cssi/SciSciGPT-SciSciNet huggingface dataset:
Separating Skills and Concepts for Novel Visual Question Answering

Generalization to out-of-distribution data has been a problem for Visual Question Answering (VQA) models. To measure generalization to novel questions, we propose to separate them into "skills" and "concepts". "Skills" are visual tasks, such as counting or attribute recognition, and are applied to "concepts" mentioned in the question, such as objects and people. VQA methods should be able to compose skills and concepts in novel ways, regardless of whether the specific composition has been seen in training, yet we demonstrate that existing models have much to improve upon towards handling new compositions. We present a novel method for learning to compose skills and concepts that separates these two factors implicitly within a model by learning grounded concept representations and disentangling the encoding of skills from that of concepts. We enforce these properties with a novel contrastive learning procedure that does not rely on external annotations and can be learned from unlabeled image-question pairs. Experiments demonstrate the effectiveness of our approach for improving compositional and grounding performance.1
The MICRO-BOSS scheduling system: Current status and future efforts

In this paper, a micro-opportunistic approach to factory scheduling was described that closely monitors the evolution of bottlenecks during the construction of the schedule and continuously redirects search towards the bottleneck that appears to be most critical. This approach differs from earlier opportunistic approaches, as it does not require scheduling large resource subproblems or large job subproblems before revising the current scheduling strategy. This micro-opportunistic approach was implemented in the context of the MICRO-BOSS factory scheduling system. A study comparing MICRO-BOSS against a macro-opportunistic scheduler suggests that the additional flexibility of the micro-opportunistic approach to scheduling generally yields important reductions in both tardiness and inventory. Current research efforts include: adaptation of MICRO-BOSS to deal with sequence-dependent setups and development of micro-opportunistic reactive scheduling techniques that will enable the system to patch the schedule in the presence of contingencies such as machine breakdowns, raw materials arriving late, job cancellations, etc.
Crossing the Boundaries in Planetary Atmospheres - From Earth to Exoplanets

The past decade has been an especially exciting time to study atmospheres, with a renaissance in fundamental studies of Earths general circulation and hydrological cycle, stimulated by questions about past climates and the urgency of projecting the future impacts of humankinds activities. Long-term spacecraft and Earth-based observation of solar system planets have now reinvigorated the study of comparative planetary climatology. The explosion in discoveries of planets outside our solar system has made atmospheric science integral to understanding the diversity of our solar system and the potential habitability of planets outside it. Thus, the AGU Chapman Conference Crossing the Boundaries in Planetary Atmospheres From Earth to Exoplanets, held in Annapolis, MD from June 24-27, 2013 gathered Earth, solar system, and exoplanet scientists to share experiences, insights, and challenges from their individual disciplines, and discuss areas in which thinking broadly might enhance our fundamental understanding of how atmospheres work.
A dynamic model of the human postural control system.

Description of a digital simulation of the pitch axis dynamics of a stick man. The difficulties encountered in linearizing the equations of motion are discussed; the conclusion reached is that a completely linear simulation is of such restricted validity that only a nonlinear simulation is of any practical use. Typical simulation results obtained from the full nonlinear model are illustrated.
Distinctive features of SARS-CoV-2-specific T cells predict recovery from severe COVID-19.

Although T cells are likely players in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunity, little is known about the phenotypic features of SARS-CoV-2-specific T cells associated with recovery from severe coronavirus disease 2019 (COVID-19). We analyze T cells from 34 individuals with COVID-19 with severity ranging from mild (outpatient) to critical, culminating in death. Relative to individuals who succumbed, individuals who recovered from severe COVID-19 harbor elevated and increasing numbers of SARS-CoV-2-specific T cells capable of homeostatic proliferation. In contrast, fatal COVID-19 cases display elevated numbers of SARS-CoV-2-specific regulatory T cells and a time-dependent escalation in activated bystander CXCR4+ T cells, as assessed by longitudinal sampling. Together with the demonstration of increased proportions of inflammatory CXCR4+ T cells in the lungs of individuals with severe COVID-19, these results support a model where lung-homing T cells activated through bystander effects contribute to immunopathology, whereas a robust, non-suppressive SARS-CoV-2-specific T cell response limits pathogenesis and promotes recovery from severe COVID-19.
The elastic potential function of slightly compressible rubberlike materials

In order to obtain an analytical expression for the stored energy function W of slightly compressible rubberlike materials, two recent results are used to obtain a unified expression where Γi are a new set of invariants, and H(Γ1,Γ2) is the shear part of the stored energy function which is now assumed to be a separate symmetric function of the modified stretch ratio, λ = λi/I, i.e., Various analytical forms of h(λ) are proposed. Also, a straightforward transformation technique is formulated such that one can easily relate the stress-strain relation in terms of modified stretch ratio λ to the new invariants Γi. Thus, by a combination of the above equations and the transformation technique, one may readily determine the elastic strain energy function of slightly compressible materials from careful measurements of the volume change in multiaxial deformations.
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph

We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical process that facilitates: 1) the high-level network raising a sub-goal towards achieving a designated final goal; 2) the low-level network towards an optimal policy; and 3) the overall system generalizing unseen environments and goals. We evaluate our approach with two settings of partially observable goal-driven tasks — a grid-world domain and a robotic object search task. Our experimental results show that our approach exhibits superior generalization performance on both unseen environments and new goals 1.
EVALUATION OF IN-PERSON COUNSELING STRATEGIES TO DEVELOP PHYSICAL ACTIVITY CHATBOT FOR WOMEN

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people's behavior. To develop intervention chatbots, the first step is to understand natural language conversation strategies in human conversation. This work introduces an intervention conversation dataset collected from a real-world physical activity intervention program for women. We designed comprehensive annotation schemes in four dimensions (domain, strategy, social exchange, and task-focused exchange) and annotated a subset of dialogs. We built a strategy classifier with context information to detect strategies from both trainers and participants based on the annotation. To understand how human intervention induces effective behavior changes, we analyzed the relationships between the intervention strategies and the participants' changes in the barrier and social support for physical activity. We also analyzed how participant's baseline weight correlates to the amount of occurrence of the corresponding strategy. This work lays the foundation for developing a personalized physical activity intervention bot. The dataset and code are available at this https URL
On Approximating Polynomial-Quadratic Regulator Problems

Abstract Feedback control problems involving autonomous polynomial systems are prevalent, yet there are limited algorithms and software for approximating their solution. This paper represents a step forward by considering the special case of the regulator problem where the state equation has polynomial nonlinearity, control costs are quadratic, and the feedback control is approximated by low-degree polynomials. As this represents the natural extension of the linear-quadratic regulator (LQR) and quadratic-quadratic regulator (QQR) problems, we denote this class as polynomial-quadratic regulator (PQR) problems. The present approach is amenable to feedback approximations with low degree polynomials and to problems of modest model dimension. This setting can be achieved in many problems using modern model reduction methods. The Al’Brekht algorithm, when applied to polynomial nonlinearities represented as Kronecker products leads to an elegant formulation. The terms of the feedback control lead to large linear systems that can be effectively solved with an N-way generalization of the Bartels-Stewart algorithm. We demonstrate our algorithm with numerical examples that include the Lorenz equations, a ring of van der Pol oscillators, and a discretized version of the Burgers equation. The software described here is available on Github.
Real-time Anomaly Detection and Classification in Streaming PMU Data

Phasor measurement units (PMUs) are being pervasively deployed in the grid to provide fast-sampled operational data to aid control and decision-making for reliable operation of the electric grid. This work presents a general and interpretable framework for analyzing PMU data in real-time. The proposed framework enables grid operators to understand changes to the current state and to identify anomalies in the PMU measurement data. We first learn an effective dynamical model to describe the current behavior of the system by applying statistical learning tools on the streaming PMU data. Next, we use the probabilistic predictions of our learned model to principally define an efficient anomaly detection tool. Finally, our framework produces real-time classification of the detected anomalies into common occurrence classes. We demonstrate the efficacy of our proposed framework through numerical experiments on real PMU data collected from a transmission operator in the USA.
Simulation of Electron-Proton Scattering Events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN).

We apply generative adversarial network (GAN) technology to build an event generator that simulates particle production in electron-proton scattering that is free of theoretical assumptions about underlying particle dynamics. The difficulty of efficiently training a GAN event simulator lies in learning the complicated patterns of the distributions of the particles physical properties. We develop a GAN that selects a set of transformed features from particle momenta that can be generated easily by the generator, and uses these to produce a set of augmented features that improve the sensitivity of the discriminator. The new Feature-Augmented and Transformed GAN (FAT-GAN) is able to faithfully reproduce the distribution of final state electron momenta in inclusive electron scattering, without the need for input derived from domain-based theoretical assumptions. The developed technology can play a significant role in boosting the science of the Jefferson Lab 12 GeV program and the future Electron-Ion Collider.
Data Collection and Labeling of Real-Time IoT-Enabled Bio-Signals in Everyday Settings for Mental Health Improvement

Real-time physiological data collection and analysis play a central role in modern well-being applications. Personalized classifiers and detectors have been shown to outperform general classifiers in many contexts. However, building effective personalized classifiers in everyday settings - as opposed to controlled settings - necessitates the online collection of a labeled dataset by interacting with the user. This need leads to several challenges, ranging from building an effective system for the collection of the signals and labels, to developing strategies to interact with the user and building a dataset that represents the many user contexts that occur in daily life. Based on a stress detection use case, this paper (1) builds a system for the real-time collection and analysis of photoplethysmogram, acceleration, gyroscope, and gravity data from a wearable sensor, as well as self-reported stress labels based on Ecological Momentary Assessment (EMA), and (2) collects and analyzes a dataset to extract statistics of users' response to queries and the quality of the collected signals as a function of the context, here defined as the user's activity and the time of the day.
THE BEGINNING OF THE END OF COVERTURE: A REAPPRAISAL OF THE MARRIED WOMAN’S SEPARATE ESTATE

Before statutory enactments in the nineteenth century granted married women a limited set of property rights, the separate estate trust was, by and large, the sole form of married women's property. Although the separate estate allowed married women to circumvent the law of coverture, historians have generally viewed the separate estate as an ineffective vehicle for extending property rights to married women. In this Article, I reappraise the separate estate's utility and argue that Chancery's separate estate jurisprudence during the eighteenth century was a critical first step in the establishment of married women as property-holders. Separate estates guaranteed critical financial provisioning for wives seeking to escape unhappy marriages, allowed wives to recover debts from their husbands, and enabled wives to alienate property, thereby controlling transfers of wealth. Moreover, Chancery's jurisprudence inscribed a married woman's property rights into legal precedent, and, ultimately, ascribed individual identity to the married woman. Accordingly, Chancery's support for the married woman's separate estate can be seen as the beginning of the end of coverture. t Associate-in-Law, Columbia Law School. I would like to thank John Langbein for his significant help in shaping this paper. For comments and conversation, I am also indebted to Hamilton Bryson, Ann Dailey, Ariela Dubler, Amy Erickson, Katherine Franke, Corinna Lain, Shari Motro, Luke Norris, Claire Priest, Judith Resnik, Brian Soucek, Tim Stretton and the members of the Columbia Fellows Associates Workshop as well as the participants in the November 2013 American Society for Legal History panel on Inheritance and Legal Actors. I also owe great thanks to Rachel Dempsey for her excellent editorial work. Copyright © 2014 by the Yale Journal of Law and Feminism Yale Journal of Law and Feminism IN TRO D UCTIO N 166 I. THE TRADITIONAL STORY OF THE SEPARATE ESTATE 170 A. The Legal Mechanics of the Separate Estate 170 B. The Story of Women's Property Rights in Decline 174 II. UNHAPPY MARRIAGES AND UNPAID CREDITORS 177 A. Marital Breakdown and a Wife's Separate Estate 178 1. Provisioning Abandoned and Abused Wives 179 2. Tolerance for Spouses Living Apart 184 B. Creditor Claims and Divided Household Interests 189 1. The Separate Estate as Creditor and Debtor 190 2. "Kicking or Kissing" a Wife's Separate Estate from Her ........ 195 C. A Wife's Right to Devise Her Separate Estate 199 III. A REVISED ASSESSMENT OF THE SEPARATE ESTATE 203 A. M arried W omen as Economic Actors 205 B. M arried W om en as Litigants 207 1. Chancery as a Forum for W ives 208 2. The Separate Estate as Legal Template 211 C O N C LU SIO N 2 15
Blind Backdoors in Deep Learning Models

We investigate a new method for injecting backdoors into machine learning models, based on compromising the loss-value computation in the model-training code. We use it to demonstrate new classes of backdoors strictly more powerful than those in the prior literature: single-pixel and physical backdoors in ImageNet models, backdoors that switch the model to a covert, privacy-violating task, and backdoors that do not require inference-time input modifications. Our attack is blind: the attacker cannot modify the training data, nor observe the execution of his code, nor access the resulting model. The attack code creates poisoned training inputs "on the fly," as the model is training, and uses multi-objective optimization to achieve high accuracy on both the main and backdoor tasks. We show how a blind attack can evade any known defense and propose new ones.
Maximum density of vertex-induced perfect cycles and paths in the hypercube

Abstract Let H and K be subsets of the vertex set V ( Q d ) of the d-cube Q d (we call H and K configurations in Q d ). We say K is an exact copy of H if there is an automorphism of Q d which sends H to K. If d is a positive integer and H is a configuration in Q d , we define λ ( H , d ) to be the limit as n goes to infinity of the maximum fraction, over all subsets S of V ( Q n ) , of sub-d-cubes of Q n whose intersection with S is an exact copy of H. We determine λ ( C 8 , 4 ) and λ ( P 4 , 3 ) where C 8 is a “perfect” 8-cycle in Q 4 and P 4 is a “perfect” path with 4 vertices in Q 3 , and make conjectures about λ ( C 2 d , d ) and λ ( P d + 1 , d ) for larger values of d. In our proofs there are connections with counting the number of sequences with certain properties and with the inducibility of certain small graphs. In particular, we needed to determine the inducibility of two vertex disjoint edges in the family of bipartite graphs.
Merged-Element Transmons: Design and Qubit Performance

We demonstrate a superconducting transmon qubit in which a Josephson junction has been engineered to act as its own parallel shunt capacitor. This merged-element transmon potentially offers a smaller footprint than conventional transmons. Because it concentrates the electromagnetic energy inside the junction, it reduces the relative electric field participation from other interfaces. By combining micrometer-scale $\mathrm{Al}$/$\mathrm{Al}\mathrm{O}$${}_{x}$/$\mathrm{Al}$ junctions with long oxidations, we produce functional devices with ${E}_{J}/{E}_{C}$ in the low-transmon regime (${E}_{J}/{E}_{C}\ensuremath{\lesssim}30$). Cryogenic $I$-$V$ measurements show a sharp $dI/dV$ structure with low subgap conduction. Qubit spectroscopy of tunable versions shows a small number of avoided level crossings, suggesting the presence of two-level systems. We observe mean ${T}_{1}$ times typically in the range of $10--90\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{s}$, with some annealed devices exhibiting ${T}_{1}g100\phantom{\rule{0.2em}{0ex}}\ensuremath{\mu}\mathrm{s}$ over several hours. The results suggest that energy relaxation in conventional small-junction transmons is not limited by junction loss.
Cocycle Invariants and Oriented Singular Knots

We extend the quandle cocycle invariant to oriented singular knots and links using algebraic structures called oriented singquandles and assigning weight functions at both regular and singular crossings. This invariant coincides with the classical cocycle invariant for classical knots, but provides extra information about singular knots and links. The new invariant distinguishes the singular granny knot from the singular square knot.
Improved prophet inequalities for combinatorial welfare maximization with (approximately) subadditive agents

Abstract We give a framework for designing prophet inequalities for combinatorial welfare maximization. Instantiated with different parameters, our framework implies (1) an O ( log ⁡ m / log ⁡ log ⁡ m ) -competitive prophet inequality for subadditive agents, (2) an O ( D log ⁡ m / log ⁡ log ⁡ m ) -competitive prophet inequality for D-approximately subadditive agents, where D ∈ { 1 , … , m − 1 } measures the maximum number of items that complement each other, and (3) as a byproduct, an O ( 1 ) -competitive prophet inequality for submodular or fractionally subadditive (a.k.a. XOS) agents, matching the optimal ratio asymptotically. Our framework is computationally efficient given sample access to the prior, value queries and demand queries.
The Effects and Patterns among Mobile Health, Social Determinants, and Physical Activity: A Nationally Representative Cross-Sectional Study

Mobile health (mHealth) technologies and applications are becoming more and more accessible. The increased prevalence of wearable and embeddable sensors has opened up new opportunities to collect health data continuously outside of the clinical environment. Meanwhile, wearable devices and smartphone health apps are useful to address the issues of health disparities and inequities. This study aims to identify different characteristics of individuals who use different mHealth technologies (wearable devices and smartphone apps) and explore the effectiveness and patterns of mHealth for impacting physical activities. We found that social determinants are significantly associated with the use of mHealth; mHealth is helping people to exercise more regularly and for a longer time. Smartphone app users are older while wearable device users are younger. Health disparities exist in mHealth use and physical activity level. Social determinants like education and income are associated with mHealth use and physical activity. The integration of passively-tracked patient-generated health data (PGHD) holds promise in increasing physical activities. Physical activity interventions that comprise wearable devices and smartphone apps may be more beneficial, since health goals, data visualization, real-time support and feedback, results interpretation, and group education could be embedded in the integrated “smart system”. These findings may be useful for stakeholders like wearable device and smartphone app companies, researchers, health care workers, and public health practitioners, who should work together to design and develop “precision mobile health” products with higher personalized and participatory levels, thus improving the population health.
Deep Model Compression via Two-Stage Deep Reinforcement Learning.

Besides accuracy, the model size of convolutional neural networks (CNN) models is another important factor considering limited hardware resources in practical applications. For example, employing deep neural networks on mobile systems requires the design of accurate yet fast CNN for low latency in classification and object detection. To fulfill the need, we aim at obtaining CNN models with both high testing accuracy and small size to address resource constraints in many embedded devices. In particular, this paper focuses on proposing a generic reinforcement learning-based model compression approach in a two-stage compression pipeline: pruning and quantization. The first stage of compression, i.e., pruning, is achieved via exploiting deep reinforcement learning (DRL) to co-learn the accuracy and the FLOPs updated after layer-wise channel pruning and element-wise variational pruning via information dropout. The second stage, i.e., quantization, is achieved via a similar DRL approach but focuses on obtaining the optimal bits representation for individual layers. We further conduct experimental results on CIFAR-10 and ImageNet datasets. For the CIFAR-10 dataset, the proposed method can reduce the size of VGGNet by \(9\times \) from 20.04 MB to 2.2 MB with a slight accuracy increase. For the ImageNet dataset, the proposed method can reduce the size of VGG-16 by \(33\times \) from 138 MB to 4.14 MB with no accuracy loss.
Counting actin in contractile rings reveals novel contributions of cofilin and type II myosins to fission yeast cytokinesis.

Cytokinesis by animals, fungi and amoebas depends on actomyosin contractile rings, which are stabilized by continuous turnover of actin filaments. Remarkably little is known about the amount of polymerized actin in contractile rings, so we used low concentration of GFP-Lifeact to count total polymerized actin molecules in the contractile rings of live fission yeast cells. Contractile rings of wild-type cells accumulated polymerized actin molecules at 4,900/min to a peak number of ∼198,000 followed by a loss of actin at 5,400/min throughout ring constriction. In adf1-M3 mutant cells with cofilin that severs actin filaments poorly, contractile rings accumulated polymerized actin at twice the normal rate and eventually had almost two-fold more actin along with a proportional increase in type II myosins Myo2, Myp2 and formin Cdc12. Although 30% of adf1-M3 mutant cells failed to constrict their rings fully, the rest lost actin from the rings at the wild-type rates. Mutations of type II myosins Myo2 and Myp2 reduced contractile ring actin filaments by half and slowed the rate of actin loss from the rings.
Duality and mapping tori in Heegaard Floer homology

We show that the graph TQFT for Heegaard Floer homology satisfies a strong version of Atiyah's duality axiom for a TQFT. As an application, we compute some Heegaard Floer mixed invariants of 4-dimensional mapping tori in terms of Lefschetz numbers on $HF^+$.
Chromatic zeros on hierarchical lattices and equidistribution on parameter space

Associated to any finite simple graph $\Gamma$ is the chromatic polynomial $P_\Gamma(q)$ whose complex zeroes are called the chromatic zeros of $\Gamma$. A hierarchical lattice is a sequence of finite simple graphs $\{\Gamma_n\}_{n=0}^\infty$ built recursively using a substitution rule expressed in terms of a generating graph. For each $n$, let $\mu_n$ denote the probability measure that assigns a Dirac measure to each chromatic zero of $\Gamma_n$. Under a mild hypothesis on the generating graph, we prove that the sequence $\mu_n$ converges to some measure $\mu$ as $n$ tends to infinity. We call $\mu$ the limiting measure of chromatic zeros associated to $\{\Gamma_n\}_{n=0}^\infty$. In the case of the Diamond Hierarchical Lattice we prove that the support of $\mu$ has Hausdorff dimension two. The main techniques used come from holomorphic dynamics and more specifically the theories of activity/bifurcation currents and arithmetic dynamics. We prove a new equidistribution theorem that can be used to relate the chromatic zeros of a hierarchical lattice to the activity current of a particular marked point. We expect that this equidistribution theorem will have several other applications.
Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System

Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unobservability. The proposed approach exploits both the low rank structure and a suitable transform domain representation to leverage the correlation structure of the spatio-temporal data matrix while incorporating the powerflow constraints of the distribution grid. Simulations are carried out on three phase unbalanced IEEE 37 test system to verify the effectiveness of the proposed approach. The performance results reveal - (1) the superiority over traditional matrix completion and (2) very low state estimation errors for high compression ratios representing very low observability.
Soft-Attention Improves Skin Cancer Classification Performance

In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network to achieve this goal. This paper investigates the effectiveness of Soft-Attention in deep neural architectures. The central aim of Soft-Attention is to boost the value of important features and suppress the noise-inducing features. We compare the performance of VGG, ResNet, Inception ResNet v2 and DenseNet architectures with and without the Soft-Attention mechanism, while classifying skin lesions. The original network when coupled with Soft-Attention outperforms the baseline [16] by 4.7% while achieving a precision of 93.7% on HAM10000 dataset [25]. Additionally, Soft-Attention coupling improves the sensitivity score by 3.8% compared to baseline [31] and achieves 91.6% on ISIC-2017 dataset [2]. The code is publicly available at github (https://github.com/skrantidatta/Attention-based-Skin-Cancer-Classification).
A Joint Network for Grasp Detection Conditioned on Natural Language Commands

We consider the task of grasping a target object based on a natural language command query. Previous work primarily focused on localizing the object given the query, which requires a separate grasp detection module to grasp it. The cascaded application of two pipelines incurs errors in overlapping multi-object cases due to ambiguity in the individal outputs. This work proposes a model named Command Grasping Network (CGNet) to directly output command satisficing grasps from RGB image and textual command inputs. A dataset with ground truth (image, command, grasps) tuple is generated based on the VMRD dataset to train the proposed network. Experimental results on the generated test set show that CGNet outperforms a cascaded object-retrieval and grasp detection baseline by a large margin. Three physical experiments demonstrate the functionality and performance of CGNet.
A multi-level optimization model of infrastructure-dependent technology adoption: Overcoming the chicken-and-egg problem

Abstract Policymakers seeking to promote the diffusion of infrastructure-dependent technologies are often confronted with the “chicken-and-egg” problem: consumers are reluctant to adopt the technology without adequate infrastructure available, and firms are reluctant to invest in infrastructure without a sufficient number of adopters. This chicken-and-egg problem can hinder the diffusion of new technologies and prolong the timeframe over which existing technological systems remain locked-in. In this paper, we formulate a stylized model of technology policy decision-making from the perspective of a policymaker who seeks to stimulate the market penetration of an infrastructure-dependent technology. Our model is a multi-level optimization problem in which a policymaker (first level) maximizes net social benefits by setting the levels of two incentives: a subsidy for a profit-maximizing firm (in equilibrium with two other firms, the second level) to invest in infrastructure that raises the benefit of adoption to consumers, and a direct subsidy for consumers to adopt the technology (third level). Under certain assumptions about functional forms, we solve the full multi-level problem analytically through backward induction and derive closed-form expressions for the policymaker’s optimal subsidy levels. Then, we present a case study on policies to promote battery electric vehicle (BEV) diffusion. Our results reveal three main insights: (1) the optimal policy portfolio often subsidizes charging infrastructure more than BEV purchases, (2) infrastructure and adoption subsidies tend to be substitutes rather than complements, and (3) an increase in the marginal social benefit of BEV adoption shifts the optimal policy away from infrastructure subsidies and toward BEV subsidies.
Asteroid magnetization from the early solar wind

Magnetic fields provide an important probe of the thermal, material, and structural history of planetary and sub-planetary bodies. Core dynamos are a potential source of magnetic field amplification in differentiated bodies, but evidence of magnetization in undifferentiated bodies requires a different mechanism. Here we study stellar wind-induced magnetization (WIM) of an initially unmagnetized body using analytic theory and numerical simulations, employing the resistive MHD AstroBEAR adaptive mesh refinement (AMR) multiphysics code. We obtain a broadly applicable scaling relation for the peak magnetization achieved once a wind advects, piles-up, and drapes a body with magnetic field, reaching a quasi-steady state. We find that the dayside magnetic field for a sufficiently conductive body saturates when it balances the sum of incoming solar wind ram, magnetic, and thermal pressures. Stronger amplification results from pileup by denser and faster winds. Careful quantification of numerical diffusivity is required for accurately interpreting the peak magnetic field strength from simulations and corroborating with theory. As specifically applied to the Solar System, we find that early solar wind-induced magnetization is a viable explanation for observed paleointensities in some undifferentiated bodies. This magnetism mechanism may also be applicable for other Solar System bodies, including metal-rich bodies to be visited in future space missions such as the asteroid (16) Psyche.
Outer automorphism groups of graph products: subgroups and quotients

We show that the outer automorphism groups of graph products of finitely generated abelian groups satisfy the Tits alternative, are residually finite, their so-called Torelli subgroups are finitely generated, and they satisfy a dichotomy between being virtually nilpotent and containing a non-abelian free subgroup that is determined by a graphical condition on the underlying labelled graph. Graph products of finitely generated abelian groups simultaneously generalize right-angled Artin groups (RAAGs) and right-angled Coxter groups (RACGs), providing a common framework for studying these groups. Our results extend a number of known results for the outer automorphism groups of RAAGs and/or RACGs by a variety of authors, including Caprace, Charney, Day, Ferov, Guirardel, Horbez, Minasyan, Vogtmann, Wade, and the current authors.
Hematopoietic Stem and Progenitor Cells Improve Survival from Sepsis By Boosting Immunomodulatory Cells

Sepsis accounts for 1 in 5 deaths worldwide, and new therapeutic strategies to reduce sepsis- related mortality are urgently needed. Since hematopoietic stem and progenitor cells (HSPCs) are responsible for producing blood and immune cells, including in response to immunological stress, we explored their potential for treating sepsis. In a mouse model of Group A Streptococcus (GAS)-induced sepsis, severe immunological stress was associated with significant depletion of bone marrow HSPCs and mortality within approximately 5-7 days. We hypothesized that the inflammatory environment of GAS infection drives rapid HSPC differentiation and depletion that can be rescued by infusion of donor HSPCs. Indeed, infusion of 10,000 naive HSPCs into GAS-infected mice resulted in rapid myelopoiesis and a 50-60% increase in overall survival. Surprisingly, mice receiving donor HSPCs displayed a similar pathogen load compared to untreated mice. Flow cytometric analysis revealed a significantly increased number of myeloid-derived suppressor cells in HSPC-infused mice, which correlated with reduced inflammatory cytokine levels and restored HSPC levels. These findings suggest that HSPCs play an essential immunomodulatory role that may translate into new therapeutic strategies for sepsis.
Phases of cortical actomyosin dynamics coupled to the neuroblast polarity cycle

The Par complex dynamically polarizes to the apical cortex of asymmetrically dividing Drosophila neuroblasts where it directs fate determinant segregation. Previously we showed that apically directed cortical movements that polarize the Par complex require F-actin (Oon and Prehoda, 2019). Here we report the discovery of cortical actomyosin dynamics that begin in interphase when the Par complex is cytoplasmic but ultimately become tightly coupled to cortical Par dynamics. Interphase cortical actomyosin dynamics are unoriented and pulsatile but rapidly become sustained and apically-directed in early mitosis when the Par protein aPKC accumulates on the cortex. Apical actomyosin flows drive the coalescence of aPKC into an apical cap that is depolarized in anaphase when the flow reverses direction. Together with the previously characterized role of anaphase flows in specifying daughter cell size asymmetry, our results indicate that multiple phases of cortical actomyosin dynamics regulate asymmetric cell division.
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent

We study first-order optimization algorithms for computing the barycenter of Gaussian distributions with respect to the optimal transport metric. Although the objective is geodesically non-convex, Riemannian GD empirically converges rapidly, in fact faster than off-the-shelf methods such as Euclidean GD and SDP solvers. This stands in stark contrast to the best-known theoretical results for Riemannian GD, which depend exponentially on the dimension. In this work, we prove new geodesic convexity results which provide stronger control of the iterates, yielding a dimension-free convergence rate. Our techniques also enable the analysis of two related notions of averaging, the entropically-regularized barycenter and the geometric median, providing the first convergence guarantees for Riemannian GD for these problems.
Hessenberg varieties associated to ad-nilpotent ideals

We consider Hessenberg varieties in the flag variety of $GL_n(\mathbb{C})$ with the property that the corresponding Hessenberg function defines an ad-nilpotent ideal. Each such Hessenberg variety is contained in a Springer fiber. We extend a theorem of Tymoczko to this setting, showing that these varieties have an affine paving obtained by intersecting with Schubert cells. Our method of proof constructs an an affine paving for each Springer fiber that restricts to an affine paving of the Hessenberg variety. We use the combinatorial properties of this paving to prove that Hessenberg varieties of this kind are connected.
Evolution of decaying particles and decay products in various scenarios for the future expansion of the Universe

We examine nonrelativistic particles that decay into relativistic products in big rip, little rip, and pseudo-rip models for the future evolution of the universe. In contrast to decays that occur in standard $\Lambda$CDM, the evolution of the ratio $r$ of the energy density of the relativistic decay products to the energy density of the initially decaying particles can decrease with time in all of these models. In big rip and little rip models, $r$ always goes to zero asymptotically, while this ratio evolves to infinity or a constant in pseudo-rip models.
Learning-based framework for sensor fault-tolerant building HVAC control with model-assisted learning

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort and reducing energy consumption. These HVAC systems in smart buildings rely 'on real-time sensor readings, which in practice often suffer from various faults and could also be vulnerable to malicious attacks. Such faulty sensor inputs may lead to the violation of indoor environment requirements (e.g., temperature, humidity, etc.) and the increase of energy consumption. While many model-based approaches have been proposed in the literature for building HVAC control, it is costly to develop accurate physical models for ensuring their performance and even more challenging to address the impact of sensor faults. In this work, we present a novel learning-based framework for sensor fault-tolerant HVAC control, which includes three deep learning based components for 1) generating temperature proposals with the consideration of possible sensor faults, 2) selecting one of the proposals based on the assessment of their accuracy, and 3) applying reinforcement learning with the selected temperature proposal. Moreover, to address the challenge of training data insufficiency in building-related tasks, we propose a model-assisted learning method leveraging an abstract model of building physical dynamics. Through extensive experiments, we demonstrate that the proposed fault-tolerant HVAC control framework can significantly reduce building temperature violations under a variety of sensor fault patterns while maintaining energy efficiency.
Peanut oral immunotherapy differentially suppresses clonally distinct subsets of T helper cells.

Food allergy affects an estimated 8% of children in the US. Oral immunotherapy (OIT) is a recently approved treatment, with outcomes ranging from sustained tolerance to food allergen to no apparent benefit. The immunological underpinnings that influence clinical outcomes of OIT still remain largely unresolved. Using single-cell RNA sequencing and paired TCRα/β sequencing, we assessed the transcriptomes of CD154+ and CD137+ peanut-reactive T helper cells from 12 peanut-allergic patients longitudinally throughout OIT. We observed expanded populations of cells expressing Th1, Th2, and Th17 signatures that further separated into six clonally distinct subsets. Four of these subsets demonstrated convergence of TCR sequences, suggesting antigen-driven T cell fate. Over the course of OIT, we observed suppression of Th2 and Th1 gene signatures in effector clonotypes but not Tfh-like clonotypes. Positive outcomes were associated with stronger suppression of Th2 signatures in Th2A-like cells, while treatment failure was associated with the expression of baseline inflammatory gene signatures that were present in Th1 and Th17 populations and unmodulated by OIT. These results demonstrate that differential clinical responses to OIT are associated both with pre-existing characteristics of peanut-reactive CD4+ T cells and with suppression of a subset of Th2 cells.
Robust linear classification from limited training data

We consider the problem of linear classification under general loss functions in the limited-data setting. Overfitting is a common problem here. The standard approaches to prevent overfitting are dimensionality reduction and regularization. But dimensionality reduction loses information, while regularization requires the user to choose a norm, or a prior, or a distance metric. We propose an algorithm called RoLin that needs no user choice and applies to a large class of loss functions. RoLin combines “reliable” information from the top principal components with a robust optimization to extract any useful information from “unreliable” subspaces. It also includes a new robust cross-validation that is better than existing cross-validation methods in the limited-data setting. Experiments on 25 real-world datasets and three standard loss functions show that RoLin broadly outperforms both dimensionality reduction and regularization. Dimensionality reduction has $$14\%-40\%$$ worse test loss on average as compared to RoLin. Against $$L_1$$ and $$L_2$$ regularization, RoLin can be up to 3x better for logistic loss and 12x better for squared hinge loss. The differences are greatest for small sample sizes, where RoLin achieves the best loss on 2x to 3x more datasets than any competing method. For some datasets, RoLin with 15 training samples is better than the best norm-based regularization with 1500 samples.
Novel genome characteristics contribute to the invasiveness of Phragmites australis (common reed).

The rapid invasion of the non-native Phragmites australis (Poaceae, subfamily Arundinoideae) is a major threat to native wetland ecosystems in North America and elsewhere. We describe the first reference genome for P. australis and compare invasive (ssp. australis) and native (ssp. americanus) genotypes collected from replicated populations across the Laurentian Great Lakes to deduce genomic bases driving its invasive success. We report novel genomic features including a Phragmites lineage-specific whole genome duplication, followed by gene loss and preferential retention of genes associated with transcription factors and regulatory functions in the remaining duplicates. Comparative transcriptomic analyses revealed that genes associated with biotic stress and defense responses were expressed at a higher basal level in invasive genotypes, but native genotypes showed a stronger induction of defense responses when challenged by a fungal endophyte. The reference genome and transcriptomes, combined with previous ecological and environmental data, add to our understanding of mechanisms leading to invasiveness and support the development of novel, genomics-assisted management approaches for invasive Phragmites.
Estimation of distributed parameter systems

The estimation and control of large flexible space structures pose significant new control technology problems. One major problem area is the closed-loop stability of a structure that has been modeled with truncated modal dynamics. Stability problems arise due to the control design process beginning with a deficient model. This work provides the necessary conditions for the optimal estimation of infinite dimensional (partial-differential equation) systems. This approach can be particularly useful for initial control studies and for gaining considerable insight into what the optimal estimator for a truly infinite dimensional system should be. A detailed example of the estimation of the continuous shape of a string in tension is presented.
Opiate overdose deaths in the District of Columbia. II. Methadone-related fatalities.

With widespread clinical utilization of methadone as a treatment modality for heroin addiction, public attention has been drawn increasingly of late to its potential for diversion to nontherapeutic situations. Deaths directly related to the abuse of black-market methadone have received considerable notoriety in the District of Columbia and elsewhere. Much can and has been learned relating to the complex epidemiological dynamics of the situation, where careful examination of a cohort of methadone-related fatalities has afforded promulgation of reasoned public policy decisions relative to community methadone utilization. Countermeasures taken to control the problem here have proven remarkably effective.
Supramolecular regulation of bioorthogonal catalysis in cells using nanoparticle-embedded transition metal catalysts

Regulation of bioorthogonal catalysis in living systems is challenging because of the complex intracellular environment. Now, the activity of protein-sized bioorthogonal nanozymes has been regulated by binding a supramolecular cucurbit[7]uril ‘gate-keeper’ onto the monolayer surface. This arrangement enables the controlled activation of profluorophores and prodrugs inside living cells for imaging and therapeutic applications.
Absence of capsule reveals glycan‐mediated binding and recognition of salivary mucin MUC7 by Streptococcus pneumoniae

Salivary proteins modulate bacterial colonization in the oral cavity and interact with systemic pathogens that pass through the oropharynx. An interesting example is the opportunistic respiratory pathogen Streptococcus pneumoniae that normally resides in the nasopharynx, but belongs to the greater Mitis group of streptococci, most of which colonize the oral cavity. Streptococcus pneumoniae also expresses a serine-rich repeat (SRR) adhesin, PsrP, which is a homologue to oral Mitis group SRR adhesins, such as Hsa of Streptococcus gordonii and SrpA of Streptococcus sanguinis. As the latter bind to salivary glycoproteins through recognition of terminal sialic acids, we wanted to determine whether S. pneumoniae also binds to salivary proteins through possibly the same mechanism. We found that only a capsule-free mutant of S. pneumoniae TIGR4 binds to salivary proteins, most prominently to mucin MUC7, but that this binding was not mediated through PsrP or recognition of sialic acid. We also found, however, that PsrP is involved in agglutination of human red blood cells (RBCs). After removal of PsrP, an additional previously masked lectin-like adhesin activity mediating agglutination of sialidase-treated RBCs becomes revealed. Using a custom-spotted glycoprotein and neoglycoprotein dot blot array, we identify candidate glycan motifs recognized by PsrP and by the putative S. pneumoniae adhesin that could perhaps be responsible for pneumococcal binding to salivary MUC7 and glycoproteins on RBCs.