cssi/SciSciGPT-SciSciNet huggingface dataset:
Stability Analysis of Conewise Affine Dynamical Systems Using Conewise Linear Lyapunov Functions

This letter proposes computational algorithms for analyzing conewise affine dynamical systems, where every neighborhood of the origin contains an affine mode. These algorithms are based on conewise linear Lyapunov functions. To make such algorithms useful, we present an algorithm to automatically search over partitions defining these conewise Linear functions. This algorithm is sound, although we present a counter-example to its completeness. We show that this approach verifies stability of 2D and 3D examples of conewise affine dynamical systems, including combinations of the harmonic and nonsmooth oscillators.
Fully Decentralized Reinforcement Learning-Based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power

In this paper, we introduce a new framework to address the problem of voltage regulation in unbalanced distribution grids with deep photovoltaic penetration. In this framework, both real and reactive power setpoints are explicitly controlled at each solar panel smart inverter, and the objective is to simultaneously minimize system-wide voltage deviation and maximize solar power output. We formulate the problem as a Markov decision process with continuous action spaces and use proximal policy optimization, a reinforcement learning-based approach, to solve it, without the need for any forecast or explicit knowledge of network topology or line parameters. By representing the system in a quasi-steady state manner, and by carefully formulating the Markov decision process, we reduce the complexity of the problem and allow for fully decentralized (communication-free) policies, all of which make the trained policies much more practical and interpretable. Numerical simulations on a 240-node unbalanced distribution grid, based on a real network in Midwest U.S., are used to validate the proposed framework and reinforcement learning approach.
Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network

Midlatitude prediction on subseasonal timescales is difficult due to the chaotic nature of the atmosphere and often requires the identification of favorable atmospheric conditions that may lead to ...
How Simple Changes to Language and Tick Marks Can Curtail the Ghost Ticking of Audit Procedures

Prior research shows that ghost ticking, or documenting audit work not actually performed, is a persistent threat to audit quality. We examine whether requiring self-references in audit workpapers (i.e., “I” performed the test) can effectively curtail ghost ticking, compared to other workpaper language that can be used in practice. We also design and test an alternative to tick marks (symbol-based notation commonly used in workpapers to describe the results of audit procedures), in which auditors select the same description of the procedure performed, but from a pre-populated drop-down list. Consistent with our hypotheses, we find that using both self-references and descriptions (as opposed to tick marks) jointly reduces ghost ticking, compared to when only one (or neither) of these are used. Overall, we demonstrate how two simple and easily implementable changes to language and tick marks can significantly curtail ghost ticking and therefore reduce its threat to audit quality.
Violence, development, and migration waves: Evidence from Central American child migrant apprehensions

Abstract Does violence lead to flight from cities in the developing world? This paper estimates the effect of violent crime in municipalities of Honduras, El Salvador, and Guatemala on the irregular emigration of children to the United States. It studies the full universe of 178,825 unaccompanied child migrants from those countries apprehended in the U.S. from 2011 to 2016. In the average municipality, generalized insecurity that produces 10 additional homicides in the origin city caused between three and six cumulative additional child-migrant apprehensions in the U.S.—a measure of irregular migration. Diffusion of migration experience through peer and family networks produces self-reinforcing, city-specific waves of migration that can rise even after violence subsides.
Results of a demonstration of the use of Differential Very Long Baseline Interferometry data for precise navigation of interplanetary spacecraft

A set of experiments in the use of Differential Very Long Baseline Interferometry for spacecraft navigation have been completed. Data using both Voyager spacecraft and a single quasar were acquired during the Jupiter encounter time period. The data were processed and analyzed in order to assess the navigation accuracy of Differential Very Long Baseline Interferometry. The paper focuses on the data reduction and techniques for assessing data quality and consistency.
Is Pessimism Provably Efficient for Offline RL

We study offline reinforcement learning (RL), which aims to learn an optimal policy based on a dataset collected a priori. Due to the lack of further interactions with the environment, offline RL suffers from the insufficient coverage of the dataset, which eludes most existing theoretical analysis. In this paper, we propose a pessimistic variant of the value iteration algorithm (PEVI), which incorporates an uncertainty quantifier as the penalty function. Such a penalty function simply flips the sign of the bonus function for promoting exploration in online RL, which makes it easily implementable and compatible with general function approximators. Without assuming the sufficient coverage of the dataset, we establish a data-dependent upper bound on the suboptimality of PEVI for general Markov decision processes (MDPs). When specialized to linear MDPs, it matches the information-theoretic lower bound up to multiplicative factors of the dimension and horizon. In other words, pessimism is not only provably efficient but also minimax optimal. In particular, given the dataset, the learned policy serves as the "best effort" among all policies, as no other policies can do better. Our theoretical analysis identifies the critical role of pessimism in eliminating a notion of spurious correlation, which emerges from the "irrelevant" trajectories that are less covered by the dataset and not informative for the optimal policy.
Seeding Supermassive Black Holes with Self-interacting Dark Matter: A Unified Scenario with Baryons

Observations show that supermassive black holes (SMBHs) with a mass of $\sim10^9 M_\odot$ exist when the Universe is just $6\%$ of its current age. We propose a scenario where a self-interacting dark matter halo experiences gravothermal instability and its central region collapses into a seed black hole. The presence of baryons in protogalaxies could significantly accelerate the gravothermal evolution of the halo and shorten collapse timescales. The central halo could dissipate its angular momentum remnant via viscosity induced by the self-interactions. The host halo must be on high tails of density fluctuations, implying that high-$z$ SMBHs are expected to be rare in this scenario. We further derive conditions for triggering general relativistic instability of the collapsed region. Our results indicate that self-interacting dark matter can provide a unified explanation for diverse dark matter distributions in galaxies today and the origin of SMBHs at redshifts $z\sim6-7$.
Q-Learning Lagrange Policies for Multi-Action Restless Bandits

Multi-action restless multi-armed bandits (RMABs) are a powerful framework for constrained resource allocation in which N independent processes are managed. However, previous work only study the offline setting where problem dynamics are known. We address this restrictive assumption, designing the first algorithms for learning good policies for Multi-action RMABs online using combinations of Lagrangian relaxation and Q-learning. Our first approach, MAIQL, extends a method for Q-learning the Whittle index in binary-action RMABs to the multi-action setting. We derive a generalized update rule and convergence proof and establish that, under standard assumptions, MAIQL converges to the asymptotically optimal multi-action RMAB policy as t → ∞. However, MAIQL relies on learning Q-functions and indexes on two timescales which leads to slow convergence and requires problem structure to perform well. Thus, we design a second algorithm, LPQL, which learns the well-performing and more general Lagrange policy for multi-action RMABs by learning to minimize the Lagrange bound through a variant of Q-learning. To ensure fast convergence, we take an approximation strategy that enables learning on a single timescale, then give a guarantee relating the approximation's precision to an upper bound of LPQL's return as t → ∞. Finally, we show that our approaches always outperform baselines across multiple settings, including one derived from real-world medication adherence data.
Megaverse: Simulating Embodied Agents at One Million Experiences per Second

We present Megaverse, a new 3D simulation platform for reinforcement learning and embodied AI research. The efficient design of our engine enables physics-based simulation with high-dimensional egocentric observations at more than 1,000,000 actions per second on a single 8-GPU node. Megaverse is up to 70x faster than DeepMind Lab in fully-shaded 3D scenes with interactive objects. We achieve this high simulation performance by leveraging batched simulation, thereby taking full advantage of the massive parallelism of modern GPUs. We use Megaverse to build a new benchmark that consists of several single-agent and multi-agent tasks covering a variety of cognitive challenges. We evaluate model-free RL on this benchmark to provide baselines and facilitate future research. The source code is available at this https URL
Secure Software Leasing

Formulating cryptographic definitions to protect against software piracy is an important research direction that has not received much attention. Since natural definitions using classical cryptography are impossible to achieve (as classical programs can always be copied), this directs us towards using techniques from quantum computing. The seminal work of Aaronson [CCC’09] introduced the notion of quantum copy-protection precisely to address the problem of software anti-piracy. However, despite being one of the most important problems in quantum cryptography, there are no provably secure solutions of quantum copy-protection known for any class of functions.
Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.
Constrained Online Learning to Mitigate Distortion Effects in Pulse-Agile Cognitive Radar

Pulse-agile radar systems have demonstrated favorable performance in dynamic electromagnetic scenarios. However, the use of non-identical waveforms within a radar's coherent processing interval may lead to harmful distortion effects when pulse-Doppler processing is used. This paper presents an online learning framework to optimize detection performance while mitigating harmful sidelobe levels. The radar waveform selection process is formulated as a linear contextual bandit problem, within which waveform adaptations which exceed a tolerable level of expected distortion are eliminated. The constrained online learning approach is effective and computationally feasible, evidenced by simulations in a radar-communication coexistence scenario and in the presence of intentional adaptive jamming. This approach is applied to both stochastic and adversarial contextual bandit learning models and the detection performance in dynamic scenarios is evaluated.
A Stochastic Primal-Dual Method for Optimization with Conditional Value at Risk Constraints

We study a first-order primal-dual subgradient method to optimize risk-constrained risk-penalized optimization problems, where risk is modeled via the popular conditional value at risk (CVaR) measure. The algorithm processes independent and identically distributed samples from the underlying uncertainty in an online fashion and produces an $$\eta /\sqrt{K}$$ -approximately feasible and $$\eta /\sqrt{K}$$ -approximately optimal point within K iterations with constant step-size, where $$\eta $$ increases with tunable risk-parameters of CVaR. We find optimized step sizes using our bounds and precisely characterize the computational cost of risk aversion as revealed by the growth in $$\eta $$ . Our proposed algorithm makes a simple modification to a typical primal-dual stochastic subgradient algorithm. With this mild change, our analysis surprisingly obviates the need to impose a priori bounds or complex adaptive bounding schemes for dual variables to execute the algorithm as assumed in many prior works. We also draw interesting parallels in sample complexity with that for chance-constrained programs derived in the literature with a very different solution architecture.
Fairness and Transparency in Recommendation: The Users’ Perspective

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these new algorithmic objectives must be communicated transparently in a fairness-aware recommender system. While explanation has a long history in recommender systems research, there has been little work that attempts to explain systems that use a fairness objective. Even though the previous work in other branches of AI has explored the use of explanations as a tool to increase fairness, this work has not been focused on recommendation. Here, we consider user perspectives of fairness-aware recommender systems and techniques for enhancing their transparency. We describe the results of an exploratory interview study that investigates user perceptions of fairness, recommender systems, and fairness-aware objectives. We propose three features – informed by the needs of our participants – that could improve user understanding of and trust in fairness-aware recommender systems.
Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning

Fast and accurate reconstruction of magnetic resonance (MR) images from under-sampled data is important in many clinical applications. In recent years, deep learning-based methods have been shown to produce superior performance on MR image reconstruction. However, these methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. In order to overcome this challenge, we propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients’ privacy. However, the generalizability of models trained with the FL setting can still be suboptimal due to domain shift, which results from the data collected at multiple institutions with different sensors, disease types, and acquisition protocols, etc. With the motivation of circumventing this challenge, we propose a cross-site modeling for MR image reconstruction in which the learned intermediate latent features among different source sites are aligned with the distribution of the latent features at the target site. Extensive experiments are conducted to provide various insights about FL for MR image reconstruction. Experimental results demonstrate that the proposed framework is a promising direction to uti-lize multi-institutional data without compromising patients’ privacy for achieving improved MR image reconstruction. Our code is available at https://github.com/guopengf/FL-MRCM.
Optimum Launch Trajectories for the ATS-E Mission

Optimum launch trajectories for ATS-E mission with noncircular parking orbits, considering apogee motor size, perigee radius and duration constraints
Teacher Expectancy or My Fair Lady

(1972). Teacher Expectancy or My Fair Lady. School Psychology Review: Vol. 1, No. 2, pp. 17-21.
Early Outcomes After Carotid Endarterectomy and Carotid Artery Stenting: A Propensity-Matched Cohort Analysis.

Background Carotid endarterectomy (CEA) and carotid artery stenting (CAS) represent options to treat many patients with carotid stenosis. Although randomized trial data are plentiful, estimated rates of morbidity and mortality for both CEA and CAS have varied substantially. Objective To evaluate rates of adverse outcomes after CAS and CEA in a large national database. Methods We analyzed 84 191 adult patients undergoing elective, nonemergent CAS (n = 81 361) or CEA (n = 2830), from 2011 to 2018, in the American College of Surgeons' National Surgical Quality Improvement Program database. Odds of adverse outcomes (30-d rates of stroke, myocardial infarction (MI), cardiac arrest, prolonged length of stay (LOS), readmission, reoperation, and mortality) were evaluated in propensity-matched (n = 2821) cohorts through logistic regression. Results In the propensity-matched cohorts, CAS had increased odds of periprocedural stroke (odds ratio [OR] 1.97, 95% CI 1.32-2.95) and decreased odds of cardiac arrest (OR 0.33, 95% CI 0.13-0.84) and 30-d reoperation (OR 0.59, 95% CI 0.44-0.80) compared to CEA. Relative odds of MI, prolonged LOS, discharge to destination other than home, 30-d readmission, or 30-d mortality were statistically similar. In the unmatched patient population, rates of adverse outcomes with CEA were constant over time; however, for CAS, rates of stroke increased over time. In both the matched and unmatched patient cohorts, patients 70 yr and older had lower rates of post-procedural stroke with CEA, but not with CAS, compared to younger patients. Conclusion In a propensity-matched analysis of a large, prospectively collected, national, surgical database, CAS was associated with increased odds of periprocedural stroke, which increased over time. Rates of MI and death were not significantly different between the 2 procedures.
New Stick Number Bounds from Random Sampling of Confined Polygons

The stick number of a knot is the minimum number of segments needed to build a polygonal version of the knot. Despite its elementary definition and relevance to physical knots, the stick number is ...
MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.
Sham: A DSL for Fast DSLs

Domain-specific languages (DSLs) are touted as both easy to embed in programs and easy to optimize. Yet these goals are often in tension. Embedded or internal DSLs fit naturally with a host language, while inheriting the host's performance characteristics. External DSLs can use external optimizers and languages but sit apart from the host. We present Sham, a toolkit designed to enable internal DSLs with high performance. Sham provides a domain-specific language (embedded in Racket) for implementing other high-performance DSLs, with transparent compilation to assembly code at runtime. Sham is well suited as both a compilation target for other embedded DSLs and for transparently replacing DSL support code with faster versions. Sham provides seamless inter-operation with its host language without requiring any additional effort from its users. Sham also provides a framework for defining language syntax which implements Sham's own language interface as well. We validate Sham's design on a series of case studies, ranging from Krishnamurthi's classic automata DSL to a sound synthesis DSL and a probabilistic programming language. All of these are existing DSLs where we replaced the backend using Sham, resulting in major performance gains. We present an example-driven description of how Sham can smoothly enhance an existing DSL into a high-performance one. When compared to existing approaches for implementing high-performance DSLs, Sham's design aims for both simplicity and programmer control. This makes it easier to port our techniques to other languages and frameworks, or borrow Sham's innovations "a la carte" without adopting the whole approach. Sham builds a sophisticated and powerful DSL construction toolkit atop fundamental language features including higher-order functions, data structures, and a foreign-function interface (FFI), all readily available in other languages. Furthermore, Sham's approach allows DSL developers to simply write functions, either using Sham or generating Sham, without needing to work through complex staging or partial evaluation systems.
Quantized nonlinear Thouless pumping.

The topological protection of wave transport, originally observed in the context of the quantum Hall effect in two-dimensional electron gases1, has been shown to apply broadly to a range of physical platforms, including photonics2–5, ultracold atoms in optical lattices6–8 and others9–12. That said, the behaviour of such systems can be very different from the electronic case, particularly when interparticle interactions or nonlinearity play a major role13–22. A Thouless pump23 is a one-dimensional model that captures the topological quantization of transport in the quantum Hall effect using the notion of dimensional reduction: an adiabatically, time-varying potential mathematically maps onto a momentum coordinate in a conceptual second dimension24–34. Importantly, quantization assumes uniformly filled electron bands below a Fermi energy, or an equivalent occupation for non-equilibrium bosonic systems. Here we theoretically propose and experimentally demonstrate quantized nonlinear Thouless pumping of photons with a band that is decidedly not uniformly occupied. In our system, nonlinearity acts to quantize transport via soliton formation and spontaneous symmetry-breaking bifurcations. Quantization follows from the fact that the instantaneous soliton solutions centred upon a given unit cell are identical after each pump cycle, up to translation invariance; this is an entirely different mechanism from traditional Thouless pumping. This result shows that nonlinearity and interparticle interactions can induce quantized transport and topological behaviour without a linear counterpart. Nonlinearity is shown to induce quantized topological transport via soliton motion; specifically, we demonstrate nonlinear Thouless pumping of photons in waveguide arrays with a non-uniformly occupied energy band.
Accelerating the Development of Multimodal, Integrative-AI Systems with Platform for Situated Intelligence

We describe Platform for Situated Intelligence, an open-source framework for multimodal, integrative-AI systems. The framework provides infrastructure, tools, and components that enable and accelerate the development of applications that process multimodal streams of data and in which timing is critical. The framework is particularly well-suited for developing physically situated interactive systems that perceive and reason about their surroundings in order to better interact with people, such as social robots, virtual assistants, smart meeting rooms, etc. In this paper, we provide a brief, high-level overview of the framework and its main affordances, and discuss its implications for HRI.
Improved Delegation of Computation using Fully Homomorphic Encryption.

Following Gennaro, Gentry, and Parno (Cryptology ePrint Archive 2009/547), we use fully homomorphic encryption to design improved schemes for delegating computation. In such schemes, a delegator outsources the computation of a function F on many, dynamically chosen inputs xi to a worker in such a way that it is infeasible for the worker to make the delegator accept a result other than F (xi). The \online stage" of the Gennaro et al. scheme is very ecient: the parties exchange two messages, the delegator runs in time poly(logT ), and the worker runs in time poly(T ), where T is the time complexity of F . However, the \oine stage" (which depends on the function F but not the inputs to be delegated) is inecient: the delegator runs in time poly(T ) and generates a public key of length poly(T ) that needs to be accessed by the worker during the online stage. Our rst construction eliminates the large public key from the Gennaro et al. scheme. The delegator still invests poly(T ) time in the oine stage, but does not need to communicate or publish anything. Our second construction reduces the work of the delegator in the oine stage to poly(logT ) at the price of a 4-message (oine) interaction with a poly( T )-time worker (which need not be the same as the workers used in the online stage). Finally, we describe a \pipelined" implementation of the second construction that avoids the need to re-run the oine construction after errors are detected (assuming errors are not too frequent).
Comparison of Approaches to Transcriptomic Analysis in Multi-Sampled Tumors

Intratumoral heterogeneity is a well-documented feature of human cancers and is associated with outcome and treatment resistance. However, a heterogeneous tumor transcriptome contributes an unknown level of variability to analyses of differentially expressed genes (DEGs) that may contribute to phenotypes of interest, including treatment response. Although current clinical practice and the vast majority of research studies use a single sample from each patient, decreasing costs of sequencing technologies and computing power have made repeated-measures analyses increasingly economical. Repeatedly sampling the same tumor increases the statistical power of DEG analysis, which is indispensable toward downstream analysis and also increases one's understanding of within-tumor variance, which may affect conclusions. Here, we compared five different methods for analyzing gene expression profiles derived from repeated sampling of human prostate tumors in two separate cohorts of patients. We also benchmarked the sensitivity of generalized linear models to linear mixed models for identifying DEGs contributing to relevant prostate cancer pathways based on a ground-truth model.
The Supreme Court’s Un-Americanism Pendulum

Examining the Bill of Rights through the post-WWII Red Scare opinions of the Supreme Court reveals an array of strategies of judging and interplay between the judiciary, the legislature, and the electorate. The transitions are more gradual than appointments of justices would suggest and hint toward judicial sensitivity to political undercurrents. Legislative action that mostly failed to pass had full impact on the Court’s majority. The choices of the liberal justices may have undermined their long-term interests.
Sidon sets and perturbations

A subset A of an additive abelian group is an h-Sidon set if every element in the h-fold sumset hA has a unique representation as the sum of h not necessarily distinct elements of A. Let \(\mathbf {F}\) be a field of characteristic 0 with a nontrivial absolute value, and let \(A = \{a_i :i \in \mathbf {N} \}\) and \(B = \{b_i :i \in \mathbf {N} \}\) be subsets of \(\mathbf {F}\). Let \(\varepsilon = \{ \varepsilon _i:i \in \mathbf {N} \}\), where \(\varepsilon _i > 0\) for all \(i \in \mathbf {N}\). The set B is an \(\varepsilon \)-perturbation of A if \(|b_i-a_i| 0\), every set \(A = \{a_i :i \in \mathbf {N} \}\) has an \(\varepsilon \)-perturbation B that is an h-Sidon set. This result extends to sets of vectors in \( \mathbf{F}^n\).
Learning social networks from text data using covariate information

Accurately describing the lives of historical figures can be challenging, but unraveling their social structures perhaps is even more so. Historical social network analysis methods can help in this regard and may even illuminate individuals who have been overlooked by historians, but turn out to be influential social connection points. Text data, such as biographies, are a useful source of information for learning historical social networks but the identifcation of links based on text data can be challenging. The Local Poisson Graphical Lasso model models social networks by conditional independence structures, and leverages the number of name co-mentions in the text to infer relationships. However, this method does not take into account the abundance of covariate information that is often available in text data. Conditional independence structure like Poisson Graphical Model, which makes use name mention counts in the text can be useful tools to avoid false positive links due to the co-mentions but given historical tendency of frequently used or common names, without additional distinguishing information, we may introduce incorrect connections. In this work, we therefore extend the Local Poisson Graphical Lasso model with a (multiple) penalty structure that incorporates covariates, opening up the opportunity for similar individuals to have a higher probability of being connected. We propose both greedy and Bayesian approaches to estimate the penalty parameters. We present results on data simulated with characteristics of historical networks and show that this type of penalty structure can improve network recovery as measured by precision and recall. We also illustrate the approach on biographical data of individuals who lived in early modern Britain between 1500 and 1575. We will show how these covariates affect the statistical model’s performance using simulations, discuss how it helps to better identify links for the people with common names and those who are traditionally underrepresented in the biography text data.
Is a Classification Procedure Good Enough?-A Goodness-of-Fit Assessment Tool for Classification Learning

In recent years, many non-traditional classification methods, such as Random Forest, Boosting, and neural network, have been widely used in applications. Their performance is typically measured in ...
Through the Looking Glass: Insights into Visualization Pedagogy through Sentiment Analysis of Peer Review Text.

Peer review is a widely utilized feedback mechanism for engaging students. As a pedagogical method, it has been shown to improve educational outcomes, but we have found limited empirical measurement of peer review in visualization courses. In addition to increasing engagement, peer review provides diverse feedback and reinforces recently-learned course concepts through critical evaluation of others' work. We discuss the construction and application of peer review in two visualization courses from different colleges at the University of South Florida. We then analyze student projects and peer review text via sentiment analysis to infer insights for visualization educators, including the focus of course content, engagement across student groups, student mastery of concepts, course trends over time, and expert intervention effectiveness. Finally, we provide suggestions for adapting peer review to other visualization courses to engage students and increase instructor understanding of the peer review process.
Task-Level Authoring for Remote Robot Teleoperation

Remote teleoperation of robots can broaden the reach of domain specialists across a wide range of industries such as home maintenance, health care, light manufacturing, and construction. However, current direct control methods are impractical, and existing tools for programming robot remotely have focused on users with significant robotic experience. Extending robot remote programming to end users, i.e., users who are experts in a domain but novices in robotics, requires tools that balance the rich features necessary for complex teleoperation tasks with ease of use. The primary challenge to usability is that novice users are unable to specify complete and robust task plans to allow a robot to perform duties autonomously, particularly in highly variable environments. Our solution is to allow operators to specify shorter sequences of high-level commands, which we call task-level authoring, to create periods of variable robot autonomy. This approach allows inexperienced users to create robot behaviors in uncertain environments by interleaving exploration, specification of behaviors, and execution as separate steps. End users are able to break down the specification of tasks and adapt to the current needs of the interaction and environments, combining the reactivity of direct control to asynchronous operation. In this paper, we describe a prototype system contextualized in light manufacturing and its empirical validation in a user study where 18 participants with some programming experience were able to perform a variety of complex telemanipulation tasks with little training. Our results show that our approach allowed users to create flexible periods of autonomy and solve rich manipulation tasks. Furthermore, participants significantly preferred our system over comparative more direct interfaces, demonstrating the potential of our approach for enabling end users to effectively perform remote robot programming.
Hybrid Generative Models for Two-Dimensional Datasets

Two-dimensional array-based datasets are pervasive in a variety of domains. Current approaches for generative modeling have typically been limited to conventional image datasets and performed in the pixel domain which does not explicitly capture the correlation between pixels. Additionally, these approaches do not extend to scientific and other applications where each element value is continuous and is not limited to a fixed range. In this paper, we propose a novel approach for generating two-dimensional datasets by moving the computations to the space of representation bases and show its usefulness for two different datasets, one from imaging and another from scientific computing. The proposed approach is general and can be applied to any dataset, representation basis, or generative model. We provide a comprehensive performance comparison of various combinations of generative models and representation basis spaces. We also propose a new evaluation metric which captures the deficiency of generating images in pixel space.
A statistical approach for enhancing security in VANETs with efficient rogue node detection using fog computing

Abstract Rogue nodes broadcasting false information in beacon messages may lead to catastrophic consequences in Vehicular Ad Hoc Networks (VANETs). Previous researchers used either cryptography, trust scores, or past vehicle data to detect rogue nodes; however, these methods suffer from high processing delay, overhead, and False–Positive Rate (FPR). We propose herein Greenshield’s traffic model–based fog computing scheme called Fog–based Rogue Node Detection (F–RouND), which dynamically utilizes the On–Board Units (OBUs) of all vehicles in the region for rogue node detection. We aim to reduce the data processing delays and FPR in detecting rogue nodes at high vehicle densities. The performance of the F–RouND framework was evaluated via simulations. Results show that the F–RouND framework ensures 45% lower processing delays, 12% lower overhead, and 36% lower FPR at the urban scenario compared to the existing rogue node detection schemes even when the number of rogue nodes increases by up to 40% in the region.
On the Wrongness of Lies

Why are lies wrong? The Kantian answer sees lies as a close cousin to coercion; they are a violation of individual autonomy and a demonstration of contempt. By contrast, the utilitarian answer is t...
Propagation of singularities for the stochastic wave equation

Abstract We study the existence and propagation of singularities of the solution to a one-dimensional linear stochastic wave equation driven by an additive Gaussian noise that is white in time and colored in space. Our approach is based on a simultaneous law of the iterated logarithm and general methods for Gaussian processes.
Annoyance to Noise Produced by a Distributed Electric Propulsion High-Lift System

A psychoacoustic test was performed using simulated sounds from a distributed electric propulsion aircraft concept to help understand factors associated with human annoyance. A design space spanning the number of high-lift leading edge propellers and their relative operating speeds, inclusive of time varying effects associated with motor controller error and atmospheric turbulence, was considered. It was found that the mean annoyance response varies in a statistically significant manner with the number of propellers and with the inclusion of time varying effects, but does not differ significantly with the relative RPM between propellers. An annoyance model was developed, inclusive of confidence intervals, using the noise metrics of loudness, roughness, and tonality as predictors.
Extended shallow-water theories with thermodynamics and geometry

Driven by growing momentum in two-dimensional geophysical flow modeling, this paper introduces a general family of “thermal” rotating shallow-water models. The models are capable of accommodating thermodynamic processes, such as those acting in the ocean mixed layer, by allowing buoyancy to vary in horizontal position and time as well as with depth, in a polynomial fashion up to an arbitrary degree. Moreover, the models admit Euler–Poincare variational formulation and possess Lie–Poisson Hamiltonian structure. Such a geometric property provides solid fundamental support to the theories described with consequences for numerical implementation and the construction of unresolved motion parametrizations. In particular, it is found that stratification halts the development of small-scale filament rollups recently observed in a popular model, which, having vertically homogeneous density, represents a special case of the models presented here.
Special Repo Rates and the Cross-Section of Bond Prices: The Role of the Special Collateral Risk Premium

We estimate the joint term-structure of U.S. Treasury cash and repo rates using daily prices of all outstanding Treasury securities and corresponding special collateral (SC) repo rates. This allows us to derive a risk premium associated to the SC value of Treasuries and quantitatively link this premium to various price anomalies, such as the on-the-run premium. We show that a time-varying SC risk premium can explain between 74%â€“90% of the on-the-run premium, and is highly correlated with a number of other Treasury market anomalies. This suggests a commonality across these price anomalies, explicitly linked to the SC value of the highest-quality securitiesâ€”recently-issued U.S. nominal Treasuries.
Material matters in superconducting qubits

Abstract The progress witnessed within the field of quantum computing has been enabled by the identification and understanding of interactions between the state of the quantum bit (qubit) and the materials within its environment. Beginning with an introduction of the parameters used to differentiate various quantum computing approaches, we discuss the evolution of the key components that comprise superconducting qubits, where the methods of fabrication can play as important a role as the composition in dictating the overall performance. We describe several mechanisms that are responsible for the relaxation or decoherence of superconducting qubits and the corresponding methods that can be utilized to characterize their influence. In particular, the effects of dielectric loss and its manifestation through the interaction with two-level systems (TLS) are discussed. We elaborate on the methods that are employed to quantify dielectric loss through the modeling of energy flowing through the surrounding dielectric materials, which can include contributions due to both intrinsic TLS and extrinsic aspects, such as those generated by processing. The resulting analyses provide insight into identifying the relative participation of specific sections of qubit designs and refinements in construction that can mitigate their impact on qubit quality factors. Additional prominent mechanisms that can lead to energy relaxation within qubits are presented along with experimental techniques which assess their importance. We close by highlighting areas of future research that should be addressed to help facilitating the successful scaling of superconducting quantum computing.
Multi-group SIS Epidemics with Simplicial and Higher-Order Interactions

This paper analyzes a Susceptible-Infected-Susceptible (SIS) model of epidemic propagation over hypergraphs and, motivated by an important special case, we refer to the model as to the simplicial SIS model. Classically, the multi-group SIS model has assumed pairwise interactions of contagion across groups and thus has been vastly studied in the literature. It is only recently that a renewed special attention has been drawn to the study of contagion dynamics over higher-order interactions and over more general graph structures, like simplexes. Previous work on mean-field approximation scalar models of the simplicial SIS model has indicated that a new dynamical behavior domain, compared to the classical SIS model, appears due to the newly introduced higher order interaction terms: both a disease-free equilibrium and an endemic equilibrium co-exist and are both locally asymptotically stable. This paper formally establishes that bistability (as a new epidemiological behavior) also appears in the multi-group simplicial SIS model. We give sufficient conditions over the model's parameters for the appearance of this and the other behavioral domains present in the classical
Learning in Multi-Stage Decentralized Matching Markets

Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article develops a framework for learning optimal strategies in such settings, based on a nonparametric statistical approach and variational analysis. We propose an efficient algorithm, built upon concepts of "lower uncertainty bound" and "calibrated decentralized matching," for maximizing the participants' expected payoff. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. Participants will strategically act in favor of a low uncertainty level to reduce competition and increase expected payoff. We prove that participants can be better off with multi-stage matching compared to single-stage matching. We demonstrate aspects of the theoretical predictions through simulations and an experiment using real data from college admissions.
Grain splitting is a mechanism for grain coarsening in colloidal polycrystals

In established theories of grain coarsening, grains disappear either by shrinking or by rotating as a rigid object to coalesce with an adjacent grain. Here we report a third mechanism for grain coarsening, in which a grain splits apart into two regions that rotate in opposite directions to match two adjacent grains' orientations. We experimentally observe both conventional grain rotation and grain splitting in 2D colloidal polycrystals. We find that grain splitting occurs via independently rotating "granules" whose shapes are determined by the underlying triangular lattices of the two merging crystal grains. These granules are so small that existing continuum theories of grain boundary energy are inapplicable, so we introduce a hard sphere model for the free energy of a colloidal polycrystal. We find that during splitting, the system overcomes a free energy barrier before ultimately reaching a lower free energy when splitting is complete. Using simulated splitting events and a simple scaling prediction, we find that the barrier to grain splitting decreases as grain size decreases. Consequently, grain splitting is likely to play an important role in polycrystals with small grains. This discovery suggests that mesoscale models of grain coarsening may offer better predictions in the nanocrystalline regime by including grain splitting.
Controlled Evaluation of Grammatical Knowledge in Mandarin Chinese Language Models

Prior work has shown that structural supervision helps English language models learn generalizations about syntactic phenomena such as subject-verb agreement. However, it remains unclear if such an inductive bias would also improve language models’ ability to learn grammatical dependencies in typologically different languages. Here we investigate this question in Mandarin Chinese, which has a logographic, largely syllable-based writing system; different word order; and sparser morphology than English. We train LSTMs, Recurrent Neural Network Grammars, Transformer language models, and Transformer-parameterized generative parsing models on two Mandarin Chinese datasets of different sizes. We evaluate the models’ ability to learn different aspects of Mandarin grammar that assess syntactic and semantic relationships. We find suggestive evidence that structural supervision helps with representing syntactic state across intervening content and improves performance in low-data settings, suggesting that the benefits of hierarchical inductive biases in acquiring dependency relationships may extend beyond English.
Persistently foliar composite knots

A knot $\kappa$ in $S^3$ is persistently foliar if, for each non-trivial boundary slope, there is a co-oriented taut foliation meeting the boundary of the knot complement transversely in a foliation by curves of that slope. For rational slopes, these foliations may be capped off by disks to obtain a co-oriented taut foliation in every manifold obtained by non-trivial Dehn surgery on that knot. We show that any composite knot with a persistently foliar summand is persistently foliar and that any nontrivial connected sum of fibered knots is persistently foliar. As an application, it follows that any composite knot in which each of two summands is fibered or at least one summand is nontorus alternating or Montesinos is persistently foliar. We note that, in constructing foliations in the complements of fibered summands, we build branched surfaces whose complementary regions agree with those of Gabai's product disk decompositions, except for the one containing the boundary of the knot complement. It is this boundary region which provides for persistence.