cssi/SciSciGPT-SciSciNet huggingface dataset:
Training for Long-Duration Space Missions: A Literature Review into Skill Retention and Generalizability

In long-duration space missions, skill retention and generalizability become ever more important as mission length increases, for it is through these capabilities that astronaut crews achieve autonomy. A framework is proposed for future research based on three types of training commonly found in operational scenarios: initial training, refresher training, and just-in-time training. One of the key points of interest identified from the body of literature is the need for a structured and quantifiable approach to characterize skill decay. Such an approach would allow gaining a deeper understanding of the mechanisms through which skill decay operates. Because simulators are used extensively in all types of training, the effects of simulator fidelity on skill retention and generalizability are paramount to understand. Some common fidelity types are defined and their effects on the human operator are discussed. From a review of the literature on all these topics, several research gaps were identified that led to three key opportunities for future research: (1) developing skill decay functions provides theoretical insights into skill decay and allows for several practical applications, such as planning refresher training, (2) the effects of simulator fidelity on skill decay functions should be investigated empirically, and (3) investigating the generalizability of skills learned in initial training to other tasks could provide space crews with greater autonomy. A cybernetic approach might give new insights.
Multivariate Uncertainty in Deep Learning.

Deep learning has the potential to dramatically impact navigation and tracking state estimation problems critical to autonomous vehicles and robotics. Measurement uncertainties in state estimation systems based on Kalman and other Bayes filters are typically assumed to be a fixed covariance matrix. This assumption is risky, particularly for ``black box'' deep learning models, in which uncertainty can vary dramatically and unexpectedly. Accurate quantification of multivariate uncertainty will allow for the full potential of deep learning to be used more safely and reliably in these applications. We show how to model multivariate uncertainty for regression problems with neural networks, incorporating both aleatoric and epistemic sources of heteroscedastic uncertainty. We train a deep uncertainty covariance matrix model in two ways: directly using a multivariate Gaussian density loss function and indirectly using end-to-end training through a Kalman filter. We experimentally show in a visual tracking problem the large impact that accurate multivariate uncertainty quantification can have on the Kalman filter performance for both in-domain and out-of-domain evaluation data. We additionally show, in a challenging visual odometry problem, how end-to-end filter training can allow uncertainty predictions to compensate for filter weaknesses.
Duals of Semisimple Poisson–Lie Groups and Cluster Theory of Moduli Spaces of G-local Systems

We study the dual ${\rm G}^\ast$ of a standard semisimple Poisson-Lie group ${\rm G}$ from a perspective of cluster theory. We show that the coordinate ring $\mathcal{O}({\rm G}^\ast)$ can be naturally embedded into a cluster Poisson algebra with a Weyl group action. We prove that $\mathcal{O}({\rm G}^\ast)$ admits a natural basis which has positive integer structure coefficients and satisfies an invariance property with respect to a braid group action. We continue the study of the moduli space $\mathscr{P}_{{\rm G},\mathbb{S}}$ of ${\rm G}$-local systems introduced in \cite{GS3}, and prove that the coordinate ring of $\mathscr{P}_{{\rm G}, \mathbb{S}}$ coincides with its underlying cluster Poisson algebra.
Partisan endorsement experiments do not affect mass opinion on COVID-19

The partisan politics and polarized messaging surrounding COVID-19 have attracted wide interest. We present the findings of a novel survey experiment, fielded March 21-23, 2020, on a nationally-representative sample of Americans. We found no statistically significant effects of partisan endorsements or messaging from President Trump on a wide range of health behaviors and policy attitudes. We speculate on potential explanations for these null results, such as America's saturated media environment or heterogeneous effects by party. Our results suggest that priming experiments face serious obstacles when implemented at the same time as a national crisis is unfolding. [ABSTRACT FROM AUTHOR] Copyright of Journal of Elections, Public Opinion & Parties is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
A Numerical Method for Solving the Equations of Compressible Viscous Flow

Although much progress has already been made In solving problems in aerodynamic design, many new developments are still needed before the equations for unsteady compressible viscous flow can be solved routinely. This paper describes one such development. A new method for solving these equations has been devised that 1) is second-order accurate in space and time, 2) is unconditionally stable, 3) preserves conservation form, 4) requires no block or scalar tridiagonal inversions, 5) is simple and straightforward to program (estimated 10% modification for the update of many existing programs), 6) is more efficient than present methods, and 7) should easily adapt to current and future computer architectures. Computational results for laminar and turbulent flows at Reynolds numbers from 3 x 10(exp 5) to 3 x 10(exp 7) and at CFL numbers as high as 10(exp 3) are compared with theory and experiment.
Language-Mediated, Object-Centric Representation Learning

We present Language-mediated, Object-centric Representation Learning (LORL), learning disentangled, object-centric scene representations from vision and language. LORL builds upon recent advances in unsupervised object segmentation, notably MONet and Slot Attention. Just like these algorithms, LORL also learns an object-centric representation by reconstructing the input image. But LORL further learns to associate the learned representations to concepts, i.e., words for object categories, properties, and spatial relationships, from language input. These object-centric concepts derived from language facilitate the learning of object-centric representations. LORL can be integrated with various unsupervised segmentation algorithms that are language-agnostic. Experiments show that LORL consistently improves the performance of MONet and Slot Attention on two datasets via the help of language. We also show that concepts learned by LORL aid downstream tasks such as referential expression interpretation.
Stock Prices and Economic Activity in the Time of Coronavirus

Stock prices and workplace mobility trace out striking clockwise paths in daily data from mid-February to late May 2020. Global stock prices fell 30% from 17 February to 12 March, before mobility declined. Over the next 11 days, stocks fell another 10 percentage points as mobility dropped 40%. From 23 March to 9 April, stocks recovered half their losses and mobility fell further. From 9 April to late May, both stocks and mobility rose modestly. This dynamic plays out across the 35 countries in our sample, with notable departures in China, South Korea and Taiwan. The size of the global stock market crash in reaction to the pandemic is many times larger than a standard asset-pricing model implies. Looking more closely at the world’s two largest economies, the pandemic had greater effects on stock market levels and volatilities in the USA than in China even before it became evident that early US containment efforts would flounder. Newspaper-based narrative evidence confirms the dominant—and historically unprecedented—role of pandemic-related developments in the stock market behavior of both countries.
Object classification from randomized EEG trials

New results suggest strong limits to the feasibility of object classification from human brain activity evoked by image stimuli, as measured through EEG. Considerable prior work suffers from a confound between the stimulus class and the time since the start of the experiment. A prior attempt to avoid this confound using randomized trials was unable to achieve results above chance in a statistically significant fashion when the data sets were of the same size as the original experiments. Here, we attempt object classification from EEG using an array of methods that are representative of the state-of-the-art, with a far larger (20×) dataset of randomized EEG trials, 1,000 stimulus presentations of each of forty classes, all from a single subject. To our knowledge, this is the largest such EEG data-collection effort from a single subject and is at the bounds of feasibility. We obtain classification accuracy that is marginally above chance and above chance in a statistically significant fashion, and further assess how accuracy depends on the classifier used, the amount of training data used, and the number of classes. Reaching the limits of data collection with only marginally above-chance performance suggests that the prevailing literature substantially exaggerates the feasibility of object classification from EEG.
Assessing Emoji Use in Modern Text Processing Tools

Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors. This also leads to an increased need for systems and tools to operate on text containing emojis. In this study, we assess this support by considering test sets of tweets with emojis, based on which we perform a series of experiments investigating the ability of prominent NLP and text processing tools to adequately process them. In particular, we consider tokenization, part-of-speech tagging, dependency parsing, as well as sentiment analysis. Our findings show that many systems still have notable shortcomings when operating on text containing emojis.
A reference-free approach for cell type classification with scRNA-seq

Single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to characterize cells under different biological conditions. Unlike bulk RNA-seq, gene expression from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that attribute to gene quantification. To overcome data sparsity and fully utilize original reads, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types with k-mer level features. The compressed k-mer groups (CKGs), identified by the simhash method, contain k-mers with similar abundance profiles and serve as the cells' features. Our experiments demonstrate that CKG features lend themselves to better performance than gene expression features in scRNA-seq classification accuracy in the majority of experimental cases. Because CKGs are derived from raw reads without alignment to reference genome, scSimClassify offers an effective alternative to existing methods especially when reference genome is incomplete or insufficient to represent subject genomes.
Competitive Perimeter Defense on a Line

We consider a perimeter defense problem in which a single vehicle seeks to defend a compact region from intruders in a one-dimensional environment parameterized by the perimeter size and the intruder-to-vehicle speed ratio. The intruders move inward with fixed speed and direction to reach the perimeter. We provide both positive and negative worst-case performance results over the parameter space using competitive analysis. We first establish fundamental limits by identifying the most difficult parameter combinations that admit no c-competitive algorithms for any constant $c$ ≥ 1 and slightly easier parameter combinations in which every algorithm is at best 2-competitive. We then design three classes of algorithms and prove they are 1, 2, and 4-competitive, respectively, for increasingly difficult parameter combinations. Finally, we present numerical studies that provide insights into the performance of these algorithms against stochastically generated intruders.
A Simple Robust MPC for Linear Systems with Parametric and Additive Uncertainty

We propose a simple and computationally efficient approach for designing a robust Model Predictive Controller (MPC) for constrained uncertain linear systems. The uncertainty is modeled as an additive disturbance and an additive error on the system dynamics matrices. Set based bounds for each component of the model uncertainty are assumed to be known. We separate the constraint tightening strategy into two parts, depending on the length of the MPC horizon. For a horizon length of one, the robust MPC problem is solved exactly, whereas for other horizon lengths, the model uncertainty is over-approximated with a net-additive component. The resulting MPC controller guarantees robust satisfaction of state and input constraints in closed-loop with the uncertain system. With appropriately designed terminal components and an adaptive horizon strategy, we prove the controller's recursive feasibility and stability of the origin. With numerical simulations, we demonstrate that our proposed approach gains up to 15x online computation speedup over a tube MPC strategy, while stabilizing about 98% of the latter's region of attraction.
Radio frequency mixing modules for superconducting qubit room temperature control systems.

As the number of qubits in nascent quantum processing units increases, the connectorized RF (radio frequency) analog circuits used in first generation experiments become exceedingly complex. The physical size, cost, and electrical failure rate all become limiting factors in the extensibility of control systems. We have developed a series of compact RF mixing boards to address this challenge by integrating I/Q quadrature mixing, intermediate frequency/LO (local oscillator)/RF power level adjustments, and direct current bias fine tuning on a 40 × 80 mm2 four-layer printed circuit board with electromagnetic interference shielding. The RF mixing module is designed to work with RF and LO frequencies between 2.5 and 8.5 GHz. The typical image rejection and adjacent channel isolation are measured to be ∼27 dBc and ∼50 dB. By scanning the drive phase in a loopback test, the module short-term amplitude and phase linearity are typically measured to be 5 ×10-4 (Vpp/Vmean) and 1 ×10-3 radian (pk-pk). The operation of the RF mixing board was validated by integrating it into the room temperature control system of a superconducting quantum processor and executing randomized benchmarking characterization of single and two qubit gates. We measured a single-qubit process infidelity of 9.3(3) × 10-4 and a two-qubit process infidelity of 2.7(1) × 10-2.
Grey models for short-term queue length predictions for adaptive traffic signal control

Abstract Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather, and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using six variations of Grey systems: (i) the first-order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), (iv) GVM with Fourier error corrections (EGVM), (v) the Grey model with cosine term (GM(1,1 | c o s ( ω t ) or GMC), and (vi) GMC with Fourier error corrections (EGMC). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they can adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, Adaboost (ADA), Bagging (BAG), Gradient Boost (GB), Neural Network (NN), Random Forest (RF), Support Vector Regression (SVR), and Long Short Term Memory (LSTM) models. Based on our analyses, we found that GMC can produce better or competing performance compared to more complex (i.e., LSTM and NN models) in predicting average and maximum queue lengths in terms of root mean squared and mean absolute errors. GMC model needs only 4 data values and can be used in real-time as the computational time to generate a prediction in less than 0.001 s.
Ionization cross sections of silane and disilane by electron impact

Partial ionization cross sections for silane and disilane by electron impact have been measured for electron energies in the range of threshold to 1000 eV using a crossed beam geometry and employing a pulsed electron beam and a pulsed ion extraction technique. The cross sections were normalized by using the relative flow technique. For ease of use in various plasma modeling we have fitted the cross sections to an empirical formula.
PLSO: A generative framework for decomposing nonstationary time-series into piecewise stationary oscillatory components

To capture the slowly time-varying spectral content of real-world time-series, a common paradigm is to partition the data into approximately stationary intervals and perform inference in the time-frequency domain. However, this approach lacks a corresponding nonstationary time-domain generative model for the entire data and thus, time-domain inference occurs in each interval separately. This results in distortion/discontinuity around interval boundaries and can consequently lead to erroneous inferences based on any quantities derived from the posterior, such as the phase. To address these shortcomings, we propose the Piecewise Locally Stationary Oscillation (PLSO) model for decomposing time-series data with slowly time-varying spectra into several oscillatory, piecewise-stationary processes. PLSO, as a nonstationary time-domain generative model, enables inference on the entire time-series without boundary effects and simultaneously provides a characterization of its time-varying spectral properties. We also propose a novel two-stage inference algorithm that combines Kalman theory and an accelerated proximal gradient algorithm. We demonstrate these points through experiments on simulated data and real neural data from the rat and the human brain.
Resolvent analysis of stratification effects on wall-bounded shear flows

The resolvent framework for the Navier-Stokes equations with the Boussinesq approximation was applied to a stratified turbulent boundary layer. The results show that despite using only a very limited range of representative scales, the resolvent model was able to reproduce the relative magnitude of turbulence intensities and the balance of the energy budget as well as provide meaningful analysis of structures in the flow. The resolvent response modes were able to predict the relative variation in turbulence intensities as a function of wall-normal distance and Richardson number (Ri${}_{\ensuremath{\tau}}$) for the Ri${}_{\ensuremath{\tau}}$ under consideration in this study.
Energy-saving Cross-layer Optimization of Big Data Transfer Based on Historical Log Analysis

With the proliferation of data movement across the Internet, global data traffic per year has already exceeded the Zettabyte scale. The network infrastructure and end-systems facilitating the vast data movement consume an extensive amount of electricity, measured in terawatt-hours per year. This massive energy footprint costs the world economy billions of dollars partially due to energy consumed at the network end-systems. Although extensive research has been done on managing power consumption within the core networking infrastructure, there is little research on reducing the power consumption at the end-systems during active data transfers. This paper presents a novel cross-layer optimization framework, called Cross-LayerHLA, to minimize energy consumption at the end-systems by applying machine learning techniques to historical transfer logs and extracting the hidden relationships between different parameters affecting both the performance and resource utilization. It utilizes offline analysis to improve online learning and dynamic tuning of application-level and kernel-level parameters with minimal overhead. This approach minimizes end-system energy consumption and maximizes data transfer throughput. Our experimental results show that Cross-LayerHLA outperforms other state-of-the-art solutions in this area.
Safe Planning and Control Under Uncertainty for Self-Driving

Motion planning under uncertainty is critical for safe self-driving. This paper proposes a unified obstacle avoidance framework that deals with 1) uncertainty in ego-vehicle motion; and 2) prediction uncertainty of dynamic obstacles from the environment. A two-stage traffic participant trajectory predictor comprising short-term and long-term prediction is used in the planning layer to generate safe but not over-conservative trajectories for the ego-vehicle. The prediction module cooperates well with existing planning approaches. Our work showcases its effectiveness in a Frenet frame planner. A robust controller using tube MPC guarantees safe execution of the trajectory in the presence of state noise and dynamic model uncertainty. A Gaussian process regression model is used for on-line identification of the uncertainty's bound. We demonstrate the effectiveness, safety, and real-time performance of our framework in the CARLA simulator.
Contribution of receptive field center and surround to repetition suppression in macaque visual area V2

Author(s): Williams, Nathaniel; Olson, Carl | Abstract: Primate inferotemporal cortex (ITC) neurons respond with declining strength to repeated presentations of large natural images. This phenomenon - repetition suppression - has been assumed to arise at the level of ITC because ITC neurons possess the large receptive fields and sophisticated selectivity to recognize images as repetitions. It was recently discovered that V2 neurons exhibit repetition suppression under identical conditions. How do V2 neurons, with classical receptive fields encompassing only a small fraction of the image, recognize it as a repetition? One possibility is that they are sensitive to repetition of content outside the classical receptive field, in the surround. To assess this, we recorded neuronal responses to displays while independently controlling repetition of elements in the classical receptive field and the surround. We found that content in the surround contributed to repetition suppression and that this occurred relatively late in the response, consistent with being mediated by feedback.
Learning by example: Fast reliability-aware seismic imaging with normalizing flows

Uncertainty quantification provides quantitative measures on the reliability of candidate solutions of ill-posed inverse problems. Due to their sequential nature, Monte Carlo sampling methods require large numbers of sampling steps for accurate Bayesian inference and are often computationally infeasible for large-scale inverse problems, such as seismic imaging. Our main contribution is a data-driven variational inference approach where we train a normalizing flow (NF), a type of invertible neural net, capable of cheaply sampling the posterior distribution given previously unseen seismic data from neighboring surveys. To arrive at this result, we train the NF on pairs of low- and high-fidelity migrated images. In our numerical example, we obtain high-fidelity images from the Parihaka dataset and low-fidelity images are derived from these images through the process of demigration, followed by adding noise and migration. During inference, given shot records from a new neighboring seismic survey, we first compute the reverse-time migration image. Next, by feeding this low-fidelity migrated image to the NF we gain access to samples from the posterior distribution virtually for free. We use these samples to compute a high-fidelity image including a first assessment of the image's reliability. To our knowledge, this is the first attempt to train a conditional network on what we know from neighboring images to improve the current image and assess its reliability.
P-elements strengthen reproductive isolation within the Drosophila simulans species complex.

Determining mechanisms that underlie reproductive isolation is key to understanding how species boundaries are maintained in nature. Transposable elements (TEs) are ubiquitous across eukaryotic genomes. However, the role of TEs in modulating the strength of reproductive isolation between species is poorly understood. Several species of Drosophila have been found to harbor P-elements (PEs), yet only D. simulans is known to be currently polymorphic for their presence in wild populations. PEs can cause reproductive isolation between PE-containing (P) and PE-lacking (M) lineages of the same species. However, it is unclear whether they also contribute to the magnitude of reproductive isolation between species. Here, we use the simulans species complex to assess whether differences in PE status between D. simulans and its sister species, which do not harbor PEs, contribute to multiple barriers to gene flow between species. We show that crosses involving a P D. simulans father and an M mother from a sister species exhibit lower F1 female fecundity than crosses involving an M D. simulans father and an M sister-species mother. We also find that another TE, I-element, might play a minor role on determining the frequency of dysgenesis between species. Our results suggest that the presence of PEs in a species can strengthen isolation from its sister species, providing evidence that TEs can play a role in reproductive isolation. This article is protected by copyright. All rights reserved.
Health, Longevity, and Welfare Inequality of Older Americans

We estimate the distribution of well-being among the older U.S. population using an expected utility framework that incorporates differences in consumption, leisure, health, and mortality. We find large disparities in welfare that have increased over time. Incorporating the cost of living with poor health into elderly welfare substantially increases the overall inequality. Disparity measures based on cross-sectional income or consumption underestimate the growth in aggregate welfare inequality. Moreover, health is a better indicator of an individual’s relative welfare position than income or consumption.
A Weakly Supervised Amodal Segmenter With Boundary Uncertainty Estimation

This paper addresses weakly supervised amodal instance segmentation, where the goal is to segment both visible and occluded (amodal) object parts, while training provides only ground-truth visible (modal) segmentations. Following prior work, we use data manipulation to generate occlusions in training images and thus train a segmenter to predict amodal segmentations of the manipulated data. The resulting predictions on training images are taken as the pseudo-ground truth for the standard training of Mask-RCNN, which we use for amodal instance segmentation of test images. For generating the pseudo-ground truth, we specify a new Amodal Segmenter based on Boundary Uncertainty estimation (ASBU) and make two contributions. First, while prior work uses the occluder's mask, our ASBU uses the occlusion boundary as input. Second, ASBU estimates an uncertainty map of the prediction. The estimated uncertainty regularizes learning such that lower segmentation loss is incurred on regions with high uncertainty. ASBU achieves significant performance improvement relative to the state of the art on the COCOA and KINS datasets in three tasks: amodal instance segmentation, amodal completion, and ordering recovery.
Controlling Costs: Feature Selection on a Budget

The traditional framework for feature selection treats all features as costing the same amount. However, in reality, a scientist often has considerable discretion regarding what variables to measure, and the decision involves a tradeoff between model accuracy and cost (where cost can refer to money, time, difficulty, or intrusiveness). In particular, unnecessarily including an expensive feature in a model is worse than unnecessarily including a cheap feature. We propose a procedure, which we call cheap knockoffs, for performing feature selection in a cost-conscious manner. The key idea behind our method is to force higher cost features to compete with more knockoffs than cheaper features. We derive an upper bound on the weighted false discovery proportion associated with this procedure, which corresponds to the fraction of the feature cost that is wasted on unimportant features. We prove that this bound holds simultaneously with high probability over a path of selected variable sets of increasing size. A user may thus select a set of features based, for example, on the overall budget, while knowing that no more than a particular fraction of feature cost is wasted. We investigate, through simulation and a biomedical application, the practical importance of incorporating cost considerations into the feature selection process.
In Defense of the Standard Picture: The Basic Challenge

In this article I defend what Mark Greenberg has labeled the standard picture of law against the attack on it by Greenberg and Scott Hershovitz. I point out that law on the standard picture's conception of it has moral virtues that Greenberg's own moral impact theory and Hershovitz's similar theory lack. Moreover, it avoids a vicious circularity that bedevils Greenberg's theory.
Generalizing Object-Centric Task-Axes Controllers using Keypoints

To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. To achieve this it is often infeasible to train monolithic neural network policies across such large variations in object properties. Towards this generalization challenge, we propose to learn modular task policies which compose object-centric task-axes controllers. These task-axes controllers are parameterized by properties associated with underlying objects in the scene. We infer these controller parameters directly from visual input using multi-view dense correspondence learning. Our overall approach provides a simple and yet powerful framework for learning manipulation tasks. We empirically evaluate our approach on 3 different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry.
Compact reconfigurable kirigami

Kirigami involves cutting a flat, thin sheet that allows it to morph from a closed, compact configuration into an open deployed structure via coordinated rotations of the internal tiles. By recognizing and generalizing the geometric constraints that enable this art form, we propose a design framework for compact reconfigurable kirigami patterns, which can morph from a closed and compact configuration into a deployed state conforming to any prescribed target shape, and subsequently be contracted into a different closed and compact configuration. We further establish a condition for producing kirigami patterns and mechanisms which are reconfigurable and rigid deployable allowing us to connect the compact states via a zero-energy family of deployed states. All together, our inverse design framework lays out a path for the creation of shape-morphing material structures.
Interpretable machine learning applied to on-farm biosecurity and porcine reproductive and respiratory syndrome virus

Effective biosecurity practices in swine production are key in preventing the introduction and dissemination of infectious pathogens. Ideally, on-farm biosecurity practices should be chosen by their impact on bio-containment and bio-exclusion; however, quantitative supporting evidence is often unavailable. Therefore, the development of methodologies capable of quantifying and ranking biosecurity practices according to their efficacy in reducing disease risk has the potential to facilitate better-informed choices of biosecurity practices. Using survey data on biosecurity practices, farm demographics, and previous outbreaks from 139 herds, a set of machine learning algorithms were trained to classify farms by porcine reproductive and respiratory syndrome virus status, depending on their biosecurity practices and farm demographics, to produce a predicted outbreak risk. A novel interpretable machine learning toolkit, MrIML-biosecurity, was developed to benchmark farms and production systems by predicted risk and quantify the impact of biosecurity practices on disease risk at individual farms. By quantifying the variable impact on predicted risk, 50% of 42 variables were associated with fomite spread while 31% were associated with local transmission. Results from machine learning interpretations identified similar results, finding substantial contribution to predicted outbreak risk from biosecurity practices relating to the turnover and number of employees, the surrounding density of swine premises and pigs, the sharing of haul trailers, distance from the public road and farm production type. In addition, the development of individualized biosecurity assessments provides the opportunity to better guide biosecurity implementation on a case-by-case basis. Finally, the flexibility of the MrIML-biosecurity toolkit gives it the potential to be applied to wider areas of biosecurity benchmarking, to address biosecurity weaknesses in other livestock systems and industry-relevant diseases.
Spectrum of weighted composition operators, Part VII: Essential spectra of weighted composition operators on C(K). The case of non-invertible homeomorphisms

Abstract We provide a complete description of the spectrum and the essential spectra of weighted composition operators T = w T φ on C ( K ) in the case when the map φ is a non-invertible homeomorphism of K into itself.
The Power of Random Symmetry-Breaking in Nakamoto Consensus

Nakamoto consensus underlies the security of many of the world's largest cryptocurrencies, such as Bitcoin and Ethereum. Common lore is that Nakamoto consensus only achieves consistency and liveness under a regime where the difficulty of its underlying mining puzzle is very high, negatively impacting overall throughput and latency. In this work, we study Nakamoto consensus under a wide range of puzzle difficulties, including very easy puzzles. We first analyze an adversary-free setting and show that, surprisingly, the common prefix of the blockchain grows quickly even with easy puzzles. In a setting with adversaries, we provide a small backwards-compatible change to Nakamoto consensus to achieve consistency and liveness with easy puzzles. Our insight relies on a careful choice of \emph{symmetry-breaking strategy}, which was significantly underestimated in prior work. We introduce a new method -- \emph{coalescing random walks} -- to analyzing the correctness of Nakamoto consensus under the uniformly-at-random symmetry-breaking strategy. This method is more powerful than existing analysis methods that focus on bounding the number of {\it convergence opportunities}.
Faster Matchings via Learned Duals

A recent line of research investigates how algorithms can be augmented with machine-learned predictions to overcome worst case lower bounds. This area has revealed interesting algorithmic insights into problems, with particular success in the design of competitive online algorithms. However, the question of improving algorithm running times with predictions has largely been unexplored. We take a first step in this direction by combining the idea of machine-learned predictions with the idea of "warm-starting" primal-dual algorithms. We consider one of the most important primitives in combinatorial optimization: weighted bipartite matching and its generalization to $b$-matching. We identify three key challenges when using learned dual variables in a primal-dual algorithm. First, predicted duals may be infeasible, so we give an algorithm that efficiently maps predicted infeasible duals to nearby feasible solutions. Second, once the duals are feasible, they may not be optimal, so we show that they can be used to quickly find an optimal solution. Finally, such predictions are useful only if they can be learned, so we show that the problem of learning duals for matching has low sample complexity. We validate our theoretical findings through experiments on both real and synthetic data. As a result we give a rigorous, practical, and empirically effective method to compute bipartite matchings.
Metallization of colloidal crystals

Colloidal crystals formed by size-asymmetric binary particles coassemble into a wide variety of colloidal compounds with lattices akin to ionic crystals. Recently, a transition from a compound phase with a sublattice of small particles to a metal-like phase in which the small particles are delocalized has been predicted computationally and observed experimentally. In this colloidal metallic phase, the small particles roam the crystal maintaining the integrity of the lattice of large particles, as electrons do in metals. A similar transition also occurs in superionic crystals, termed sublattice melting. Here, we use energetic principles and a generalized molecular dynamics model of a binary system of functionalized nanoparticles to analyze the transition to sublattice delocalization in different coassembled crystal phases as a function of temperature ($T$), number of grafted chains on the small particles, and number ratio between the small and large particles ${n}_{s}:{n}_{l}$. We find that ${n}_{s}:{n}_{l}$ is the primary determinant of crystal type due to energetic interactions and interstitial site filling, while the number of grafted chains per small particle determines the stability of these crystals. We observe first-order sublattice delocalization transitions as $T$ increases, in which the host lattice transforms from low- to high-symmetry crystal structures, including A20 $\ensuremath{\rightarrow}$ bct $\ensuremath{\rightarrow}$ bcc, ${\mathrm{A}}_{\mathrm{d}}$ $\ensuremath{\rightarrow}$ bct $\ensuremath{\rightarrow}$ bcc, and bcc $\ensuremath{\rightarrow}$ bcc/fcc $\ensuremath{\rightarrow}$ fcc transitions and lattices. Analogous sublattice transitions driven primarily by lattice vibrations have been seen in some atomic materials exhibiting an insulator-metal transition also referred to as metallization. We also find minima in the lattice vibrations and diffusion coefficient of small particles as a function of ${n}_{s}:{n}_{l}$, indicating enhanced stability of certain crystal structures for ${n}_{s}:{n}_{l}$ values that form compounds.
Good-Enough Example Extrapolation.

This paper asks whether extrapolating the hidden space distribution of text examples from one class onto another is a valid inductive bias for data augmentation. To operationalize this question, I propose a simple data augmentation protocol called “good-enough example extrapolation” (GE3). GE3 is lightweight and has no hyperparameters. Applied to three text classification datasets for various data imbalance scenarios, GE3 improves performance more than upsampling and other hidden-space data augmentation methods.
Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations

When compared to the image classification models, black-box adversarial attacks against video classification models have been largely understudied. This could be possible because, with video, the temporal dimension poses significant additional challenges in gradient estimation. Query-efficient black-box attacks rely on effectively estimated gradients towards maximizing the probability of misclassifying the target video. In this work, we demonstrate that such effective gradients can be searched for by parameterizing the temporal structure of the search space with geometric transformations. Specifically, we design a novel iterative algorithm Geometric TRAnsformed Perturbations (GEO-TRAP), for attacking video classification models. GEO-TRAP employs standard geometric transformation operations to reduce the search space for effective gradients into searching for a small group of parameters that define these operations. This group of parameters describes the geometric progression of gradients, resulting in a reduced and structured search space. Our algorithm inherently leads to successful perturbations with surprisingly few queries. For example, adversarial examples generated from GEO-TRAP have better attack success rates with ~73.55% fewer queries compared to the state-of-the-art method for video adversarial attacks on the widely used Jester dataset. Overall, our algorithm exposes vulnerabilities of diverse video classification models and achieves new state-of-the-art results under black-box settings on two large datasets.
Prairie Politics: Black Life In The Midwest, 1890-1940

Harold Washington's maverick rise to power in Chicago in the 1980s is the culmination of nearly a century of black politics in the Windy City. Politicians have heralded his success as symbolic of the transition of the black political power base to urban centers. In a recent discussion of the historic significance of this transformation, Vernon Jarrett warned that the urban arena remains a contested terrain albeit one where blacks have at last realized power.' It should surprise no one that this shift in urban power came in the Midwest, where the inroads in urban politics appeared first in Gary, Indiana. Yet, historically the struggle for this power has been difficult to achieve. Once home to the sterling personalities upon whom Ray Ginger bestowed the Lincoln ideal, the Midwest has had a checkered career in race relations to say the least. The books reviewed here represent the most recent scholarship on blacks in Chicago, Detroit, and the small midwestern town of Monroe, Michigan. Moving beyond previous accounts of the famous race riots in Chicago and Detroit they give us an account of the process of twentiethcentury urban problems through the black experience. Relying on oral history, some quantitative research, and careful genealogical searches, all three monographs contribute a new richness to the social history of black America.
A Framework for Agreement

Over the years, the Byzantine Generals Problem has become notorious for the complexity of its solutions. This is particularly true for protocols with the early-stopping property. Moreover, minor changes in the fault model resulted in drastically different protocols. In this paper, we expose a fundamental technique that underlies most of these protocols. We then derive a simple, intuitive, failure-model-independent scheme for solving the Byzantine Generals Problem. The derivation reveals when it is possible to stop early. Using this result, we extend the basic framework into one that yields simple, early-stopping protocols for the Eventual Byzantine Agreement problem. We demonstrate the utility of our results by deriving an early-stopping protocol.
Midlatitude sub-auroral ionospheric small scale structure during a magnetic storm

[1] We develop a model for small scale structure in the midlatitude sub-auroral polarization stream (SAPS)-driven density trough ionosphere during a magnetic storm. We include the effects of ring current precipitation, parallel currents, rapid subauroral ion drifts, storm-induced neutral winds, ion temperature and density gradients. We find that ionospheric density, electric field, ion and electron temperature irregularities can be driven in the stormtime SAPS-driven density trough (1) wall region from the ion temperature gradient convective instability, which is driven by density and ion temperature gradients, in concert with the gradient-drift instability and (2) central region from an ion frictional heating instability which is driven by strong ion convection.
A Hierarchical ICA Method for Unsupervised Learning of Nonlinear Dependencies in Natural Images

Capturing dependencies in images in an unsupervised manner is important for many image-processing applications and understanding the structure of natural image signals. Linear generative models such as independent component analysis (ICA) have shown to capture low level features such as oriented edges in images. However ICA only captures linear dependency due to its linear model constraints and its modeling capability is limited. We propose a new method for capturing nonlinear dependencies in natural images. It is an extension of the linear ICA method and builds on a hierarchical representation. It makes use of lower level linear ICA representation and a subsequent mixture of Laplacian distribution for learning the nonlinear dependencies. The model is learned via the EM algorithm and it can capture variance correlation and high order structures in a consistent manner. We visualize the learned variance structure and demonstrate applications to image segmentation and denoising.
Duality Based Characterizations of Efficient Facets

Most practical applications of multicriteria decision making can be formulated in terms of efficient points determined by preference cones with polyhedral closure. Using linear approximations and duality from mathematical programming, we characterize a family of supporting hyperplanes that define the efficient facets of a set of alternatives with respect to such preference cones. We show that a subset of these hyperplanes generate maximal efficient facets. These characterizations permit us to devise a new algorithm for generating all maximal efficient facets of multicriteria optimization problems with polyhedral structure.
Optimal nonmyopic value of information in graphical models: efficient algorithms and theoretical limits

Many real-world decision making tasks require us to choose among several expensive observations. In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty. It has been general practice to use heuristic-guided procedures for selecting observations. In this paper, we present the first efficient optimal algorithms for selecting observations for a class of graphical models containing Hidden Markov Models (HMMs). We provide results for both selecting the optimal subset of observations, and for obtaining an optimal conditional observation plan. For both problems, we present algorithms for the filtering case, where only observations made in the past are taken into account, and the smoothing case, where all observations are utilized. Furthermore we prove a surprising result: In most graphical models tasks, if one designs an efficient algorithm for chain graphs, such as HMMs, this procedure can be generalized to polytrees. We prove that the value of information problem is NPPP-hard even for discrete polytrees. It also follows from our results that even computing conditional entropies, which are widely used to measure value of information, is a #P-complete problem on polytrees. Finally, we demonstrate the effectiveness of our approach on several real-world datasets.