cssi/SciSciGPT-SciSciNet huggingface dataset:
The longitudinal stability of fMRI activation during reward processing in adolescents and young adults.

Abstract Background : The use of functional neuroimaging has been an extremely fruitful avenue for investigating the neural basis of human reward function. This approach has included identification of potential neurobiological mechanisms of psychiatric disease and examination of environmental, experiential, and biological factors that may contribute to disease risk via effects on the reward system. However, a central and largely unexamined assumption of much of this research is that neural reward function is an individual difference characteristic that is relatively stable and trait-like over time. Methods In two independent samples of adolescents and young adults studied longitudinally (Ns = 145 & 139, 100% female and 100% male, ages 15-21 and 20-22, 2-4 scans and 2 scans respectively), we tested within-person stability of reward-task BOLD activation, with a median of 1 and 2 years between scans. We examined multiple commonly used contrasts of active states and baseline in both the anticipation and feedback phases of a card-guessing reward task. We examined the effects of cortical parcellation resolution, contrast, network (reward regions and resting-state networks), region-size, and activation strength and variability on the stability of reward-related activation. Results In both samples, contrasts of an active state relative to a baseline were more stable (ICC: intra-class correlation; e.g., Win>Baseline; mean ICC = 0.13 – 0.33) than contrasts of two active states (e.g., Win>Loss; mean ICC = 0.048 – 0.05). Additionally, activation in reward regions was less stable than in many non-task networks (e.g., dorsal attention), and activation in regions with greater between-subject variability showed higher stability in both samples. Conclusions These results show that some contrasts from functional neuroimaging activation during a card guessing reward task have partially trait-like properties in adolescent and young adult samples over 1-2 years. Notably, results suggest that contrasts intended to map cognitive function and show robust group-level effects (i.e. Win > Loss) may be less effective in studies of individual differences and disease risk. The robustness of group-level activation should be weighed against other factors when selecting regions of interest in individual difference fMRI studies.
Persuasion-Based Robust Sensor Design Against Attackers With Unknown Control Objectives

We introduce a robust sensor design framework to provide “persuasion-based” defense in stochastic control systems against an unknown type attacker with a control objective exclusive to its type. We design a robust “linear-plus-noise” signaling strategy in order to persuade the attacker to take actions that lead to minimum damage with respect to the system's objective. The specific model we adopt is a Gauss–Markov process driven by a controller with a (partially) “unknown” malicious/benign control objective. We seek to defend against the worst possible distribution over control objectives in a robust way under the solution concept of Stackelberg equilibrium, where the sensor is the leader. We show that a necessary and sufficient condition on the covariance matrix of the posterior belief is a certain linear matrix inequality. This enables us to formulate an equivalent tractable problem, indeed a semidefinite program, to compute the robust sensor design strategies “globally” even though the original optimization problem is nonconvex and highly nonlinear. We also extend this result to scenarios where the sensor makes noisy or partial measurements.
Quantifying a frequency modulation response biomarker in responsive neurostimulation

Objective.Responsive neurostimulation (RNS) is an effective treatment for controlling seizures in patients with drug-resistant focal epilepsy who are not suitable candidates for resection surgery. A lack of tools for detecting and characterizing potential response biomarkers, however, contributes to a limited understanding of mechanisms by which RNS improves seizure control. We developed a method to quantify ictal frequency modulation, previously identified as a biomarker of clinical responsiveness to RNS.Approach.Frequency modulation is characterized by shifts in power across spectral bands during ictal events, over several months of neurostimulation. This effect was quantified by partitioning each seizure pattern into segments with distinct spectral content and measuring the extent of change from the baseline distribution of spectral content using the squared earth mover's distance.Main results.We analyzed intracranial electroencephalography data from 13 patients who received RNS therapy, six of whom exhibited frequency modulation on expert evaluation. Patients in the frequency modulation group had, on average, significantly larger and more sustained changes in their squared earth mover's distances (mean = 13.97 × 10-3± 1.197 × 10-3). In contrast, those patients without expert-identified frequency modulation exhibited statistically insignificant or negligible distances (mean = 4.994 × 10-3± 0.732 × 10-3).Significance.This method is the first step towards a quantitative, feedback-driven system for systematically optimizing RNS stimulation parameters, with an ultimate goal of truly personalized closed-loop therapy for epilepsy.
de Sitter Complementarity, TCC, and the Swampland

Motivated by the coincidence of scrambling time in de Sitter and maximum lifetime given by the Trans-Planckian Censorship Conjecture (TCC), we study the relation between the de Sitter complementarity and the Swampland conditions. We study thermalization in de Sitter space from different perspectives and show that TCC implies de Sitter space cannot live long enough to be considered a thermal background. We also revisit \(\alpha\)-vacua in light of this work and show that TCC imposes multiple initial condition/fine-tuning problems on any conventional inflationary scenario.
Area minimization for hierarchical floorplans

In this paper we study the area-minimization problem for hierarchical floorplans. We settle an open problem on the complexity of the area-minimization problem for hierarchical floorplans by showing it to be NP-complete (even for balanced hierarchical floorplans). We then present a new algorithm for determining the nonredundant realizations of a wheel. The algorithm has time costO(k2 logk) and space cost0(k2) if each block in a wheel has at mostk realizations. Based on the new algorithm for a wheel, we design a new pseudopolynomial area-minimization algorithm for hierarchical floorplans of order-5. The time and space costs of the algorithm are0((nM)2log(nM) and0(n2M), respectively, wheren is the number of basic blocks andM is an upper bound on the dimensions of the realizations of the basic blocks. The area-minimization algorithm was implemented. Experimental results show that it is very fast.
On-chip random spectrometer

Spectrometers are widely used tools in chemical and biological sensing, material analysis, and light source characterization. The development of a high-resolution on-chip spectrometer could enable compact, low-cost spectroscopy for portable sensing as well as increasing lab-on-a-chip functionality. However, the spectral resolution of traditional grating-based spectrometers scales with the optical pathlength, which translates to the linear dimension or footprint of the system, which is limited on-chip. In this work, we utilize multiple scattering in a random photonic structure fabricated on a silicon chip to fold the optical path, making the effective pathlength much longer than the linear dimension of the system and enabling high spectral resolution with a small footprint. Of course, the random spectrometer also requires a different operating paradigm, since different wavelengths are not spatially separated by the random structure, as they would be by a grating. Instead, light transmitted through the random structure produces a wavelengthdependent speckle pattern which can be used as a fingerprint to identify the input spectra after calibration. In practice, these wavelength-dependent speckle patterns are experimentally measured and stored in a transmission matrix, which describes the spectral-to-spatial mapping of the spectrometer. After calibrating the transmission matrix, an arbitrary input spectrum can be reconstructed from its speckle pattern. We achieved sub-nm resolution with 25 nm bandwidth at a wavelength of 1500 nm using a scattering medium with largest dimension of merely 50 μm.
Verification of the Jones unknot conjecture up to 24 crossings

Extending upon our previous work, we verify the Jones Unknot Conjecture for all knots up to 24 crossings. We describe the method of our approach and analyze the growth of the computational complexi...
Quiz-Style Question Generation for News Stories

A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metrics (e.g., click-through rates or reading time) to track their performance. As a first step towards measuring news informedness at a scale, we study the problem of quiz-style multiple-choice question generation, which may be used to survey users about their knowledge of recent news. In particular, we formulate the problem as two sequence-to-sequence tasks: question-answer generation (QAG) and distractor, or incorrect answer, generation (DG). We introduce NewsQuizQA, the first dataset intended for quiz-style question-answer generation, containing 20K human written question-answer pairs from 5K news article summaries. Using this dataset, we propose a series of novel techniques for applying large pre-trained Transformer encoder-decoder models, namely PEGASUS and T5, to the tasks of question-answer generation and distractor generation. We show that our models outperform strong baselines using both automated metrics and human raters. We provide a case study of running weekly quizzes on real-world users via the Google Surveys platform over the course of two months. We found that users generally found the automatically generated questions to be educational and enjoyable. Finally, to serve the research community, we are releasing the NewsQuizQA dataset.
Ethnicity-specific alterations of plasma and hepatic lipidomic profiles are related to high NAFLD rate and severity in Hispanic Americans, a pilot study

Nonalcoholic fatty liver disease (NAFLD) is a progressive condition that includes steatosis (NAFL) and nonalcoholic steatohepatitis (NASH). In the U.S., Hispanics (HIS) are afflicted with NAFLD at a higher rate and severity compared to other ethnicities. To date, the mechanisms underlying this disparity have not been elucidated. In this pilot study, we compared untargeted plasma metabolomic profiles for primary metabolism, complex lipids, choline and related compounds between a group of HIS (n = 7) and White Caucasian (CAU, n = 8) subjects with obesity and biopsy-characterized NAFL to ethnicity-matched lean healthy controls (n = 14 HIS and 8 CAU). We also compared liver and plasma metabolomic profiles in a group of HIS and CAU subjects with obesity and NASH of comparable NAFLD Activity Scores, to BMI-matched NASH-free subjects in both ethnicities. Results highlight signs of metabolic dysregulation observed in HIS, independent of obesity, including higher plasma triglycerides, acylcarnitines, and free fatty acids. With NASH progression, there were ethnicity-related differences in the hepatic profile, including higher free fatty acids and lysophospholipids seen in HIS, suggesting lipotoxicity is involved in the progression of NASH. We also observed greater hepatic triglyceride content, higher plasma triglyceride concentrations and lower hepatic phospholipids with signs of impaired hepatic mitochondrial β-oxidation. These findings provide preliminary evidence indicating ethnicity-related variations that could potentially modulate the risk for progression of NALD to NASH.
Planning as Persuasive Storytelling in the Context of “the Network Society”

In Planning as Persuasive Storytelling (1996), I argued that planning can usefully be construed as persuasive and constitutive storytelling about the future. Three broad lines of critique have been directed against that claim. One argues that to think of planning as storytelling is to open the floodgates to fabrications. The second essentially argues that power, not stories, is what matters. And the third suggests that there is more to storytelling than first meets the eye. Furthermore, my claim was not well connected to contemporary scholarship about economic restructuring and the emergence of a globalized “network society” and the postmodern, global, or transnational city.
A stabilizer-free pressure-robust finite element method for the Stokes equations

In this paper, we introduce a new finite element method for solving the Stokes equations in the primary velocity-pressure formulation using H(div) finite elements to approximate velocity. Like other finite element methods with velocity discretized by H(div) conforming elements, our method has the advantages of an exact divergence-free velocity field and pressure-robustness. However, most of H(div) conforming finite element methods for the Stokes equations require stabilizers to enforce the weak continuity of velocity in tangential direction. Some stabilizers need to tune penalty parameter and some of them do not. Our method is stabilizer free although discontinuous velocity fields are used. Optimal-order error estimates are established for the corresponding numerical approximation in various norms. Extensive numerical investigations are conducted to test accuracy and robustness of the method and to confirm the theory. The numerical examples cover low- and high-order approximations up to the degree four, and 2D and 3D cases.
Multidimensional configurations in the primes with shifted prime steps

Let $$\mathcal{P}$$ denote the set of primes. For a fixed dimension $$d$$ , Cook– Magyar–Titichetrakun, Tao–Ziegler and Fox–Zhao independently proved that any subset of positive relative density of $$\mathcal{P}^d$$ contains an arbitrary linear configuration. In this paper, we prove that there exists such configuration with the step being a shifted prime (prime minus $$1$$ or plus $$1$$ ).
Convolutional neural nets in chemical engineering: Foundations, computations, and applications

In this paper we review the mathematical foundations of convolutional neural nets (CNNs) with the goals of: i) highlighting connections with techniques from statistics, signal processing, linear algebra, differential equations, and optimization, ii) demystifying underlying computations, and iii) identifying new types of applications. CNNs are powerful machine learning models that highlight features from grid data to make predictions (regression and classification). The grid data object can be represented as vectors (in 1D), matrices (in 2D), or tensors (in 3D or higher dimensions) and can incorporate multiple channels (thus providing high flexibility in the input data representation). CNNs highlight features from the grid data by performing convolution operations with different types of operators. The operators highlight different types of features (e.g., patterns, gradients, geometrical features) and are learned by using optimization techniques. In other words, CNNs seek to identify optimal operators that best map the input data to the output data. A common misconception is that CNNs are only capable of processing image or video data but their application scope is much wider; specifically, datasets encountered in diverse applications can be expressed as grid data. Here, we show how to apply CNNs to new types of applications such as optimal control, flow cytometry, multivariate process monitoring, and molecular simulations.
Monocular Visual Mapping for Obstacle Avoidance on UAVs

An unmanned aerial vehicle requires adequate knowledge of its surroundings in order to operate in close proximity to obstacles. UAVs also have strict payload and power constraints which limit the number and variety of sensors available to gather this information. It is desirable, therefore, to enable a UAV to gather information about potential obstacles or interesting landmarks using common and lightweight sensor systems. This paper presents a method of fast terrain mapping with a monocular camera. Features are extracted from camera images and used to update a sequential extended Kalman filter. The features locations are parameterized in inverse depth to enable fast depth convergence. Converged features are added to a persistent terrain map which can be used for obstacle avoidance and additional vehicle guidance. Simulation results, results from recorded flight test data, and flight test results are presented to validate the algorithm.
Tight Regret Bounds for Infinite-armed Linear Contextual Bandits

Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks. While there is a lot of prior research, tight regret bounds of linear contextual bandit with infinite action sets remain open. In this paper, we address this open problem by considering the linear contextual bandit with (changing) infinite action sets. We prove a regret upper bound on the order of $O(\sqrt{d^2T\log T})\times \text{poly}(\log\log T)$ where $d$ is the domain dimension and $T$ is the time horizon. Our upper bound matches the previous lower bound of $\Omega(\sqrt{d^2 T\log T})$ in [Li et al., 2019] up to iterated logarithmic terms.
A spatiotemporal molecular switch governs plant asymmetric cell division

Asymmetric cell division (ACD) requires protein polarization in the mother cell to produce daughter cells with distinct identities (cell-fate asymmetry). Here, we define a previously undocumented mechanism for establishing cell-fate asymmetry in Arabidopsis stomatal stem cells. In particular, we show that polarization of the protein phosphatase BSL1 promotes stomatal ACD by establishing kinase-based signalling asymmetry in the two daughter cells. BSL1 polarization in the stomatal ACD mother cell is triggered at the onset of mitosis. Polarized BSL1 is inherited by the differentiating daughter cell, where it suppresses cell division and promotes cell-fate determination. Plants lacking BSL proteins exhibit stomatal overproliferation, which demonstrates that the BSL family plays an essential role in stomatal development. Our findings establish that BSL1 polarization provides a spatiotemporal molecular switch that enables cell-fate asymmetry in stomatal ACD daughter cells. We propose that BSL1 polarization is triggered by an ACD checkpoint in the mother cell that monitors the establishment of division-plane asymmetry. Stomatal development requires asymmetric cell division and cell-fate determination, in which BASL is an essential regulator. The binding partners of BASL are now identified and characterized to reveal the molecular mechanism of cell-fate asymmetry in stomata.
Treatment Effect Estimation Using Invariant Risk Minimization

Inferring causal individual treatment effect (ITE) from observational data is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias. In this work, we propose a new way to estimate the ITE using the domain generalization framework of invariant risk minimization (IRM). IRM uses data from multiple domains, learns predictors that do not exploit spurious domain-dependent factors, and generalizes better to unseen domains. We propose an IRM-based ITE estimator aimed at tackling treatment assignment bias when there is little support overlap between the control group and the treatment group. We accomplish this by creating diversity: given a single dataset, we split the data into multiple domains artificially. These diverse domains are then exploited by IRM to more effectively generalize regression-based models to data regions that lack support overlap. We show gains over classical regression approaches to ITE estimation in settings when support mismatch is more pronounced.
Programmable system on chip for controlling an atomic physics experiment

Most atomic physics experiments are controlled by a digital pattern generator used to synchronize all equipment by providing triggers and clocks. Recently, the availability of well-documented open-source development tools has lifted the barriers to using programmable systems on chip (PSoCs), making them a convenient and versatile tool for synthesizing digital patterns. Here, we take advantage of these advancements in the design of a versatile clock and pattern generator using a PSoC. We present our design with the intent of highlighting the new possibilities that PSoCs have to offer in terms of flexibility. We provide a robust hardware carrier and basic firmware implementation that can be expanded and modified for other uses.
Domains of Interference Between Working Bodies in Mechanisms and Manipulators

Criteria for the set of all points in a pair of working bodies in a mechanism or manipulator that can coincide for any kinematically admissible configuration of the underlying mechanism, called the domain of interference between the bodies, are formulated. Kinematic equations for the mechanism and parameterizations of the domains of the working bodies are used to derive analytical criteria for domains of interference. Three complementary problems are formulated and analyzed to characterize (1) the set of points in one of the interfering bodies that are occupied by any point in the second body, (2) the set of points in one of the interfering bodies that are occupied by any point on the boundary of the second body, and (3) the set of all points in space that are simultaneously occupied by points in the interfering bodies; each condition occurring for any kinematically admissible configuration of the mechanism. Analytical criteria for the boundaries of domains of interference for each of the three problems are derived, based on row-rank deficiency of a sub-Jacobian matrix associated with the kinematic equations for each of the problems. Numerical methods for mapping boundaries of domains of interference are presented and illustrated for planar Stewart platforms with domes attached that are characteristic of flingt or ground vehicle simulators.
Unified Gradient Reweighting for Model Biasing with Applications to Source Separation

Recent deep learning approaches have shown great improvement in audio source separation tasks. However, the vast majority of such work is focused on improving average separation performance, often neglecting to examine or control the distribution of the results. In this paper, we propose a simple, unified gradient reweighting scheme, with a lightweight modification to bias the learning process of a model and steer it towards a certain distribution of results. More specifically, we reweight the gradient updates of each batch, using a user-specified probability distribution. We apply this method to various source separation tasks, in order to shift the operating point of the models towards different objectives. We demonstrate different parameterizations of our unified reweighting scheme can be used towards addressing several real-world problems, such as unreliable separation estimates. Our framework enables the user to control a robustness trade-off between worst and average performance. Moreover, we experimentally show that our unified reweighting scheme can also be used in order to shift the focus of the model towards being more accurate for user-specified sound classes or even towards easier examples in order to enable faster convergence.
Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.
Reconstructing Malice in the Law of Punitive Damages

Punitive damages present two related puzzles. One concerns their object. If they are punitive, their object is to punish tortfeasors. If they are damages, their object is to compensate tort victims. If they are both, as the Supreme Court has recently stated, the problem is to reconcile these different objects in applying them. A second puzzle involves their subject. Punitive damages are awarded for egregious wrongdoing. But the nature of that egregiousness is nebulous and contested, implicating many poorly understood terms. The two puzzles are connected, because the subject of punitive damages will inform their object. Once we know the type of wrongfulness that punitive damages deal with, we can understand better whether and how they are punishing, compensating, or both. This Article reconstructs one of punitive damages’ central subjects: malice. In so doing, it clarifies one key object of punitive damages: to offer redress to a victim of cruelty. Malice is a ubiquitous textual element in the state law of punitive damages. But there has been little scholarly commentary about what malice means for punitive damages. Drawing from the common history of tort and criminal law, this Article identifies two core meanings of malice: a desire or motive to do wrong, and a disposition of callous indifference to the wrong inflicted. Though distinct, these meanings broadly coalesce in the concept of cruelty. The Article argues that this reconstructed account of the wrong of malice represents a powerful justification for awarding punitive damages. Malice as cruelty as a justification for punitive damages also fits within a broader view of tort law as redress for specific private wrongs. But malice as a subject of punitive damages clarifies and enriches this account of their object. A victim of a tort done with malice, and who is aware of it, has been wronged more gravely than a victim of a tort done without malice and is therefore entitled to greater redress.
Improving Fault Localization by Integrating Value and Predicate Based Causal Inference Techniques

Statistical fault localization (SFL) techniques use execution profiles and success/failure information from software executions, in conjunction with statistical inference, to automatically score program elements based on how likely they are to be faulty. SFL techniques typically employ one type of profile data: either coverage data, predicate outcomes, or variable values. Most SFL techniques actually measure correlation, not causation, between profile values and success/failure, and so they are subject to confounding bias that distorts the scores they produce. This paper presents a new SFL technique, named UniVal, that uses causal inference techniques and machine learning to integrate information about both predicate outcomes and variable values to more accurately estimate the true failure-causing effect of program statements. UniVal was empirically compared to several coverage-based, predicate-based, and value-based SFL techniques on 800 program versions with real faults.
Characterizing the tumor microenvironment of metastatic ovarian cancer by single-cell transcriptomics.

Summary Understanding the cellular composition of the tumor microenvironment and the interactions of the cells is essential to the development of successful immunotherapies in cancer. We perform single-cell RNA sequencing (scRNA-seq) of 9,885 cells isolated from the omentum in 6 patients with ovarian cancer and identify 9 major cell types, including cancer, stromal, and immune cells. Transcriptional analysis of immune cells stratifies our patient samples into 2 groups: (1) high T cell infiltration (high Tinf) and (2) low T cell infiltration (low Tinf). TOX-expressing resident memory CD8+ T (CD8+ Trm) and granulysin-expressing CD4+ T cell clusters are enriched in the high Tinf group. Concurrently, we find unique plasmablast and plasma B cell clusters, and finally, NR1H2+IRF8+ and CD274+ macrophage clusters, suggesting an anti-tumor response in the high Tinf group. Our scRNA-seq study of metastatic tumor samples provides important insights in elucidating the immune response within ovarian tumors.
Structural basis of complex formation between mitochondrial anion channel VDAC1 and Hexokinase-II.

Complex formation between hexokinase-II (HKII) and the mitochondrial VDAC1 is crucial to cell growth and survival. We hypothesize that HKII first inserts into the outer membrane of mitochondria (OMM) and then interacts with VDAC1 on the cytosolic leaflet of OMM to form a binary complex. To systematically investigate this process, we devised a hybrid approach. First, we describe membrane binding of HKII with molecular dynamics (MD) simulations employing a membrane mimetic model with enhanced lipid diffusion capturing membrane insertion of its H-anchor. The insertion depth of the H-anchor was then used to derive positional restraints in subsequent millisecond-scale Brownian dynamics (BD) simulations to preserve the membrane-bound pose of HKII during the formation of the HKII/VDAC1 binary complex. Multiple BD-derived structural models for the complex were further refined and their structural stability probed with additional MD simulations, resulting in one stable complex. A major feature in the complex is the partial (not complete) blockade of VDAC1’s permeation pathway, a result supported by our comparative electrophysiological measurements of the channel in the presence and absence of HKII. We also show how VDAC1 phosphorylation disrupts HKII binding, a feature that is verified by our electrophysiology recordings and has implications in mitochondria-mediated cell death. Haloi et al. investigate the role of the hydrophobic N-terminus of hexokinase II (HKII) in their binding to mitochondrial VDAC1. They use a hybrid approach combining molecular and Brownian dynamics to show that HKII first inserts into the outer membrane of mitochondria (OMM) and then interacts with VDAC1 on the cytosolic surface of OMM to form a binary complex.
Surfactant interactions and solvent phase solubility modulate small molecule release from emulsion electrospun fibers

Emulsion electrospinning represents a tunable system for the fabrication of porous scaffolds for controlled, localized drug delivery in tissue engineering applications. This study aimed to elucidate the role of model drug interactions with emulsion chemistry on loading and release rates from fibers with controlled fiber diameter and fiber volume fraction. Nile Red and Rhodamine B were used as model drugs and encapsulation efficiency and release rates were determined from poly(caprolactone) (PCL) electrospun fibers spun either with no surfactant (Span 80), surfactant, or water-in-oil emulsions. Drug loading efficiency and release rates were modulated by both surfactant and aqueous internal phase in the emulsions as a function of drug molecule hydrophobicity. Overall, these results demonstrate the role of intermolecular interactions and drug phase solubility on the release from emulsion electrospun fibers and highlight the need to independently control these parameters when designing fibers for use as tunable drug delivery systems.
Active Control and Sustained Oscillations in actSIS Epidemic Dynamics

Abstract An actively controlled Susceptible-Infected-Susceptible (actSIS) contagion model is presented for studying epidemic dynamics with continuous-time feedback control of infection rates. Our work is inspired by the observation that epidemics can be controlled through decentralized disease-control strategies such as quarantining, sheltering in place, social distancing, etc., where individuals can actively modify their contact rates in response to observations of the infection levels in the population. Accounting for a time lag in observations and categorizing individuals into distinct sub-populations based on their risk profiles, we show that the actSIS model manifests qualitatively different features as compared with the SIS model. In a homogeneous population of risk-averters, the endemic equilibrium is always reduced, although the transient infection level can overshoot or undershoot. In a homogeneous population of risk-tolerating individuals, the system exhibits bistability, which can also lead to reduced infection. For a heterogeneous population comprised of risk-tolerators and risk-averters, we prove conditions on model parameters for the existence of a Hopf bifurcation and sustained oscillations in the infected population.
Recent Hypersonic Studies of Wings and Bodies

Even though a great deal of theoretical and experimental information has been obtained in recent years on the flow over simple shapes in hypersonic flow a great deal of confusion still exists on how to interpret and extrapolate the results obtained. This paper offers information recently obtained at Langley at Mach numbers ranging from 7 to 21 encompassing both work in air and helium on shapes ranging from rods to delta wings. The results indicate that in most cases methods for making useful estimates of pressure are in hand for simple shapes. However, three-dimensional effects and the interaction between the components considerably complicates the flow fields over delta wings at low angles of attack.
Boosting for Online Convex Optimization

We consider the decision-making framework of online convex optimization with a very large number of experts. This setting is ubiquitous in contextual and reinforcement learning problems, where the size of the policy class renders enumeration and search within the policy class infeasible. Instead, we consider generalizing the methodology of online boosting. We define a weak learning algorithm as a mechanism that guarantees multiplicatively approximate regret against a base class of experts. In this access model, we give an efficient boosting algorithm that guarantees near-optimal regret against the convex hull of the base class. We consider both full and partial (a.k.a. bandit) information feedback models. We also give an analogous efficient boosting algorithm for the i.i.d. statistical setting. Our results simultaneously generalize online boosting and gradient boosting guarantees to contextual learning model, online convex optimization and bandit linear optimization settings.
ABC-LMPC: Safe Sample-Based Learning MPC for Stochastic Nonlinear Dynamical Systems with Adjustable Boundary Conditions.

Sample-based learning model predictive control (LMPC) strategies have recently attracted attention due to their desirable theoretical properties and good empirical performance on robotic tasks. However, prior analysis of LMPC controllers for stochastic systems has mainly focused on linear systems in the iterative learning control setting. We present a novel LMPC algorithm, Adjustable Boundary Condition LMPC (ABC-LMPC), which enables rapid adaptation to novel start and goal configurations and theoretically show that the resulting controller guarantees iterative improvement in expectation for stochastic nonlinear systems. We present results with a practical instantiation of this algorithm and experimentally demonstrate that the resulting controller adapts to a variety of initial and terminal conditions on 3 stochastic continuous control tasks.
Access to public capital markets and employment growth

Abstract This paper examines the effect of going public on firm-level employment. To establish a causal effect, we employ a novel data set of private firms to investigate employment growth in IPO firms relative to a group of firms that file for an IPO but subsequently withdraw their offering. We find that employment increases significantly after going public, and the increase is more pronounced in industries with requirements for highly skilled labor and greater dependence on external finance. Improved ability to undertake acquisitions and a strategic shift toward commercialization, rather than agency problems, explain employment growth. Overall, these results highlight the importance of going public for firms’ employment policies.
Outlier-Robust Optimal Transport

Optimal transport (OT) provides a way of measuring distances between distributions that depends on the geometry of the sample space. In light of recent advances in solving the OT problem, OT distances are widely used as loss functions in minimum distance estimation. Despite its prevalence and advantages, however, OT is extremely sensitive to outliers. A single adversarially-picked outlier can increase OT distance arbitrarily. To address this issue, in this work we propose an outlier-robust OT formulation. Our formulation is convex but challenging to scale at a first glance. We proceed by deriving an equivalent formulation based on cost truncation that is easy to incorporate into modern stochastic algorithms for regularized OT. We demonstrate our model applied to mean estimation under the Huber contamination model in simulation as well as outlier detection on real data.
Graph-of-Tweets: A Graph Merging Approach to Sub-event Identification

Graph structures are powerful tools for modeling the relationships between textual elements. Graph-of-Words (GoW) has been adopted in many Natural Language tasks to encode the association between terms. However, GoW provides few document-level relationships in cases when the connections between documents are also essential. For identifying sub-events on social media like Twitter, features from both word- and document-level can be useful as they supply different information of the event. We propose a hybrid Graph-of-Tweets (GoT) model which combines the word- and document-level structures for modeling Tweets. To compress large amount of raw data, we propose a graph merging method which utilizes FastText word embeddings to reduce the GoW. Furthermore, we present a novel method to construct GoT with the reduced GoW and a Mutual Information (MI) measure. Finally, we identify maximal cliques to extract popular sub-events. Our model showed promising results on condensing lexical-level information and capturing keywords of sub-events.
Precision formation delta-v requirements for distributed platforms in Earth orbit

Distributed spacecraft flying in formation can overcome the resolution limitations of monolithic, Earth-sensing systems. However, formation spacecraft must now expend fuel to counteract disturbances and the gravity gradients between spacecraft. We consider three different formation architectures and determine the delta-v required to maintain relative positions at accuracies ranging from 0.1 to 10 m (1 sigma). The three architectures considered are: (i) Leader/Follower, in which individual spacecraft controllers track with respect to a passive, leader spacecraft, (ii) Center of Formation, in which individual spacecraft controllers track with respect to the geometric center of the formation, and (iii) Iterated Virtual Structure, in which a formation template is fit each timestep and individual spacecraft controllers track with respect to the fitted template. We show that in the presence of relative and inertial sensor noise and disturbances (e.g., Earth oblateness and aerodynamic drag) relative positions can be maintained to the 10 m level for 4 mm/s/orbit.
Sensitivity and Specificity Evaluation of Deep Learning Models for Detection of Pneumoperitoneum on Chest Radiographs

Deep learning has great potential to assist with detecting and triaging critical findings such as pneumoperitoneum on medical images. To be clinically useful, the performance of this technology still needs to be validated for generalizability across different types of imaging systems. This retrospective study included 1,287 chest X-ray images of patients who underwent initial chest radiography at 13 different hospitals between 2011 and 2019. State-of-the-art deep learning models were trained on a subset of this dataset, and the automated classification performance was evaluated on the rest of the dataset by measuring the AUC, sensitivity, and specificity. All deep learning models performed well for identifying radiographs with pneumoperitoneum, while DenseNet161 achieved the highest AUC of 95.7%, Specificity of 89.9%, and Sensitivity of 91.6%. The DenseNet161 model was able to accurately classify radiographs from different imaging systems (Accuracy of 90.8%), while it was trained on images captured from a specific imaging system from a single institution. This result suggests the generalizability of our model for learning salient features in chest X-ray images to detect pneumoperitoneum, independent of the imaging system. If verified in clinical settings, this model could assist practitioners with the diagnosis and management of patients with this urgent condition.
Adapting Surprise Minimizing Reinforcement Learning Techniques for Transactive Control

Optimizing prices for energy demand response requires a flexible controller with ability to navigate complex environments. We propose a reinforcement learning controller with surprise minimizing modifications in its architecture. We suggest that surprise minimization can be used to improve learning speed, taking advantage of predictability in peoples' energy usage. Our architecture performs well in a simulation of energy demand response. We propose this modification to improve functionality and savin a large-scale experiment.
Minimizing Age of Information via Scheduling over Heterogeneous Channels

In this paper, we study the problem of minimizing the age of information when a source can transmit status updates over two heterogeneous channels. Our work is motivated by recent developments in 5G mmWave technology, where transmissions may occur over an unreliable but fast (e.g., mmWave) channel or a slow reliable (e.g., sub-6GHz) channel. The unreliable channel is modeled as a time-correlated Gilbert-Elliot channel, where information can be transmitted at a high rate when the channel is in the "ON" state. The reliable channel provides a deterministic but lower data rate. The scheduling strategy determines the channel to be used for transmission with the aim to minimize the time-average age of information (AoI). The optimal scheduling problem is formulated as a Markov Decision Process (MDP), which in our setting poses some significant challenges because e.g., supermodularity does not hold for part of the state space. We show that there exists a multi-dimensional threshold-based scheduling policy that is optimal for minimizing the age. A low-complexity bisection algorithm is further devised to compute the optimal thresholds. Numerical simulations are provided to compare different scheduling policies.
Mobile Internet usage and usage-based pricing

Using data on mobile Internet usage of thousands of individuals, we provide some of the first analyses linking mobile usage to key demographics such as income. We find a reverse‐U relationship between mobile Internet usage and income—notably different than the monotonically declining relationship found on home devices. This pattern suggests that data caps are particularly binding on low‐income users. We then construct a simple model of mobile Internet usage that incorporates demand features suggested by our empirical finding and prior empirical findings on device adoption and usage. After abstracting away from cost and two‐sided market considerations, we solve the model, and through comparative statics, identify demand conditions for which usage‐based pricing (via a data cap) is, or is not, revenue enhancing. Key insights from this analysis are: (1) the tendency toward demand‐driven price discrimination is hill‐shaped (increasing then decreasing) in the number of low‐income users and eventually declining in high‐income users, and (2) a relatively high (low) proportion of total value attained from relatively low usage levels by low‐income (high‐income) users increases the tendency toward demand‐driven price discrimination. Hence, to the extent that a market has a large number of low‐income users and/or high‐income users attain a higher proportion of their total value at the cap than low‐income users, the use of caps by providers is more likely driven by cost (or other non‐demand‐side) considerations than by revenue enhancement. Lastly, additional analysis shows a largely monotonically increasing relationship between income and usage intensity (measured as page views in a session), suggesting that, ceteris paribus, price discrimination strategies may be more effective if tied to usage intensity rather than duration.
Push-SAGA: A Decentralized Stochastic Algorithm With Variance Reduction Over Directed Graphs

In this letter, we propose Push-SAGA, a decentralized stochastic first-order method for finite-sum minimization over a directed network of nodes. Push-SAGA combines node-level variance reduction to remove the uncertainty caused by stochastic gradients, network-level gradient tracking to address the distributed nature of the data, and push-sum consensus to tackle directed information exchange. We show that Push-SAGA achieves linear convergence to the exact solution for smooth and strongly convex problems and is thus the first linearly-convergent stochastic algorithm over arbitrary strongly connected directed graphs. We also characterize the regime in which Push-SAGA achieves a linear speed-up compared to its centralized counterpart and achieves a network-independent convergence rate. We illustrate the behavior and convergence properties of Push-SAGA with the help of numerical experiments on strongly convex and non-convex problems.
Block What You Can, Except When You Shouldn’t

Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the precision of est...
An Automated Approach to the Collatz Conjecture

We explore the Collatz conjecture and its variants through the lens of termination of string rewriting. We construct a rewriting system that simulates the iterated application of the Collatz function on strings corresponding to mixed binary–ternary representations of positive integers. Termination of this rewriting system is equivalent to the Collatz conjecture. To show the feasibility of our approach in proving mathematically interesting statements, we implement a minimal termination prover that uses the automated method of matrix/arctic interpretations and we perform experiments where we obtain proofs of nontrivial weakenings of the Collatz conjecture. Finally, we adapt our rewriting system to show that other open problems in mathematics can also be approached as termination problems for relatively small rewriting systems. Although we do not succeed in proving the Collatz conjecture, we believe that the ideas here represent an interesting new approach.