cssi/SciSciGPT-SciSciNet huggingface dataset:
The many Shapley values for model explanation

The Shapley value has become a popular method to attribute the prediction of a machine-learning model on an input to its base features. The use of the Shapley value is justified by citing [16] showing that it is the \emph{unique} method that satisfies certain good properties (\emph{axioms}). There are, however, a multiplicity of ways in which the Shapley value is operationalized in the attribution problem. These differ in how they reference the model, the training data, and the explanation context. These give very different results, rendering the uniqueness result meaningless. Furthermore, we find that previously proposed approaches can produce counterintuitive attributions in theory and in practice---for instance, they can assign non-zero attributions to features that are not even referenced by the model. In this paper, we use the axiomatic approach to study the differences between some of the many operationalizations of the Shapley value for attribution, and propose a technique called Baseline Shapley (BShap) that is backed by a proper uniqueness result. We also contrast BShap with Integrated Gradients, another extension of Shapley value to the continuous setting.
Strong self-concordance and sampling

Motivated by the Dikin walk, we develop aspects of the interior-point theory for sampling in high dimension. Specifically, we introduce the notions of strong self-concordance and symmetry for a barrier. These properties imply that the Dikin walk defined using a strongly self-concordant barrier with symmetry parameter ν mixes in O(nν) steps from a warm start for a convex body in ℝ n . For many natural barriers, ν is roughly bounded by ν, the standard self-concordance parameter. We also show that these properties hold for the Lee-Sidford barrier. As a consequence, we obtain the first walk that mixes in O(n 2) steps for an arbitrary polytope in ℝ n . Strong self-concordance for other barriers leads to an interesting (and unexpected) connection — for the universal and entropic barriers, it is implied by the KLS conjecture.
Programmable RNA Targeting Using CasRx in Flies

CRISPR-Cas genome editing technologies have revolutionized the fields of functional genetics and genome engineering, but with the recent discovery and optimization of RNA-targeting Cas ribonucleases, we may soon see a similar revolution in the study of RNA function and transcriptome engineering. However, to date, successful proof of principle for Cas ribonuclease RNA targeting in eukaryotic systems has been limited. Only recently has successful modification of RNA expression by a Cas ribonuclease been demonstrated in animal embryos. This previous work, however, did not evaluate endogenous expression of Cas ribonucleases and only focused on function in early developmental stages. A more comprehensive evaluation of this technology is needed to assess its potential impact. Here we report on our efforts to develop a programmable platform for RNA targeting using a Cas ribonuclease, CasRx, in the model organism Drosophila melanogaster. By genetically encoding CasRx in flies, we demonstrate moderate transcript targeting of known phenotypic genes in addition to unexpected toxicity and lethality. We also report on the off-target effects following on-target transcript cleavage by CasRx. Taken together, our results present the current state and limitations of a genetically encoded programmable RNA-targeting Cas system in Drosophila melanogaster, paving the way for future optimization of the system.
Stressful Life Events and Social Capital During the Early Phase of COVID-19 in the U.S

Abstract Background A novel coronavirus disease (COVID-19) was reported in Wuhan, China late December 2019. The disease has as of the end of March 2020, affected over 35 countries (with over 570,000 cases and 26,000 deaths) worldwide. This includes the U.S., where cases are increasing by the thousands every day (100,000 cases with 1500 deaths as of April 2020). We set out to investigate new or increased stressful life events (SLEs) as a result of this pandemic in the U.S. Methods In this exploratory qualitative study, we examined new or heightened SLEs during an active phase of this outbreak. We used a list of SLEs acquired from the first phase of our study, whereby we conducted open-ended surveys and performed an in-depth focus group. We applied Lazarus and Folkman’s transactional model of stress and coping to understand diverse focus-group participants’ appraisal of events. We coded survey data and applied sentiment analysis. Results Participants varied in perceived threat and challenge appraisals of COVID-19, indicating both calm and fear. From 267 coded and sentiment analyzed events from survey text, 95% were predominantly negative; 112 (42%) very negative and 142 (53%) moderately negative. Social capital was unanimously emphasized upon as monumental for example: family, friends or technology mediated. We additionally identified seven major themes of SLEs due to the pandemic. Limitations Our sample profile is not inclusive of all subsets of the population. Conclusions Participants mostly shared similar frustrations and a variety of SLEs such as fear of the unknown and concern for loved ones as a result of COVID-19.
Likelihood Ratio Test in Multivariate Linear Regression: from Low to High Dimension

Multivariate linear regressions are widely used statistical tools in many applications to model the associations between multiple related responses and a set of predictors. To infer such associations, it is often of interest to test the structure of the regression coefficients matrix, and the likelihood ratio test (LRT) is one of the most popular approaches in practice. Despite its popularity, it is known that the classical $\chi^2$ approximations for LRTs often fail in high-dimensional settings, where the dimensions of responses and predictors $(m,p)$ are allowed to grow with the sample size $n$. Though various corrected LRTs and other test statistics have been proposed in the literature, the fundamental question of when the classic LRT starts to fail is less studied, an answer to which would provide insights for practitioners, especially when analyzing data with $m/n$ and $p/n$ small but not negligible. Moreover, the power performance of the LRT in high-dimensional data analysis remains underexplored. To address these issues, the first part of this work gives the asymptotic boundary where the classical LRT fails and develops the corrected limiting distribution of the LRT for a general asymptotic regime. The second part of this work further studies the test power of the LRT in the high-dimensional setting. The result not only advances the current understanding of asymptotic behavior of the LRT under alternative hypothesis, but also motivates the development of a power-enhanced LRT. The third part of this work considers the setting with $p>n$, where the LRT is not well-defined. We propose a two-step testing procedure by first performing dimension reduction and then applying the proposed LRT. Theoretical properties are developed to ensure the validity of the proposed method. Numerical studies are also presented to demonstrate its good performance.
Fast Algorithms for Computational Optimal Transport and Wasserstein Barycenter.

We provide theoretical complexity analysis for new algorithms to compute the optimal transport (OT) distance between two discrete probability distributions, and demonstrate their favorable practical performance over state-of-art primal-dual algorithms and their capability in solving other problems in large-scale, such as the Wasserstein barycenter problem for multiple probability distributions. First, we introduce the \emph{accelerated primal-dual randomized coordinate descent} (APDRCD) algorithm for computing the OT distance. We provide its complexity upper bound $\bigOtil(\frac{n^{5/2}}{\varepsilon})$ where $n$ stands for the number of atoms of these probability measures and $\varepsilon > 0$ is the desired accuracy. This complexity bound matches the best known complexities of primal-dual algorithms for the OT problems, including the adaptive primal-dual accelerated gradient descent (APDAGD) and the adaptive primal-dual accelerated mirror descent (APDAMD) algorithms. Then, we demonstrate the better performance of the APDRCD algorithm over the APDAGD and APDAMD algorithms through extensive experimental studies, and further improve its practical performance by proposing a greedy version of it, which we refer to as \emph{accelerated primal-dual greedy coordinate descent} (APDGCD). Finally, we generalize the APDRCD and APDGCD algorithms to distributed algorithms for computing the Wasserstein barycenter for multiple probability distributions.
The Return To Tax Simplification: an Econometric Analysis

This article provides estimates of the probable saving in the resource cost of complying with the tax law that would result from simplifying the individual Abstract income tax law. These estimates are based on an econometric analysis of the tax-filing behavior in 1982 of a sample of Minnesota taxpayers. A simple model of tax-compliance behavior based on utility maximization is first presented in order to suggest the important determinants of compliance behavior. The empirical model treats the discrete choices of whether to itemize deductions and whether to hire professional tax advice, and the choice of how much time and money to spend, conditional on the discrete choices made. Simulations based on the econometric results suggest that significant resource saving could be expected from eliminating the system of itemized deductions, although no significant saving from changing to a single-rate tax structure can be confidently predicted. Results suggest that the Tax Reform Act of 1986 will, in the long run, decrease the use ofprofessional tax assistance, but its net effect on the use of taxpayer's own time is unclear.
Active Sector Funds and Fund Manager Skill

The authors investigate the performance of active sector funds whose potential outperformance has not been exhausted entirely by decreasing returns to scale. The authors document that despite good track records, most sector funds are relatively smaller than their equilibrium fund sizes, at which they are expected to generate zero net alphas. In particular, from 1998 to 2016, a passive indexation strategy of actively managed sector funds earns an annual benchmark-adjusted return of 5.70% and a monthly alpha of 27 bps. Moreover, the strategy’s outperformance is present in market downturns (i.e., resilient to tail risk) and robust to change of rebalancing frequency and inclusion of expenses. Efficient diversification and underappreciated skill, illustrated by an alpha-arithmetic to guide similar strategies, explain the strategy’s success. TOPICS:Manager selection, mutual fund performance, exchange-traded funds and applications Key Findings • Using data on active equity sector mutual funds, the authors find that an equal-weighted portfolio of sector funds delivers reliable alpha that is resilient to market downturns and only requires infrequent rebalancing. • Active sector funds individually show only weak evidence of selection skill, which may make them somewhat undersized, but an equal-weighted portfolio of them brings out the skill signal and diversifies away much of the alpha-seeking noise. • A portfolio of sector funds benefits from two layers of diversification: It not only washes off the sector-specific risk, it also diversifies the manager-specific active risk because managers in different sectors often make independent active bets.
Improved Oxidative C–C Bond Formation Reactivity of High-Valent Pd Complexes Supported by a Pseudo-Tridentate Ligand

There is a large interest in developing oxidative transformations catalyzed by palladium complexes that employ environmentally friendly and economical oxidizing reagents such as dioxygen. Recently,...
Approximation Schemes for ReLU Regression

We consider the fundamental problem of ReLU regression, where the goal is to output the best fitting ReLU with respect to square loss given access to draws from some unknown distribution. We give the first efficient, constant-factor approximation algorithm for this problem assuming the underlying distribution satisfies some weak concentration and anti-concentration conditions (and includes, for example, all log-concave distributions). This solves the main open problem of Goel et al., who proved hardness results for any exact algorithm for ReLU regression (up to an additive $\epsilon$). Using more sophisticated techniques, we can improve our results and obtain a polynomial-time approximation scheme for any subgaussian distribution. Given the aforementioned hardness results, these guarantees can not be substantially improved.\n \nOur main insight is a new characterization of {\em surrogate losses} for nonconvex activations. While prior work had established the existence of convex surrogates for monotone activations, we show that properties of the underlying distribution actually induce strong convexity for the loss, allowing us to relate the global minimum to the activation's {\em Chow parameters.}
Saddle pulmonary embolism and thrombus-in-transit straddling the patent foramen ovale 28 days after COVID symptom onset.

We present a late presentation of saddle pulmonary embolism and thrombus-in-transit straddle the patent foramen on patient who successfully recovered from severe acute respiratory syndrome coronavirus-2 (COVID-19) pneumonia. Seven days postdischarge (ie, 28 days after initial COVID-19 symptom onset), she was readmitted to hospital for severe dyspnea. Computer tomography angiogram and echocardiography confirmed the diagnosis. Severe pro-inflammatory and pro-thrombotic states with endothelial involvement have been reported associated with severe COVID-19 infection. However, the duration of hypercoagulable state has not yet known. This case highlights the risk of thromboembolic phenomena for prolonged periods of times after recovering from COVID-19 pneumonia.
Enterprise Architecture for Accounting and Finance Transformation: Using Strategy Maps to Develop High-Performance Finance

Enterprise architecture has received limited attention in the accounting information systems literature. However, enterprise architecture can provide an important approach for transforming an organization and bringing strategy into organizations. Recently, The Hartford implemented an approach that used Kaplan and Norton’s well known “strategy maps” as part of the enterprise architecture process to transform the finance department and embed strategy into accounting and finance. The process generated a creative future state that included a linear flow of information that mirrored the information life cycle, while providing a “single source of truth” archive for financial information. This paper reviews and analyzes The Hartford’s unique use of strategy maps to redesign the finance function in order to attain “high performance finance.” In so doing, the paper investigates a unique approach, an “innovative practice,” that allows grounding accounting and financial information systems in an organization’s strategy formulation process.
A Hoeffding Inequality for Finite State Markov Chains and its Applications to Markovian Bandits

This paper develops a Hoeffding inequality for the partial sums $\sum\nolimits_{k = 1}^n f \left( {{X_k}} \right)$, where ${\left\{ {{X_k}} \right\}_{k \in {\mathbb{Z}_{ > 0}}}}$ is an irreducible Markov chain on a finite state space S, and f : S → [a,b] is a real-valued function. Our bound is simple, general, since it only assumes irreducibility and finiteness of the state space, and powerful. In order to demonstrate its usefulness we provide two applications in multi-armed bandit problems. The first is about identifying an approximately best Markovian arm, while the second is concerned with regret minimization in the context of Markovian bandits.
Maximal single-frequency electromagnetic response

Modern nanophotonic and meta-optical devices utilize a tremendous number of structural degrees of freedom to enhance light–matter interactions. A fundamental question is how large such enhancements can be. We develop an analytical framework to derive upper bounds to single-frequency electromagnetic response, across near- and far-field regimes, for any materials, naturally incorporating the tandem effects of material- and radiation-induced losses. Our framework relies on a power-conservation law for the polarization fields induced in any scatterer. It unifies previous theories on optical scattering bounds and reveals new insight for optimal nanophotonic design, with applications including far-field scattering, near-field local-density-of-states engineering, optimal wavefront shaping, and the design of perfect absorbers. Our bounds predict strikingly large minimal thicknesses for arbitrarily patterned perfect absorbers, ranging from 50–100 nm for typical materials at visible wavelengths to micrometer-scale thicknesses for polar dielectrics at infrared wavelengths. We use inverse design to discover metasurface structures approaching the minimum-thickness perfect-absorber bounds.
The Power of the Hybrid Model for Mean Estimation

We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary real-valued distributions with bounded support. When the curator knows the distribution's variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines. We then analytically characterize how the estimator's utility is parameterized by the problem setting and parameter choices. When the distribution's variance is unknown, we design a heuristic hybrid estimator and analyze how it compares to the baselines. We find that it often performs better than the baselines, and sometimes almost as well as the known-variance estimator. We then answer the question of how our estimator's utility is affected when users' data are not drawn from the same distribution, but rather from distributions dependent on their trust model preference. Concretely, we examine the implications of the two groups' distributions diverging and show that in some cases, our estimators maintain fairly high utility. We then demonstrate how our hybrid estimator can be incorporated as a sub-component in more complex, higher-dimensional applications. Finally, we propose a new privacy amplification notion for the hybrid model that emerges due to interaction between the groups, and derive corresponding amplification results for our hybrid estimators.
IoT Platforms for 5G Network and Practical Considerations: A Survey

The fifth generation (5G) mobile network will enable the Internet of Things (IoT) to take a large leap into the age of future computing. As a result of extended connectivity, high speed, and reduced latency services being provided by 5G, IoT has experienced and will continue to undergo a remarkable transition in every field of daily life. Furthermore, fog computing will revolutionize the IoT platforms by decentralizing the operations through the cloud and ensuring sustainability with big data, mobility, and reduced processing lag. 5G is ubiquitous, reliable, scalable, and economic in nature. The features will not only globalize IoT in a broader spectrum, but also make common people interact smartly and efficiently with the environment in real time. In this study, a combined survey is presented on different IoT applications coupled with cloud platforms. Moreover, the capabilities of IoT in the influence of 5G are explored as well as how the IoT platform and services will adopt through 5G are envisaged. Additionally, some open issues triggered by 5G have been introduced to harness the maximum benefit out of this network. Finally, a platform is proposed to implement in the telepresence project based on the investigation and findings.
The Multidimensional Damped Wave Equation: Maximal Weak Solutions for Nonlinear Forcing via Semigroups and Approximation

The damped nonlinear wave equation, also known as the nonlinear telegraph equation, is studied within the framework of semigroups and eigenfunction approximation. The linear semigroup assumes a cen...
Rumor Source Detection With Multiple Observations Under Adaptive Diffusions

Recent work, motivated by anonymous messaging platforms, has introduced adaptive diffusion protocols which can obfuscate the source of a rumor: a “snapshot adversary” with access to the subgraph of “infected” nodes can do no better than randomly guessing the entity of the source node. What happens if the adversary has access to multiple independent snapshots? We study this question when the underlying graph is the infinite $d$ -regular tree. We show that (1) a weak form of source obfuscation is still possible in the case of two independent snapshots, but (2) already with three observations there is a simple algorithm that finds the rumor source with constant probability, regardless of the adaptive diffusion protocol. We also characterize the tradeoff between local spreading and source obfuscation for adaptive diffusion protocols (under a single snapshot). These results raise questions about the robustness of anonymity guarantees when spreading information in social networks.
Transition fronts in unbounded domains with multiple branches

This paper is concerned with the existence and uniqueness of transition fronts of a general reaction-diffusion-advection equation in domains with multiple branches. In this paper, every branch in the domain is not necessary to be straight and we use the notions of almost-planar fronts to generalize the standard planar fronts. Under some assumptions of existence and uniqueness of almost-planar fronts with positive propagating speeds in extended branches, we prove the existence of entire solutions emanating from some almost-planar fronts in some branches. Then, we get that these entire solutions converge to almost-planar fronts in some of the rest branches as time increases if no blocking occurs in these branches. Finally, provided by the complete propagation of every front-like solution emanating from one almost-planar front in every branch, we prove that there is only one type of transition fronts, that is, the entire solutions emanating from some almost-planar fronts in some branches and converging to almost-planar fronts in the rest branches.
Resolving Information Asymmetry Through Contractual Risk Sharing: The Case of Private Firm Acquisitions

When private firms are acquired, buyers commonly rely on seller financing and earnouts. Using a novel database of private acquisitions, I find that seller financing and earnouts become more common as information asymmetry increases between the acquirer and the target. Financial statement audits of the targets attenuate these results, which suggests that audits decrease information asymmetry in firm acquisitions. Seller‐financed acquisitions also close faster and at higher prices, reducing the private firm discount. These findings suggest that these contract structures are an important channel through which privately held firms mitigate adverse selection that arises from information asymmetry.
Petermann-Factor Limited Sensing near an Exceptional Point

We show that enhanced responsivity of a laser gyroscope operating near an exceptional point is accompanied by laser linewidth broadening related to the Petermann factor. The broadening imposes a fundamental sensitivity limit.
Similarity between mutation spectra in hypermutated genomes of rubella virus and in SARS-CoV-2 genomes accumulated during the COVID-19 pandemic

Genomes of tens of thousands of SARS-CoV2 isolates have been sequenced across the world and the total number of changes (predominantly single base substitutions) in these isolates exceeds ten thousand. We compared the mutational spectrum in the new SARS-CoV-2 mutation dataset with the previously published mutation spectrum in hypermutated genomes of rubella-another positive single stranded (ss) RNA virus. Each of the rubella virus isolates arose by accumulation of hundreds of mutations during propagation in a single subject, while SARS-CoV-2 mutation spectrum represents a collection events in multiple virus isolates from individuals across the world. We found a clear similarity between the spectra of single base substitutions in rubella and in SARS-CoV-2, with C to U as well as A to G and U to C being the most prominent in plus strand genomic RNA of each virus. Of those, U to C changes universally showed preference for loops versus stems in predicted RNA secondary structure. Similarly, to what was previously reported for rubella virus, C to U changes showed enrichment in the uCn motif, which suggested a subclass of APOBEC cytidine deaminase being a source of these substitutions. We also found enrichment of several other trinucleotide-centered mutation motifs only in SARS-CoV-2-likely indicative of a mutation process characteristic to this virus. Altogether, the results of this analysis suggest that the mutation mechanisms that lead to hypermutation of the rubella vaccine virus in a rare pathological condition may also operate in the background of the SARS-CoV-2 viruses currently propagating in the human population.
Verification of a Rapidly Multiplexed Circuit for Scalable Action Potential Recording

This report presents characterizations of in vivo neural recordings performed with a CMOS multichannel chip that uses rapid multiplexing directly at the electrodes, without any pre-amplification or buffering. Neural recordings were taken from a 16-channel microwire array implanted in rodent cortex, with comparison to a gold-standard commercial bench-top recording system. We were able to record well-isolated threshold crossings from 10 multiplexed electrodes and typical local field potential waveforms from 16, with strong agreement with the standard system (average SNR = 2.59 and 3.07 respectively). For 10 electrodes, the circuit achieves an effective area per channel of 0.0077 mm2, which is >5× smaller than typical multichannel chips. Extensive characterizations of noise and signal quality are presented and compared to fundamental theory, as well as results from in vivo and in vitro experiments. By demonstrating the validation of rapid multiplexing directly at the electrodes, this report confirms it as a promising approach for reducing circuit area in massively-multichannel neural recording systems, which is crucial for scaling recording site density and achieving large-scale sensing of brain activity with high spatiotemporal resolution.
Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization

Mapping and localization, preferably from a small number of observations, are fundamental tasks in robotics. We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN). The DMN constructs a spatially structured view-embedding map and uses it for subsequent visual localization with a particle filter. Since the DMN architecture is end-to-end differentiable, we can jointly learn the map representation and localization using gradient descent. We apply the DMN to sparse visual localization, where a robot needs to localize in a new environment with respect to a small number of images from known viewpoints. We evaluate the DMN using simulated environments and a challenging real-world Street View dataset. We find that the DMN learns effective map representations for visual localization. The benefit of spatial structure increases with larger environments, more viewpoints for mapping, and when training data is scarce. Project website: https://sites.google.com/view/differentiable-mapping.
Kinematic Modeling and Compliance Modulation of Redundant Manipulators Under Bracing Constraints

Collaborative robots should ideally use low torque actuators for passive safety reasons. However, some applications require these collaborative robots to reach deep into confined spaces while assisting a human operator in physically demanding tasks. In this paper, we consider the use of in-situ collaborative robots (ISCRs) that balance the conflicting demands of passive safety dictating low torque actuation and the need to reach into deep confined spaces. We consider the judicious use of bracing as a possible solution to these conflicting demands and present a modeling framework that takes into account the constrained kinematics and the effect of bracing on the endeffector compliance. We then define a redundancy resolution framework that minimizes the directional compliance of the end-effector while maximizing end-effector dexterity. Kinematic simulation results show that the redundancy resolution strategy successfully decreases compliance and improves kinematic conditioning while satisfying the constraints imposed by the bracing task. Applications of this modeling framework can support future research on the choice of bracing locations and support the formation of an admittance control framework for collaborative control of ISCRs under bracing constraints. Such robots can benefit workers in the future by reducing the physiological burdens that contribute to musculoskeletal injury.
Classes of barren extensions

Henle, Mathias, and Woodin proved in [21] that, provided that holds in a model M of ZF, then forcing with over M adds no new sets of ordinals, thus earning the name a “barren” extension. Moreover, under an additional assumption, they proved that this generic extension preserves all strong partition cardinals. This forcing thus produces a model , where is a Ramsey ultrafilter, with many properties of the original model M. This begged the question of how important the Ramseyness of is for these results. In this paper, we show that several classes of -closed forcings which generate non-Ramsey ultrafilters have the same properties. Such ultrafilters include Milliken–Taylor ultrafilters, a class of rapid p-points of Laflamme, k-arrow p-points of Baumgartner and Taylor, and extensions to a class of ultrafilters constructed by Dobrinen, Mijares, and Trujillo. Furthermore, the class of Boolean algebras , , forcing non-p-points also produce barren extensions.
Modeling short‐ranged dependence in block extrema with application to polar temperature data

The block maxima approach is an important method in univariate extreme value analysis. While assuming that block maxima are independent results in straightforward analysis, the resulting inferences maybe invalid when a series of block maxima exhibits dependence. We propose a model, based on a first-order Markov assumption, that incorporates dependence between successive block maxima through the use of a bivariate logistic dependence structure while maintaining generalized extreme value (GEV) marginal distributions. Modeling dependence in this manner allows us to better estimate extreme quantiles when block maxima exhibit short-ranged dependence. We demonstrate via a simulation study that our first-order Markov GEV model performs well when successive block maxima are dependent, while still being reasonably robust when maxima are independent. We apply our method to two polar annual minimum air temperature data sets that exhibit short-ranged dependence structures, and find that the proposed model yields modified estimates of high quantiles.
Selling effective violence prevention policies to the public: a nationally representative framing experiment

After years of decreasing public punitiveness and declining crime rates, politicians are seeking evidence-based crime policies to reduce mass incarceration without increasing crime. One such policy that has been implemented in several US cities is the Operation Peacemaker Fellowship (OPF), which incentivizes conformity and program participation by providing monetary stipends to individuals at risk of violent offending, thereby simultaneously reducing violence and incarceration. Yet, there is no evidence about public support for such policies. Using a nationally representative survey experiment, we examine public support for violence prevention stipends. We employ a referendum-style, contingent valuation design to measure the impact of tax increases versus tax savings on public opinion, and we randomize message framing that emphasizes the stipend program’s risky versus protective features. Both tax changes and risk framing matter. The public is willing to vote for stipends when they reduce taxes and are framed as a method to save lives. Most Republicans oppose stipends under all conditions. Reformers can increase public support for effective, nonpunitive policies that target violent offenders by emphasizing both their economic and social benefits. However, such policies are likely to face consistent opposition from certain portions of the public.
Integrated multi-wavelength control of an ion qubit

Monolithic integration of control technologies for atomic systems is a promising route to the development of quantum computers and portable quantum sensors1–4. Trapped atomic ions form the basis of high-fidelity quantum information processors5,6 and high-accuracy optical clocks7. However, current implementations rely on free-space optics for ion control, which limits their portability and scalability. Here we demonstrate a surface-electrode ion-trap chip8,9 using integrated waveguides and grating couplers, which delivers all the wavelengths of light required for ionization, cooling, coherent operations and quantum state preparation and detection of Sr+ qubits. Laser light from violet to infrared is coupled onto the chip via an optical-fibre array, creating an inherently stable optical path, which we use to demonstrate qubit coherence that is resilient to platform vibrations. This demonstration of CMOS-compatible integrated photonic surface-trap fabrication, robust packaging and enhanced qubit coherence is a key advance in the development of portable trapped-ion quantum sensors and clocks, providing a way towards the complete, individual control of larger numbers of ions in quantum information processing systems. A surface-electrode ion-trap chip is demonstrated, which delivers all the wavelengths of light required for the preparation and operation of ion qubits.
Message Complexity of Population Protocols.

The standard population protocol model assumes that when two agents interact, each observes the entire state of the other. We initiate the study of message complexity for population protocols, where an agent’s state is divided into an externally-visible message and externally-hidden local state. We consider the case of O(1) message complexity. When time is unrestricted, we obtain an exact characterization of the stably computable predicates based on the number of internal states s(n): If s(n) = o(n) then the protocol computes semilinear predicates (unlike the original model, which can compute non-semilinear predicates with s(n) = O(log n)), and otherwise it computes a predicate decidable by a nondeterministic O(n log s(n))-space-bounded Turing machine. We then introduce novel O(polylog(n)) expected time protocols for junta/leader election and general purpose broadcast correct with high probability, and approximate and exact population size counting correct with probability 1. Finally, we show that the main constraint on the power of bounded-message-size protocols is the size of the internal states: with unbounded internal states, any computable function can be computed with probability 1 in the limit by a protocol that uses only 1-bit messages.
Measuring phase errors in the presence of scintillation.

Strong turbulence conditions create amplitude aberrations through the effects of near-field diffraction. When integrated over long optical path lengths, amplitude aberrations (seen as scintillation) can nullify local areas in the recorded image of a coherent beam, complicating the wavefront reconstruction process. To estimate phase aberrations experienced by a telescope beam control system in the presence of strong turbulence, the wavefront sensor (WFS) of an adaptive optics must be robust to scintillation. We have designed and built a WFS, which we refer to as a “Fresnel sensor,” that uses near-field diffraction to measure phase errors under moderate to strong turbulent conditions. Systematic studies of its sensitivity were performed with laboratory experiments using a point source beacon. The results were then compared to a Shack-Hartmann WFS (SHWFS). When the SHWFS experiences irradiance fade in the presence of moderate turbulence, the Fresnel WFS continues to routinely extract phase information. For a scintillation index of S = 0.55, we show that the Fresnel WFS offers a factor of 9 × gain in sensitivity over the SHWFS. We find that the Fresnel WFS is capable of operating with extremely low light levels, corresponding to a signal-to-noise ratio of only SNR≈2−3 per pixel. Such a device is well-suited for coherent beam propagation, laser communications, remote sensing, and applications involving long optical path-lengths, site-lines along the horizon, and faint signals.
The pandemic economic crisis, precautionary behavior, and mobility constraints: an application of the dynamic disequilibrium model with randomness

This article analyzes the economic impact of the pandemic, providing insights into the consequences of alternative policies Our framework focuses on three key features: (i) COVID-19 is a sectoral shock of unknown depth and duration affecting some sectors and technologies more than others;(ii) there are constraints in shifting resources across sectors;and (iii) there is a high level of uncertainty about the disease and its economic aftermath, inducing a high level of precautionary behavior by some agents and leading to others facing more severe credit constraints Because of macroeconomic externalities, precautionary behavior exacerbates the downturn, and even sectors where COVID-19 does not directly affect consumption or production may face unemployment Multipliers associated with different government expenditure programs differ markedly The article describes policies that can mitigate precautionary behavior, leading to reduced unemployment Greater wage flexibility may lead to increased unemployment The precautionary behavior is the antithesis of equilibrium behavior, suggesting that standard equilibrium approaches may not provide the appropriate framework for analyzing the pandemic: individuals know that they don’t know the future, that existing and newly made contracts and plans may be broken, and that they need to be able to respond to these unknowable contingencies
Correcting for experiment-specific variability in expression compendia can remove underlying signals.

Motivation In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technical variability, or "batch effects," may result from combining samples collected and processed at different times and in different settings. Such variability may distort our ability to extract true underlying biological patterns. As more integrative analysis methods arise and data collections get bigger, we must determine how technical variability affects our ability to detect desired patterns when many experiments are combined. Objective We sought to determine the extent to which an underlying signal was masked by technical variability by simulating compendia comprising data aggregated across multiple experiments. Method We developed a generative multi-layer neural network to simulate compendia of gene expression experiments from large-scale microbial and human datasets. We compared simulated compendia before and after introducing varying numbers of sources of undesired variability. Results The signal from a baseline compendium was obscured when the number of added sources of variability was small. Applying statistical correction methods rescued the underlying signal in these cases. However, as the number of sources of variability increased, it became easier to detect the original signal even without correction. In fact, statistical correction reduced our power to detect the underlying signal. Conclusion When combining a modest number of experiments, it is best to correct for experiment-specific noise. However, when many experiments are combined, statistical correction reduces our ability to extract underlying patterns.
Quantum topological invariants, gravitational instantons and the topological embedding

Certain topological invariants of the moduli space of gravitational instantons are defined and studied. Several amplitudes of two- and four-dimensional topological gravity are computed. A notion of puncture in four dimensions, which is particularly meaningful in the class of Weyl instantons, is introduced. The topological embedding, a theoretical framework for constructing physical amplitudes that are well defined order-by-order in perturbation theory around instantons, is explicitly applied to the computation of the correlation functions of Dirac fermions in a punctured gravitational background, as well as to the most general QED and QCD amplitude. Various alternatives are worked out, discussed and compared. The quantum background affects the propagation by generating a certain effective `quantum' metric. The topological embedding could represent a new chapter of quantum field theory.
Deformation Theory of Periodic Monopoles (With Singularities)

Cherkis and Kapustin (Commun Math Phys 218(2): 333–371, 2001 and Commun Math Phys 234(1):1–35, 2003) introduced periodic monopoles (with singularities), i.e. monopoles on $${\mathbb{R}^{2} \times \mathbb{S}^{1}}$$ possibly singular at a finite collection of points. In this paper we show that for generic choices of parameters the moduli spaces of periodic monopoles (with singularities) with structure group $${SO(3)}$$ are either empty or smooth hyperkahler manifolds. Furthermore, we prove an index theorem and therefore compute the dimension of the moduli spaces.
On Functions Computed on Trees.

Any function can be constructed using a hierarchy of simpler functions through compositions. Such a hierarchy can be characterized by a binary rooted tree. Each node of this tree is associated with...
A Sharp Inequality for the Strichartz Norm

Let be the solution of the linear Schrodinger equationIn the first part of this paper, we obtain a sharp inequality for the Strichartz norm , where , and , that admits only Gaussian maximizers. As corollaries, we obtain sharp forms of the classical Strichartz inequalities in low dimensions (works of Foschi [4] and Hundertmark-Zharnitsky [6]) and also sharp forms of some Sobolev-Strichartz inequalities. In the second part of the paper, we express Foschi's [4] sharp inequalities for the Schrodinger and wave equations in the broader setting of sharp restriction/extension estimates for the paraboloid and the cone.
Sub-kelvin thermometer for on-chip measurements of microwave devices utilizing two-level systems in superconducting microresonators

We present a superconducting microresonator thermometer based on two-level systems (TLSs), which is drop-in compatible with cryogenic microwave systems. The operational temperature range is 50–1000 mK (which may be extended to 5 mK), and the sensitivity (50–75 μK/ Hz) is relatively uniform across this range. The miniature footprint that conveniently attaches to the feedline of a cryogenic microwave device facilitates the measurement of on-chip device temperature and requires no additional thermometry wiring or readout electronics. We demonstrate the practical use of these TLS thermometers to investigate static and transient chip heating in a kinetic inductance traveling-wave parametric amplifier operated with a strong pump tone. TLS thermometry may find broad application in cryogenic microwave devices such as superconducting qubits and detectors.
Colors of Trans-Neptunian Contact Binaries

The g'r'i' colors of seven likely and potential contact binaries in the Kuiper belt were acquired with the Magellan-Baade telescope and combined with colors from the literature to understand contact binary surfaces. The likely and potential contact binaries discovered in the dynamically Cold Classical population display very-red/ultra-red colors. Such a color is common in this sub-population and infers that the Cold Classical contact binaries were formed in-situ. The likely contact binaries found in several mean motion resonances with Neptune have colors from moderately to ultra-red suggesting different formation regions. Among the nine contact binaries discovered in resonances, five have very-red/ultra-red colors and four have moderately-red surfaces. Based on the very-red/ultra-red colors and low to moderate inclination of the contact binaries in resonances, these contact binaries are maybe escaped dynamically Cold Classicals that are now trapped in resonances. Moderately-red surfaces are common in diverse sub-populations of the Kuiper belt and thus pinpointing their origin is difficult though they are most likely captured objects formed in the giant planet area. Finally, for the contact binary population we report an anti-correlation between inclination and g'-r', as noticed in the rest of this belt. We also have hints for trends between eccentricity, perihelion distance, rotational period and g'-r', but as we are still dealing with a limited sample, additional data are required to confirm them.
Identifying Documents In-Scope of a Collection from Web Archives

Web archive data usually contains high-quality documents that are very useful for creating specialized collections of documents, e.g., scientific digital libraries and repositories of technical reports. In doing so, there is a substantial need for automatic approaches that can distinguish the documents of interest for a collection out of the huge number of documents collected by web archiving institutions. In this paper, we explore different learning models and feature representations to determine the best performing ones for identifying the documents of interest from the web archived data. Specifically, we study both machine learning and deep learning models and "bag of words" (BoW) features extracted from the entire document or from specific portions of the document, as well as structural features that capture the structure of documents. We focus our evaluation on three datasets that we created from three different Web archives. Our experimental results show that the BoW classifiers that focus only on specific portions of the documents (rather than the full text) outperform all compared methods on all three datasets.
Chaotic maps, Hamiltonian flows and holographic methods

Holographic functional methods are introduced as probes of discrete time-stepped maps that lead to chaotic behavior. The methods provide continuous time interpolation between the time steps, thereby revealing the maps to be quasi-Hamiltonian systems underlain by novel potentials that govern the motion of a perceived point particle. Between turning points, the particle is strictly driven by Hamiltonian dynamics, but at each encounter with a turning point the potential changes abruptly, loosely analogous to the switchbacks on a mountain road. A sequence of successively deepening switchback potentials explains, in physical terms, the frequency cascade and trajectory folding that occur on the particular route to chaos revealed by the logistic map.