cssi/SciSciGPT-SciSciNet huggingface dataset:
More on Average Case vs Approximation Complexity

We consider the problem to determine the maximal number of satisfiable equations in a linear system chosen at random. We make several plausible conjectures about the average case hardness of this problem for some natural distributions on the instances, and relate them to several interesting questions in the theory of approximation algorithms and in cryptography. Namely we show that our conjectures imply the following facts: ◦ Feige’s hypothesis about the hardness of refuting a random 3CNF is true, which in turn implies inapproximability within a constant for several combinatorial problems, for which no NP-hardness of approximation is known. ◦ It is hard to approximate the NEAREST CODEWORD within factor n1-e. ◦ It is hard to estimate the rigidity of a matrix. More exactly, it is hard to distinguish between matrices of low rigidity and random ones. ◦ There exists a secure public-key (probabilistic) cryptosystem, based on the intractability of decoding of random binary codes.
Exact conservation laws for gauge-free electromagnetic gyrokinetic equations

The exact energy and angular momentum conservation laws are derived by the Noether method for the Hamiltonian and symplectic representations of the gauge-free electromagnetic gyrokinetic Vlasov–Maxwell equations. These gyrokinetic equations, which are solely expressed in terms of electromagnetic fields, describe the low-frequency turbulent fluctuations that perturb a time-independent toroidally-axisymmetric magnetized plasma. The explicit proofs presented here provide a complete picture of the transfer of energy and angular momentum between the gyrocentres and the perturbed electromagnetic fields, in which the crucial roles played by gyrocentre polarization and magnetization effects are highlighted. In addition to yielding an exact angular momentum conservation law, the gyrokinetic Noether equation yields an exact momentum transport equation, which might be useful in more general equilibrium magnetic geometries.
The academic wanderer: structure of collaboration network and relation with research performance

Thanks to the widespread availability of large-scale datasets on scholarly outputs, science itself has come under the microscope with the aim of capturing a quantitative understanding of its workings. In this study, we leverage well-established cognitive models coming from anthropology in order to characterise the personal network of collaborations between scientists, i.e., the network considered from the standpoint of each individual researcher (referred to as ego network), in terms of the cognitive investment they devote to the different collaborations. Building upon these models, we study the interplay between the structure of academic collaborations, academic performance, and academic mobility at different career stages. We take into account both purely academic mobility (i.e., the number of affiliation changes) and geographical mobility (i.e., physical relocations to different countries). For our investigation, we rely on a dataset comprising the geo-referenced publications of a group of 81,500 authors extracted from Scopus, one of the biggest repositories of academic knowledge. Our main finding is that there is a clear correlation between the structure of co-authorship ego networks and academic performance indices: the more one publishes and the higher their impact, the larger their collaboration network. However, we observe a capacity bound effect, whereby, beyond a certain point, higher performances become increasingly less correlated with large collaboration networks. We also find that international academic migrants are better at growing their networks than researchers that only migrate within the same country, but the latter seem to be better in exploiting their collaboration to achieve higher impact. High academic mobility does not appear to translate into better academic performance or larger collaboration networks. This shows a different finding with respect to related literature, where scientific productivity is seen as directly linked to mobility. Our results show that, when looking at impact of research, this is not necessarily the case.
Genotype‐dependent and heat‐induced grain chalkiness in rice correlates with the expression patterns of starch biosynthesis genes

To understand the molecular basis of environment-induced and genotype-dependent chalkiness, six rice genotypes showing variable chalk levels were subjected to gene expression analysis during reproductive stages. In the high chalk genotypes, the peak expressions of ADP-Glucose Pyrophosphorylase (AGPase) Large Subunit 4 (AGPL4) occurred in the stages before grain filling commenced, creating a temporal gap with the upregulation of Granule Bound Starch Synthase I (GBSSI) and Starch Synthase IIA (SSIIA). Whereas, in the low chalk genotypes, AGPL4 expression generally occurred in later stages, close to the upregulation of GBSSI and SSIIA. However, heat treatment altered the expression pattern and created a gap between the expression peaks of AGPL4, and GBSS1 and SSIIA. This change was accompanied by transformed granular morphology, increased protein content, and chalkiness in the grains. AGPL4 expression pattern may partially explain chalkiness as it contributes to the pool of ADP-Glucose for producing amylose and amylopectin, the major components of the starch. Down-regulation of AGPase during grain filling stages could result in a limited pool of ADP-Glucose leading to inefficient grain filling and air pockets that contribute to chalkiness. The study suggests a mechanism of grain chalkiness based on the coordination of the three starch biosynthesis genes in rice. Significance statementGenotype-dependent and heat-induced grain chalkiness in rice is partially based on the increased gap between the upregulation AGPase and that of GBSSI and SSIIA through reproductive stages. This temporal gap could limit starch accumulation and alter granular morphology, eventually leading to grain chalkiness.
Lower-curing-Temperature PMR polyimides

Studies were performed to achieve lower-curing temperature PMR (polymerization of monomer reactants) polyimides. Partial substitution of a p-aminostyrene end-cap for the monomethyl ester of 5-norbornene-2,3-dicarboxylic acid lowered the final cure temperature of typical PMR resins from 600 to 500°F. The weight-loss characteristics of neat resins and graphite fiber composites prepared by using the mixed end-cap approach were determined at 600°F. The room temperature and short-term elevated temperature mechanical properties of the composites at 550°F and 600°F were determined. The mechanical-property-retention characteristics of the composites at 550°F and 600°F are also discussed.
Twisted complexes and simplicial homotopies

We consider the dg-category of twisted complexes over simplicial ringed spaces. It is clear that a simplicial map $$f:({\mathscr {U}},{\mathscr {R}})\rightarrow ({\mathscr {V}}, {\mathscr {S}})$$ between simplicial ringed spaces induces a dg-functor $$f^*:\mathrm{Tw}({\mathscr {V}}, {\mathscr {S}})\rightarrow \mathrm{Tw}({\mathscr {U}}, {\mathscr {R}})$$ where $$\mathrm{Tw}({\mathscr {U}}, {\mathscr {R}})$$ denotes the dg-category of twisted complexes on $$({\mathscr {U}},{\mathscr {R}})$$ . We prove that for simplicial homotopic maps f and g, there exists an $$A_{\infty }$$ -natural transformation $$\Phi :f^*\Rightarrow g^*$$ between induced dg-functors. Moreover, the 0th component of $$\Phi $$ is an objectwise weak equivalence. If we restrict ourselves to the full dg-subcategory of twisted perfect complexes, then we prove that $$\Phi $$ admits an $$A_{\infty }$$ -quasi-inverse when $$({\mathscr {U}},{\mathscr {R}})$$ satisfies some additional conditions.
Lin28a Regulates Pathological Cardiac Hypertrophic Growth through Pck2‐mediated Enhancement of Anabolic Synthesis

Background: Hypertrophic response to pathological stimuli is a complex biological process that involves transcriptional and epigenetic regulation of the cardiac transcriptome. Although previous stu...
Temporal Patterns in Latin American Truth Commission Recommendation Formulation and Implementation

Remarkably little attention has focused on the formulation and implementation of truth commission (TC) recommendations. We use Skaar et al.’s (2021) original data on approximately 1000 recommendations produced by 13 truth commissions established in 11 Latin American countries between 1983 and 2014 to examine how recommendations and government responses to them have evolved over nearly 40 years. Truth commissions appear to be regularly influenced by major global transitional justice and human rights developments as they formulate recommendations. They target specific marginalised identity groups in their recommendations, particularly after major global initiatives to recognise the rights of such groups. Yet, governments often forego implementing such recommendations. Recommendations also appear to be shaped by whether the commission was established right after a political transition. Post-transitional commissions, which come five or more years after transition, issue more recommendations dealing with reparations of all sorts. However, whether overwhelmed by the number of proposals or more immune to pressure to enact such measures, governments implement these recommendations less regularly. These commissions also do not invoke the importance of reconciliation as transitional commissions do.
Structure-Aware Machine Learning over Multi-Relational Databases

We consider the problem of computing machine learning models over multi-relational databases. The mainstream approach involves a costly repeated loop that data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and learn the desired model using this tool. In this thesis, we advocate for an alternative approach that avoids this loop and instead tightly integrates the query and learning tasks into one unified solution. By integrating these two tasks, we can exploit structure in the data and the query to optimize the end-to-end learning problem. We provide a framework for structure-aware learning for a variety of commonly used machine learning models that achieves runtime guarantees that can be asymptotically faster than the mainstream approach that first constructs the training dataset. In practice, this asymptotic gap translates into several orders of magnitude performance improvements over state-of-the-art machine learning packages such as TensorFlow, MADlib, scikit-learn, and mlpack. The thesis is composed of three parts. First, we present the methodology and theoretical foundation of structure-aware learning. Then, we report on the design and implementation of LMFAO, an in-memory engine for structure-aware learning over databases. Finally, we present an extensive experimental evaluation. In following, we briefly highlight each of these three parts.
Generating Poisson‐distributed differentially private synthetic data

The dissemination of synthetic data can be an effective means of making information from sensitive data publicly available while reducing the risk of disclosure associated with releasing the sensitive data directly. While mechanisms exist for synthesizing data that satisfy formal privacy guarantees, the utility of the synthetic data is often an afterthought. More recently, the use of methods from the disease mapping literature has been proposed to generate spatially-referenced synthetic data with high utility, albeit without formal privacy guarantees. The objective for this paper is to help bridge the gap between the disease mapping and the formal privacy literatures. In particular, we extend an existing approach for generating formally private synthetic data to the case of Poisson-distributed count data in a way that allows for the infusion of prior information. To evaluate the utility of the synthetic data, we conducted a simulation study inspired by publicly available, county-level heart disease-related death counts. The results of this study demonstrate that the proposed approach for generating differentially private synthetic data outperforms a popular technique when the counts correspond to events arising from subgroups with unequal population sizes or unequal event rates.
The Atiyah–Singer index theorem

The Atiyah-Singer index theorem, a landmark achievement of the early 1960s, brings together ideas in analysis, geometry, and topology. We recount some antecedents and motivations; various forms of the theorem; and some of its implications, which extend to the present.
Equilibrium Computation of Generalized Nash Games: A New Lagrangian-Based Approach

This paper presents a new primal-dual method for computing an equilibrium of generalized (continuous) Nash game (referred to as generalized Nash equilibrium problem (GNEP)) where each player's feasible strategy set depends on the other players' strategies. The method is based on a new form of Lagrangian function with a quadratic approximation. First, we reformulate a GNEP as a saddle point computation problem using the new Lagrangian and establish equivalence between a saddle point of the Lagrangian and an equilibrium of the GNEP. We then propose a simple algorithm that is convergent to the saddle point. Furthermore, we establish global convergence by assuming that the Lagrangian function satisfies the Kurdyka-Ł ojasiewicz property. A distinctive feature of our analysis is to make use of the new Lagrangian as a potential function to guide the iterate convergence, which is based on the idea of turning a descent method into a multiplier method. Our method has two novel features over existing approaches: (i) it requires neither boundedness assumptions on the strategy set and the set of multipliers of each player, nor any boundedness assumptions on the iterates generated by the algorithm; (ii) it leads to a Jacobi-type decomposition scheme, which, to the best of our knowledge, is the first development of a distributed algorithm to solve a general class of GNEPs. Numerical experiments are performed on benchmark test problems, and the results demonstrate the effectiveness of the proposed method.
The Limits to Learning a Diffusion Model

This paper provides the first sample complexity lower bounds for the estimation of simple diffusion models which seek to explain the diffusion of an epidemic in a network. The Susceptible-Infected-Recovered (SIR) model is a classic example, proposed nearly a century ago [2]. The SIR model remains a cornerstone for the forecasting of epidemics. The so-called Bass model [1] remains a basic building block in forecasting consumer adoption of new products and services. The durability of these models arises from the fact that they have shown an excellent fit to data, in numerous studies spanning both the epidemiology and marketing literatures. Somewhat paradoxically, using these same models as reliable forecasting tools presents a challenge. While we are ultimately motivated by the problem of forecasting a diffusion model, this paper asks a more basic question that is surprisingly unanswered: What are the limits to learning a diffusion model? We answer this question by characterizing sample complexity lower bounds for a class of stochastic diffusion models that encompass both the Bass model and the SIR model. We show that the time to collect a number of observations that exceeds these lower bounds is too large to allow for accurate forecasts early in the process. In the context of the Bass model our results imply that when adoption is driven by imitation, one cannot hope to predict the eventual number of adopting customers until one is at least two-thirds of the way to the time at which the rate of new adopters is at its peak. In a similar vein, our results imply that in the case of an SIR model, one cannot hope to predict the eventual number of infections until one is approximately two-thirds of the way to the time at which the infection rate has peaked. Our analysis is conceptually simple and relies on the Cramer-Rao bound. The core technical difficulty in our analysis rests in characterizing the Fisher information in the observations available due to the fact that they have a non-trivial correlation structure. Maximum likelihood estimation of diffusion models on product adoption datasets (for products on Amazon.com), and epidemic data (from the ongoing COVID-19 epidemic) illustrate precisely the behavior predicted by our theory. As a byproduct of our analysis, we see that the difficulty in learning a diffusion model stems solely from uncertainty in a single unknown 'effective population size' parameter. In particular, other parameters, including those related to the 'rate of imitation' (in the Bass model) or the 'reproduction number' (in the SIR model) are easy to learn. This suggests that estimators that rely on an (informative) bias in this population size parameter can in fact overcome the limitations presented by our analysis. Although not a primary contribution of the present work, we describe a heuristic procedure used to construct such a biased estimator that yielded one of the first US county-level forecasters available for COVID-19. The full paper is available at https://arxiv.org/abs/2006.06373.
Model-independent extraction of the proton charge radius from PRad data

The proton radius puzzle has motivated several new experiments that aim to extract the proton charge radius and resolve the puzzle. Recently, PRad, a new electron–proton scattering experiment at Je...
Codimensional incremental potential contact

We extend the incremental potential contact (IPC) model [Li et al. 2020a] for contacting elastodynamics to resolve systems composed of codimensional degrees-of-freedoms in arbitrary combination. This enables a unified, interpenetration-free, robust, and stable simulation framework that couples codimension-0,1,2, and 3 geometries seamlessly with frictional contact. Extending the IPC model to thin structures poses new challenges in computing strain, modeling thickness and determining collisions. To address these challenges we propose three corresponding contributions. First, we introduce a C2 constitutive barrier model that directly enforces strain limiting as an energy potential while preserving rest state. This provides energetically-consistent strain limiting models (both isotropic and anisotropic) for cloth that enable strict satisfaction of strain-limit inequalities with direct coupling to both elastodynamics and contact via minimization of the incremental potential. Second, to capture the geometric thickness of codimensional domains we extend the IPC model to directly enforce distance offsets. Our treatment imposes a strict guarantee that mid-surfaces (respectively mid-lines) of shells (respectively rods) will not move closer than applied thickness values, even as these thicknesses become characteristically small. This enables us to account for thickness in the contact behavior of codimensional structures and so robustly capture challenging contacting geometries; a number of which, to our knowledge, have not been simulated before. Third, codimensional models, especially with modeled thickness, mandate strict accuracy requirements that pose a severe challenge to all existing continuous collision detection (CCD) methods. To address these limitations we develop a new, efficient, simple-to-implement additive CCD (ACCD) method that applies conservative advancement [Mirtich 1996; Zhang et al. 2006] to iteratively refine a lower bound for deforming primitives, converging to time of impact. In combination these contributions enable codimensional IPC (C-IPC). We perform extensive benchmark experiments to validate the efficacy of our method in capturing intricate behaviors of thin-structure contact and resulting bulk effects. In our experiments C-IPC obtains feasible, convergent, and so artifact-free solutions for all time steps, across all tested examples - producing robust simulations. We test C-IPC across extreme deformations, large time steps, and exceedingly close contact over all possible pairings of codimensional domains. Finally, with our strain-limit model, we confirm C-IPC guarantees non-intersection and strain-limit satisfaction for all reasonable (and well below - verified down to 0.1%) strain limits throughout all time steps.
Beyond RCP8.5: Marginal mitigation using quasi-representative concentration pathways

Abstract Assessments of decreases in economic damages from climate change mitigation typically rely on climate output from computationally expensive pre-computed runs of general circulation models under a handful of scenarios with discretely varying targets, such as the four representative concentration pathways for CO2 and other anthropogenically emitted gases. Although such analyses are valuable in informing scientists and policymakers about massive multilateral mitigation goals, we add to the literature by considering potential outcomes from more modest policy changes that may not be represented by any well-known concentration pathway. Specifically, we construct computationally efficient Quasi-representative Concentration Pathways (QCPs) to leverage concentration pathways of existing peer-reviewed scenarios. Computational efficiency allows for bootstrapping to assess uncertainty. We illustrate our methodology by considering the impact on the relative risk of mortality from heat stress in London from the United Kingdom’s net zero emissions goal. More than half of our interval estimate for the business-as-usual scenario covers an annual risk at least that of a COVID-19-like mortality event by 2100. Success of the UK’s policy alone would do little to mitigate the risk.
Consensus under Network Interruption and Effective Resistance Interdiction

We study the problem of network robustness under consensus dynamics. We first show that maximizing the consensus time subject to removing limited network edges can be cast as an effective resistance interdiction problem. We then show that the effective resistance interdiction problem is strongly NP-hard, even for bipartite graphs of diameter three, hence correcting some claims in the existing literature.
Fourth- and fifth-order virial expansion of harmonically trapped fermions at unitarity

By generalizing our automated algebra approach from homogeneous space to harmonically trapped systems, we have calculated the fourth- and fifth-order virial coefficients of universal spin-$\frac{1}{2}$ fermions in the unitary limit, confined in an isotropic harmonic potential. We present results for said coefficients as a function of trapping frequency (or, equivalently, temperature), which compare favorably with previous Monte Carlo calculations (available only at fourth order) as well as with our previous estimates in the untrapped limit (high temperature, low frequency). In addition to the conventional coefficients, we provide estimates for the contributions from subspaces with varying polarization. We use our results for the virial coefficients, together with resummation techniques, to calculate the compressibility and spin susceptibility.
The Regener-Pfotzer Maximum During a Total Solar Eclipse

The Regener-Pfotzer (RP) maximum is the altitude at which cosmic radiation intensity is the greatest. A decrease of the altitude of the interaction layer, assumed to be measured by the RP maximum, has been suggested to account for a reduction in the secondary cosmic ray flux measured at the surface of the Earth during a total solar eclipse. To investigate this suggestion, high altitude cosmic radiation was measured using Geiger Mueller (GM) counters carried beneath weather balloons both before and during the total solar eclipse on 21 August 2017. The 19 and 20 August 2017 omnidirectional RP maxima occurred at an average altitude of 20.2 km ± 0.9 km. During the eclipse of 21 August 2017 the omnidirectional RP maxima occurred at an altitude of 20.4 km ± 0.8 km. The 19 and 20 August 2017 vertical coincidence RP maxima occurred at an altitude of 18.3 km ± 1.0 km. During the eclipse the vertical coincidence RP maxima occurred at 18.0 km ± 1.0 km. Our results do not show any decrease in the altitude of either the omnidirectional or the vertical coincidence RP maximum outside the range of our measurements before the eclipse.
Undercutting of defects in thin film protective coatings on polymer surfaces exposed to atomic oxygen

Summary Protection for polymeric surfaces is needed to make them durable in the low Earth orbital environment, where oxidation by atomic oxygen is the predominant failure mechanism. Thin film coatings of oxides such as SiO 2 are viable candidates to provide this protection, but concern has been voiced over the ability of these coatings to protect when defects are present in the coating due to surface anomalies occurring during the deposition process, handling or micrometeoroid and debris bombardment in low Earth orbit. When a defected coating protecting a polymer substrate is exposed to atomic oxygen, the defect provides a pathway to the underlying polymer allowing oxidation and subsequent undercutting to occur. Defect undercutting was studied for sputter deposited coatings of SiO 2 on polyimide Kapton®. Preliminary results indicate that undercutting may be limited as long as the coating remains intact with the substrate. Therefore, coatings may not need to be defect free to give protection to the underlying surface.
Online Route Choice Modeling for Mobility-as-a-Service Networks With Non-Separable, Congestible Link Capacity Effects

With the prevalence of MaaS systems, route choice models need to consider characteristics unique to them. MaaS systems tend to involve service systems with fleets of vehicles; as a result, the available service capacity depends on the choices of other travelers in different parts of the system. We model this with a new concept of ``congestible capacity''; that is, link capacities are a function of flow instead of link costs. This dependency is also non-separable; the capacity in one link can depend on flows from multiple links. An offline-online estimation method is introduced to capture the structural effects that flows have on capacities and the resulting impacts on route choice utilities. The method is first applied to obtain unique congestible capacity shadow prices in a multimodal network to verify the capability to capture congestion effects on capacities. The capacities are shown to vary and impact the utility of a route. The method is validated using real system data from Citi Bike in New York City. The results show that the model can fit to the data quite well and performs better than a baseline modeling approach that ignores congestible capacity effects. By relating the route choice to congestible capacities using a random utility model, modelers can monitor and quantify the impacts to traveler consumer surplus in real time. Applications of the model and online method include monitoring capacity effects on consumer surplus, using the model to direct incentives programs for rebalancing and other revenue management strategies, and to guide resource allocation to mitigate consumer surplus impacts due to disruptions from incidents.
Auditing Non‐GAAP Measures: Signaling More Than Intended*

Many companies regularly disclose non-GAAP performance measures to communicate firm-specific information that does not fit within the mold of GAAP reporting. However, these non-GAAP measures may have low information content or even be misleading to investors. Thus, the question arises of whether auditors should play a larger role in the reporting of non-GAAP measures, which currently are not audited. We run an experiment to provide ex ante evidence on the effect of auditing non-GAAP measures. Specifically, we present investor-participants with a non-GAAP measure that should be used when making investment judgments (more informative) or should not be used when making investment judgments (less informative) and is either audited or is not audited. As predicted, we find that, when participants view a non-GAAP measure that is more informative, they appropriately use the non-GAAP measure in their investment-related judgments, regardless of whether the measure is audited. However, also as predicted, we find that, while participants appropriately do not use a less informative non-GAAP measure when it is not audited, participants inappropriately do use the less informative non-GAAP measure in their investment-related judgments when it is audited. Mediation results provide evidence consistent with audits affecting investors’ reliance on non-GAAP measures. Specifically, our results are consistent with audits of non-GAAP measures signaling more than is intended, evidenced by investors perceiving an audited non-GAAP measure as being useful in their investment decisions when the measure is less informative to them. Our findings suggest that regulators should exercise caution when it comes to prescribing assurance over non-GAAP measures.
Continued Investigation of Leakage and Power Loss Test Results for Competing Turbine Engine Seals

Secondary seal leakage in jet engine applications results in power losses to the engine cycle. Likewise, seal power loss in jet engines not only result in efficiency loss but also increase the heat input into the engine resulting in reduced component lives. Experimental work on labyrinth and annular seals was performed at NASA Glenn Research Center to quantify seal leakage and power loss at various temperatures, seal pressure differentials, and surface speeds. Data from annular and labyrinth seals are compared with previous brush and finger seal test results. Data are also compared to literature. Annular and labyrinth seal leakage rates are 2 to 3 times greater than brush and finger seal rates. Seal leakage decreases with increasing speed but increases with increasing test temperature due to thermal expansion mismatch. Also seal power loss increases with surface speed, seal pressure differential, mass flow rate, and radial clearance. Annular and labyrinth seal power losses were higher than those of brush or finger seal data. The brush seal power loss was 15 to 30 percent lower than annular and labyrinth seal power loss.
Citrullination of a phage-displayed human peptidome library reveals the fine specificities of rheumatoid arthritis-associated autoantibodies.

Abstract Background Post-translational modifications (PTMs) on proteins can be targeted by antibodies associated with autoimmunity. Despite a growing appreciation for their intrinsic role in disease, there is a lack of highly multiplexed serological assays to characterize the fine specificities of PTM-directed autoantibodies. Methods In this study, we used the programmable phage display technology, Phage ImmunoPrecipitation Sequencing (PhIP-Seq), to profile rheumatoid arthritis (RA) associated anti-citrullinated protein antibody (ACPA) reactivities. Findings Using both unmodified and peptidylarginine deiminase (PAD)-modified phage display libraries consisting of ~250,000 overlapping 90 amino acid peptide tiles spanning the human proteome, PTM PhIP-Seq robustly identified antibodies to citrulline-dependent epitopes. Interpretation PTM PhIP-Seq was used to quantify key differences among RA patients, including PAD isoform specific ACPA profiles, and thus represents a powerful tool for proteome-scale antibody-binding analyses. Funding This research is based upon work supported in part by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of ODNI, IARPA, or the US Government. The US Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. This study was made possible by a National Institute of General Medical Sciences (NIGMS) grant R01 GM136724 (HBL). MFK was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) grant T32AR048522. ED was supported by the Rheumatology Research Foundation.
Zarankiewicz’s problem for semilinear hypergraphs

A bipartite graph with is semilinear if for some and the edge relation E consists of the pairs of points satisfying a fixed Boolean combination of s linear equalities and inequalities in variables for some s. We show that for a fixed k, the number of edges in a -free semilinear H is almost linear in n, namely for any ; and more generally, for a -free semilinear r-partite r-uniform hypergraph. As an application, we obtain the following incidence bound: given points and open boxes with axis-parallel sides in such that their incidence graph is -free, there can be at most incidences. The same bound holds if instead of boxes, one takes polytopes cut out by the translates of an arbitrary fixed finite set of half-spaces. We also obtain matching upper and (superlinear) lower bounds in the case of dyadic boxes on the plane, and point out some connections to the model-theoretic trichotomy in o-minimal structures (showing that the failure of an almost-linear bound for some definable graph allows one to recover the field operations from that graph in a definable manner).
Characterizing the First-Arriving Multipath Component in 5G Millimeter Wave Networks: TOA, AOA, and Non-Line-of-Sight Bias

This paper presents a stochastic geometry-based analysis of propagation statistics for 5G millimeter wave (mm-wave) cellular. In particular, the time-of-arrival (TOA) and angle-of-arrival (AOA) distributions of the first-arriving multipath component (MPC) are derived. These statistics find their utility in many applications such as cellular-based localization, channel modeling, and link establishment for mm-wave initial access (IA). Leveraging tools from stochastic geometry, a Boolean model is used to statistically characterize the random locations, orientations, and sizes of reflectors, i.e., buildings. Assuming non-line-of-sight (NLOS) propagation is due to first-order (i.e., single-bounce) reflections, and that reflectors can either facilitate or block reflections, the distribution of the path length (i.e., absolute time delay) of the first-arriving MPC is derived. This result is then used to obtain the first NLOS bias distribution in the localization literature that is based on the absolute delay of the first-arriving MPC for outdoor time-of-flight (TOF) range measurements. This distribution is shown to match exceptionally well with commonly assumed gamma and exponential NLOS bias models in the literature, which were only attained previously through heuristic or indirect methods. Continuing under this analytical framework, the AOA distribution of the first-arriving MPC is derived, which gives novel insight into how environmental obstacles affect the AOA and also represents the first AOA distribution derived under the Boolean model.
Optimal statistical inference for individualized treatment effects in high‐dimensional models

The ability to predict individualized treatment effects (ITEs) based on a given patient's profile is essential for personalized medicine. We propose a hypothesis testing approach to choosing between two potential treatments for a given individual in the framework of high-dimensional linear models. The methodological novelty lies in the construction of a debiased estimator of the ITE and establishment of its asymptotic normality uniformly for an arbitrary future high-dimensional observation, while the existing methods can only handle certain specific forms of observations. We introduce a testing procedure with the type-I error controlled and establish its asymptotic power. The proposed method can be extended to making inference for general linear contrasts, including both the average treatment effect and outcome prediction. We introduce the optimality framework for hypothesis testing from both the minimaxity and adaptivity perspectives and establish the optimality of the proposed procedure. An extension to high-dimensional approximate linear models is also considered. The finite sample performance of the procedure is demonstrated in simulation studies and further illustrated through an analysis of electronic health records data from patients with rheumatoid arthritis.
Test Driven Development of Scientific Models

Test-Driven Development (TDD) is a software development process that promises many advantages for developer productivity and has become widely accepted among professional software engineers. As the name suggests, TDD practitioners alternate between writing short automated tests and producing code that passes those tests. Although this overly simplified description will undoubtedly sound prohibitively burdensome to many uninitiated developers, the advent of powerful unit-testing frameworks greatly reduces the effort required to produce and routinely execute suites of tests. By testimony, many developers find TDD to be addicting after only a few days of exposure, and find it unthinkable to return to previous practices. Of course, scientific/technical software differs from other software categories in a number of important respects, but I nonetheless believe that TDD is quite applicable to the development of such software and has the potential to significantly improve programmer productivity and code quality within the scientific community. After a detailed introduction to TDD, I will present the experience within the Software Systems Support Office (SSSO) in applying the technique to various scientific applications. This discussion will emphasize the various direct and indirect benefits as well as some of the difficulties and limitations of the methodology. I will conclude with a brief description of pFUnit, a unit testing framework I co-developed to support test-driven development of parallel Fortran applications.
Vaccination intentions generate racial disparities in the societal persistence of COVID-19.

We combined survey, mobility, and infections data in greater Boston, MA to simulate the effects of racial disparities in the inclination to become vaccinated on continued infection rates and the attainment of herd immunity. The simulation projected marked inequities, with communities of color experiencing infection rates 3 times higher than predominantly White communities and reaching herd immunity 45 days later on average. Persuasion of individuals uncertain about vaccination was crucial to preventing the worst inequities but could only narrow them so far because 1/5th of Black and Latinx individuals said that they would never vaccinate. The results point to a need for well-crafted, compassionate messaging that reaches out to those most resistant to the vaccine.
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-Directional Metric Learning

While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-based model by Attention Guided Knowledge Distillation and Bi-directional Metric Learning (AGKD-BML). The attention knowledge is obtained from a weight-fixed model trained on a clean dataset, referred to as a teacher model, and transferred to a model that is under training on adversarial examples (AEs), referred to as a student model. In this way, the student model is able to focus on the correct region, as well as correcting the intermediate features corrupted by AEs to eventually improve the model accuracy. Moreover, to efficiently regularize the representation in feature space, we propose a bidirectional metric learning. Specifically, given a clean image, it is first attacked to its most confusing class to get the forward AE. A clean image in the most confusing class is then randomly picked and attacked back to the original class to get the backward AE. A triplet loss is then used to shorten the representation distance between original image and its AE, while enlarge that between the forward and backward AEs. We conduct extensive adversarial robustness experiments on two widely used datasets with different attacks. Our proposed AGKD-BML model consistently outperforms the state-of-the-art approaches. The code of AGKD-BML will be available at: this https URL.
Inverse Dynamics Control of Compliant Hybrid Zero Dynamic Walking

We present a trajectory planning and control architecture for bipedal locomotion at a variety of speeds on a highly underactuated and compliant bipedal robot. A library of compliant walking trajectories are planned offline, and stored as compact arrays of polynomial coefficients for tracking online. The control implementation uses a floating-base inverse dynamics controller which generates dynamically consistent feedforward torques to realize walking using information obtained from the trajectory optimization. The effectiveness of the controller is demonstrated in simulation and on hardware for walking both indoors on flat terrain and over unplanned disturbances outdoors. Additionally, both the controller and optimization source code are made available on GitHub.
Situated Live Programming for Human-Robot Collaboration

We present situated live programming for human-robot collaboration, an approach that enables users with limited programming experience to program collaborative applications for human-robot interaction. Allowing end users, such as shop floor workers, to program collaborative robots themselves would make it easy to “retask” robots from one process to another, facilitating their adoption by small and medium enterprises. Our approach builds on the paradigm of trigger-action programming (TAP) by allowing end users to create rich interactions through simple trigger-action pairings. It enables end users to iteratively create, edit, and refine a reactive robot program while executing partial programs. This live programming approach enables the user to utilize the task space and objects by incrementally specifying situated trigger-action pairs, substantially lowering the barrier to entry for programming or reprogramming robots for collaboration. We instantiate situated live programming in an authoring system where users can create trigger-action programs by annotating an augmented video feed from the robot’s perspective and assign robot actions to trigger conditions. We evaluated this system in a study where participants (n = 10) developed robot programs for solving collaborative light-manufacturing tasks. Results showed that users with little programming experience were able to program HRC tasks in an interactive fashion and our situated live programming approach further supported individualized strategies and workflows. We conclude by discussing opportunities and limitations of the proposed approach, our system implementation, and our study and discuss a roadmap for expanding this approach to a broader range of tasks and applications.
Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives

Robot manipulation in cluttered scenes often requires contact-rich interactions with objects. It can be more economical to interact via non-prehensile actions, for example, push through other objects to get to the desired grasp pose, instead of deliberate prehensile rearrangement of the scene. For each object in a scene, depending on its properties, the robot may or may not be allowed to make contact with, tilt, or topple it. To ensure that these constraints are satisfied during non-prehensile interactions, a planner can query a physics-based simulator to evaluate the complex multi-body interactions caused by robot actions. Unfortunately, it is infeasible to query the simulator for thousands of actions that need to be evaluated in a typical planning problem as each simulation is time-consuming. In this work, we show that (i) manipulation tasks (specifically pick-and-place style tasks from a tabletop or a refrigerator) can often be solved by restricting robot-object interactions to adaptive motion primitives in a plan, (ii) these actions can be incorporated as subgoals within a multi-heuristic search framework, and (iii) limiting interactions to these actions can help reduce the time spent querying the simulator during planning by up to 40× in comparison to baseline algorithms. Our algorithm is evaluated in simulation and in the real-world on a PR2 robot using PyBullet as our physics-based simulator. Supplementary video: https://youtu.be/ABQc7JbeJPM.
Remarks on Anomalous Symmetries of C*-Algebras

For a group G and $$\omega \in Z^{3}(G, \text {U}(1))$$ , an $$\omega $$ -anomalous action on a C*-algebra B is a $$\text {U}(1)$$ -linear monoidal functor between 2-groups , where the latter denotes the 2-group of $$*$$ -automorphisms of B. The class $$[\omega ]\in H^{3}(G, \text {U}(1))$$ is called the anomaly of the action. We show that for every $$n\ge 2$$ and every finite group G, every anomaly can be realized on the stabilization of a commutative C*-algebra $$C(M)\otimes {\mathcal {K}}$$ for some closed connected n-manifold M. We also show that although there are no anomalous symmetries of Roe C*-algebras of coarse spaces, for every finite group G, every anomaly can be realized on the Roe corona $$C^{*}(X)/{\mathcal {K}}$$ of some bounded geometry metric space X with property A.
Online Connectivity-aware Dynamic Deployment for Heterogeneous Multi-Robot Systems

In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of units per category representing heterogeneous capabilities. Every task area with different importance demands a total number of units contributed by all of the robots within its area. Moreover, we assume the importance and the total number of units requested from each task area is initially unknown. The robots need first to explore, i.e., reach those areas, and then be allocated to the tasks so to fulfill the requirements. The multi-robot distribution problem is formulated as designing controllers to distribute the robots that maximize the overall task fulfillment while minimizing the traveling costs in presence of connectivity constraints. We propose a novel connectivity-aware multi-robot redistribution approach that accounts for dynamic task allocation and connectivity maintenance for a heterogeneous robot team. Such an approach could generate sub-optimal robot controllers so that the amount of total unfulfilled requirements of the tasks weighted by their importance is minimized and robots stay connected at all times. Simulation and numerical results are provided to demonstrate the effectiveness of the proposed approaches.
Reinforcement Learning for Mixed Autonomy Intersections

We propose a model-free reinforcement learning method for controlling mixed autonomy traffic in simulated traffic networks with through-traffic-only two-way and four-way intersections. Our method utilizes multi-agent policy decomposition which allows decentralized control based on local observations for an arbitrary number of controlled vehicles. We demonstrate that, even without reward shaping, reinforcement learning learns to coordinate the vehicles to exhibit traffic signal-like behaviors, achieving near-optimal throughput with 33-50% controlled vehicles. With the help of multi-task learning and transfer learning, we show that this behavior generalizes across inflow rates and size of the traffic network. Our code, models, and videos of results are available at https://github.com/ZhongxiaYan/mixed.autonomy_intersections.