-indoor deployments. However, THz has multiple caveats, such as high absorption rate, limited coverage range, low transmit power, sensitivity to mobility, and frequent outages, making it challenging to deploy. THz might compel networks to be dependent on additional infrastructure, which might not be profitable for network operators and can even result in inefficient resource utilization for devices demanding low to moderate data rates. Using distributed Device-to-Device (D2D) communication in the THz, we can cater to these ultra-dense low data rate type applications in a constrained resource situation. We propose a 2-Layered distributed D2D model, where devices use coordinated multi-agent reinforcement learning (MARL) to maximize efficiency and user coverage for dense-indoor deployment. We explore the choice of features required to train the algorithms and how it impacts the system efficiency. We show that densification and mobility in a network can be used to further the limited coverage range of THz devices, without the need for extra infrastructure or resources.
Suppressing the ferroelectric switching barrier in hybrid improper ferroelectrics

Integration of ferroelectric materials into novel technological applications requires low coercive field materials, and consequently, design strategies to reduce the ferroelectric switching barriers. In this first principles study, we show that biaxial strain, which has a strong effect on the ferroelectric ground states, can also be used to tune the switching barrier of hybrid improper ferroelectric Ruddlesden–Popper oxides. We identify the region of the strain-tolerance factor phase diagram where this intrinsic barrier is suppressed, and show that it can be explained in relation to strain-induced phase transitions to nonpolar phases.
Colliding Kaluza–Klein bubbles

We construct an exact solution describing the collision of two Kaluza–Klein 'bubbles of nothing' in 3 + 1 dimensions. When the bubbles collide, a curvature singularity forms which is hidden inside an event horizon. However, unlike the formation of ordinary black holes, in this case the spacetime resembles the entire maximally extended Schwarzschild solution. We also point out that there are inequivalent bubbles that can be constructed from Kerr black holes.
Multisolitons for the Defocusing Energy Critical Wave Equation with Potentials

We construct multi-soliton solutions to the defocusing energy critical wave equation with potentials in $${\mathbb{R}^{3}}$$ and study their asymptotic stability in the energy space based on both regular and reversed Strichartz estimates developed in [GC3]. We also study related scattering problems in the stable case. Since each soliton decays slowly with rate $${\frac{1}{\left\langle x\right\rangle }}$$ , some refined estimates for the charge transfer model are also established.
Modular invariants for real quadratic fields and Kloosterman sums

We investigate the asymptotic distribution of integrals of the j-function that are associated to ideal classes in a real quadratic field. To estimate the error term in our asymptotic formula, we prove a bound for sums of Kloosterman sums of half-integral weight that is uniform in every parameter. To establish this estimate we prove a variant of Kuznetsov’s formula where the spectral data is restricted to half-integral weight forms in the Kohnen plus space, and we apply Young’s hybrid subconvexity estimates for twisted modular L-functions.