cssi/SciSciGPT-SciSciNet huggingface dataset:
Software Factories: Assembling Applications with Patterns, Models, Frameworks and Tools

Increasingly complex and rapidly changing requirements and technologies are making application development increasingly difficult. This talk examines this phenomenon, and presents a simple pattern for building languages, patterns, frameworks and tools for specific domains, such as user interface construction or database design. Software Factories integrate critical innovations in adaptive assembly, software product lines, and model driven development to reduce the cost of implementing this pattern, making it cost effective for narrower and more specialized domains, such as B2B commerce and employee self service portals.
A Phillips curve for the euro area

This paper asks whether a textbook Phillips curve can explain the behavior of core inflation in the euro area. A critical feature of the analysis is that we measure core inflation with the weighted median of industry inflation rates, which is less volatile than the common measure of inflation excluding food and energy prices. We find that fluctuations in core inflation since the creation of the euro are well explained by three factors: expected inflation (as measured by surveys of forecasters); the output gap (as measured by the Organisation for Economic Co‐operation and Development); and the pass‐through of movements in headline inflation. Our specification resolves the puzzle of a “missing disinflation” after the Great Recession, and it diminishes the puzzle of a “missing inflation” during the recent economic recovery.
Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE

The optimization of electric machines at multiple operating points is crucial for applications that require frequent changes on speeds and loads, such as the electric vehicles, to strive for the machine optimal performance across the entire driving cycle. However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification. Therefore, this paper proposes the utilization of t-distributed stochastic neighbor embedding (t-SNE) to visualize all of the optimization objectives of various electric machines design candidates with various operating conditions, which constitute a high-dimensional set of data that would lie on several different, but related, low-dimensional manifolds. Finally, two case studies of switched reluctance machines (SRM) are presented to illustrate the superiority of then t-SNE when compared to traditional visualization techniques used in electric machine optimizations.
Unmatter Plasma Discovered

Unmmatter Plasma is a novel form of plasma, exclusively made of matter and its antimatter counterpart. It was first generated in the 2015 experiment [1, 2] based on the 2004 considerations [3]. There are four fundamental states of matter: solid, liquid, gas, and plasma. Plasma consists of positive ions and free electrons (negative particles), typically at low pressures, and it is overall almost neutral. Plasma is an ionized gas (as in fluorescent neon, in lightning, in stars, in nuclear reactors). An ion is a positive or negative charged particle. A positive ion is called cation, while a negative ion is called anion. If the ion is an atom, then it may contain less electrons than needed for being neutrally charged (hence one has a cation), or more electrons than needed for being neutrally charged (hence one has an anion). Similarly if the ion is a molecule or a group (of atoms or molecules). The process of forming ions is called ionization. The degree of ionization depends on the proportion of atoms that have lost or gained electrons. By applying a strong electromagnetic field to a gas, or by heating a gas, one obtains plasma. Unmatter as theoretically predicted in the framework of the neutrosophic logic and statistics [4–6] is considered as a combination of matter and antimatter that bound together, or a long-range mixture of matter and antimatter forming a weakly-coupled phase. For example, the electron-positron pair is a type of unmatter. We coined the word unmatter that means neither matter nor antimatter, but something in between. Besides matter and antimatter there may exist unmatter (as a new form of matter) in accordance with the neutrosophy theory that between an entity and its opposite there exist intermediate entities. The 2015 experiment [1, 2] on matter-antimatter plasma (unmatter plasma, in terms of the neutrosophic logic and statistics) was recently successful in the Astra Gemini laser facility of the Rutherford Appleton Laboratory in Oxford, United Kingdom. The 2015 experiment has produced electronpositron plasma. The positron is the antimatter of the electron, having an opposite charge of the electron, but the other properties are the same. Also, the meson is a clear example of unmatter whose configuration includes a pair quark-antiquark. Unmatter is mostly expected to emerge in exotic states outside the boundaries of the Standard Model for particle physics (for example in the Dark Matter sector) and in the regime of high-energy astrophysical objects [7]. “It is definitely a jet of unmatter, because a plasma consisting of the electrons and the positrons is neither matter nor antimatter in the same time. This experiment is the truly verification of unmatter as the theoretical achievements of neutrosophic logic and statistics. This experiment is a milestone of both experimental physics and pure mathematics” [8].
Detecting the Effects of Early-Life Exposures: Why Fecundity Matters

Prenatal exposures have meaningful effects on health across the lifecourse. Innovations in causal inference have shed new light on these effects. Here, we motivate the importance of innovation in the characterization of fecundity, and prenatal selection in particular. We argue that such innovation is crucial for expanding knowledge of the fetal origins of later life health. Pregnancy loss is common, responsive to environmental factors, and closely related to maternal and fetal health outcomes. As a result, selection into live birth is driven by many of the same exposures that shape the health trajectories of survivors. Lifecourse effects that are inferred without accounting for these dynamics may be significantly distorted by survival bias. We use a set of Monte Carlo simulations with realistic parameters to examine the implications of prenatal survival bias. We find that even in conservatively specified scenarios, true fetal origin effects can be underestimated by 50% or more. In contrast, effects of exposures that reduce the probability of prenatal survival but improve the health of survivors will be overestimated. The absolute magnitude of survival bias can even exceed small effect sizes, resulting in inferences that beneficial exposures are harmful or vice-versa. We also find reason for concern that moderately sized true effects, underestimated due to failure to account for selective survival, are missing from scientific knowledge because they do not clear statistical significance filters. This bias has potential real-world costs; policy decisions about interventions to improve maternal and infant health will be affected by underestimated program impact.
The growing landscape of FLT3 inhibition in AML

Midostaurin and gilteritinib are FLT3 inhibitors that have been recently approved for use in FLT3-mutant acute myeloid leukemia (AML). These approved drugs represent a new standard of care for patients with FLT3 mutations in both the first-line and salvage settings. The success of midostaurin used in combination with induction chemotherapy has prompted exploration of newer, more potent and targeted inhibitors (including gilteritinib) in the first-line setting in combination with chemotherapy. At the same time, the success of gilteritinib and other newer FLT3 inhibitors as monotherapy in the salvage setting has been tempered by the development of resistance because of diverse mechanisms. Investigational strategies that incorporate FLT3 inhibitors in combination with hypomethylating agents and as maintenance therapy after allogeneic stem cell transplantation have shown promise. Other novel combination strategies are also undergoing clinical investigation. In this article, we review the current landscape of approved and investigational FLT3 inhibitors in AML, including the current standard of care and investigational strategies.
Sensitivity and Distortion Analysis of a 125-GHz Interferometry Radar for Submicrometer Motion Sensing Applications

In all quadrature direct-conversion receivers, signal deterioration due to $I/Q$ mismatch may happen, causing a mirrored signal to appear superimposed onto the desired signal, thereby destroying the orthogonality of the demodulated signal. This effect strongly affects radar systems over 100 GHz, since they are sensitive to small fabrication errors. In addition, when slow movements are measured with ac-coupled interferometric radars, coupling distortions are observed. In this article, these distortions were analyzed for interferometric radars and then resolved using a novel phase imbalance correction method for a 125-GHz radar. The effectiveness of the proposed solution was verified experimentally for speech information sensing and for small motion detection applications. By successfully measuring a 95-nm sinusoidal movement, the high sensitivity of millimeter-wave (mm-wave) radars was demonstrated. Moreover, noise analyses for small-motion interferometry radars have been presented and a model for sensitivity estimation was proposed.
Prefrontal Cortex Regulates Chronic Stress-Induced Cardiovascular Susceptibility.

Background The medial prefrontal cortex is necessary for appropriate appraisal of stressful information, as well as coordinating visceral and behavioral processes. However, prolonged stress impairs...
Causal network perturbations for instance-specific analysis of single cell and disease samples.

MOTIVATION Complex diseases involve perturbation in multiple pathways and a major challenge in clinical genomics is characterizing pathway perturbations in individual samples. This can lead to patient-specific identification of the underlying mechanism of disease thereby improving diagnosis and personalizing treatment. Existing methods rely on external databases to quantify pathway activity scores. This ignores the data dependencies and that pathways are incomplete or condition-specific. RESULTS ssNPA is a new approach for subtyping samples based on deregulation of their gene networks. ssNPA learns a causal graph directly from control data. Sample-specific network neighborhood deregulation is quantified via the error incurred in predicting the expression of each gene from its Markov blanket. We evaluate the performance of ssNPA on liver development single-cell RNA-seq data, where the correct cell timing is recovered; and two TCGA datasets, where ssNPA patient clusters have significant survival differences. In all analyses ssNPA consistently outperforms alternative methods, highlighting the advantage of network-based approaches. AVAILABILITY AND IMPLEMENTATION http://www.benoslab.pitt.edu/Software/ssnpa/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
The disengaging brain: Dynamic transitions from cognitive engagement and alcoholism risk

Human functional brain connectivity is usually measured either at "rest" or during cognitive tasks, ignoring life's moments of mental transition. We propose a different approach to understanding brain network transitions. We applied a novel independent component analysis of functional connectivity during motor inhibition (stop signal task) and during the continuous transition to an immediately ensuing rest. A functional network reconfiguration process emerged that: (i) was most prominent in those without familial alcoholism risk, (ii) encompassed brain areas engaged by the task, yet (iii) appeared only transiently after task cessation. The pattern was not present in a pre-task rest scan or in the remaining minutes of post-task rest. Finally, this transient network reconfiguration related to a key behavioral trait of addiction risk: reward delay discounting. These novel findings illustrate how dynamic brain functional reconfiguration during normally unstudied periods of cognitive transition might reflect addiction vulnerability, and potentially other forms of brain dysfunction.
MIPaaL: Mixed Integer Program as a Layer.

Machine learning components commonly appear in larger decision-making pipelines; however, the model training process typically focuses only on a loss that measures average accuracy between predicted values and ground truth values. Decision-focused learning explicitly integrates the downstream decision problem when training the predictive model, in order to optimize the quality of decisions induced by the predictions. It has been successfully applied to several limited combinatorial problem classes, such as those that can be expressed as linear programs (LP), and submodular optimization. However, these previous applications have uniformly focused on problems with simple constraints. Here, we enable decision-focused learning for the broad class of problems that can be encoded as a mixed integer linear program (MIP), hence supporting arbitrary linear constraints over discrete and continuous variables. We show how to differentiate through a MIP by employing a cutting planes solution approach, an algorithm that iteratively tightens the continuous relaxation by adding constraints removing fractional solutions. We evaluate our new end-to-end approach on several real world domains and show that it outperforms the standard two phase approaches that treat prediction and optimization separately, as well as a baseline approach of simply applying decision-focused learning to the LP relaxation of the MIP. Lastly, we demonstrate generalization performance in several transfer learning tasks.
The targeting benefit of conditional cash transfers

Conditional cash transfers (CCTs) are a popular type of social welfare program that make payments to households conditional on human capital investments in children. Compared to unconditional cash transfers (UCTs), CCTs may exclude some low-income households as access is tied to normal investments in children. This paper argues that conditionalities on children's school enrollment offer an unexplored targeting benefit over UCTs: CCTs target money to households that forgo a discrete amount of child income. This paper shows that the size of this targeting benefit is directly related to the distribution of parental incomes, the size of forgone child incomes, and two elasticities already popular in the literature: the income effect of a UCT and the price effect of a CCT. These elasticities are estimated for a large CCT program in rural Mexico, Progresa, using variation in transfers to younger siblings to identify income effects. In this setting, the analysis finds that the targeting benefit is almost as large as the cost of excluding some low-income households; this implies that 41 percent of the Progresa budget should go to a CCT over a UCT based on targeting grounds alone.
Characterization of Directionally Solidified Gray Iron

Even with decades of study, the complex development of solidification microstructures in cast iron is incompletely understood. Because Fe-C eutectic can produce different morphologies, and even different phases, depending on growth velocities and composition, understanding the conditions under which each forms is important. Directional solidification was used to investigate the effects of alloying additions and solidification velocity on graphite spacing in gray iron. Average and minimum spacing for five compositions, containing varied amounts of Si and Mn, and velocities from 0.5 to 5 µm/s are reported. A critical velocity of around 1 µm/s was observed, above which the graphite structure loses directionality and austenite dendrites appear. A semi-automated MATLAB code was developed for quickly and objectively measuring graphite spacing. The automated results compare favorably with traditional manual measurements, and will allow for more robust measurement of eutectic spacing in systems where the spacing is highly irregular.
Iterative methods for solving fourth‐ and sixth‐order time‐fractional Cahn‐Hillard equation

This paper presents analytical-approximate solutions of the time-fractional Cahn-Hilliard (TFCH) equations of fourth and sixth-order using the new iterative method (NIM) and q-homotopy analysis method (q-HAM). We obtained convergent series solutions using these iterative methods. The simplicity and accuracy of these methods in solving strongly nonlinear fractional differential equations is displayed through the examples provided. In the case where exact solution exists, error estimates are also investigated.
On the stability of the solutions of the general problem of three bodies

The following question is investigated: By how much may the initial conditions of a given three-body system be varied before the subsequent evolution of the new system completely differs from that of the original? Stated somewhat differently, how big is the ‘island’ in the phase space of initial conditions throughout which the parameters describing the evolution of the systems are continuous functions of the initial conditions? The extent of one such island is determined numerically and found to be surprisingly large. It is conjectured, however, that this result is due to the fact that the corresponding systems have very short disintegration times, so that the total motion is not very complex.
An MPC Scheme for Traffic Coordination in Open and Irreversible, Zone-Controlled, Guidepath-Based Transport Systems

Zone-controlled, guidepath-based transport system (ZC-GBTS) is a modeling abstraction that has been used extensively for the modeling of the safe interaction of a number of agents that circulate in a constricted medium. The traffic scheduling problem in these transport systems is very hard, and in some recent work of ours, we have proposed a model predictive control (MPC) scheme for simplifying this problem. The detailed implementation of this MPC scheme depends on certain structural and operational properties of the underlying ZC-GBTS. In this article, we detail the aforementioned MPC scheme for a ZC-GBTS subclass that is characterized as “open and irreversible”; the presented results leverage some earlier similar developments of ours for the subclass of “open and reversible” ZC-GBTS.
Repeated partnerships with multiple equilibria and imperfect monitoring: An experimental study

Abstract I investigate finitely repeated partnership games with imperfect monitoring where both mutual effort and mutual shirking are Nash equilibria of the stage game. The treatment variable is the number of repetitions. I find that period 1 effort rates are increasing in the number of repetitions, but subjects use trigger strategies that switch to permanent shirking after enough failed projects so effort rates decrease across time in all treatments. Additionally, the rate of decrease is less when there are more repetitions. Total effort does not vary much across the treatments, because the increased period 1 effort when the number of repetitions is large is mostly canceled out by the erosion of effort as the game progresses.
Nonlinear Effects on Coexistence Phenomenon in Parametric Excitation

We investigate the effect of nonlinearites on a parametrically excited ordinary differential equation whose linearization exhibits the phenomenon of coexistence. The differential equation studied governs the stability of a mode of vibration in an unforced conservative two degree of freedom system used to model thefree vibrations of a thin elastica. Using perturbation methods, we show thatat parameter values corresponding to coexistence, nonlinear terms can cause the origin to become nonlinearly unstable,even though linear stability analysis predicts the origin to be stable.We also investigate the bifurcations associated with this instability.
Computer Systems Have 99 Problems, Let's Not Make Machine Learning Another One

Machine learning techniques are finding many applications in computer systems, including many tasks that require decision making: network optimization, quality of service assurance, and security. We believe machine learning systems are here to stay, and to materialize on their potential we advocate a fresh look at various key issues that need further attention, including security as a requirement and system complexity, and how machine learning systems affect them. We also discuss reproducibility as a key requirement for sustainable machine learning systems, and leads to pursuing it.
Fast Algorithms for Joint Multicast Beamforming and Antenna Selection in Massive MIMO

Massive MIMO is currently a leading physical layer technology candidate that can dramatically enhance throughput in 5G systems, for both unicast and multicast transmission modalities. As antenna elements are becoming smaller and cheaper in the mmW range compared to radio frequency (RF) chains, it is crucial to perform antenna selection at the transmitter, such that the available RF chains are switched to an appropriate subset of antennas. This paper considers the joint problem of multicast beamforming and antenna selection for a single multicast group in massive MIMO systems. The prior state-of-art for this problem relies on semi-definite relaxation (SDR), which cannot scale up to the massive MIMO regime. A successive convex approximation (SCA) based approach is proposed to tackle max-min fair joint multicast beamforming and antenna selection. The key idea of SCA is to successively approximate the non-convex problem by a class of non-smooth, convex optimization problems. Two fast and memory efficient first-order methods are proposed to solve each SCA subproblem. Simulations demonstrate that the proposed algorithms outperform the existing state-of-art approach in terms of solution quality and run time, in both traditional and especially in massive MIMO settings.
Optimal Deceptive and Reference Policies for Supervisory Control

The use of deceptive strategies is important for an agent that attempts not to reveal his intentions in an adversarial environment. We consider a setting in which a supervisor provides a reference policy and expects an agent to follow the reference policy and perform a task. The agent may instead follow a different, deceptive policy to achieve a different task. We model the environment and the behavior of the agent with a Markov decision process, represent the tasks of the agent and the supervisor with linear temporal logic formulae, and study the synthesis of optimal deceptive policies for such agents. We also study the synthesis of optimal reference policies that prevents deceptive strategies of the agent and achieves the supervisor’s task with high probability. We show that the synthesis of deceptive policies has a convex optimization problem formulation, while the synthesis of reference policies requires solving a nonconvex optimization problem.
Algorithmic Social Engineering

We examine the microeconomics of using algorithms to nudge decision-makers toward particular social outcomes. We refer to this as "algorithmic social engineering." In this article, we apply classic strategic communication models to this strategy. Manipulating predictions to express policy preferences strips the predictions of informational content and can lead decision-makers to ignore them. When social problems stem from decision-makers' objectives (rather than their information sets), algorithmic social engineering exhibits clear limitations. Our framework emphasizes separating preferences and predictions in designing algorithmic interventions. This distinction has implications for software architecture, organizational structure, and regulation.
Rulers or Rules? International Law, Elite Cues and Public Opinion

One of the mechanisms by which international law can shape domestic politics is through its effects on public opinion. However, a growing number of national leaders have begun to advocate policies that ignore or even deny international law constraints. This article investigates whether international law messages can still shift public opinion even in the face of countervailing elite cues. It reports results from survey experiments conducted in three countries, the United States, Australia and India, which examined attitudes on a highly salient domestic political issue: restrictions on refugee admissions. In each experimental vignette, respondents were asked about their opinion on a proposed or ongoing restrictive refugee policy that was endorsed by the government but also likely contravened international refugee law. Respondents were randomly exposed to messages highlighting the policy’s illegality and/or the elite endorsement. The results show that, on average, the international law messages had a small but significant persuasive effect in reducing support for the restrictive policy – at most 10 percentage points. Surprisingly, there was no evidence that the countervailing elite endorsement was a significant moderator of this effect. However, in the case of the United States and among Republican co-partisans of the President, the elite endorsement independently increased respondents’ beliefs that the restriction was legal under international law while having no effect on support for the policy. The results suggest that cues from domestic elites do not strictly trump those from international sources and that despite cues about national leaders’ policy advocacy, international law can affect the attitudes of some voters even on an issue as heavily politicized as refugee policy.
Probing behavior of Diaphorina citri (Hemiptera: Liviidae) on Valencia orange influenced by sex, color, and size

Candidatus Liberibacter asiaticus Jagoueix, Bove, and Garnier (Rhizobiales: Rhizobiaceae) is transmitted by the psyllid Diaphorina citri Kuwayama and putatively causes Huanglongbing disease in citrus. Huanglongbing has reduced yields by 68% relative to pre-disease yields in Florida. Disease management is partly through vector control. Understanding vector biology is essential in this endeavor. Our goal was to document differences in probing behavior linked to sex. Based on both a literature review and our results, we conclude that there is either no effect of sex or that identifying such an effect requires a sample size at least four times larger than standard methodologies. Including both color and sex in statistical models did not improve model performance. Both sex and color are correlated with body size, and body size has not been considered in previous studies on sex in D. citri in terms of probing behavior. An effect of body size was found wherein larger psyllids took longer to reach ingestion behaviors and larger individuals spent more time-ingesting phloem, but these relationships explained little of the variability in these data. We suggest that the effects of sex can be ignored when running EPG experiments on healthy psyllids.
Real-Time, Universal, and Robust Adversarial Attacks Against Speaker Recognition Systems

As the popularity of voice user interface (VUI) exploded in recent years, speaker recognition system has emerged as an important medium of identifying a speaker in many security-required applications and services. In this paper, we propose the first real-time, universal, and robust adversarial attack against the state-of-the-art deep neural network (DNN) based speaker recognition system. Through adding an audio-agnostic universal perturbation on arbitrary enrolled speaker’s voice input, the DNN-based speaker recognition system would identify the speaker as any target (i.e., adversary-desired) speaker label. In addition, we improve the robustness of our attack by modeling the sound distortions caused by the physical over-the-air propagation through estimating room impulse response (RIR). Experiment using a public dataset of 109 English speakers demonstrates the effectiveness and robustness of our proposed attack with a high attack success rate of over 90%. The attack launching time also achieves a 100× speedup over contemporary non-universal attacks.
News that takes your breath away: risk perceptions during an outbreak of vaping-related lung injuries

We study the impact of new information on people’s perceptions of the risks of e-cigarettes. In September 2019 the U.S. experienced an outbreak of e-cigarette, or vaping, associated lung injuries (EVALI). The EVALI outbreak created an information shock, which was followed by additional new information in a later CDC recommendation. We use data on consumer risk perceptions from two sets of surveys conducted before (HINTS survey data) and during the EVALI outbreak (Google Survey data). The empirical model examines changes in risk perceptions during the early crisis period when the CDC was warning consumers that they should avoid all vaping products and during a later period when the message was refined and focused on a narrower set of illegal vaping products that contain THC (the main psychoactive compound in marijuana). Econometric results suggest that the immediate impact of the first information shock was to significantly increase the fraction of respondents who perceived e-cigarettes as more harmful than smoking. As the outbreak subsided and the CDC recommendation changed to emphasize the role of THC e-cigarette products, e-cigarette risk perceptions were only partially revised downwards. Individuals who had higher risk perceptions showed a weaker response to the first information shock but were more likely to later revise their risk perceptions downwards. We conclude the paper by discussing the public policy issues that stem from having risk perceptions of e-cigarettes relative to combustible cigarettes remain at these elevated levels where a substantial portion of consumers believe that e-cigarettes are more harmful than cigarettes.
Analyzing process flexibility: A distribution-free approach with partial expectations

Abstract We develop a distribution-free model to evaluate the performance of process flexibility structures when only the mean and partial expectation of the demand are known. We characterize the worst-case demand distribution under general concave objective functions, and apply it to derive tight lower bounds for the performance of chaining structures under the balanced systems (systems with the same number of plants and products). We also derive a simple lower bound for chaining-like structures under unbalanced systems with different plant capacities.
Degradation of the Repetitive Genomic Landscape in a Close Relative of Caenorhabditis elegans.

The abundance, diversity, and genomic distribution of repetitive elements is highly variable among species. These patterns are thought to be driven in part by reproductive mode and the interaction of selection and recombination, and recombination rates typically vary by chromosomal position. In the nematode Caenorhabditis elegans, repetitive elements are enriched at chromosome arms and depleted on centers, and this mirrors the chromosomal distributions of other genomic features such as recombination rate. How conserved is this genomic landscape of repeats, and what evolutionary forces maintain it? To address this, we compared the genomic organization of repetitive elements across five Caenorhabditis species with chromosome-level assemblies. As previously reported, repeat content is enriched on chromosome arms in most Caenorhabditis species, and no obvious patterns of repeat content associated with reproductive mode were observed. However, the fig-associated C. inopinata has experienced repetitive element expansion and reveals no association of global repeat density with chromosome position. Patterns of repeat superfamily specific distributions reveal this global pattern is driven largely by a few repeat superfamilies that in C. inopinata have expanded in number and have weak associations with chromosome position. Additionally, 15% of predicted protein-coding genes in C. inopinata align to transposon-related proteins. When these are excluded, C. inopinata has no enrichment of genes in chromosome centers, in contrast to its close relatives who all have such clusters. Forward evolutionary simulations reveal that chromosomal heterogeneity in recombination rate alone can generate structured repetitive genomic landscapes when insertions are weakly deleterious, whereas chromosomal heterogeneity in the fitness effects of transposon insertion can promote such landscapes across a variety of evolutionary scenarios. Thus, patterns of gene density along chromosomes likely contribute to global repetitive landscapes in this group, although other historical or genomic factors are needed to explain the idiosyncrasy of genomic organization of various transposable element taxa within C. inopinata. Taken together, these results highlight the power of comparative genomics and evolutionary simulations in testing hypotheses regarding the causes of genome organization.
Joint estimation of sequential labor force participation and fertility decisions using Markov chain Monte Carlo techniques

In this paper we estimate the causal effect of children on the labor supply of women using panel data on women from the 1979 National Longitudinal Survey of Youth (NLSY79). We examine the effect of children both prior to and after birth as well as how the effect of children varies with the number of children. We also decompose the total effect of children into the direct and indirect components and separately examine the dynamics of these components. Sequential participation decisions for four levels of labor market involvement and fertility decisions are jointly modeled. We allow decisions to be correlated in a general fashion both across time and across choices. The estimation is performed using Markov chain Monte Carlo methods. We find that children have a strong effect on a women’s labor market behavior in the post-birth period and that differences in expected fertility have a strong effect on labor market behavior in the pre-birth period. We also find that both the direct and indirect effects are large immediately after the birth of a child but that the indirect effect declines quickly over time. The effects of children vary by education and race.
Activity of Epigenetic Inhibitors against Plasmodium falciparum Asexual and Sexual Blood Stages.

Earlier genetic and inhibitor studies showed that epigenetic regulation of gene expression is critical for malaria parasite survival in multiple life stages and a promising target for new antimalarials. We therefore evaluated the activity of 350 diverse epigenetic inhibitors against multiple stages of Plasmodium falciparum We observed ≥90% inhibition at 10 μM for 28% of compounds against asexual blood stages and early gametocytes, of which a third retained ≥90% inhibition at 1 μM.
Group-Twisted Alexander-Whitney and Eilenberg-Zilber Maps

We define group-twisted Alexander-Whitney and Eilenberg-Zilber maps for converting between bimodule resolutions of skew group algebras. These algebras are the natural semidirect products recording actions of finite groups by automorphisms. The group-twisted chain maps allow us to transfer information between resolutions for use in homology theories, for example, in those governing deformation theory. We show how to translate in particular from the default (but often cumbersome) reduced bar resolution to a more convenient twisted product resolution. This provides a more universal approach to some known results classifying PBW deformations.
Anions Enhance Rare Earth Adsorption at Negatively Charged Surfaces

Anions are expected to be repelled from negatively charged surfaces. At aqueous interfaces, however, ion-specific effects can dominate over direct electrostatic interactions. Using multiple in situ surface sensitive experimental techniques, we show that surface affinities of SCN- anions are so strong that they can adsorb at a negatively charged floating monolayer at the air-aqueous interface. This extreme example of ion-specific effects may be very important for understanding complex processes at aqueous interfaces, such as chemical separations of rare earth metals. Adsorbed SCN- ions at the floating monolayer increase the overall negative charge density, leading to enhanced trivalent rare earth adsorption. Surface sensitive X-ray fluorescence measurements show that the surface coverage of Lu3+ ions can be triple the apparent surface charge of the floating monolayer in the presence of SCN-. Comparison to NO3- samples shows that the effects are strongly dependent on the character of the anion, providing further evidence of ion-specific effects dominating over electrostatics.
The Expressive Power of a Class of Normalizing Flow Models

Normalizing flows have received a great deal of recent attention as they allow flexible generative modeling as well as easy likelihood computation. While a wide variety of flow models have been proposed, there is little formal understanding of the representation power of these models. In this work, we study some basic normalizing flows and rigorously establish bounds on their expressive power. Our results indicate that while these flows are highly expressive in one dimension, in higher dimensions their representation power may be limited, especially when the flows have moderate depth.
Halloysite and Gibbsite Formation in Hawaii1

Desilication and removal of bases from the basalitic rocks of the Hawaiian Islands produces large amounts of halloysite and gibbsite, together with variable quantities of allophane and nontronite, iron and titanium oxides, and amorphous mineral and gel material. The nature and relative abundance of intermediate and end products of weathering depend primarily upon the amount of rainfall, angle of slope, and texture of the rock. The formation of clay minerals is the common step in the transformation of primary silicates to oxides and hydroxides, but the amount and relative importance of the clay “stage” depend on the intensity of the weathering process. Halloysite forms from plagioclase usually by alteration of first the core and subsequently the rim of the feldspar laths. No kaolinite was observed, its absence being attributed to the absence of fine-grained mica as an intermediate weathering product. Gibbsite is produced by (1) removal of silica from halloysite, (2) dehydration of Al-gel, and (3) precipitation from solution. Although it is possible that the mineral may form directly from feldspar, halloysite is the common crystalline intermediate on both the megascopie and microscopic scales. An amorphous transition state, probably ranging in composition from allophane to Al-gel, exists as part of the change from halloysite to gibbsite as evidenced by electron microscope and diffraction work on pseudomorphs after halloysite tubes found in certain samples studied in more detail than others. Volcanic glass is the apparent source of most Al-Fe-Si gel material which upon dehydration becomes allophane, cliachite, or gibbsite depending on the kind and relative proportion of the cations present.
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout

Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find optimal sampling policies through solving a dynamic programming (DP) formulation that maximizes a long-term reward over a rolling horizon. Though promising, lookahead BO faces the risk of error propagation through its increased dependence on a possibly mis-specified model. In this work we focus on the rollout approximation for solving the intractable DP. We first prove the improving nature of rollout in tackling lookahead BO and provide a sufficient condition for the used heuristic to be rollout improving. We then provide both a theoretical and practical guideline to decide on the rolling horizon stagewise. This guideline is built on quantifying the negative effect of a mis-specified model. To illustrate our idea, we provide case studies on both single and multi-information source BO. Empirical results show the advantageous properties of our method over several myopic and non-myopic BO algorithms.
Clinical Features of Critical Coronavirus Disease 2019 in Children.

Objectives We sought to describe the presentation, course, and outcomes of hospitalized pediatric coronavirus disease 2019 patients, with detailed description of those requiring mechanical ventilation, and comparisons between critically ill and noncritical hospitalized pediatric patients. Design Observational cohort study. Setting Riley Hospital for Children at Indiana University Health in Indianapolis in the early weeks of the coronavirus disease 2019 pandemic. Patients All hospitalized pediatric patients with confirmed coronavirus disease 2019 as of May 4, 2020, were included. Interventions Patients received therapies including hydroxychloroquine, remdesivir, tocilizumab, and convalescent serum and were managed according to an institutional algorithm based on evidence available at the time of presentation. Measurements and main results Of 407 children tested for severe acute respiratory syndrome-coronavirus 2 at our hospital, 24 were positive, and 19 required hospitalization. Seven (36.8%) were critically ill in ICU, and four (21%) required mechanical ventilation. Hospitalized children were predominantly male (14, 74%) and African-American or Hispanic (14, 74%), with a bimodal distribution of ages among young children less than or equal to 2 years old (8, 42%) and older adolescents ages 15-18 (6, 32%). Five of seven (71.4%) of critically ill patients were African-American (n = 3) or Hispanic (n = 2). Critical illness was associated with older age (p = 0.017), longer duration of symptoms (p = 0.036), and lower oxygen saturation on presentation (p = 0.016); with more thrombocytopenia (p = 0.015); higher C-reactive protein (p = 0.031); and lower WBC count (p = 0.039). Duration of mechanical ventilation averaged 14.1 days. One patient died. Conclusions Severe, protracted coronavirus disease 2019 is seen in pediatric patients, including those without significant comorbidities. We observed a greater proportion of hospitalized children requiring mechanical ventilation than has been reported to date. Older children, African-American or Hispanic children, and males may be at risk for severe coronavirus disease 2019 requiring hospitalization. Hypoxia, thrombocytopenia, and elevated C-reactive protein may be useful markers of critical illness. Data regarding optimal management and therapies for pediatric coronavirus disease 2019 are urgently needed.
Recurrent and Spiking Modeling of Sparse Surgical Kinematics

Robot-assisted minimally invasive surgery is improving surgeon performance and patient outcomes. This innovation is also turning what has been a subjective practice into motion sequences that can be precisely measured. A growing number of studies have used machine learning to analyze video and kinematic data captured from surgical robots. In these studies, models are typically trained on benchmark datasets for representative surgical tasks to assess surgeon skill levels. While they have shown that novices and experts can be accurately classified, it is not clear whether machine learning can separate highly proficient surgeons from one another, especially without video data. In this study, we explore the possibility of using only kinematic data to predict surgeons of similar skill levels. We focus on a new dataset created from surgical exercises on a simulation device for skill training. A simple, efficient encoding scheme was devised to encode kinematic sequences so that they were amenable to edge learning. We report that it is possible to identify surgical fellows receiving near perfect scores in the simulation exercises based on their motion characteristics alone. Further, our model could be converted to a spiking neural network to train and infer on the Nengo simulation framework with no loss in accuracy. Overall, this study suggests that building neuromorphic models from sparse motion features may be a potentially useful strategy for identifying surgeons and gestures with chips deployed on robotic systems to offer adaptive assistance during surgery and training with additional latency and privacy benefits.
Deep Quantized Representation For Enhanced Reconstruction

While machine learning approaches have shown remarkable performance in biomedical image analysis, most of these methods rely on high-quality and accurate imaging data. However, collecting such data requires intensive and careful manual effort. One of the major challenges in imaging the Shoot Apical Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the z-stack suffer from different perpetual quality related problems like poor contrast and blurring. These quality related issues often lead to disposal of the painstakingly collected data with little to no control on quality while collecting the data. Therefore, it becomes necessary to employ and design techniques that can enhance the images to make it more suitable for further analysis. In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana. Our proposed framework utilizes multiple consecutive slices in the z-stack to learn a low dimensional latent space, quantize it and subsequently perform reconstruction using the quantized representation to obtain sharper images. Experiments on a publicly available dataset validate our methodology showing promising results. Our code is available at github.com/agupt013/enhancedRec.git.
Ecological Economics Beyond Markets.

Abstract Non-market practices and institutions make up much of every economy. Even in today's most developed capitalist societies, people produce things that are not for sale and allocate them through sharing, gifts, and redistribution rather than buying and selling. This article is about why and how ecological economists should study these non-market economies. Historically, markets only emerge when states forcibly create them; community members do not tend to spontaneously start selling each other goods and services. Markets work well for coordinating complex industrial webs to satisfy individual tastes, but they are not appropriate for governing the production or distribution of entities that are non-rival, non-excludable, not produced for sale, essential need satisfiers, or culturally important. Moreover, we argue, markets do not serve justice, sustainability, efficiency, or value pluralism, the foundations of ecological economics. We sketch an agenda for research on economic practices and institutions without markets by posing nine broad questions about non-market food systems and exploring the evidence and theory around each. By ignoring and demeaning non-market economies, researchers contribute to creating markets' dominance over social life. Observing, analyzing, theorizing, supporting, promoting, creating, and envisioning non-market economies challenges market hegemony.