cssi/SciSciGPT-SciSciNet huggingface dataset:
TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic

This paper presents TrollHunter, an automated reasoning mechanism we used to hunt for trolls on Twitter during the COVID-19 pandemic in 2020. Trolls quickly seized the opportunity to create a COVID-19 infodemic by promulgating dubious content on Twitter. To counter the COVID-19 infodemic, the TrollHunter leverages a unique linguistic analysis of a multi-dimensional set of Twitter content features to detect whether or not a tweet was meant to troll. TrollHunter achieved 98.5% accuracy, 75.4% precision and 69.8% recall over a dataset of 1.3 million tweets. Without a final resolution of the pandemic in sight, it is unlikely that the trolls will go away, although they might be forced to evade automated hunting. To explore the plausibility of this strategy, we developed and tested an adversarial machine learning mechanism called TrollHunter-Evader. TrollHunter-Evader employs a Test Time Evasion (TTE) approach in a combination with a Markov chain-based mechanism to recycle originally trolling tweets. The recycled tweets were able to achieve a remarkable 40% decrease in the TrollHunter’s ability to correctly identify trolling tweets. Because the COVID-19 infodemic could have a harmful impact on the COVID-19 pandemic, we provide an elaborate discussion about the implications of employing adversarial machine learning to evade Twitter troll hunts.
Nash Social Welfare Approximation for Strategic Agents

We study the problem of allocating divisible resources to agents with different preferences. We analyze a market game known as Trading Post, first considered by Shapley and Shubik, where each agent...
Massive Coded-NOMA for Low-Capacity Channels: A Low-Complexity Recursive Approach

In this paper, we present a low-complexity recursive approach for massive and scalable code-domain nonorthogonal multiple access (NOMA) with applications to emerging low-capacity scenarios. The problem definition in this paper is inspired by three major requirements of the next generations of wireless networks. Firstly, the proposed scheme is particularly beneficial in low-capacity regimes which is important in practical scenarios of utmost interest such as the Internet-of-Things (IoT) and massive machine-type communication (mMTC). Secondly, we employ code-domain NOMA to efficiently share the scarce common resources among the users. Finally, the proposed recursive approach enables code-domain NOMA with low-complexity detection algorithms that are scalable with the number of users to satisfy the requirements of massive connectivity. To this end, we propose a novel encoding and decoding scheme for code-domain NOMA based on factorizing the pattern matrix, for assigning the available resource elements to the users, as the Kronecker product of several smaller factor matrices. As a result, both the pattern matrix design at the transmitter side and the mixed symbols’ detection at the receiver side can be performed over matrices with dimensions that are much smaller than the overall pattern matrix. Consequently, this leads to significant reduction in both the complexity and the latency of the detection. We present the detection algorithm for the general case of factor matrices. The proposed algorithm involves several recursions each involving certain sets of equations corresponding to a certain factor matrix. We then characterize the system performance in terms of average sum rate, latency, and detection complexity. Our latency and complexity analysis confirm the superiority of our proposed scheme in enabling large pattern matrices. Moreover, our numerical results for the average sum rate show that the proposed scheme provides better performance compared to straightforward code-domain NOMA with comparable complexity, especially at low-capacity regimes.
Magnetic Rotational Spectroscopy for Probing Rheology of Nanoliter Droplets and Thin Films

In situ characterization of minute amounts of complex fluids is a challenge. Magnetic rotational spectroscopy (MRS) with submicron probes offers flexibility and accuracy providing desired spatial and temporal resolution in characterization of nanoliter droplets and thin films when other methods fall short. MRS analyzes distinct features of the in-plane rotation of a magnetic probe, when its magnetic moment makes full revolution following an external rotating magnetic field. The probe demonstrates a distinguishable movement which changes from rotation to tumbling to trembling as the frequency of rotation of the driving magnetic field changes. In practice, MRS has been used in analysis of gelation of thin polymer films, ceramic precursors, and nanoliter droplets of insect biofluids. MRS is a young field, but it has many potential applications requiring rheological characterization of scarcely available, chemically reacting complex fluids.
Nucleation is more than critical: A case study of the electroweak phase transition in the NMSSM

Electroweak baryogenesis is an attractive mechanism to generate the baryon asymmetry of the Universe via a strong first order electroweak phase transition. We compare the phase transition patterns suggested by the vacuum structure at the critical temperatures, at which local minima are degenerate, with those obtained from computing the probability for nucleation via tunneling through the barrier separating local minima. Heuristically, nucleation becomes difficult if the barrier between the local minima is too high, or if the distance (in field space) between the minima is too large. As an example of a model exhibiting such behavior, we study the Next-to-Minimal Supersymmetric Standard Model, whose scalar sector contains two SU(2) doublets and one gauge singlet. We find that the calculation of the nucleation probabilities prefers different regions of parameter space for a strong first order electroweak phase transition than the calculation based solely on the critical temperatures. Our results demonstrate that analyzing only the vacuum structure via the critical temperatures can provide a misleading picture of the phase transition patterns, and, in turn, of the parameter space suitable for electroweak baryogenesis.
On corner scattering for operators of divergence form and applications to inverse scattering

We consider the scattering problem governed by the Helmholtz equation with inhomogeneity in both “conductivity” in the divergence form and “potential” in the lower order term. The support of the in...
Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic

The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe, introducing numerous social challenges. Twitter platforms have been used to identify public opinion about events at the local and global scale. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020 and explored topic modeling to identify the most discussed topics and themes during this period. Additionally, we analyzed the temporal changes in the topics with respect to the events that occurred. We found eight topics were sufficient to identify the themes in our corpus. The dominant topics were found to vary over time and align with the events related to the COVID-19 pandemic.
What if you build it and they don't come? How the ghost of transit past haunts transit present

Abstract In the last quarter-century, both Bogota and Jakarta built bus rapid transit systems. Bogota's is widely credited as a success; Jakarta's not. To understand why, we look back more than a century to the roots of initial transit investments in these two cities. We credit Bogota's current success in large part to pre-existing land use patterns – commercial streets, residential density, and pedestrian-friendly infrastructure – determined by its streetcar system of the 1910s that remain hospitable to successful transit today. Similarly, we suggest that Jakarta's struggles stem from the difficulty of adapting areas built for private transport, particularly the car, to public transit. We conclude by using these insights to look forward and contemplate more generally whether and how transit can succeed in areas constructed during the period of auto hegemony and its pedestrian-unfriendly land uses.
Exchange Rates and Foreign Direct Investment: An Imperfect Capital Markets Approach

We examine the connection between exchange rates and foreign direct investment that arises when globally integrated capital markets are subject to informational imperfections. These imperfections cause external financing to be more expensive than internal financing, so that changes in wealth translate into changes in the demand for direct investment. By systematically lowering the relative wealth of domestic agents, a depreciation of the domestic currency can lead to foreign acquisitions of certain domestic assets. We develop a simple model of this phenomenon and test for its relevance in determining international capital flows.
What Do We Want in a Presidential Primary - An Election Law Perspective

Although the 2008 presidential primaries were in many ways a resounding success -in terms of turnout, attention, and sheer excitement -many noted the pressing need for reform. States were rushing to hold their primaries sooner than ever, giving rise to “Super Duper Tuesday,” where twenty-four states had their primary on the same day. The Democratic nominee at one point looked like it might be decided by the votes of socalled “Superdelegates” -party regulars beholden to no one. As the Democratic nomination contest wore on, Rush Limbaugh, in “Operation Chaos” encouraged his “dittoheads” to raid the party primaries of the Democrats, tilting the vote against Obama, the presumptive nominee. And there were continued grumblings about the disproportionate influence Iowa and New Hampshire had on the whole process. Fortunately, reform is in the air. The Democratic National Committee’s “Commission for Change” has just released its proposals for changing how the Democrats run their primaries. Republicans are sure to follow suit. But how are we to evaluate such proposals? What do we want in a presidential primary? My article, borrowing from the vital field of election law, proposes a set of values by which we can evaluate the presidential primaries. By investigating the various players in the nomination process -voters, parties, and the state -I isolate three major sets of “constitutional values” that are implicated in the presidential primary system: (1) the rights of voters to an effective and meaningful vote, (2) the interests of the political parties in their autonomy and ideological purity, and (3) the concern of state and federal governments that the nomination process be legitimate, competitive, and produce a candidate who is capable of governing. Finally, I propose a master value -that of “deliberation” -that both explains and unifies the various competing values at play in the primaries. And I analyze the two major proposals for reforming the primaries, a national primary and a series of regional primaries, as well as the recommendations of the Democratic National Committee, in light of the goal of achieving “deliberative primaries.” * Assistant professor of Law, Saint Louis University School of Law, Saint Louis, MO. J.D. 2007, Yale; PhD. 2004, University of Chicago (philosophy). I thank Michael Neblo for conversations and collaboration over many years on the topics in this essay. He should not be taken to agree with everything I say. Melanie J. Springer gave a guest lecture to my election law seminar, which helped spur my thinking about reforming the presidential primaries. I am indebted to Uduak Ifon and Terrence Burek for timely research assistance, and to Christopher Bradley and William Baude for comments.
Extended space and time correlations in strongly magnetized plasmas

Molecular dynamics simulations are used to show that strong magnetization significantly increases the space and time scales associated with interparticle correlations. The physical mechanism responsible is a channeling effect whereby particles are confined to move along narrow cylinders with a width characterized by the gyroradius and a length characterized by the collision mean free path. The predominant interactions are the 180 ° collisions at the ends of the collision cylinders, resulting in a long-range correlation parallel to the magnetic field. Its influence is demonstrated via the dependence of the velocity autocorrelation functions and self-diffusion coefficients on the domain size and run time in simulations of the one-component plasma. A very large number of particles, and therefore domain size, must be used to resolve the long-range correlations, suggesting that the number of charged particles in the collection must increase in order to constitute a plasma. Correspondingly, this effect significantly delays the time it takes to reach a diffusive regime, in which the mean square displacement of particles increases linearly in time. This result presents challenges for connecting measurements in non-neutral and ultracold neutral plasma experiments, as well as molecular dynamics simulations, with fluid transport properties due to their finite size.
A transformation-free linear regression for compositional outcomes and predictors.

Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compositional is limited, and the existing models are often difficult to interpret in the compositional space, due to their use of complex log-ratio transformations. We develop a transformation-free linear regression model where the expected value of the compositional outcome is expressed as a single Markov transition from the compositional predictor. Our approach is based on estimating equations thereby not requiring complete specification of data likelihood and is robust to different data-generating mechanisms. Our model is simple to interpret, allows for 0s and 1s in both the compositional outcome and covariates, and subsumes several interesting subcases of interest. We also develop permutation tests for linear independence and equality of effect sizes of two components of the predictor. Finally, we show that despite its simplicity, our model accurately captures the relationship between compositional data using two datasets from education and medical research.
Isochrones of M67 with an Expanded Set of Parameters

We create isochrones of M67 using the Yale Rotating Stellar Evolution Code. In addition to metallicity, parameters that are traditionally held fixed, such as the mixing length parameter and initial helium abundance, also vary. The amount of convective overshoot is also changed in different sets of isochrones. Models are constructed both with and without diffusion. From the resulting isochrones that fit the cluster, the age range is between 3.6 and 4.8 Gyr and the distance is between 755 and 868 pc. We also confirm Michaud et al. (2004) claim that M67 can be fit without overshoot if diffusion is included.
Critical Thinking: A Model of Intelligence for Solving Real-World Problems

Most theories of intelligence do not directly address the question of whether people with high intelligence can successfully solve real world problems. A high IQ is correlated with many important outcomes (e.g., academic prominence, reduced crime), but it does not protect against cognitive biases, partisan thinking, reactance, or confirmation bias, among others. There are several newer theories that directly address the question about solving real-world problems. Prominent among them is Sternberg's adaptive intelligence with "adaptation to the environment" as the central premise, a construct that does not exist on standardized IQ tests. Similarly, some scholars argue that standardized tests of intelligence are not measures of rational thought-the sort of skill/ability that would be needed to address complex real-world problems. Other investigators advocate for critical thinking as a model of intelligence specifically designed for addressing real-world problems. Yes, intelligence (i.e., critical thinking) can be enhanced and used for solving a real-world problem such as COVID-19, which we use as an example of contemporary problems that need a new approach.
The Five-Tool Mediator: Game Theory, Baseball Practices, and Southpaw Scouting

This essay borrows heavily from the fields of game theory, baseball business strategy and neuropsychology. Knitting these together, the author advocates that mediators become inciters and advocates for an outcome that solves problems, irrespective of the amount in controversy and the initial gap between offer and counter-offers of settlement. This is not an essay on how to do facilitator’s tasks in settlement negotiations; instead, the reader should consider how to think about the mediator’s role in the process, advancing the value proposition in negotiations. This essay does not propose that mediators become group therapists but instead urges them to relentlessly expose (1) the essence of each party’s intentions and purpose within the controversy, and (2) a range of satisfactory outcomes from the perspective of each party. Once that is accomplished, the second, incitement, phase may commence. In that phase, three transitions must occur. First, the concept of wounding must fade into the background while the concept of amelioration – the path of most proximate to making each party whole – assumes the foreground. In that initial transition, the warriors – those for whom the encounter’s savagery matters equally with the outcome – must be disarmed and converted into fellow seekers of imaginative solutions to the joint problems to be resolved. This requires foremost that the mediators alter the mind-set of the adversaries from the binary thinking realm. The second transition converts each party’s belief that a win is the ultimate goal to understanding that there is a problem to be solved at the lowest possible cost to him in the most expeditious manner feasible – and that such an outcome is as close to victory as he may be realizable. This transition requires moving from pragmatic to imaginative thinking about a controversy’s resolution. A realistic perspective in a controversy, while helpful, is not all-sufficient to achieving a resolution in many cases unless a third-party adjudicator intervenes and directs the dispute’s outcome. The pragmatist’s perspective, that the dispute is a transaction whose terms have been written down but not agreed to, yet, will not guide the parties down the path to resolution. The appropriate perspective sees the dispute environment as writ on a white board, the problem set forth at its top margin and the resolution schematic remaining to be written. Here, every possible solution is available for capture, evaluation and incorporation into an overall solution. The third transition relates to trust: Learning to accept evidence of trust extended by the other disputant and to extend indications of trust without expectation of reciprocity from the adversary. Adopting these attitudes will set the facilitator on the path to becoming a five-tool mediator. For the person not engaged in the business of facilitation, this essay offers a lens through which to evaluate the talent of a prospective facilitator or to gauge a current facilitator’s ongoing performance.
Sample canonical correlation coefficients of high-dimensional random vectors: Local law and Tracy–Widom limit

Consider two random vectors C11/2x ∈ ℝp and C21/2y ∈ Rq, where the entries of x and y are i.i.d. random variables with mean zero and variance one, and C1 and C2 are respectively, p × p and q × q de...
Interband tunneling effects on materials transport properties using the first principles Wigner distribution

Abstract Electronic transport in narrow gap semiconductors is characterized by spontaneous vertical transitions between carriers in the valence and conduction bands, a phenomenon also known as Zener tunneling. However, this effect is not captured by existing models based on the Boltzmann transport equation. In this work, we propose a new fully first principles model for electronic transport using the Wigner distribution function and implement it to solve the equations of motion for electrons. The formalism generalizes the Boltzmann equation to materials with strong interband coupling and include transport contributions from off-diagonal components of the charge current operator. We illustrate the method with a study of Bi2Se3, showing that interband tunneling dominates the electron transport dynamics at experimentally relevant small doping concentrations, a behavior that is likely shared with other semiconductors, including topological insulators. Surprisingly, Zener tunneling occurs also between band subvalleys separated by energy much larger than the band gap.
On Asymptotic Analysis of Energy-Distortion Tradeoff for Low-Delay Transmission Over Gaussian Channels

Asymptotic energy-distortion performance of zero- and low-delay transmission of Gaussian sources over energy-limited Gaussian channels is studied. A lower bound for the leading term in the negative logarithm of the distortion, termed the energy-distortion exponent , is derived through an achievable scheme based on high-resolution quantization coupled with orthogonal signaling. The higher-order term in the negative logarithm of the distortion, termed the energy-distortion dispersion , is optimized while keeping the leading term, the energy-distortion exponent, at its optimal (respectively, the best known) value for the zero-delay (respectively, low-delay) regime. In contrast with the decaying dispersion previously reported in the literature, the proposed coding scheme achieves a constant dispersion. When the scheme is optimized, this constant can be improved with respect to its naive value, i.e., that achieved by optimizing purely the source coding performance instead of the end-to-end distortion. Lastly, a tradeoff of achievable energy-distortion exponents is derived for broadcast scenarios by extending the point-to-point scheme to include a successive refinement source coder coupled with two rounds of orthogonal signaling. A simple parametric computation algorithm is derived for the tradeoff.
Can Good Politicians Compensate for Bad Institutions? Evidence from an Original Survey of Italian Mayors

Can competent political leaders bring significant policy changes to communities otherwise doomed by “bad” informal institutions? This question has remained unanswered because of the lack of a convi...
A penalized likelihood approach for efficiently estimating a partially linear additive transformation model with current status data

Current status data are commonly encountered in medical and epidemiological studies in which the failure time for study units is the outcome variable of interest. Data of this form are characterized by the fact that the failure time is not directly observed but rather is known relative to an observation time, i.e., the failure times are either left- or right-censored. Due to its structure, the analysis of such data can be challenging. To circumvent these challenges and to provide for a flexible modeling construct which can be used to analyze current status data, herein a partially linear additive transformation model is proposed. In the formulation of this model, constrained B-splines are employed to model the monotone transformation function and nonparametric covariate effects. To provide for more efficient estimators, a penalization technique is used to regularize the estimation of all unknown functions. An easy to implement hybrid algorithm is developed for model fitting, and a simple and consistent estimator of the large-sample variance-covariance matrix for regression parameter estimators is proposed. It is shown theoretically that the proposed estimators of the finite-dimensional regression coefficients are root-n consistent, asymptotically normal, and achieve the semiparametric information bound, while the estimators of the nonparametric components attain the optimal rate of convergence. The finite-sample performance of the proposed methodology is evaluated through extensive numerical studies and is further demonstrated through the analysis of human papillomavirus (HPV) data.
N-sided radial Schramm–Loewner evolution

We use the interpretation of the Schramm–Loewner evolution as a limit of path measures tilted by a loop term in order to motivate the definition of n-radial SLE going to a particular point. In order to justify the definition we prove that the measure obtained by an appropriately normalized loop term on n-tuples of paths has a limit. The limit measure can be described as n paths moving by the Loewner equation with a driving term of Dyson Brownian motion. While the limit process has been considered before, this paper shows why it naturally arises as a limit of configurational measures obtained from loop measures.
Racial and ethnic differences in left atrial appendage occlusion wait time, complications, and periprocedural management.

PURPOSE Non-white patients are underrepresented in left atrial appendage occlusion (LAAO) trials, and racial disparities in LAAO periprocedural management are unknown. METHODS We assessed sociodemographics and comorbidities of consecutive patients at our institution undergoing LAAO between 2015 and 2020, then in adjusted analyses, compared procedural wait time, procedural complications, and post-procedure oral anticoagulation (OAC) use in whites versus non-whites. RESULTS Among 109 patients undergoing LAAO (45% white), whites had lower CHA2 DS2 VASc scores, on average, than non-whites (4.0 vs. 4.8, p = .006). There was no difference in median time from index event (IE) or initial outpatient cardiology encounter to LAAO procedure (whites 10.5 vs. non-whites 13.7 months, p = .9; 1.9 vs. 1.8 months, p = .6, respectively), and there was no difference in procedural complications (whites 4% vs. non-whites 5%, p = .33). After adjusting for CHA2 DS2 VASc score, OAC use at discharge tended to be higher in whites (OR 2.4, 95% CI [0.9-6.0], p = .07). When restricting the analysis to those with prior gastrointestinal (GI) bleed, adjusting for CHA2 DS2 VASc score and GI bleed severity, whites had a nearly five-fold odds of being discharged on OAC (OR 4.6, 95% CI [1-21.8], p = 0.05). The association between race and discharge OAC was not mediated through income category (total mediation effect 19% 95% CI [-.04-0.11], p = .38). CONCLUSION Despite an increased prevalence of comorbidities amongst non-whites, wait time for LAAO and procedural complications were similar in whites versus non-whites. Among those with prior GI bleed, whites were nearly five-fold more likely to be discharged on OAC than non-whites, independent of income.
APS: Active Pretraining with Successor Features

We introduce a new unsupervised pretraining objective for reinforcement learning. During the unsupervised reward-free pretraining phase, the agent maximizes mutual information between tasks and states induced by the policy. Our key contribution is a novel lower bound of this intractable quantity. We show that by reinterpreting and combining variational successor features~\citep{Hansen2020Fast} with nonparametric entropy maximization~\citep{liu2021behavior}, the intractable mutual information can be efficiently optimized. The proposed method Active Pretraining with Successor Feature (APS) explores the environment via nonparametric entropy maximization, and the explored data can be efficiently leveraged to learn behavior by variational successor features. APS addresses the limitations of existing mutual information maximization based and entropy maximization based unsupervised RL, and combines the best of both worlds. When evaluated on the Atari 100k data-efficiency benchmark, our approach significantly outperforms previous methods combining unsupervised pretraining with task-specific finetuning.
Route lengths in invariant spatial tree networks

Is there a constant r0 such that, in any invariant tree network linking rate-1 Poisson points in the plane, the mean within-network distance between points at Euclidean distance r is infinite for r>r0? We prove a slightly weaker result. This is a continuum analog of a result of Benjamini et al (2001) on invariant spanning trees of the integer lattice.
A Structured Observation Distribution for Generative Biological Sequence Prediction and Forecasting

Generative probabilistic modeling of biological sequences has widespread existing and potential application across biology and biomedicine, from evolutionary biology to epidemiology to protein design. Many standard sequence analysis methods preprocess data using a multiple sequence alignment (MSA) algorithm, one of the most widely used computational methods in all of science. However, as we show in this article, training generative probabilistic models with MSA preprocessing leads to statistical pathologies in the context of sequence prediction and forecasting. To address these problems, we propose a principled drop-in alternative to MSA preprocessing in the form of a structured observation distribution (the ``MuE" distribution). The MuE is a latent alignment model in which not only the alignment variable but also the regressor sequence can be latent. We prove theoretically that the MuE distribution comprehensively generalizes popular methods for inferring biological sequence alignments, and provide a precise characterization of how such biological models have differed from natural language latent alignment models. We show empirically that models that use the MuE as an observation distribution outperform comparable methods across a variety of datasets, and apply MuE models to a novel problem for generative probabilistic sequence models: forecasting pathogen evolution.
Limits of high-density, low-force pressure contacts

Using a custom-built apparatus we have been characterizing extremely small pressure contacts with applied forces down to a few nN and corresponding contact areas down to 10/sup -13/ cm/sup 2/. At these forces, which are many orders of magnitude smaller than those currently employed in electrical contacts, nominally clean gold-gold and iridium-gold contacts in air exhibit resistivities higher than that predicted from a simple model based on the plastic deformation of the gold. A film of the insulating polymer PMMA three monolayers (2.7 mm) thick deposited on a gold surface affects its conductance significantly, and a film of seven monolayers insulates it completely. >
The Generalized Mean Densest Subgraph Problem

Finding dense subgraphs of a large graph is a standard problem in graph mining that has been studied extensively both for its theoretical richness and its many practical applications. In this paper we introduce a new family of dense subgraph objectives, parameterized by a single parameter p, based on computing generalized means of degree sequences of a subgraph. Our objective captures both the standard densest subgraph problem and the maximum k-core as special cases, and provides a way to interpolate between and extrapolate beyond these two objectives when searching for other notions of dense subgraphs. In terms of algorithmic contributions, we first show that our objective can be minimized in polynomial time for all p ≥ 1 using repeated submodular minimization. A major contribution of our work is analyzing the performance of different types of peeling algorithms for dense subgraphs both in theory and practice. We prove that the standard peeling algorithm can perform arbitrarily poorly on our generalized objective, but we then design a more sophisticated peeling method which for p ≥ 1 has an approximation guarantee that is always at least 1/2 and converges to 1 as p ⟶ ₶. In practice, we show that this algorithm obtains extremely good approximations to the optimal solution, scales to large graphs, and highlights a range of different meaningful notions of density on graphs coming from numerous domains. Furthermore, it is typically able to approximate the densest subgraph problem better than the standard peeling algorithm, by better accounting for how the removal of one node affects other nodes in its neighborhood.
Venus Topography and Boundary Conditions in 3D General Circulation Modeling

We describe how one ingests 3D topographic data from NASA’s Venus Magellan Spacecraft radar observations into the ROCKE-3D Planetary General Circulation Model. We also explain how boundary condition choices such as ocean/lake coverage/depth, rotation rate, atmospheric constituents, and other factors influence surface conditions in ROCKE-3D paleo-Venus simulations. Studies such as these should also be considered when examining liquid water habitability in similar exoplanet experiments.
The Global Precipitation Measurement (GPM) Mission: Overview and U.S. Science Status

The Global Precipitation Measurement (GPM) Mission, an international satellite mission to unify and advance space-based precipitation measurements around the globe, is a science mission with integrated application goals. The mission is designed to (1) advance the knowledge of the global water cycle and freshwater availability, and (2) improve weather, climate, and hydrological prediction capabilities through more accurate and frequent measurements of global precipitation. The cornerstone of GPM is the deployment of a Core Spacecraft in a unique 65 deg-inclined orbit to serve as a physics observatory and a calibration reference to improve the accuracy of precipitation measurements by a heterogeneous constellation of dedicated and operational passive microwave sensors. The Core Spacecraft will carry a dual-frequency (Ku-Ka band) radar and a multi-channel microwave radiometer with high-frequency capabilities to provide measurements of 3-D precipitation structures and microphysical properties, which are key to achieving a better understanding of precipitation processes and improved retrieval algorithms for passive microwave radiometers. The GPM constellation is envisioned to comprise 5 or more conical-scanning microwave radiometers provided by partners, augmented by cross-track microwave sounders on operational satellites such as the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), POES, NPOESS, and MetOp satellites for improved sampling over land. The GPM Mission is currently a partnership between NASA and the Japan Aerospace Exploration Agency (JAXA), with opportunities for additional international partners in constellation satellites and ground validation. An overview of the GPM mission concept and science activities in the United States will be presented.
Learning Syntax from Naturally-Occurring Bracketings

Naturally-occurring bracketings, such as answer fragments to natural language questions and hyperlinks on webpages, can reflect human syntactic intuition regarding phrasal boundaries. Their availability and approximate correspondence to syntax make them appealing as distant information sources to incorporate into unsupervised constituency parsing. But they are noisy and incomplete; to address this challenge, we develop a partial-brackets-aware structured ramp loss in learning. Experiments demonstrate that our distantly-supervised models trained on naturally-occurring bracketing data are more accurate in inducing syntactic structures than competing unsupervised systems. On the English WSJ corpus, our models achieve an unlabeled F1 score of 68.9 for constituency parsing.
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information

Incorporating graph side information into recommender systems has been widely used to better predict ratings, but relatively few works have focused on theoretical guarantees. Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters. In this work, we propose a new model in which 1) the unknown latent preference matrix can have any discrete values, and 2) users can be clustered into multiple clusters, thereby relaxing the assumptions made in prior work. Under this new model, we fully characterize the optimal sample complexity and develop a computationally-efficient algorithm that matches the optimal sample complexity. Our algorithm is robust to model errors and outperforms the existing algorithms in terms of prediction performance on both synthetic and real data.
Quantum-accelerated imaging of N stars

Imaging point sources with low angular separation near or below the Rayleigh criterion are important in astronomy, e.g., in the search for habitable exoplanets near stars. However, the measurement time required to resolve stars in the sub-Rayleigh region via traditional direct imaging is usually prohibitive. Here we propose quantum-accelerated imaging (QAI) to significantly reduce the measurement time using an information-theoretic approach. QAI achieves quantum acceleration by adaptively learning optimal measurements from data to maximize Fisher information per detected photon. Our approach can be implemented experimentally by linear-projection instruments followed by single-photon detectors. We estimate the position, brightness, and the number of unknown stars 10∼100 times faster than direct imaging with the same aperture. QAI is scalable to a large number of incoherent point sources and can find widespread applicability beyond astronomy to high-speed imaging, fluorescence microscopy, and efficient optical read-out of qubits.
Learning Task Informed Abstractions

Current model-based reinforcement learning methods struggle when operating from complex visual scenes due to their inability to prioritize task-relevant features. To mitigate this problem, we propose learning Task Informed Abstractions (TIA) that explicitly separates reward-correlated visual features from distractors. For learning TIA, we introduce the formalism of Task Informed MDP (TiMDP) that is realized by training two models that learn visual features via cooperative reconstruction, but one model is adversarially dissociated from the reward signal. Empirical evaluation shows that TIA leads to significant performance gains over state-of-the-art methods on many visual control tasks where natural and unconstrained visual distractions pose a formidable challenge.
An Enhanced Global Precipitation Measurement (GPM) Validation Network Prototype

A Validation Network (VN) prototype is currently underway that compares data from the Precipitation Radar (PR) instrument on NASA's Tropical Rainfall Measuring Mission (TRMM) satellite to similar measurements from the U.S. national network of operational weather radars. This prototype is being conducted as part of the ground validation activities of NASA's Global Precipitation Measurement (GPM) mission. GPM will carry a Dual-frequency Precipitation Radar instrument (DPR) with similar characteristics to the TRMM PR. The purpose of the VN is to identify and resolve significant discrepancies between the U.S. national network of ground radar (GR) observations and satellite observations. The ultimate goal of such comparisons is to understand and resolve the first order variability and bias of precipitation retrievals in different meteorological/hydrological regimes at large scales. This paper presents a description of, and results from, an improved algorithm for volume matching and comparison of PR and ground radar observations.
Consistency, Verification, and Validation of Turbulence Models for Reynolds-Averaged Navier—stokes Applications:

In current practice, it is often difficult to draw firm conclusions about turbulence model accuracy when performing multi-code computational fluid dynamics studies ostensibly using the same model because of inconsistencies in model formulation or implementation in different codes. This article describes an effort to improve the consistency, verification, and validation of turbulence models within the aerospace community through a website database of verification and validation cases. Some of the variants of two widely used turbulence models are described, and two independent computer codes (one structured and one unstructured) are used in conjunction with two specific versions of these models to demonstrate consistency with grid refinement for several representative problems. Naming conventions, implementation consistency, and thorough grid resolution studies are key factors necessary for success.
Initialization Process of a Power System Transient Simulation Scheme for Stability Studies

The initialization process of a novel power system transient simulation scheme for stability studies is put forward, by further developing a “time-domain harmonic power-flow algorithm”. The initialization process is formulated as an algebraic problem to ensure that the power system under study is in steady state and operated at a specified operating point, at the beginning of a transient simulation run. The algebraic problem is then solved efficiently by a preconditioned finite difference Newton-GMRES method. Case studies verify the validity and efficiency of the initialization process. The proposed initialization process is general-purpose and can be applied to other power system transient simulation schemes.
NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning.

Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI). And at this time, symbolic approaches to NLI are receiving less attention. Both approaches (symbolic and DL) have their advantages and weaknesses. However, currently, no method combines them in a system to solve the task of NLI. To merge symbolic and deep learning methods, we propose an inference framework called NeuralLog, which utilizes both a monotonicity-based logical inference engine and a neural network language model for phrase alignment. Our framework models the NLI task as a classic search problem and uses the beam search algorithm to search for optimal inference paths. Experiments show that our joint logic and neural inference system improves accuracy on the NLI task and can achieve state-of-art accuracy on the SICK and MED datasets.
Minimal Differential Difference Realizations of Delay Differential, Differential Difference, and Neutral Delay Systems

Delay-Differential Equations (DDEs) are often used to represent control of and over large networks. However, the presence of delay makes the problems of analysis and control of such networks challenging. Recently, Differential Difference Equations (DDFs) have been proposed as a modelling framework which allows us to more efficiently represent the low-dimensional nature of delayed channels in a network or large-scale delayed system. Unfortunately, however, the standard conversion formulae from DDE to DDF do not account for this low-dimensional structure - hence any efficient DDF representation of a large delayed network or system must be hand-crafted. In this paper, we propose an algorithm for constructing DDF realizations of both DDE and DDF systems wherein the dimension of the delayed channels has been minimized. Furthermore, we provide a convenient PIETOOLS implementation of these algorithms and show that the algorithm significantly reduces the complexity of the model for several illustrative examples, including Neutral Delay Systems (NDSs).
Improved Approximation for Longest Common Subsequence over Small Alphabets.

This paper investigates the approximability of the Longest Common Subsequence (LCS) problem. The fastest algorithm for solving the LCS problem exactly runs in essentially quadratic time in the length of the input, and it is known that under the Strong Exponential Time Hypothesis the quadratic running time cannot be beaten. There are no such limitations for the approximate computation of the LCS however, except in some limited scenarios. There is also a scarcity of approximation algorithms. When the two given strings are over an alphabet of size k, returning the subsequence formed by the most frequent symbol occurring in both strings achieves a 1/k approximation for the LCS. It is an open problem whether a better than 1/k approximation can be achieved in truly subquadratic time (O(n^{2-δ}) time for constant δ > 0). A recent result [Rubinstein and Song SODA'2020] showed that a 1/2+e approximation for the LCS over a binary alphabet is possible in truly subquadratic time, provided the input strings have the same length. In this paper we show that if a 1/2+e approximation (for e > 0) is achievable for binary LCS in truly subquadratic time when the input strings can be unequal, then for every constant k, there is a truly subquadratic time algorithm that achieves a 1/k+δ approximation for k-ary alphabet LCS for some δ > 0. Thus the binary case is the hardest. We also show that for every constant k, if one is given two strings of equal length over a k-ary alphabet, one can obtain a 1/k+e approximation for some constant e > 0 in truly subquadratic time, thus extending the Rubinstein and Song result to all alphabets of constant size.
Trajectory Optimization for High-Dimensional Nonlinear Systems under STL Specifications

Signal Temporal Logic (STL) has gained popularity in recent years as a specification language for cyber-physical systems, especially in robotics. Beyond being expressive and easy to understand, STL is appealing because the synthesis problem—generating a trajectory that satisfies a given specification—can be formulated as a trajectory optimization problem. Unfortunately, the associated cost function is nonsmooth and non-convex. As a result, existing synthesis methods scale poorly to high-dimensional nonlinear systems. In this letter, we present a new trajectory optimization approach for STL synthesis based on Differential Dynamic Programming (DDP). It is well known that DDP scales well to extremely high-dimensional nonlinear systems like robotic quadrupeds and humanoids: we show that these advantages can be harnessed for STL synthesis. We prove the soundness of our proposed approach, demonstrate order-of-magnitude speed improvements over the state-of-the-art on several benchmark problems, and demonstrate the scalability of our approach to the full nonlinear dynamics of a 7 degree-of-freedom robot arm.
H\"older parameterization of iterated function systems and a self-affine phenomenon

We investigate the H\"older geometry of curves generated by iterated function systems (IFS) in a complete metric space. A theorem of Hata from 1985 asserts that every connected attractor of an IFS is locally connected and path-connected. We give a quantitative strengthening of Hata's theorem. First we prove that every connected attractor of an IFS is $(1/s)$-H\"older path-connected, where $s$ is the similarity dimension of the IFS. Then we show that every connected attractor of an IFS is parameterized by a $(1/\alpha)$-H\"older curve for all $\alpha>s$. At the endpoint, $\alpha=s$, a theorem of Remes from 1998 already established that connected self-similar sets in Euclidean space that satisfy the open set condition are parameterized by $(1/s)$-H\"older curves. In a secondary result, we show how to promote Remes' theorem to self-similar sets in complete metric spaces, but in this setting require the attractor to have positive $s$-dimensional Hausdorff measure in lieu of the open set condition. To close the paper, we determine sharp H\"older exponents of parameterizations in the class of connected self-affine Bedford-McMullen carpets and build parameterizations of self-affine sponges. An interesting phenomenon emerges in the self-affine setting. While the optimal parameter $s$ for a self-similar curve in $\mathbb{R}^n$ is always at most the ambient dimension $n$, the optimal parameter $s$ for a self-affine curve in $\mathbb{R}^n$ may be strictly greater than $n$.