cssi/SciSciGPT-SciSciNet huggingface dataset:
Dealers and changing obligations: the case of stub quoting

We examine the liquidity providing behavior of NASDAQ market makers surrounding two periods of changing dealer obligation. The first change in November, 2007 is the relaxation of rule 4613, which required NASDAQ market makers to place two-sided quotes “reasonably related” to the current best bid and offer. The relaxation of this rule permitted NASDAQ market makers to post quotes far away from the prevailing market, a practice frequently referred to as stub quoting. The second is the Securities and Exchange Commission ban on stub quoting in December, 2010, which requires market makers to quote within a predefined distance from market prices. We find evidence that placing restrictions on stub quoting alters market makers’ liquidity providing behavior in both the 2007 and 2010 rule change periods. Stub quoting restrictions increase the time that market makers quote at the NBBO. We also find evidence that the proportion daily volume executed by market makers increases during the 2007 stub quoting restriction. We also find evidence that restrictions on stub quoting narrows spreads and reduces the price impact of trades. However, we find little evidence that stub quoting rules impact the participation of market makers during days with excessive volatility.
Reducing bias of MLE in a dynamic panel model

This paper investigates a simple dynamic linear panel regression model with both fixed effects and time effects. Using “large n and large T” asymptotics, we approximate the distribution of the fixed effect estimator of the autoregressive parameter in the dynamic linear panel model and derive its asymptotic bias. We find that the same higher order bias correction approach proposed by Hahn and Kuersteiner (2002, Econometrica 70, 1639–1659) can be applied to the dynamic linear panel model even when time specific effects are present.We thank Peter Phillips and three anonymous referees for helpful comments. The first author gratefully acknowledges financial support from NSF grant SES-0313651. The second author appreciates the Faculty Development Awards of USC for research support.
Shock Wave Collisions and Thermalization in AdS_5

We study heavy ion collisions at strong ’t Hooft coupling using AdS/CFT correspondence. According to the AdS/CFT dictionary heavy ion collisions correspond to gravitational shock wave collisions in AdS5. We construct the metric in the forward light cone after the collision perturbatively through expansion of Einstein equations in graviton exchanges. We obtain an analytic expression for the metric including all-order graviton exchanges with one shock wave, while keeping the exchanges with another shock wave at the lowest order. We read off the corresponding energy-momentum tensor of the produced medium. Unfortunately this energy-momentum tensor does not correspond to ideal hydrodynamics, indicating that higher order graviton exchanges are needed to construct the full solution of the problem. We also show that shock waves must completely stop almost immediately after the collision in AdS5, which, on the field theory side, corresponds to complete nuclear stopping due to strong coupling effects, likely leading to Landau hydrodynamics. Finally, we perform trapped surface analysis of the shock wave collisions demonstrating that a bulk black hole, corresponding to ideal hydrodynamics on the boundary, has to be created in such collisions, thus constructing a proof of thermalization in heavy ion collisions at strong coupling.
The joint determination of leverage and maturity

Abstract We examine theories of leverage and debt maturity, focusing on the impact of firms' investment opportunity sets and regulatory environments in determining these policies. Using results on strategic complementarities, we identify sufficient conditions for the theory to have testable implications for reduced-form and structural-equation regression coefficients. Obtaining testable implications for structural equations requires less from the theory but more from the data than the reduced-form specification because it requires an instrumental-variables approach. We examine this trade-off between theory and statistical methods and provide tests using two decades of data for over 5000 industrial firms.
TARP announcement, bank health, and borrowers’ credit risk

Theory suggests that unhealthy banks exhibit more pronounced flight-to-quality behavior during financial crises and, hence, the infusion of capital through unhealthy banks is less effective in relieving the liquidity shocks of vulnerable borrowers. We test these predictions by investigating how the financial health of leading US banks influenced their borrowers’ credit risk surrounding the announcement of the Troubled Asset Relief Program (TARP). Changes in borrower credit risk, measured by credit default swap (CDS) spreads, should reflect the expected relief from liquidity shocks and other benefits of rescuing banks, such as maintaining the existing lending relationships. Consistent with the theory, prior to the TARP capital infusions, unhealthy banks’ borrowers with high leverage experienced a greater increase in their credit risk relative to similar healthy banks’ borrowers. Following the event, the CDS market anticipated less liquidity relief to these vulnerable unhealthy banks’ borrowers, but more liquidity relief to the vulnerable healthy banks’ borrowers.
Sexual exploitation and the Criminal Code

This article discusses a section of Canada's Criminal Code that prohibits the sexual exploitation of young persons by adults in positions of trust or authority. It argues that the language employed in the statute is problematically vague and that judicial interpretation has only added to the confusion.
Stochastic Lotka-Volterra competitive reaction-diffusion systems perturbed by space-time white noise: Modeling and analysis

Abstract Motivated by the traditional Lotka-Volterra competitive models, this paper proposes and analyzes a class of stochastic reaction-diffusion partial differential equations. In contrast to the models in the literature, the new formulation enables spatial dependence of the species. In addition, the noise process is allowed to be space-time white noise. In this work, well-posedness, regularity of solutions, existence of density, and existence of an invariant measure for stochastic reaction-diffusion systems with non-Lipschitz and non-linear growth coefficients and multiplicative noise are considered. By combining the random field approach and infinite integration theory approach in SPDEs for mild solutions, analysis is carried out. Then this paper develops a Lotka-Volterra competitive system under general setting; longtime properties are studied with the help of newly developed tools in stochastic calculus.
Unsupervised Feature Learning for Point Cloud by Contrasting and Clustering With Graph Convolutional Neural Network

To alleviate the cost of collecting and annotating large-scale point cloud datasets, we propose an unsupervised learning approach to learn features from unlabeled point cloud "3D object" dataset by using part contrasting and object clustering with deep graph neural networks (GNNs). In the contrast learning step, all the samples in the 3D object dataset are cut into two parts and put into a "part" dataset. Then a contrast learning GNN (ContrastNet) is trained to verify whether two randomly sampled parts from the part dataset belong to the same object. In the cluster learning step, the trained ContrastNet is applied to all the samples in the original 3D object dataset to extract features, which are used to group the samples into clusters. Then another GNN for clustering learning (ClusterNet) is trained to predict the cluster ID of all the training samples. The contrasting learning forces the ContrastNet to learn high-level semantic features of objects but probably ignores low-level features, while the ClusterNet improves the quality of learned features by being trained to discover objects that probably belong to the same semantic categories by the use of cluster IDs. We have conducted extensive experiments to evaluate the proposed framework on point cloud classification tasks. The proposed unsupervised learning approach obtained comparable performance to the state-of-the-art unsupervised learning methods that used much more complicated network structures. The code of this work is publicly available via: this https URL.
Theta bases and log Gromov-Witten invariants of cluster varieties

Using heuristics from mirror symmetry, combinations of Gross, Hacking, Keel, Kontsevich, and Siebert have given combinatorial constructions of canonical bases of "theta functions" on the coordinate rings of various log Calabi-Yau spaces, including cluster varieties. We prove that the theta bases for cluster varieties are determined by certain descendant log Gromov-Witten invariants of the symplectic leaves of the mirror/Langlands dual cluster variety, as predicted in the Frobenius structure conjecture of Gross-Hacking-Keel. We further show that these Gromov-Witten counts are often given by naive counts of rational curves satisfying certain geometric conditions. As a key new technical tool, we introduce the notion of "contractible" tropical curves when showing that the relevant log curves are torically transverse.
Clutter Slices Approach for Identification-on-the-Fly of Indoor Spaces.

Construction spaces are constantly evolving, dynamic environments in need of continuous surveying, inspection, and assessment. Traditional manual inspection of such spaces proves to be an arduous and time-consuming activity. Automation using robotic agents can be an effective solution. Robots, with perception capabilities can autonomously classify and survey indoor construction spaces. In this paper, we present a novel identification-on-the-fly approach for coarse classification of indoor spaces using the unique signature of clutter. Using the context granted by clutter, we recognize common indoor spaces such as corridors, staircases, shared spaces, and restrooms. The proposed clutter slices pipeline achieves a maximum accuracy of 93.6% on the presented clutter slices dataset. This sensor independent approach can be generalized to various domains to equip intelligent autonomous agents in better perceiving their environment.
The social experience of participation in a COVID-19 vaccine trial: Subjects' motivations, others' concerns, and insights for vaccine promotion.

BACKGROUND: Vaccine hesitancy could undermine the effectiveness of COVID-19 vaccination programs. Knowledge about people's lived experiences regarding COVID-19 vaccination can enhance vaccine promotion and increase uptake. AIM: To use COVID-19 vaccine trial participants' experiences to identify key themes in the lived experience of vaccination early in the vaccine approval and distribution process. METHODS: We interviewed 31 participants in the Iowa City, Iowa US site of the Pfizer/BioNTech COVID-19 vaccine phase 3 clinical trial. While trial participation differs from clinical receipt of an approved vaccine in key ways, it offers the first view of people's lived experiences of potentially receiving a COVID-19 vaccine. The trial context is also useful since decision-making about vaccination and medical research participation often involve similar hopes and concerns, and because the public appears to view even approved COVID-19 vaccines as experimental given their novelty. Semi-structured interviews addressed subjects' experiences, including decision-making and telling others about their trial participation. We analyzed verbatim transcripts of these interviews thematically and identified common themes relevant for vaccination decision-making. RESULTS: Participants across demographic groups, including age, sex/gender, race/ethnicity, and political affiliation, described largely similar experiences. Key motivations for participation included ending the pandemic/restoring normalcy, protecting oneself and others, doing one's duty, promoting/modeling vaccination, and expressing aspects of identity like being a helper, career-related motivations, and support of science/vaccines. Participants often felt uniquely qualified to help via trial participation due to personal attributes like health, sex/gender or race/ethnicity. They reported hearing concerns about side effects and the speed and politicization of vaccine development. Participants responded by normalizing and contextualizing side effects, de-politicizing vaccine development, and explaining how the rapid development process was nevertheless safe. CONCLUSION: These findings regarding participants' reported motivations for trial participation and interactions with concerned others can be incorporated into COVID-19 vaccine promotion messaging aimed at similar populations.
Effective generation of right-angled artin groups in mapping class groups

We show that given a collection $$X=\{f_1$$ , ..., $$f_m\}$$ of pure mapping classes on a surface S, there is an explicit constant N, depending only on X, such that their Nth powers $$\{f_1^N$$ , ..., $$f_m^N\}$$ generate the expected right-angled Artin subgroup of MCG(S). Moreover, we show that these subgroups are undistorted, and that each element is pseudo-Anosov on the largest possible subsurface.
Sensitivity-Based Vulnerability Assessment of State Estimation

We propose a technique to assess the vulnerability of the power system state estimator. We aim at identifying measurements that have a high potential of being the target of false data injection attacks. From an adversary's point of view, such measurements have to show the following characteristics: i) being influential on the variable estimates, and ii) corrupting their measured values is likely to be undetected. Additionally, such characteristics should not change significantly with the system's operating condition. Our technique provides a systematic way of identifying measurements with such characteristics. We illustrate our methodology on a 4-bus system, the New England 39-bus system, and the IEEE 118-bus test system.
Specifying User Preferences Using Weighted Signal Temporal Logic

We extend Signal Temporal Logic (STL) to enable the specification of importance and priorities. The extension, called Weighted STL (wSTL), has the same qualitative (Boolean) semantics as STL, but additionally defines weights associated with Boolean and temporal operators that modulate its quantitative semantics (robustness). We show that the robustness of wSTL can be defined as weighted generalizations of all known compatible robustness functionals (i.e., robustness scores that are recursively defined over formulae) that can take into account the weights in wSTL formulae. We utilize this weighted robustness to distinguish signals with respect to a desired wSTL formula that has subformulae with different importance or priorities and time preferences, and demonstrate its usefulness in problems with conflicting tasks where satisfaction of all tasks cannot be achieved. We also employ wSTL robustness in an optimization framework to synthesize controllers that maximize satisfaction of a specification with user specified preferences.
Of the World Trade Court's Burden

This article argues that in adjudicating sensitive disputes, such as those concerning human health, the WTO tribunal (Court) acts as a Dworkinian Hercules which provides its own answers on risks and science. In judging which party should win the case, this Hercules assesses parties� arguments and evidence on risks and regulatory responses through a technical rule labelled the �burden of proof� (BOP). Yet the BOP is more the Court's burden than parties� burden (who to prove) in that the final outcome of the case hinges eventually on those elements which the Court requires parties to prove (what to prove), as well as whether the Court approves that a party has discharged its BOP and allows the burden to shift to the other party (whether to prove). As long as the Court plays the role of Hercules by handing down substantive justice on issues of high controversy, such as risks and science, whatever decision it makes will hardly satisfy the parties concerned, and thus will never fully resolve their disputes. If the Court's own answer (substantive justice) cannot put an end to parties� antimonial struggle, the Court should contemplate guiding parties to discover the solution between them via constructive regulatory dialogue. The Court can achieve this new goal by transforming its current substantive hermeneutics over the BOP into a �procedural� one. The Court's new interpretation can reoperationalize the BOP in a way that brings out certain important administrative law elements, such as transparency and reason-giving, embedded in major SPS obligations such as risk assessment.
LACK OF ASSOCIATION BETWEEN EARLY FLUID INTAKE AND RISK OF BRONCHOPULMONARY DYSPLASIA IN EXTREMELY LOW BIRTH WEIGHT INFANTS

Objective To evaluate the association between fluid intake during the first 14 days of life and the risk of bronchopulmonary dysplasia (BPD) in extremely low birth weight (ELBW) infants. Methods A retrospective analysis of a cohort of ELBW admitted in our NICU from 2001 to 2006 compared demographic, clinical data and the daily nutritional intake during the first 14 days of life between the infants with BPD (oxygen at 36 wks pma) and those without BPD. Results Among the 124 infants studied, 22 (17.7%) developed BPD. The mean total fluid intake at 7 days (119±5.3 vs 118.2±8.2 ml/kg/d), at 14 days (136.5±9.1 vs 137.1±8.4 ml/kg/d) and the maximum weight loss (9.0±6.1% vs 10.3±5.2%) were similar in the two groups; BPD infants received higher mean intravenous fluid intake at 14 days (122.9±16.1 vs 109.1±21.8 ml/kg/d). The adjusted risk of higher intravenous fluid intake related to BPD was lost after a multivariate logistic regression analysis that showed GA (OR 0.40; 95% CI 0.30 to 0.52), BW z score (OR 0.47; 95% CI 0.32 to 0.69) and intravenous protein intake at 14 days (OR 0.38; 95% CI 0.18 to 0.80) as the best early independent predictors of BPD. Conclusion In this cohort of ELBW infants early fluid intake was not associated with an increased risk of BPD.
New Linear Codes as Quasi-Twisted Codes from Long Constacyclic Codes

One of the most important and challenging problems in coding theory is to determine the optimal values of the parameters of a linear code and to explicitly construct codes with optimal parameters, or as close to the optimal values as possible. The class of quasi-twisted (QT) codes has been very promising in this regard. Over the past few decades various search algorithms to construct QT codes with better parameters have been employed. Most of these algorithms (such as ASR [1]) start by joining constacyclic codes of smaller lengths to obtain QT codes of longer lengths. There has been an algorithm that works in the opposite way that constructs shorter QT codes from long constacyclic codes. We modified and generalized this algorithm and obtained new linear codes via its implementation. We also observe that the new algorithm is related to the ASR algorithm.
Kenji or Kenneth? Pearl Harbor and Japanese-American assimilation

Abstract Do immigrants assimilate in response to an exogenous shock in anti-immigrant sentiment? I investigate this question by examining the Pearl Harbor bombing as a natural experiment. I generate an index for the Americanization of first names from the 1900–1930 censuses and merge this index with records from the universe of Japanese-American internees during WW2. Regression discontinuity in day-of-birth estimates suggest that Japanese Americans born in the days after Pearl Harbor had more Americanized first names relative to internees born in the days before December 7th, 1941. There is no discontinuity in socioeconomic variables, and a within-family analysis yields similar results.
Necessity Never Made a Good Bargain: When Consumer Arbitration Agreements Prohibit Class Relief

Courts rely on the "national policy favoring arbitration" to restrict the review of arbitration agreements under state laws of unconscionability. Consequently, banks, phone companies, and other consumer businesses implement mandatory arbitration clauses that provide complete immunization from both class actions and classwide arbitrations. As potential defedants, these companies hope that courts will force individual resolution of all consumer claims against them by upholding their agreements to arbitrate. Such an exercise raises an important question, which is the subject of this Article: To what extent should courts use the "national policy favoring arbitration" to protect consumer arbitration agreements that prohibit all class relief?
The First Droplet in a Cloud Chamber Track

In a cloud chamber, the quantum measurement problem amounts to explaining the first droplet in a charged-particle track; subsequent droplets are explained by Mott’s 1929 wave-theoretic argument about collision-induced wavefunction collimation. I formulate a mechanism for how the first droplet in a cloud chamber track arises, making no reference to quantum measurement axioms. I look specifically at tracks of charged particles emitted in the simplest slow decays, because I can reason about rather than guess the form that wave packets take. The first visible droplet occurs when a randomly occurring, barely-subcritical vapor droplet is pushed past criticality by ionization triggered by the faint wavefunction of the emitted charged particle. This is possible because potential energy incurred when an ionized vapor molecule polarizes the other molecules in a droplet can balance the excitation energy needed for the emitted charged particle to create the ion in the first place. This degeneracy is a singular condition for Coulombic scattering, leading to infinite or near-infinite ionization cross sections, and from there to an emergent Born rule in position space, but not an operator projection as in the projection postulate. Analogous mechanisms may explain canonical quantum measurement behavior in detectors such as ionization chambers, proportional counters, photomultiplier tubes or bubble chambers. This work is important because attempts to understand canonical quantum measurement behavior and its limitations have become urgent in view of worldwide investment in quantum computing and in searches for super-rare processes (e.g., proton decay).
Approaching the limits of optoelectronic performance in mixed cation mixed halide perovskites by controlling surface recombination

We demonstrate the critical role of surface recombination in mixed-cation, mixed-halide perovskite, FA0.83Cs0.17Pb(I0.85Br0.15)3. By passivating non-radiative defects with the polymerizable Lewis base (3-aminopropyl)trimethoxysilane (APTMS) we transform these thin films. We demonstrate average minority carrier lifetimes > 4 {\mu}s, nearly single exponential monomolecular PL decays, and concomitantly high external photoluminescence quantum efficiencies (>20%, corresponding to ~97% of the maximum theoretical quasi-Fermi-level splitting) at low excitation fluence. We confirm both the composition and valence band edge position of the FA0.83Cs0.17Pb(I0.85Br0.15)3 perovskite using multi-institution, cross-validated, XPS and UPS measurements. We extend the APTMS surface passivation to higher bandgap double cation (FA,Cs) compositions (1.7 eV, 1.75 eV and 1.8 eV) as well as the widely used triple cation (FA,MA,Cs) composition and observe significant PL and PL lifetime improvements after surface passivation. Finally, we demonstrate that the average surface recombination velocity (SRV) decreases from ~1000 cm/s to ~10 cm/s post APTMS passivation for FA0.83Cs0.17Pb(I0.85Br0.15)3. Our results demonstrate that surface-mediated recombination is the primary non-radiative loss pathway in MA-free mixed-cation mixed-halide films with a range of different bandgaps, which is a problem observed for a wide range of perovskite active layers and reactive electrical contacts. This study indicates that surface passivation and contact engineering will enable near-theoretical device efficiencies with these materials.
Fusion yield of plasma with velocity-space anisotropy at constant energy

Velocity-space anisotropy can significantly modify fusion reactivity. The nature and magnitude of this modification depends on the plasma temperature, as well as the details of how the anisotropy is introduced. For plasmas that are sufficiently cold compared to the peak of the fusion cross section, anisotropic distributions tend to have higher yields than isotropic distributions with the same thermal energy. At higher temperatures, it is instead isotropic distributions that have the highest yields. However, the details of this behavior depend on exactly how the distribution differs from an isotropic Maxwellian. This paper describes the effects of anisotropy on fusion yield for the class of anisotropic distribution functions with the same energy distribution as a 3D isotropic Maxwellian and compares those results with the yields from bi-Maxwellian distributions. In many cases, especially for plasmas somewhat below reactor-regime temperatures, the effects of anisotropy can be substantial.
ScieNet: Deep learning with spike-assisted contextual information extraction

Abstract Spiking neural network (SNN) is a type of artificial neural network that uses biologically inspired neuron models and learning rules to develop artificial intelligence with capability parallel to human brain. Deep neural networks (DNNs), on the other hand, uses less biologically plausible neurons and training methods such as gradient descent, and has shown good accuracy in computer vision tasks. However, human brain can still outperform DNN in certain scenarios. For example, DNN experiences significant performance degradation when perturbation from various sources is present in the input, which makes DNN less reliable for systems interacting with physical world. In this paper, we present a hybrid deep net work architecture with s pike-assisted c ontextual i nformation e xtraction (ScieNet) as a solution to the problem. ScieNet integrates a front-end SNN with a novel stochastic spike-timing-dependent plasticity (STDP) algorithm that extracts visual context from images. The back-end DNN is trained for classification given the contextual information. The integrated network demonstrates high resilience to input perturbations without relying on pre-training on perturbed inputs. We demonstrate ScieNet with various back-end DNNs for image classification using different datasets and considering both stochastic and structured input perturbations. Experimental results demonstrate significant improvement in accuracy on perturbed images, while maintaining state-of-the-art accuracy on clean images.
Nonlinear stability analysis of transitional flows using quadratic constraints

The dynamics of incompressible flows are governed by an interaction between non-normal linear dynamics and a static nonlinearity. We propose a framework for stability analysis that considers the linear dynamics subject to constraints that reflect the fact that the nonlinearity is quadratic and energy conserving. The approach can be used to conduct global, local, and non-modal stability analyses and to uncover dominant nonlinear flow interactions that drive these instabilities.
Testing framework for black-box AI models

With widespread adoption of AI models for important decision making, ensuring reliability of such models remains an important challenge. In this paper, we present an end-to-end generic framework for testing AI Models which performs automated test generation for different modalities such as text, tabular, and time-series data and across various properties such as accuracy, fairness, and robustness. Our tool has been used for testing industrial AI models and was very effective to uncover issues present in those models. Demo video link- https://youtu.be/984UCU17YZI
Group Communication With Context Codec for Lightweight Source Separation

Despite the recent progress on neural network architectures for speech separation, the balance between the model size, model complexity and model performance is still an important and challenging problem for the deployment of such models to low-resource platforms. In this paper, we propose two simple modules, group communication and context codec, that can be easily applied to a wide range of architectures to jointly decrease the model size and complexity without sacrificing the performance. A group communication module splits a high-dimensional feature into groups of low-dimensional features and captures the inter-group dependency. A separation module with a significantly smaller model size can then be shared by all the groups. A context codec module, containing a context encoder and a context decoder, is designed as a learnable downsampling and upsampling module to decrease the length of a sequential feature processed by the separation module. The combination of the group communication and the context codec modules is referred to as the GC3 design. Experimental results show that applying GC3 on multiple network architectures for speech separation can achieve on-par or better performance with as small as 2.5% model size and 17.6% model complexity, respectively.
Contrastive Unsupervised Learning for Speech Emotion Recognition

Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled datasets. To circumvent this problem, we investigate how unsupervised representation learning on unlabeled datasets can benefit SER. We show that the contrastive predictive coding (CPC) method can learn salient representations from unlabeled datasets, which improves emotion recognition performance. In our experiments, this method achieved state-of-the-art concordance correlation coefficient (CCC) performance for all emotion primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on the MSP-Podcast dataset, our method obtained considerable performance improvements compared to baselines.
Stochastic Dynamic Games in Belief Space

Information gathering while interacting with other agents under sensing and motion uncertainty is critical in domains such as driving, service robots, racing, or surveillance. The interests of agents may be at odds with others, resulting in a stochastic noncooperative dynamic game. Agents must predict others’ future actions without communication, incorporate their actions into these predictions, account for uncertainty and noise in information gathering, and consider what information their actions reveal. Our solution uses local iterative dynamic programming in Gaussian belief space to solve a game-theoretic continuous partially observable Markov decision process. Solving a quadratic game in the backward pass of a game-theoretic belief-space variant of iterative linear-quadratic Gaussian control achieves a runtime polynomial in the number of agents and linear in the planning horizon. Our algorithm yields linear feedback policies for our robot, and predicted feedback policies for other agents. We present three applications: Active surveillance, guiding eyes for a blind agent, and autonomous racing. Agents with game-theoretic belief-space planning (BSP) win 44% more races than without game theory and 34% more than without BSP.
Anisotropic nanocrystal shape and ligand design for co-assembly

The use of nanocrystal (NC) building blocks to create metamaterials is a powerful approach to access emergent materials. Given the immense library of materials choices, progress in this area for anisotropic NCs is limited by the lack of co-assembly design principles. Here, we use a rational design approach to guide the co-assembly of two such anisotropic systems. We modulate the removal of geometrical incompatibilities between NCs by tuning the ligand shell, taking advantage of the lock-and-key motifs between emergent shapes of the ligand coating to subvert phase separation. Using a combination of theory, simulation, and experiments, we use our strategy to achieve co-assembly of a binary system of cubes and triangular plates and a secondary system involving two two-dimensional (2D) nanoplates. This theory-guided approach to NC assembly has the potential to direct materials choices for targeted binary co-assembly.
An optimization-based approach to parameter learning for fractional type nonlocal models

Abstract Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.
Deep Image Reconstruction Using Unregistered Measurements Without Groundtruth

One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images. This paper addresses this limitation by proposing a novel unsupervised deep registration-augmented reconstruction method (U-Dream) for training deep neural nets to reconstruct high-quality images by directly mapping pairs of unregistered and artifact-corrupted images. The ability of U-Dream to circumvent the need for accurately registered data makes it widely applicable to many biomedical image reconstruction tasks. We validate it in accelerated magnetic resonance imaging (MRI) by training an image reconstruction model directly on pairs of undersampled measurements from images that have undergone nonrigid deformations.
A critical analysis of the study of gender and technology in government

Research at the intersection of feminist organizational theory and techno-science scholarship notes the important role that gender plays in technology design, adoption, implementation, and use and how technology shapes and is shaped by gender and gendered work life. While governments are committed to advancing gender equity, feminist theory is rarely applied to the study of technology in government, thus limiting our ability to fully understand gendered outcomes of e-government. In this paper, we argue that e-government research and practice can benefit from drawing from three streams of feminist research: 1) studying gender as social construction, 2) researching gender bias in data, technology use and design, and 3) assessing gendered representation in technology management. Drawing from feminist research, we offer six propositions and several research questions for advancing research on e-government and gender. We contribute to research on gender and e-government by advocating for a feminist approach to the analysis of the adoption and implementation of technology in government settings from the perspective of public managers and employees.
Time fused coefficient SIR model with application to COVID-19 epidemic in the United States

In this paper, we propose a Susceptible–Infected–Removal (SIR) model with time fused coefficients. In particular, our proposed model discovers the underlying time homogeneity pattern for the SIR mo...
Graphs associated with orthogonal collections of k-planes over finite fields

Abstract We study graphs coming from quadratic spaces over finite fields which generalize a recent result given by Bishnoi, Ihringer, and Pepe (2020). More precisely, we study the graph Γ □ ( n , k , q ) defined as follows: the vertex set is the set of k-dimensional quadratic subspaces of a fixed quadratic space ( F q n , x 1 2 + ⋯ + x n − 1 2 + λ x n 2 ) isometrically isomorphic to x 1 2 + ⋯ + x k 2 . Here λ is a non-square in F q , and two vertices x , y are adjacent if x ⊆ y ⊥ .
Data-driven coarse-grained modeling of non-equilibrium systems.

Modeling a high-dimensional Hamiltonian system in reduced dimensions with respect to coarse-grained (CG) variables can greatly reduce computational cost and enable efficient bottom-up prediction of main features of the system for many applications. However, it usually experiences significantly altered dynamics due to loss of degrees of freedom upon coarse-graining. To establish CG models that can faithfully preserve dynamics, previous efforts mainly focused on equilibrium systems. In contrast, various soft matter systems are known to be out of equilibrium. Therefore, the present work concerns non-equilibrium systems and enables accurate and efficient CG modeling that preserves non-equilibrium dynamics and is generally applicable to any non-equilibrium process and any observable of interest. To this end, the dynamic equation of a CG variable is built in the form of the non-stationary generalized Langevin equation (nsGLE), where the two-time memory kernel is determined from the data of the auto-correlation function of the observable of interest. By embedding the nsGLE in an extended dynamics framework, the nsGLE can be solved efficiently to predict the non-equilibrium dynamics of the CG variable. To prove and exploit the equivalence of the nsGLE and extended dynamics, the memory kernel is parameterized in a two-time exponential expansion. A data-driven hybrid optimization process is proposed for the parameterization, which integrates the differential-evolution method with the Levenberg–Marquardt algorithm to efficiently tackle a non-convex and high-dimensional optimization problem.
Sound probabilistic inference via guide types

Probabilistic programming languages aim to describe and automate Bayesian modeling and inference. Modern languages support programmable inference, which allows users to customize inference algorithms by incorporating guide programs to improve inference performance. For Bayesian inference to be sound, guide programs must be compatible with model programs. One pervasive but challenging condition for model-guide compatibility is absolute continuity, which requires that the model and guide programs define probability distributions with the same support. This paper presents a new probabilistic programming language that guarantees absolute continuity, and features general programming constructs, such as branching and recursion. Model and guide programs are implemented as coroutines that communicate with each other to synchronize the set of random variables they sample during their execution. Novel guide types describe and enforce communication protocols between coroutines. If the model and guide are well-typed using the same protocol, then they are guaranteed to enjoy absolute continuity. An efficient algorithm infers guide types from code so that users do not have to specify the types. The new programming language is evaluated with an implementation that includes the type-inference algorithm and a prototype compiler that targets Pyro. Experiments show that our language is capable of expressing a variety of probabilistic models with nontrivial control flow and recursion, and that the coroutine-based computation does not introduce significant overhead in actual Bayesian inference.
Valence–Instrumentality–Expectancy Model of Motivation as an Alternative Model for Examining Ethical Leadership Behaviors:

Incessant occurrences of character failure of people in leadership positions that have characterized organizations in recent years call for critical examination of leadership behaviors. The Social ...
Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations

Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. However, one critical and fundamental challenge in fixing the incompleteness of taxonomies is the incompleteness of the extracted concepts, especially for those whose names have multiple words and consequently low frequency in the corpus. To resolve the limitations of extraction-based methods, we propose GenTaxo to enhance taxonomy completion by identifying positions in existing taxonomies that need new concepts and then generating appropriate concept names. Instead of relying on the corpus for concept embeddings, GenTaxo learns the contextual embeddings from their surrounding graph-based and language-based relational information, and leverages the corpus for pre-training a concept name generator. Experimental results demonstrate that GenTaxo improves the completeness of taxonomies over existing methods.
Dawn Ion Propulsion System: Initial Checkout After Launch

The first 80 days after launch of the Dawn mission were dedicated to the checkout of the spacecraft with a major emphasis on the ion propulsion system. All three ion thrusters, all three thruster-gimbal assemblies, both power processor units, both digital interface and control units, and the entire xenon feed system were completely checked out, and every component was found to be in good health. Direct thrust measurements agreed well with preflight expected values for all three thrusters over the entire throttle range. Measurements of the thruster-produced roll- torque verified that each thruster produced less than the maximum allowed value of 60 μNm at full power. Thruster electrical operating parameters and power processor unit efficiencies also agreed well with preflight expected values based on acceptance test data. Two of the three ion thrusters were fully checked out within 30 days after launch. Checkout of all three thrusters was completed 64 days after launch. Deterministic thrusting with the ion propulsion system began on 17 December 2007.
Holography abhors visible trapped surfaces

We prove that consistency of the holographic dictionary implies a hallmark prediction of the weak cosmic censorship conjecture: that in classical gravity, trapped surfaces lie behind event horizons. In particular, the existence of a trapped surface implies the existence of an event horizon, and that furthermore this event horizon must be outside of the trapped surface. More precisely, we show that the formation of event horizons outside of a strong gravity region is a direct consequence of causal wedge inclusion, which is required by entanglement wedge reconstruction. We make few assumptions beyond the absence of evaporating singularities in strictly classical gravity. We comment on the implication that spacetimes with naked trapped surfaces do not admit a holographic dual, note a possible application to holographic complexity, and speculate on the dual CFT interpretation of a trapped surface.
Does the Heisenberg uncertainty principle apply along the time dimension

Does the Heisenberg uncertainty principle (HUP) apply along the time dimension in the same way it applies along the three space dimensions? Relativity says it should; current practice says no. With recent advances in measurement at the attosecond scale it is now possible to decide this question experimentally. The most direct test is to measure the time-of-arrival of a quantum particle: if the HUP applies in time, then the dispersion in the time-of-arrival will be measurably increased. We develop an appropriate metric of time-of-arrival in the standard case; extend this to include the case where there is uncertainty in time; then compare. There is -- as expected -- increased uncertainty in the time-of-arrival if the HUP applies along the time axis. The results are fully constrained by Lorentz covariance, therefore uniquely defined, therefore falsifiable. So we have an experimental question on our hands. Any definite resolution would have significant implications with respect to the role of time in quantum mechanics and relativity. A positive result would also have significant practical applications in the areas of quantum communication, attosecond physics (e.g. protein folding), and quantum computing.
Substation-Level Grid Topology Optimization Using Bus Splitting

Operations of substation circuit breakers are important for maintenance needs and topology reconfiguration in power systems. Bus splitting is one type of topology change where the two bus bars at a substation can become electrically disconnected under certain actions of circuit breakers. Because these events involve detailed substation modeling, they are typically not considered in routine power system operation and control. In this paper, an improved substation-level topology optimization framework is developed by expanding traditional line switching decisions by breaker-level bus splitting, which can further reduce grid congestion and generation costs. A tight McCormick relaxation is proposed to reformulate the bilinear terms in the resultant optimization problem to linear inequality constraints. Thus, a tractable mixed-integer linear program reformulation is attained that allows for efficient solutions in real-time operations. Numerical studies on the IEEE 14-bus and 118-bus systems demonstrate the computational performance and economic benefits of the proposed topology optimization approach.
Parental plans to vaccinate children for COVID-19 in New York city.

Once COVID-19 vaccines are approved for children < 12 years of age, high pediatric vaccination coverage will be needed to help minimize the public health threat from the SARS-CoV-2 epidemic. We conducted an online survey of 1,119 parents and caregivers of children ≤ 12 years in New York City from March 9 to April 11, 2021. Among parents surveyed, 61.9% reported plans to vaccinate their youngest child for COVID-19, 14.8% said they do not plan to vaccinate their child and 23.3% were unsure. Female and non-Hispanic Black parents were least likely to report plans to vaccinate their children. Safety, effectiveness and perceptions that children do not need vaccination were the primary reasons for vaccine hesitancy/resistance. Parents who have or will vaccinate themselves were significantly more likely to report they would vaccinate their children. Efforts to increase awareness about vaccine safety and education about the importance of vaccinating children are needed.
Immune checkpoint therapy modeling of PD-1/PD-L1 blockades reveals subtle difference in their response dynamics and potential synergy in combination

Abstract Immune checkpoint therapy is one of the most promising immunotherapeutic methods that are likely able to give rise to durable treatment response for various cancer types. Despite much progress in the past decade, there are still critical open questions with particular regards to quantifying and predicting the efficacy of treatment and potential optimal regimens for combining different immune checkpoint blockades. To shed light on this issue, here we develop clinically-relevant, dynamical systems models of cancer immunotherapy with a focus on the immune checkpoint PD-1/PD-L1 blockades. Our model allows the acquisition of adaptive immune resistance in the absence of treatment, whereas immune checkpoint blockades can reverse such resistance and boost anti-tumor activities of effector cells. Our numerical analysis predicts that anti-PD-1 agents are commonly less effective than anti-PD-L1 agents for a wide range of model parameters. We also observe that combination treatment of anti-PD-1 and anti-PD-L1 blockades leads to a desirable synergistic effect. Our modeling framework lays the ground for future data-driven analysis on combination therapeutics of immune checkpoint treatment regimes and thorough investigation of optimized treatment on a patient-by-patient basis.