cssi/SciSciGPT-SciSciNet huggingface dataset:
Multiphase Porous Electrode Theory

Porous electrode theory, pioneered by John Newman and collaborators, provides a useful macroscopic description of battery cycling behavior, rooted in microscopic physical models rather than empirical circuit approximations. The theory relies on a separation of length scales to describe transport in the electrode coupled to intercalation within small active material particles. Typically, the active materials are described as solid solution particles with transport and surface reactions driven by concentration fields, and the thermodynamics are incorporated through fitting of the open circuit potential. This approach has fundamental limitations, however, and does not apply to phase-separating materials, for which the voltage is an emergent property of inhomogeneous concentration profiles, even in equilibrium. Here, we present a general theoretical framework for "multiphase porous electrode theory" implemented in an open-source software package called "MPET", based on electrochemical nonequilibrium thermodynamics. Cahn-Hilliard-type phase field models are used to describe the solid active materials with suitably generalized models of interfacial reaction kinetics. Classical concentrated solution theory is implemented for the electrolyte phase, and Newman's porous electrode theory is recovered in the limit of solid-solution active materials with Butler-Volmer kinetics. More general, quantum-mechanical models of Faradaic reactions are also included, such as Marcus-Hush-Chidsey kinetics for electron transfer at metal electrodes, extended for concentrated solutions. The full equations and numerical algorithms are described, and a variety of example calculations are presented to illustrate the novel features of the software compared to existing battery models.
A non-commutative Julia Inequality.

We prove a Julia inequality for bounded non-commutative functions on polynomial polyhedra. We use this to deduce a Julia inequality for holomorphicfunctions on classical domains in $$\mathbb {C}^d$$ . We look at differentiability at a boundary point for functions that have a certain regularity there.
Generalized Iterated Wreath Products of Symmetric Groups and Generalized Rooted Trees Correspondence

Consider the generalized iterated wreath product \(S_{r_1}\wr \ldots \wr S_{r_k}\) of symmetric groups. We give a complete description of the traversal for the generalized iterated wreath product. We also prove an existence of a bijection between the equivalence classes of ordinary irreducible representations of the generalized iterated wreath product and orbits of labels on certain rooted trees. We find a recursion for the number of these labels and the degrees of irreducible representations of the generalized iterated wreath product. Finally, we give rough upper bound estimates for fast Fourier transforms.
Higher dimensional black hole initial data with prescribed boundary metric

We obtain higher dimensional analogues of the results of Mantoulidis and Schoen in [8]. More precisely, we show that (i) any metric $g$ with positive scalar curvature on the $3$-sphere $S^3$ can be realized as the induced metric on the outermost apparent horizon of a $4$-dimensional asymptotically flat manifold with non-negative scalar curvature, whose ADM mass can be arranged to be arbitrarily close to the optimal value specified by the Riemannian Penrose inequality; (ii) any metric $g$ with positive scalar curvature on the $n$-sphere $S^n$, with $ n \ge 4 $, such that $(S^n, g)$ isometrically embeds into $\mathbb{R}^{n+1}$ as a star-shaped hypersurface, can be realized as the induced metric on the outermost apparent horizon of an $(n+1)$-dimensional asymptotically flat manifold with non-negative scalar curvature, whose ADM mass can be made to be arbitrarily close to the optimal value.
Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks

Brain age prediction based on neuroimaging data could help characterize both the typical brain development and neuropsychiatric disorders. Pattern recognition models built upon functional connectivity (FC) measures derived from resting state fMRI (rsfMRI) data have been successfully used to predict the brain age. However, most existing studies focus on coarse-grained FC measures between brain regions or intrinsic connectivity networks (ICNs), which may sacrifice fine-grained FC information of the rsfMRI data. Whole brain voxel-wise FC measures could provide fine-grained FC information of the brain and may improve the prediction performance. In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age.
Intensified Vegetation Water Use Under Acid Deposition

Despite the important role vegetation plays in the global water cycle, the exact controls of vegetation water use, especially the role of soil biogeochemistry, remain elusive. In this study, we reveal a new mechanism of soil biogeochemical control of large-scale vegetation water use. Nitrate and sulfate deposition from fossil fuel burning have caused substantial soil acidification, leading to the leaching of soil base cations. Of these, calcium has a unique role in plant cells by regulating stomatal aperture, thus affecting vegetation water use. We hypothesized that the leaching of the soil calcium supply, induced by acid deposition, would increase large-scale vegetation water use. We present evidence from a long-term whole watershed acidification experiment demonstrating that the alteration of the soil calcium supply by acid deposition can significantly intensify vegetation water use (~10% increase in evapotranspiration) and deplete available soil water. These results are critical to understanding future water availability, biogeochemical cycles, and surface energy flux and to help reduce uncertainties in terrestrial biosphere models.
Dynamic CRMP2 Regulation of CaV2.2 in the Prefrontal Cortex Contributes to the Reinstatement of Cocaine Seeking

Cocaine addiction remains a major health concern with limited effective treatment options. A better understanding of mechanisms underlying relapse may help inform the development of new pharmacotherapies. Emerging evidence suggests that collapsin response mediator protein 2 (CRMP2) regulates presynaptic excitatory neurotransmission and contributes to pathological changes during diseases, such as neuropathic pain and substance use disorders. We examined the role of CRMP2 and its interactions with a known binding partner, CaV2.2, in cocaine-seeking behavior. We employed the rodent self-administration model of relapse to drug seeking and focused on the prefrontal cortex (PFC) for its well-established role in reinstatement behaviors. Our results indicated that repeated cocaine self-administration resulted in a dynamic and persistent alteration in the PFC expression of CRMP2 and its binding partner, the CaV2.2 (N-type) voltage-gated calcium channel. Following cocaine self-administration and extinction training, the expression of both CRMP2 and CaV2.2 was reduced relative to yoked saline controls. By contrast, cued reinstatement potentiated CRMP2 expression and increased CaV2.2 expression above extinction levels. Lastly, we utilized the recently developed peptide myr-TAT-CBD3 to disrupt the interaction between CRMP2 and CaV2.2 in vivo. We assessed the reinstatement behavior after infusing this peptide directly into the medial PFC and found that it decreased cue-induced reinstatement of cocaine seeking. Taken together, these data suggest that neuroadaptations in the CRMP2/CaV2.2 signaling cascade in the PFC can facilitate drug-seeking behavior. Targeting such interactions has implications for the treatment of cocaine relapse behavior.
Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard

We study the Bayesian model of opinion exchange of fully rational agents arranged on a network. In this model, the agents receive private signals that are indicative of an unkown state of the world. Then, they repeatedly announce the state of the world they consider most likely to their neighbors, at the same time updating their beliefs based on their neighbors' announcements. This model is extensively studied in economics since the work of Aumann (1976) and Geanakoplos and Polemarchakis (1982). It is known that the agents eventually agree with high probability on any network. It is often argued that the computations needed by agents in this model are difficult, but prior to our results there was no rigorous work showing this hardness. We show that it is PSPACE-hard for the agents to compute their actions in this model. Furthermore, we show that it is equally difficult even to approximate an agent's posterior: It is PSPACE-hard to distinguish between the posterior being almost entirely concentrated on one state of the world or another.
Factored Pose Estimation of Articulated Objects using Efficient Nonparametric Belief Propagation

Robots working in human environments often encounter a wide range of articulated objects, such as tools, cabinets, and other jointed objects. Such articulated objects can take an infinite number of possible poses, as a point in a potentially high-dimensional continuous space. A robot must perceive this continuous pose in order to manipulate the object to a desired pose. This problem of perception and manipulation of articulated objects remains a challenge due to its high dimensionality and multi-modal uncertainty. In this paper, we propose a factored approach to estimate the poses of articulated objects using an efficient non-parametric belief propagation algorithm. We consider inputs as geometrical models with articulation constraints, and observed 3D sensor data. The proposed framework produces object-part pose beliefs iteratively. The problem is formulated as a pairwise Markov Random Field (MRF) where each hidden node (continuous pose variable) models an observed object-part’s pose and each edge denotes an articulation constraint between a pair of parts. We propose articulated pose estimation by a Pull Message Passing algorithm for Nonparametric Belief Propagation (PMPNBP) and evaluate its convergence properties over scenes with articulated objects.
Police killings of Black people and rates of sexually transmitted infections: a cross-sectional analysis of 75 large US metropolitan areas, 2016.

Objectives Emerging literature shows that racialised police brutality, a form of structural racism, significantly affects health and well-being of racial/ethnic minorities in the USA. While public health research suggests that structural racism is a distal determinant of sexually transmitted infections (STIs) among Black people, no studies have empirically linked police violence to STIs. To address this gap, our study measures associations between police killings and rates of STIs among Black residents of US metropolitan statistical areas (MSAs). Methods This cross-sectional ecological analysis assessed associations between the number of Black people killed by police in 2015 and rates of primary and secondary syphilis, gonorrhoea and chlamydia per 100 000 Black residents of all ages in 2016 in 75 large MSAs. Multivariable models controlled for MSA-level demographic and socioeconomic characteristics, police expenditures, violent crime, arrest and incarceration rates, insurance rates and healthcare funding. Results In 2015, the median number of Black people killed by police per MSA was 1.0. In multivariable models, police killings were positively and significantly associated with syphilis and gonorrhoea rates among Black residents. Each additional police killing in 2015 was associated with syphilis rates that were 7.5% higher and gonorrhoea rates that were 4.0% higher in 2016. Conclusions Police killings of Black people may increase MSA-level risk of STI infections among Black residents. If future longitudinal analyses support these findings, efforts to reduce STIs among Black people should include reducing police brutality and addressing mechanisms linking this violence to STIs.
Where there is smoke: solid fuel externalities, gender, and adult respiratory health in India

Chronic respiratory conditions are a leading cause of death in the world. Using data on lung obstruction from the WHO Survey of Global Ageing and Adult Health (WHO-SAGE 2007–2008), this paper studies the determinants of respiratory health in India, home to a third of all deaths from chronic obstructive pulmonary disease. First, we find that smokers and members of households that use solid fuels (wood, biomass, coal, or dung) for cooking have higher lung obstruction. Second, even if a respondent’s household uses clean fuels, their lung obstruction is higher if their neighbors use solid fuels. In neighborhoods with high solid fuel use, the lungs of members of households that use clean fuels can be as obstructed as lungs of members of households that use solid fuels. These negative externalities of solid fuel use are robust to additional controls for neighborhood socioeconomic status, falsification tests, tests with placebo measures, and tests using alternative measures of respiratory health as outcomes. Third, the influence of the determinants is patterned by gender. Smoking tobacco is an important influence on lung obstruction among men. Confirming non-linear dose–response relationships, we find that women from households that use solid fuels are the only group not further harmed by neighborhood solid fuel smoke, possibly because of high exposure to pollutants while cooking. The study improves our understanding of behavioral, social, and environmental determinants of respiratory health in India. Importantly, it makes a case for greater public investments to promote the adoption and use of cleaner fuels.
Special solutions to a fourth-order nonlinear parabolic equation in non-divergence form

In this paper we study a crystal surface model first proposed by H.~Al Hajj Shehadeh, R.V.~Kohn, and J.~Weare (2011 Physica D, 240,1771-1784). By seeking a solution of a particular function form, we are led to a boundary value problem for a fourth-order nonlinear elliptic equation. The mathematical challenge of the problem is due to the fact that the degeneracy in the equation is directly imposed by one of the two boundary conditions. An existence theorem is established in which a meaningful mathematical interpretation of one of the boundary conditions remains open. Our proof seems to suggest that this is unavoidable. We also obtain self-similar solutions to the crystal surface model which are positive and unbounded. This is in sharp contrast with the linear biharmonic heat equation.
Step edge-mediated assembly of periodic arrays of long graphene nanoribbons on Au(111).

The influence of substrate steps on the bottom-up synthesis of atomically precise graphene nanoribbons (GNRs) on an Au(111) surface is investigated. Straight surface steps are found to promote the assembly of long and compact arrays of polymers with enhanced interchain π–π stacking interactions, which create a steric hindrance effect on cyclodehydrogenation to suppress the H passivation of polymer ends. The modified two-stage growth process results in periodic arrays of GNRs with doubled average length near step edges.
The Use of Design Thinking in the U.S. Federal Government

Few organizations have more power to improve lives through innovation than the U.S. government. In this article, we focus on design thinking, a promising innovation methodology that marries a human...
Machine learning surrogate models for Landau fluid closure

The first result of applying the machine/deep learning technique to the fluid closure problem is presented in this paper. As a start, three different types of neural networks [multilayer perceptron (MLP), convolutional neural network (CNN), and two-layer discrete Fourier transform (DFT) network] were constructed and trained to learn the well-known Hammett–Perkins Landau fluid closure in configuration space. We find that in order to train a well-preformed network, a minimum size of the training data set is needed; MLP also requires a minimum number of neurons in the hidden layers that equals the degrees of freedom in Fourier space, despite the fact that training data are being fed into the configuration space. Out of the three models, DFT performs the best for the clean data, most likely due to the existence of the simple Fourier expression for the Hammett–Perkins closure, but it is the least robust with respect to input noise. Overall, with appropriate tuning and optimization, all three neural networks are able to accurately predict the Hammett–Perkins closure and reproduce the intrinsic nonlocal feature, suggesting a promising path to calculating more sophisticated closures with the machine/deep learning technique.
From Parameter Estimation to Dispersion of Nonstationary Gauss-Markov Processes

This paper provides a precise error analysis for the maximum likelihood estimate $\hat a\left( {\boldsymbol{u}} \right)$ of the parameter a given samples u = (u 1 , … , u n )⊤ drawn from a nonstationary Gauss-Markov process U i = aU i−1 + Z i , i ≥ 1, where a > 1, U 0 = 0, and Z i ’s are independent Gaussian random variables with zero mean and variance σ2. We show a tight nonasymptotic exponentially decaying bound on the tail probability of the estimation error. Unlike previous works, our bound is tight already for a sample size of the order of hundreds. We apply the new estimation bound to find the dispersion for lossy compression of nonstationary Gauss-Markov sources. We show that the dispersion is given by the same integral formula derived in our previous work [1] for the (asymptotically) stationary Gauss-Markov sources, i.e., |a| < 1. New ideas in the nonstationary case include a deeper understanding of the scaling of the maximum eigenvalue of the covariance matrix of the source sequence, and new techniques in the derivation of our estimation error bound.
Measuring the Impact of Motivations on Travelers’ Strategic Decisions in Different Traffic Conditions: Data Collection, Analysis, and Modeling:

Continued growth in travel demand and the corresponding congestion occurrence accentuates the need for active transportation and demand management (ATDM) for predictive rather than reactive congest...
Decoupling Non-Sequential Stochastic Control Problems

This paper considers the possibility of reducing a class of unconstrained non-sequential stochastic control problems, modeled within the framework of Witsenhausen's intrinsic model, to equivalent sequential stochastic control problems that are (in theory) amenable to recursive decomposition. It is shown that a sequential reduction exists whenever a causality property and a mild measurability condition are satisfied. This result implies that a large class of N-action non-sequential problems are no harder to solve than the hardest N + 1-action sequential problems.
Cross-Validation with Confidence

AbstractCross-validation is one of the most popular model and tuning parameter selection methods in statistics and machine learning. Despite its wide applicability, traditional cross-validation met...
Why Evolve Reliance on the Microbiome for Timing of Ontogeny

The timing of life history events has important fitness consequences. Since the 1950s, researchers have combined first principles and data to predict the optimal timing of life history transitions. Recently, a striking mystery has emerged. Such transitions can be shaped by a completely different branch of the tree of life: species in the microbiome. Probing these interactions using testable predictions from evolutionary theory could illuminate whether and how host-microbiome integrated life histories can evolve and be maintained. Beyond advancing fundamental science, this research program could yield important applications. In an age of microbiome engineering, understanding the contexts that lead to microbiota signaling shaping ontogeny could offer novel mechanisms for manipulations to increase yield in agriculture by manipulating plant responses to stressful environments, or to reduce pathogen transmission by affecting vector efficiency. We combine theory and evidence to illuminate the essential questions underlying the existence of microbiome-dependent ontogenetic timing (MiDOT) to fuel research on this emerging topic.
A Cantor set whose polynomial hull contains no analytic discs

A generalization of a result of Wermer concerning the existence of polynomial hulls without analytic discs is presented. As a consequence it is shown that there exists a Cantor set $X$ in $\mathbb{C}^3$ whose polynomial hull is strictly larger than $X$ but contains no analytic discs.
Investment Choice Architecture in Trust Games: When “All-in” Is Not Enough

While many economic interactions feature All-or-Nothing options nudging investors towards going all-in, such designs may unintentionally affect reciprocity. We manipulate the investors action space in two versions of the trust game. In one version investors can invest either all their endowment or nothing. In the other version, they can invest any amount of the endowment. Consistent with our intentions-based model, we show that "all-or-nothing designs coax more investment but limit investors demonstrability of intended trust. As a result, all-in investors are less generously reciprocated than when they can invest any amount, where full investments are a clearer signal of trustworthiness.
Threat imminence dictates the role of the bed nucleus of the stria terminalis in contextual fear

Abstract Recent work indicates that the bed nucleus of the stria terminalis (BNST) is critically involved in the regulation of conditioned fear responses to unpredictable threats. Here we examined whether the involvement of the BNST in contextual fear conditioning in male rats depends on the imminence of shock after placement in the conditioning chamber. Specifically, we hypothesized that the BNST supports contextual freezing after conditioning with delayed, but not imminent, footshock (relative to placement in the context). Rats were implanted with cannulae targeting the BNST and underwent a contextual fear conditioning procedure in which a single footshock unconditioned stimulus (US) was delivered either 1 min or 9 min after the rat was placed in the context; the rats received a total of four identical conditioning sessions over two days, all with equivalent exposure to the context. Contexts associated with either imminent or delayed US onsets produced distinct patterns of freezing and shock-induced activity but freezing in each case was context-dependent. Reversible inactivation of the BNST reduced the expression of contextual freezing in the context paired with delayed (9 min), but not imminent (1 min), footshock onset. Implications of these data are discussed in the light of recent conceptualizations of BNST function, as well as for anxiety behaviors.
Finite-Time Guarantees for Byzantine-Resilient Distributed State Estimation With Noisy Measurements

This article considers resilient cooperative state estimation in unreliable multiagent networks. A network of agents aim to collaboratively estimate the value of an unknown vector parameter, while an unknown subset of agents suffer Byzantine faults. We refer to the faulty agents as Byzantine agents. Byzantine agents malfunction arbitrarily and may send out highly unstructured messages to other agents in the network. As opposed to fault-free networks, reaching agreement in the presence of Byzantine agents is far from trivial. In this article, we propose a computationally efficient algorithm that is provably robust to Byzantine agents. At each iteration of the algorithm, a good agent performs a gradient descent update based on noisy local measurements, exchanges its update with other agents in its neighborhood, and robustly aggregates the received messages using coordinate-wise trimmed means. Under mild technical assumptions, we establish that good agents learn the true parameter asymptotically in almost sure sense. We further complement our analysis by proving (high probability) finite-time convergence rate, encapsulating network characteristics.
Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit

Many neuroimaging studies have investigated reward processing dysfunction in major depressive disorder. These studies have led to the common idea that major depressive disorder is associated with blunted responses within the reward circuit, particularly in the ventral striatum. Yet, the link between major depressive disorder and reward-related responses in other regions remains inconclusive, thus limiting our understanding of the pathophysiology of major depressive disorder. To address this issue, we performed a coordinate-based meta-analysis of 41 whole-brain neuroimaging studies encompassing reward-related responses from a total of 794 patients with major depressive disorder and 803 healthy controls. Our findings argue against the common idea that major depressive disorder is primarily linked to deficits within the reward system. Instead, our results demonstrate that major depressive disorder is associated with opposing abnormalities in the reward circuit: hypo-responses in the ventral striatum and hyper-responses in the orbitofrontal cortex. The current findings suggest that dysregulated corticostriatal connectivity may underlie reward-processing abnormalities in major depressive disorder, providing an empirical foundation for a more refined understanding of abnormalities in the reward circuitry in major depressive disorder.
Resonant inelastic x-ray scattering in metals: A diagrammatic approach

This work introduces a method to analyze resonant inelastic x-ray scattering data from metals. Previous methods rely on two approximations that break down in the case of charged particles. The authors test their method in a Coulomb gas and observe higher-order processes dominating the scattering spectrum
Compact high-order stencils with optimal accuracy for numerical solutions of 2-D time-independent elasticity equations

Abstract A new approach for the increase in the order of accuracy of high-order numerical techniques used for time-independent elasticity is suggested on uniform square and rectangular meshes. It is based on the optimization of the coefficients of the corresponding discrete stencil equations with respect to the local truncation error. It is shown that the second order of accuracy of 2-D linear finite elements with 9-point stencils is optimal and cannot be improved. However, the order of accuracy of 25-point stencils (similar to those for quadratic finite and isogeometric elements) can be significantly improved. We have developed new 25-point stencils for 2-D elastic problems with optimal 10th order of accuracy. The numerical results are in good agreement with the theoretical findings as well as they show a big increase in accuracy of the new stencils compared with those for high-order finite elements. At the same number of degrees of freedom, the new approach yields significantly more accurate results than those obtained by high-order (up to the tenth order) finite elements. The numerical experiments also show that the new approach with 25-point stencils yields very accurate results for nearly incompressible materials with Poisson’s ratio 0.4999.
Physics-Based Freely Scalable Continuum Deformation for UAS Traffic Coordination

This article develops a novel physics-inspired traffic coordination approach and applies it to unmanned aircraft system (UAS) traffic management. We extend available physics-inspired approaches previously applied to 1-D traffic flow on highways and urban streets to support models of traffic coordination in higher dimension airspace for cases where no predefined paths exist. This article considers airspace as a finite control volume while UAS coordination, treated as continuum deformation, is controlled at the airspace boundaries. By partitioning airspace into planned and unplanned spaces, the article models nominal coordination in the planned airspace as the solution of a partial differential equation with spatiotemporal parameters. This article also improves resilience to vehicle failures with a resilient boundary control algorithm to update the geometry of the planned space when UAS problems threaten safe coordination in existing navigable airspace channels. To support UAS coordination at the microscopic level, we propose clustering vehicles based on vehicle performance limits. UAS clusters, with each UAS treated as a particle of a virtual rigid body, use leader–follower containment to acquire the macroscopic desired trajectory.
Water limitations to large-scale desert agroforestry projects for carbon sequestration.

Vegetation and soils take up 20% of global anthropogenic greenhouse gas emission (1), and vegetation also plays an active role in regulating global water cycle (2). In PNAS, Branch and Wulfmeyer (3) conduct a detailed regional-scale simulation to show that agroforestry can significantly enhance rainfall in some desert regions and therefore could provide an effective biological engineering approach to sequestrate carbon (3). They also identify arid zones that have potential for desert agroforestry to enhance rainfall. The zones are mostly outside of the proposed areas available for potential tree restoration in a recent global analysis (4). This is an important finding. However, we argue that the conclusion misses an important piece of the puzzle: a comparison between … [↵][1]1To whom correspondence may be addressed. Email: lxwang{at}iupui.edu. [1]: #xref-corresp-1-1
Linear Model-Predictive Controller (LMPC) for Building’s Heating Ventilation and Air Conditioning (HVAC) System

Model predictive control (MPC) is a widely used technique for temperature set-point tracking and energy optimization of Heating Ventilation and Air Conditioning (HVAC) systems in buildings. Unfortunately, a nonlinear thermal building model leads to a computationally expensive nonlinear MPC problem that is not suitable for real-time control and optimization. This paper presents a novel approximate linearized model for building’s thermal dynamics and the HVAC system power consumption that leads to a computationally efficient linear model predictive controller (LMPC) for the buildings’ HVAC systems. We employ feedback linearization technique to obtain an equivalent linearized model for the nonlinear thermal building dynamics and use constraint mapping approach to obtain a linearized formulation for new control variables. Next, using piecewise linearization, we obtain a linearized analytical model for the HVAC system power consumption. The proposed LMPC technique is validated using multiple simulation case studies. We demonstrate that the proposed LMPC technique is not only computationally efficient but also accurately approximates the nonlinear optimal control decisions.
On the averaged Colmez conjecture

This is an expository article on the averaged version of Colmez's conjecture, relating Faltings heights of CM abelian varieties to Artin L-functions. It is based on the author's lectures at the Current Developments in Mathematics conference held at Harvard in 2018.
Pin(2)-equivariant Seiberg-Witten Floer homology of Seifert fibrations

We compute the -module called connected Seiberg–Witten Floer homology, whose isomorphism class is a homology cobordism invariant.
Graph Constrained Reinforcement Learning for Natural Language Action Spaces

Interactive Fiction games are text-based simulations in which an agent interacts with the world purely through natural language. They are ideal environments for studying how to extend reinforcement learning agents to meet the challenges of natural language understanding, partial observability, and action generation in combinatorially-large text-based action spaces. We present KG-A2C, an agent that builds a dynamic knowledge graph while exploring and generates actions using a template-based action space. We contend that the dual uses of the knowledge graph to reason about game state and to constrain natural language generation are the keys to scalable exploration of combinatorially large natural language actions. Results across a wide variety of IF games show that KG-A2C outperforms current IF agents despite the exponential increase in action space size.
Distributed Path Planning for Executing Cooperative Tasks with Time Windows

Abstract We investigate the distributed planning of robot trajectories for optimal execution of cooperative tasks with time windows. In this setting, each task has a value and is completed if sufficiently many robots are simultaneously present at the necessary location within the specified time window. Tasks keep arriving periodically over cycles. The task specifications (required number of robots, location, time window, and value) are unknown a priori and the robots try to maximize the value of completed tasks by planning their own trajectories for the upcoming cycle based on their past observations in a distributed manner. Considering the recharging and maintenance needs, robots are required to start and end each cycle at their assigned stations located in the environment. We map this problem to a game theoretic formulation and maximize the collective performance through distributed learning. Some simulation results are also provided to demonstrate the performance of the proposed approach.
Crime and Output: Theory and Application to the Northern Triangle of Central America

This paper presents a structural model of crime and output. Individuals make an occupational choice between criminal and legal activities. The return to becoming a criminal is endogenously determined in a general equilibrium together with the level of crime and economic activity. I calibrate the model to the Northern Triangle countries and conduct several policy experiments. I find that for a country like Honduras crime reduces GDP by about 3 percent through its negative effect on employment indirectly, in addition to direct costs of crime associated with material losses, which are in line with literature estimates. Also, the model generates a non-linear effect of crime on output and vice versa. On average I find that a one percent increase in output per capita implies about ½ percent decline in crime, while a decrease of about 5 percent in crime leads to about one percent increase in output per capita. These positive effects are larger if the initial level of crime is larger.
Time spent exercising and obesity: An application of Lewbel's instrumental variables method.

Abstract This paper examines the role physical activity plays in determining body mass using data from the American Time Use Survey. Our work is the first to address the measurement error that arises when time use during a single day—rather than average daily time use over an extended period—is used as an explanatory variable. We show that failing to account for day-to-day variation in activities results in the effects of time use on a typical day being understated. Furthermore, we account for the possibility that physical activity and body mass are jointly determined by implementing Lewbel’s instrumental variables estimator that exploits first-stage heteroskedasticity rather than traditional exclusion restrictions. While averaging 30 min of transportation-related biking or walking per day lowers the BMI of men by 1.5, we find no effect of physically active leisure on the BMI of men in our sample. In contrast, 30 min of per day of either type of physical activity lowers the BMI of women by 1.
Making Permanent Optical Matter of Plasmonic Nanoparticles by in Situ Photopolymerization

Laser-induced self-organization of colloidal metal nanoparticles holds great promise in building artificial photonic structures, yet the self-organized nanoparticles (i.e., optical matter) disassem...
Lag penalized weighted correlation for time series clustering

The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. LPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at https://github.com/gitter-lab/LPWC and CRAN under a MIT license.
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Summary Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have exposed the power of optimizing millions of synaptic weights over millions of observations to operate robustly in real-world contexts. These models do not learn simple, human-interpretable rules or representations of the world; rather, they use local computations to interpolate over task-relevant manifolds in a high-dimensional parameter space. Counterintuitively, similar to evolutionary processes, over-parameterized models can be simple and parsimonious, as they provide a versatile, robust solution for learning a diverse set of functions. This new family of direct-fit models present a radical challenge to many of the theoretical assumptions in psychology and neuroscience. At the same time, this shift in perspective establishes unexpected links with developmental and ecological psychology.
Automated Addressable Microfluidic Device for Minimally Disruptive Manipulation of Cells and Fluids within Living Cultures

Cell culturing experiments are ubiquitous to the study of biology, development of new medical treatments, and the biomanufacturing industry. However, there are still major technological barriers limiting the advancement of knowledge and ballooning the experimental costs associated with these systems. For example, currently, it is difficult to perform nondisruptive monitoring and control of the cells in the cultured samples. This often necessitates the use of sacrificial assays and results in product inconsistency. To resolve these bottlenecks, we present a prototype "addressable" microfluidic technology capable of spatiotemporal fluid and cell manipulations within living cultures. As a proof-of-concept, we demonstrate its ability to perform additive manufacturing by seeding cells in spatial patterns (including co-culturing multiple cell types) and subtractive manufacturing by removing surface adherent cells via the focused flow of trypsin. Additionally, we show that the device can sample fluids and perform cell "biopsies" (which can be subsequently sent for ex situ analysis), from any location within its culture chamber. Finally, the on-chip plumbing is completely automated using external electronics. This opens the possibility of performing long-term computer-driven experiments, where the cell behavior is modulated in response to the minimally disruptive observations (e.g., fluid sampling and cell biopsies) throughout the entire duration of the cultures. A limitation of the presented α prototype is that it is only two-dimensional (2D). However, technology serves as a foundation for ultimately extending the concept to three-dimensional (3D). Another limitation of the device is that it is currently made from poly(dimethylsiloxane) (PDMS), while more work needs to be done to manufacture from a material that degrades away or allow the cells to lay down the tissue matrix. Unfortunately, the existing biodegradable materials are typically not strong enough for the fabrication of microfluidic valves. Hence, new ones need to be developed before this technology can become mainstream. Yet, it is the hope of the authors that this will be achieved soon, and the microfluidic plumbing technology will eventually be scaled up to 3D, to overcome the limitations of the conventional cell culturing platforms.
Dust charging in dynamic ion wakes

A molecular dynamics simulation of ion flow past dust grains is used to investigate the interaction between a pair of charged dust particles and streaming ions. The charging and dynamics of the grains are coupled and derived from the ion–dust interactions, allowing for detailed analysis of the ion wakefield structure and wakefield-mediated interaction as the dust particles change position. When a downstream grain oscillates vertically within the wake, it decharges by up to 30% as it approaches the upstream grain and then recharges as it recedes. There is an apparent hysteresis in charging depending on whether the grain is approaching or receding from a region of higher ion density. Maps of the ion-mediated dust–dust interaction force show that the radial extent of the wake region, which provides an attractive restoring force on the downstream particle, increases as the ion flow velocity decreases, though the restoring effect becomes weaker. As also shown in recent numerical results, there is no net attractive vertical force between the two grains. Instead, the reduced ion drag on the downstream particle allows it to “draft” in the wakefield of the upstream particle.
Functional angiogenesis requires microenvironmental cues balancing endothelial cell migration and proliferation

Angiogenesis is a complex morphogenetic process that involves intimate interactions between multicellular endothelial structures and their extracellular milieu. In vitro models of angiogenesis can aid in reducing the complexity of the in vivo microenvironment and provide mechanistic insight into how soluble and physical extracellular matrix cues regulate this process. To investigate how microenvironmental cues regulate angiogenesis and the function of resulting microvasculature, we multiplexed an established angiogenesis-on-a-chip platform that affords higher throughput investigation of 3D endothelial cell sprouting emanating from a parent vessel through defined biochemical gradients and extracellular matrix. We found that two fundamental endothelial cell functions, migration and proliferation, dictate endothelial cell invasion as single cells vs. multicellular sprouts. Microenvironmental cues that elicit excessive migration speed incommensurate with proliferation resulted in microvasculature with poor barrier function and an inability to transport fluid across the microvascular bed. Restoring the balance between migration speed and proliferation rate rescued multicellular sprout invasion, providing a new framework for the design of pro-angiogenic biomaterials that guide functional microvasculature formation for regenerative therapies.
Sensitivity of wardrop equilibria: revisited

For single-commodity networks, the increase of the price of anarchy is bounded by a factor of $$(1+\varepsilon )^p$$ from above, when the travel demand is increased by a factor of $$1+\varepsilon $$ and the latency functions are polynomials of degree at most p. We show that the same upper bound holds for multi-commodity networks and provide a lower bound as well.
Indoor and Outdoor Penetration Loss Measurements at 73 and 81 GHz

In this paper, we present millimeter-wave (mmWave) penetration loss measurements and analysis at E- bands-73 GHz and 81 GHz. Penetration loss was measured for common building materials such as clear glass, metal, tinted glass, wood, and drywall on the campus of Boise State University in the city of Boise. A horn antenna with a gain of 24 dBi was used at the transmitter and receiver at both bands, and both antennas were boresight- aligned with respect to the test material. A total of twelve locations were selected to test five materials. We tested two indoor materials (clear glass and wood) in at least two locations to determine the effect of penetration loss of materials in similar compositions. The average penetration loss and standard deviation were estimated for these indoor materials. We measured an average penetration loss of 2 to 9 dB for wood and glass, respectively. Furthermore, we measured the penetration loss of common indoor and outdoor building materials. We studied that outdoor materials had larger penetration losses, e.g., we obtained a penetration loss of 22.69 dB for outdoor metal, where this value dropped to 16.04 dB for indoor metal at 73 GHz. Similar results were also obtained for the 81 GHz channel, where the largest penetration loss was measured to be 26.5 dB through a tinted glass door in an outdoor setting.
A Quiescent State Following Mild Sensory Arousal in Caenorhabditis Elegans Is Potentiated by Stress

An animal's behavioral and physiological response to stressors includes changes to its responses to stimuli. How such changes occur is not well understood. Here we describe a Caenorhabditis elegans quiescent behavior, post-response quiescence (PRQ), which is modulated by the C. elegans response to cellular stressors. Following an aversive mechanical or blue light stimulus, worms respond first by briefly moving, and then become more quiescent for a period lasting tens of seconds. PRQ occurs at low frequency in unstressed animals, but is more frequent in animals that have experienced cellular stress due to ultraviolet light exposure as well as in animals following overexpression of epidermal growth factor (EGF). PRQ requires the function of the carboxypeptidase EGL-21 and the calcium-activated protein for secretion (CAPS) UNC-31, suggesting it has a neuropeptidergic mechanism. Although PRQ requires the sleep-promoting neurons RIS and ALA, it is not accompanied by decreased arousability, and does not appear to be homeostatically regulated, suggesting that it is not a sleep state. PRQ represents a simple, tractable model for studying how neuromodulatory states like stress alter behavioral responses to stimuli.