cssi/SciSciGPT-SciSciNet huggingface dataset:
Acoustic cavitation rheometry.

Characterization of soft materials is challenging due to their high compliance and the strain-rate dependence of their mechanical properties. The inertial microcavitation-based high strain-rate rheometry (IMR) method [Estrada et al., J. Mech. Phys. Solids, 2018, 112, 291–317] combines laser-induced cavitation measurements with a model for the bubble dynamics to measure local properties of polyacrylamide hydrogel under high strain-rates from 103 to 108 s−1. While promising, laser-induced cavitation involves plasma formation and optical breakdown during nucleation, a process that could alter local material properties before measurements are obtained. In the present study, we extend the IMR method to another means to generate cavitation, namely high-amplitude focused ultrasound, and apply the resulting acoustic-cavitation-based IMR to characterize the mechanical properties of agarose hydrogels. Material properties including viscosity, elastic constants, and a stress-free bubble radius are inferred from bubble radius histories in 0.3% and 1% agarose gels. An ensemble-based data assimilation is used to further help interpret the obtained estimates. The resulting parameter distributions are consistent with available measurements of agarose gel properties and with expected trends related to gel concentration and high strain-rate loading. Our findings demonstrate the utility of applying IMR and data assimilation methods with single-bubble acoustic cavitation data for measurement of viscoelastic properties.
Glucan Unmasking Identifies Regulators of Temperature-Induced Translatome Reprogramming in C. neoformans.

ABSTRACT The cell walls of fungi are critical for cellular structure and rigidity but also serve as a major communicator to alert the cell to the changing environment. In response to stresses encountered in human hosts, pathogenic fungi remodel their cell walls. Masking the β-1,3-glucan component of the cell wall is critical to escape detection by innate immune cells. We previously demonstrated that β-1,3-glucan is unmasked in response to host temperature stress when translatome reprogramming is defective in Cryptococcus neoformans. Here, we used β-1,3-glucan unmasking as an output to identify signaling modules involved both in masking and in translatome reprogramming in response to host temperature stress. We reveal that the high-osmolarity glycerol (HOG) mitogen-activated protein kinase (MAPK) pathway is involved in translatome reprogramming and that mutants in this pathway display moderate unmasking when grown at 37°C. Additionally, we show that mutants of the cell wall integrity (CWI)/Mpk1 MAPK pathway extensively unmask β-1,3-glucan. While the CWI pathway does not impact translatome reprogramming, our data suggest that it may play a role in the posttranslational regulation of transcription factors that govern masking. IMPORTANCECryptococcus neoformans is a fungal pathogen that causes devastating morbidity and mortality in immunocompromised individuals. It possesses several virulence factors that aid in its evasion from the host immune system, including a large polysaccharide capsule that cloaks the antigenic cell wall. Studies investigating how the cell wall is remodeled to keep this pathogen disguised in response to stress have been limited. We previously found that host temperature stress results in translatome reprogramming that is necessary for keeping the highly antigenic β-(1, 3)-glucan component masked. Our data reveal signaling modules that trigger these responses and suggest the points of regulation at which these pathways act in achieving masking. Understanding these mechanisms may allow for therapeutic manipulation that may promote the immune recognition and clearance of this fungal pathogen.
On the non-hypercyclicity of scalar type spectral operators and collections of their exponentials

Generalizing the case of a normal operator in a complex Hilbert space, we give a straightforward proof of the non-hypercyclicity of a (bounded or unbounded) scalar type spectral operator $A$ in a complex Banach space as well as of the collection $\left\{e^{tA}\right\}_{t\ge 0}$ of the exponentials of such an operator, which, under a certain condition on the spectrum of the operator $A$, coincides with the $C_0$-semigroup generated by $A$. The spectrum of $A$ lying on the imaginary axis, we also show that non-hypercyclic is the strongly continuous group $\left\{e^{tA}\right\}_{t\in {\mathbb R}}$ of bounded linear operators generated by $A$. From the general results, we infer that, in the complex Hilbert space $L_2({\mathbb R})$, the anti-self-adjoint differentiation operator $A:=\dfrac{d}{dx}$ with the domain $D(A):=W_2^1({\mathbb R})$ is non-hypercyclic and so is the left-translation strongly continuous unitary operator group generated by $A$.
Formal Hierarchies and Informal Networks: How Organizational Structure Shapes Information Search in Local Government

Attention to informal communication networks within public organizations has grown in recent decades. While research has documented the role of individual cognition and social structure in understanding information search in organizations, this article emphasizes the importance of formal hierarchy. We argue that the structural attributes of bureaucracies are too important to be neglected when modeling knowledge flows in public organizations. Empirically, we examine interpersonal information seeking patterns among 143 employees in a small city government, using exponential random graph modeling (ERGM). The results suggest that formal structure strongly shapes information search patterns while accounting for social network variables and individual level perceptions. We find that formal status, permission pathways, and departmental membership all affect the information search of employees. Understanding the effects of organizational structure on information search networks will offer opportunities to improve information flows in public organizations via design choices.
OASIS: Optimal Analysis-Specific Importance Sampling for event generation

We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high utility to the experimental analyses. OASIS leads to a conservation of resources at all stages of the Monte Carlo pipeline, including full-detector simulation, and is complementary to approaches which seek to speed-up the simulation pipeline.
Human-Guided Robot Behavior Learning: A GAN-Assisted Preference-Based Reinforcement Learning Approach

Human demonstrations can provide trustful samples to train reinforcement learning algorithms for robots to learn complex behaviors in real-world environments. However, obtaining sufficient demonstrations may be impractical because many behaviors are difficult for humans to demonstrate. A more practical approach is to replace human demonstrations by human queries, i.e., preference-based reinforcement learning. One key limitation of the existing algorithms is the need for a significant amount of human queries because a large number of labeled data is needed to train neural networks for the approximation of a continuous, high-dimensional reward function. To reduce and minimize the need for human queries, we propose a new GAN-assisted human preference-based reinforcement learning approach that uses a generative adversarial network (GAN) to learn human preferences and then replace the role of human in assigning preferences. The adversarial neural network is simple and only has a binary output, hence requiring much less human queries to train. Moreover, a maximum entropy based reinforcement learning algorithm is designed to shape the loss towards the desired regions or away from the undesired regions. To show the effectiveness of the proposed approach, we present some studies on complex robotic tasks without access to the environment reward in a typical MuJoCo robot locomotion environment. The obtained results show our method can achieve a reduction of about 99.8% human time without performance sacrifice.
Identification of fibronectin 1 as a candidate genetic modifier in a Col4a1 mutant mouse model of Gould syndrome

Collagen type IV alpha 1 and alpha 2 (COL4A1 and COL4A2) are major components of almost all basement membranes. COL4A1 and COL4A2 mutations cause a multisystem disorder which can affect any organ but typically involves the cerebral vasculature, eyes, kidneys and skeletal muscles. In recent years, patient advocacy and family support groups have united under the name of Gould syndrome. The manifestations of Gould syndrome are highly variable and animal studies suggest that allelic heterogeneity and genetic context contribute to the clinical variability. We previously characterized a mouse model of Gould syndrome caused by a Col4a1 mutation in which the severities of ocular anterior segment dysgenesis (ASD), myopathy, and intracerebral hemorrhage (ICH) were dependent on genetic background. Here, we performed a genetic modifier screen to provide insight into the mechanisms contributing to Gould syndrome pathogenesis and identified a single locus (modifier of Gould syndrome 1; MoGS1) on Chromosome 1 that suppressed ASD. A separate screen showed that the same locus ameliorated myopathy. Interestingly, MoGS1 had no effect on ICH, suggesting that this phenotype may be mechanistically distinct. We refined the MoGS1 locus to a 4.3 Mb interval containing 18 protein coding genes, including Fn1 which encodes the extracellular matrix component fibronectin 1. Molecular analysis showed that the MoGS1 locus increased Fn1 expression raising the possibility that suppression is achieved through a compensatory extracellular mechanism. Furthermore, we show evidence of increased integrin linked kinase levels and focal adhesion kinase phosphorylation in Col4a1 mutant mice that is partially restored by the MoGS1 locus implicating the involvement of integrin signaling. Taken together, our results suggest that tissue-specific mechanistic heterogeneity contributes to the variable expressivity of Gould syndrome and that perturbations in integrin signaling may play a role in ocular and muscular manifestations.
The McCoy property in Ohm–Rush algebras

An Ohm–Rush algebra $$R \rightarrow S$$ is called McCoy if for any zero-divisor f in S, its content c(f) has nonzero annihilator in R, because McCoy proved this when $$S=R[x]$$ . We answer a question of Nasehpour by giving a class of examples of faithfully flat Ohm–Rush algebras with the McCoy property that are not weak content algebras. However, we show that a faithfully flat Ohm–Rush algebra is a weak content algebra iff $$R/I \rightarrow S/I S$$ is McCoy for all radical (resp. prime) ideals I of R. When R is Noetherian (or has the more general fidel (A) property), we show that it is equivalent that $$R/I \rightarrow S/IS$$ is McCoy for all ideals.
Road Damage Detection and Classification with Detectron2 and Faster R-CNN

The road is vital for many aspects of life, and road maintenance is crucial for human safety. One of the critical tasks to allow timely repair of road damages is to quickly and efficiently detect and classify them. This work details the strategies and experiments evaluated for these tasks. Specifically, we evaluate Detectron2’s implementation of Faster R-CNN using different base models and configurations. We also experiment with these approaches using the Global Road Damage Detection Challenge 2020, A Track in the IEEE Big Data 2020 Big Data Cup Challenge dataset. The results show that the X101-FPN base model for Faster R-CNN with Detectron2’s default configurations is efficient and general enough to be transferable to different countries in this challenge. This approach results in F1 scores of 51.0% and 51.4% for the test1 and test2 sets of the challenge, respectively. Though the visualizations show good prediction results, the F1 scores are low. Therefore, we also evaluate the prediction results against the existing annotations and discover some discrepancies. Thus, we also suggest strategies to improve the labeling process for this dataset.
Dremel: interactive analysis of web-scale datasets

Dremel is a scalable, interactive ad hoc query system for analysis of read-only nested data. By combining multilevel execution trees and columnar data layout, it is capable of running aggregation queries over trillion-row tables in seconds. The system scales to thousands of CPUs and petabytes of data, and has thousands of users at Google. In this paper, we describe the architecture and implementation of Dremel, and explain how it complements MapReduce-based computing. We present a novel columnar storage representation for nested records and discuss experiments on few-thousand node instances of the system.
Hadron Colliders and Hadron Collider Physics Symposium

This article summarizes main developments of the hadron colliders and physics results obtained since their inception around forty years ago. The increase in the collision energy of over two orders of magnitude and even larger increases in luminosity provided experiments with unique data samples. Developments of full acceptance detectors, particle identification and analysis methods provided fundamental discoveries and ultra-precise measurements which culminated in the completion and in depth verification of the Standard Model. Hadron Collider Physics symposium provided opportunities for those working at hadron colliders to share results of their research since 1979 and helped greatly to develop the field of particle physics.
Falling through the cracks: Modeling the formation of social category boundaries

Social categorizations divide people into "us" and "them", often along continuous attributes such as political ideology or skin color. This division results in both positive consequences, such as a sense of community, and negative ones, such as group conflict. Further, individuals in the middle of the spectrum can fall through the cracks of this categorization process and are seen as out-group by individuals on either side of the spectrum, becoming inbetweeners. Here, we propose a quantitative, dynamical-system model that studies the joint influence of cognitive and social processes. We model where two social groups draw the boundaries between "us" and 'them" on a continuous attribute. Our model predicts that both groups tend to draw a more restrictive boundary than the middle of the spectrum. As a result, each group sees the individuals in the middle of the attribute space as an out-group. We test this prediction using U.S. political survey data on how political independents are perceived by registered party members as well as existing experiments on the perception of racially ambiguous faces, and find support.
Experimental Validation of Eco-Driving and Eco-Heating Strategies for Connected and Automated HEVs

This paper presents experimental results that validate eco-driving and eco-heating strategies developed for connected and automated vehicles (CAVs). By exploiting vehicle-to-infrastructure (V2I) communications, traffic signal timing, and queue length estimations, optimized and smoothed speed profiles for the ego-vehicle are generated to reduce energy consumption. Next, the planned eco-trajectories are incorporated into a real-time predictive optimization framework that coordinates the cabin thermal load (in cold weather) with the speed preview, i.e., eco-heating. To enable eco-heating, the engine coolant (as the only heat source for cabin heating) and the cabin air are leveraged as two thermal energy storages. Our eco-heating strategy stores thermal energy in the engine coolant and cabin air while the vehicle is driving at high speeds, and releases the stored energy slowly during the vehicle stops for cabin heating without forcing the engine to idle to provide the heating source. To test and validate these solutions, a power-split hybrid electric vehicle (HEV) has been instrumented for cabin thermal management, allowing to regulate heating, ventilation, and air conditioning (HVAC) system inputs (cabin temperature setpoint and blower flow rate) in real-time. Experiments were conducted to demonstrate the energy-saving benefits of eco-driving and eco-heating strategies over real-world city driving cycles at different cold ambient temperatures. The data confirmed average fuel savings of 14.5% and 4.7% achieved by eco-driving and eco-heating, respectively, offering a combined energy saving of more than 19% when comparing to the baseline vehicle driven by a human driver with a constant-heating strategy.
Representations for Question Answering from Documents with Tables and Text

Tables in web documents are pervasive and can be directly used to answer many of the queries searched on the web, motivating their integration in question answering. Very often information presented in tables is succinct and hard to interpret with standard language representations. On the other hand, tables often appear within textual context, such as an article describing the table. Using the information from an article as additional context can potentially enrich table representations. In this work we aim to improve question answering from tables by refining table representations based on information from surrounding text. We also present an effective method to combine text and table-based predictions for question answering from full documents, obtaining significant improvements on the Natural Questions dataset (Kwiatkowski et al., 2019).
Equilibria Under Negligence Liability: How the Standard Claims Fall Apart

In an important and seminal contribution Shavell (1980) extended the scope of economic analysis of liability rules by providing a model that allows for the care and activity level choices by the parties. Subsequent works have extended the model to predict outcomes under various liability rules and also to compare their efficiency properties. In this paper, we dispute the mainstream claims about the existence and efficiency of equilibria under liability rules. Contrary to the claims in much of the existing literature, we use the standard model to show that none of the standard negligence based liability rules induces an equilibrium. Moreover, the rules of strict liability for injurer, and no-liability for injurer, which place all losses on just one party, are significantly more efficient than the rules that require sharing of accident loss between the parties as well as the standard negligence rules. Finally, we show that the standard models are inherently flawed. The social optimization problems induced by these models generally do not have a solution, or may have multiple solutions none of which is discoverable by the first order conditions.
Flexibility Management for Space Logistics via Decision Rules

This paper develops a flexibility management framework for space logistics mission planning under uncertainty through decision rules and multistage stochastic programming. It aims to add built-in f...
When Will Generative Adversarial Imitation Learning Algorithms Attain Global Convergence

Generative adversarial imitation learning (GAIL) is a popular inverse reinforcement learning approach for jointly optimizing policy and reward from expert trajectories. A primary question about GAIL is whether applying a certain policy gradient algorithm to GAIL attains a global minimizer (i.e., yields the expert policy), for which existing understanding is very limited. Such global convergence has been shown only for the linear (or linear-type) MDP and linear (or linearizable) reward. In this paper, we study GAIL under general MDP and for nonlinear reward function classes (as long as the objective function is strongly concave with respect to the reward parameter). We characterize the global convergence with a sublinear rate for a broad range of commonly used policy gradient algorithms, all of which are implemented in an alternating manner with stochastic gradient ascent for reward update, including projected policy gradient (PPG)-GAIL, Frank-Wolfe policy gradient (FWPG)-GAIL, trust region policy optimization (TRPO)-GAIL and natural policy gradient (NPG)-GAIL. This is the first systematic theoretical study of GAIL for global convergence.
Visualization of trigeminal ganglion sensory neuronal signaling regulated by Cdk5

The mechanisms underlying facial and oral pain are still incompletely understood, posing major therapeutic challenges. Cyclin-dependent kinase 5 (Cdk5) is a key neuronal kinase involved in pain signaling. However, the regulatory roles of Cdk5 in orofacial pain signaling and the possibility of therapeutic intervention at the level of mouse trigeminal ganglion primary neurons remain elusive. In this study, we used optimized intravital imaging to directly compare trigeminal neuronal activities after mechanical, thermal, and chemical stimulation. We then tested whether facial inflammatory pain in mice could be alleviated by the Cdk5 inhibitor peptide TFP5. We demonstrated regulation of total Ca 2+ intensities by Cdk5 activity using transgenic and knockout mouse models. In mice with orofacial inflammation, application of TFP5 specifically decreased total Ca 2+ intensities in response to noxious stimuli. It also alleviated inflammation-induced allodynia by inhibiting activation of trigeminal peripheral sensory neurons. Cdk5 inhibitors may provide promising non-opioid candidates for pain treatment.
Efficient generation of turbulent collisionless shocks in laser-ablated counter-streaming plasmas

The authors study the formation of high-Mach number collisionless shocks in the interaction of laser-driven plasma flows.
Spectral edge in sparse random graphs: Upper and lower tail large deviations

In this paper, we consider the problem of estimating the joint upper and lower tail large deviations of the edge eigenvalues of an Erdős–Renyi random graph Gn,p, in the regime of p where the edge of the spectrum is no longer governed by global observables, such as the number of edges, but rather by localized statistics, such as high degree vertices. Going beyond the recent developments in mean-field approximations of related problems, this paper provides a comprehensive treatment of the large deviations of the spectral edge in this entire regime, which notably includes the well-studied case of constant average degree. In particular, for r≥1 fixed, we pin down the asymptotic probability that the top r eigenvalues are jointly greater/less than their typical values by multiplicative factors bigger/smaller than 1, in the regime mentioned above. The proof for the upper tail relies on a novel structure theorem, obtained by building on estimates in (Combin. Probab. Comput. 12 (2003) 61–72), followed by an iterative cycle removal process, which shows, conditional on the upper tail large deviation event, with high probability the graph admits a decomposition in to a disjoint union of stars and a spectrally negligible part. On the other hand, the key ingredient in the proof of the lower tail is a Ramsey-type result which shows that if the Kth largest degree of a graph is not atypically small (for some large K depending on r), then either the top eigenvalue or the rth largest eigenvalue is larger than that allowed by the lower tail event on the top r eigenvalues, thus forcing a contradiction. The above arguments reduce the problems to developing a large deviation theory for the extremal degrees which could be of independent interest.
VDPred: Predicting Voltage Droop for Power-Effient 3D Multi-core Processor Design

Modern applications present rapid, time-varying demands to the power supply systems. Designing for worst-case voltage droop leads to inefficient operations. These inefficiencies are amplified in 3D multi-core processors. Minimizing this voltage variation is key to improving power efficiency. One widely accepted method is to apply droop compensation into the voltage regulators. However, as the design complexity rises, the response time of the droop compensation fails to keep pace with the speed of load current switching in the modern processors. In this paper, we propose VDPred, a droop prediction system integrated with a reactive control loop inside a typical on-chip buck regulator of the 3D processor. VDPred predicts and eliminates voltage droops that produce worst-case operating voltage margins. It constructs an offline learning model for power prediction based on the pipeline and cache microarchitectural events. When VDPred predicts a hazardous droop, the circuitry warms up the regulator and pulls up the supply voltage against a voltage loss before the droop happens. VDPred can deal with very fast droop events, on the order of nanoseconds, and thus the voltage guard-band and power consumption of the processor can be further reduced compared to existing state-of-the-art techniques. We evaluate VDPred in both single- and four-core configurations of a 3D processor with the cycle-accurate full-system simulator. Results show a significant reduction in voltage guard-band and up to 7% runtime power of the overall system.
Lyapunov Exponents of the Half-Line SHE

We consider the half-line stochastic heat equation (SHE) with Robin boundary parameter $$A = -\frac{1}{2}$$ . Under narrow wedge initial condition, we compute every positive (including non-integer) Lyapunov exponents of the half-line SHE. As a consequence, we prove a large deviation principle for the upper tail of the half-line KPZ equation under Neumann boundary parameter $$A = -\frac{1}{2}$$ with rate function $$\Phi _+^{\text {hf}} (s) = \frac{2}{3} s^{\frac{3}{2}}$$ . This confirms the prediction of [44, 52] for the upper tail exponent of the half-line KPZ equation.
Rapidly emerging SARS-CoV-2 B.1.1.7 sub-lineage in the United States of America with spike protein D178H and membrane protein V70L mutations.

The SARS-CoV-2 B.1.1.7 lineage is highly infectious and as of April 2021 accounted for 92% of COVID-19 cases in Europe and 59% of COVID-19 cases in the U.S. It is defined by the N501Y mutation in t...
Effect of canard position on the longitudinal aerodynamic characteristics of a close-coupled canard-wing-body configuration

The thin-layer Navier-Stokes equations are solved numerically to investigate the effects of canard vertical position on a close-coupled canard-wing-body configuration at a transonic Mach number of 0.90 and angles of attack ranging from -2 to 12 degrees. Canard-wing interactions are investigated for high-, mid- and low-canard positions. The computational results show favorable canard-wing interactions for the high- and mid-canard configurations. The unfavorable lift and drag characteristics for the low-canard configuration are examined by analyses of the low-canard flowfield structure.
Metal-Assisted Catalytic Etching (MACE) for Nanofabrication of Semiconductor Powders

Electroless etching of semiconductors has been elevated to an advanced micromachining process by the addition of a structured metal catalyst. Patterning of the catalyst by lithographic techniques facilitated the patterning of crystalline and polycrystalline wafer substrates. Galvanic deposition of metals on semiconductors has a natural tendency to produce nanoparticles rather than flat uniform films. This characteristic makes possible the etching of wafers and particles with arbitrary shape and size. While it has been widely recognized that spontaneous deposition of metal nanoparticles can be used in connection with etching to porosify wafers, it is also possible to produced nanostructured powders. Metal-assisted catalytic etching (MACE) can be controlled to produce (1) etch track pores with shapes and sizes closely related to the shape and size of the metal nanoparticle, (2) hierarchically porosified substrates exhibiting combinations of large etch track pores and mesopores, and (3) nanowires with either solid or mesoporous cores. This review discussed the mechanisms of porosification, processing advances, and the properties of the etch product with special emphasis on the etching of silicon powders.
Distinguishing open symplectic mapping tori via their wrapped Fukaya categories

We present partial results towards a classification of symplectic mapping tori using dynamical properties of wrapped Fukaya categories. More precisely, we construct a symplectic manifold Tϕ associated to a Weinstein domain M, and an exact, compactly supported symplectomorphism ϕ. The symplectic manifold Tϕ is another Weinstein domain and its contact boundary is independent of ϕ. We distinguish Tϕ from T1M, under certain assumptions (Theorem 1.1). As an application, we obtain pairs of diffeomorphic Weinstein domains with the same contact boundary and whose symplectic cohomology groups are the same, as vector spaces, but that are different as Liouville domains. To our knowledge, this is the first example of such pairs that can be distinguished by their wrapped Fukaya category. Previously, we have suggested a categorical model Mϕ for the wrapped Fukaya category W(Tϕ), and we have distinguished Mϕ from the mapping torus category of the identity. We prove W(Tϕ) and Mϕ are derived equivalent (Theorem 1.9); hence, deducing the promised Theorem 1.1. Theorem 1.9 is of independent interest as it preludes an algebraic description of wrapped Fukaya categories of locally trivial symplectic fibrations as twisted tensor products.
In vivo glucose imaging in multiple model organisms with an engineered single-wavelength sensor

Summary Glucose is arguably the most important molecule in metabolism, and its dysregulation underlies diabetes. We describe a family of single-wavelength genetically encoded glucose sensors with a high signal-to-noise ratio, fast kinetics, and affinities varying over four orders of magnitude (1 μM to 10 mM). The sensors allow mechanistic characterization of glucose transporters expressed in cultured cells with high spatial and temporal resolution. Imaging of neuron/glia co-cultures revealed ∼3-fold faster glucose changes in astrocytes. In larval Drosophila central nervous system explants, intracellular neuronal glucose fluxes suggested a rostro-caudal transport pathway in the ventral nerve cord neuropil. In zebrafish, expected glucose-related physiological sequelae of insulin and epinephrine treatments were directly visualized. Additionally, spontaneous muscle twitches induced glucose uptake in muscle, and sensory and pharmacological perturbations produced large changes in the brain. These sensors will enable rapid, high-resolution imaging of glucose influx, efflux, and metabolism in behaving animals.
Differential effects of estradiol on neural and emotional stress response in postmenopausal women with remitted Major Depressive Disorder.

Abstract Background Estrogen fluctuations throughout the lifespan may contribute to major depressive disorder (MDD) risk in women through effects on brain networks important in stress responding, and mood regulation. Although there is evidence to support ovarian hormone treatment for peri-menopausal depression, postmenopausal use has not been well examined. The objective of this study was to investigate whether estrogen modulation of the neural and emotional cognitive responses to stress differs between postmenopausal women with and without MDD history. Methods 60 postmenopausal women completed an fMRI psychosocial stress task, after receiving no drug or 3 months of daily estradiol (E2). fMRI activity and subjective mood response were examined. Results In women without a history of MDD, E2 was associated with a more negative mood response to stress and less activity in emotional regulation regions. In women with a history of MDD, E2 was associated with a less negative mood response to stress and less activity in emotion perception regions. Limitations This study was limited by open-label estradiol administration and inclusion of participants using antidepressants. Conclusions These results support a differential effect of estrogen on emotional and neural responses to psychosocial stress in postmenopausal women with MDD history and may reflect a shift in brain activity patterns related to emotion processing following menopause.
Approximating the distance to monotonicity of Boolean functions

We design a nonadaptive algorithm that, given a Boolean function f : {0, 1}n → {0, 1} which is α-far from monotone, makes poly(n, 1/α) queries and returns an estimate that, with high probability, is an [MATH HERE]-approximation to the distance of f to monotonicity. Furthermore, we show that for any constant k > 0, approximating the distance to monotonicity up to n1/2−k-factor requires [MATH HERE] nonadaptive queries, thereby ruling out a poly(n, 1/α)-query nonadaptive algorithm for such approximations. This answers a question of Seshadhri (Property Testing Review, 2014) for the case of non-adaptive algorithms. Approximating the distance to a property is closely related to tolerantly testing that property. Our lower bound stands in contrast to standard (non-tolerant) testing of monotonicity that can be done nonadaptively with [MATH HERE] queries. We obtain our lower bound by proving an analogous bound for erasure-resilient testers. An α-erasure-resilient tester for a desired property gets oracle access to a function that has at most an α fraction of values erased. The tester has to accept (with probability at least 2/3) if the erasures can be filled in to ensure that the resulting function has the property and to reject (with probability at least 2/3) if every completion of erasures results in a function that is e-far from having the property. Our method yields the same lower bounds for unateness and being a k-junta. These lower bounds improve exponentially on the existing lower bounds for these properties.
Machine Learning Explanations to Prevent Overtrust in Fake News Detection.

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust according to their perceptions of model limitations.
Phase diagram for the methane-ethane system and its implications for titan's lakes

On Titan, methane (CH4) and ethane (C2H6) are the dominant species found in the lakes and seas. In this study, we have combined laboratory work and modeling to refine the methane-ethane binary phase diagram at low temperatures and probe how the molecules interact at these conditions. We used visual inspection for the liquidus and Raman spectroscopy for the solidus. Through these methods we determined a eutectic point of 71.15$\pm$0.5 K at a composition of 0.644$\pm$0.018 methane - 0.356$\pm$0.018 ethane mole fraction from the liquidus data. Using the solidus data, we found a eutectic isotherm temperature of 72.2 K with a standard deviation of 0.4 K. In addition to mapping the binary system, we looked at the solid-solid transitions of pure ethane and found that, when cooling, the transition of solid I-III occurred at 89.45$\pm$0.2 K. The warming sequence showed transitions of solid III-II occurring at 89.85$\pm$0.2 K and solid II-I at 89.65$\pm$0.2 K. Ideal predictions were compared to molecular dynamics simulations to reveal that the methane-ethane system behaves almost ideally, and the largest deviations occur as the mixing ratio approaches the eutectic composition.
Physics Based Modeling and Rendering of Vegetation in the Thermal Infrared

We outline a procedure for rendering physically-based thermal infrared images of simple vegetation scenes. Our approach incorporates the biophysical processes that affect the temperature distribution of the elements within a scene. Computer graphics plays a key role in two respects. First, in computing the distribution of scene shaded and sunlit facets and, second, in the final image rendering once the temperatures of all the elements in the scene have been computed. We illustrate our approach for a simple corn scene where the three-dimensional geometry is constructed based on measured morphological attributes of the row crop. Statistical methods are used to construct a representation of the scene in agreement with the measured characteristics. Our results are quite good. The rendered images exhibit realistic behavior in directional properties as a function of view and sun angle. The root-mean-square error in measured versus predicted brightness temperatures for the scene was 2.1 deg C.
Towards User Friendly Medication Mapping Using Entity-Boosted Two-Tower Neural Network

Recent advancements in medical entity linking have been applied in the area of scientific literature and social media data. However, with the adoption of telemedicine and conversational agents such as Alexa in healthcare settings, medical name inference has become an important task. Medication name inference is the task of mapping user friendly medication names from a free-form text to a concept in a normalized medication list. This is challenging due to the differences in the use of medical terminology from health care professionals and user conversations coming from the lay public. We begin with mapping descriptive medication phrases (DMP) to standard medication names (SMN). Given the prescriptions of each patient, we want to provide them with the flexibility of referring to the medication in their preferred ways. We approach this as a ranking problem which maps SMN to DMP by ordering the list of medications in the patient’s prescription list obtained from pharmacies. Furthermore, we leveraged the output of intermediate layers and performed medication clustering. We present the Medication Inference Model (MIM) achieving state-of-the-art results. By incorporating medical entities based attention, we have obtained further improvement for ranking models.
Anomalous Phase Separation Behavior of Gel-Derived Soda-Silica Glasses

The effects of retained bound hydroxyl groups on amorphous immiscibility behavior and on the kinetics of phase separation were studied in glasses containing from 10 to 19 percent sodium oxide prepared by the gel process. Differences in behavior as functions of preliminary thermal treatment of the gel precursor and of melting conditions were studied, employing IR spectroscopy, SAXS and WAXD to monitor the variation in glass microstructure. Both the initial gel treatment and the OH concentration in the prepared glasses were found to affect the immiscibility temperatures, and the magnitude of the maximum temperature increase was also a function of the sodium oxide concentration. It is suggested that the variation in thermodynamic behavior may be caused by the structural arrangement attained by the OH groups during the gel condensation process, which in turn affects the extent of hydrogen bonding to nonbridging oxygen ions.
Event-Driven Receding Horizon Control for Distributed Estimation in Network Systems

This paper considers the multi-agent persistent monitoring problem defined on a network (graph) of nodes (targets) with independent uncertain states. The agent team's goal is to persistently observe the target states so that an overall measure of estimation error covariance evaluated over a finite period is minimized. Each agent's trajectory is fully defined by the sequence of targets it visits and the corresponding dwell times spent at each visited target. To find the optimal set of agent trajectories for this estimation task over the network, we develop a distributed on-line agent controller that requires each agent to solve a sequence of receding horizon control problems (RHCPs) in an event-driven manner. We use a novel objective function form for these RHCPs to optimize the effectiveness of this distributed estimation process and establish its unimodality under certain conditions. Finally, extensive numerical results are provided, indicating significant improvements compared to other agent control methods.
Toward an understanding of year-over-year changes in municipal management discussion and analysis disclosures

Abstract Synopsis Using a unique setting in which the standard setter (the Governmental Accounting Standards Board) provides guidance on the content of Management Discussion and Analysis (MD&A) disclosures, we investigate the determinants of content changes in municipal government MD&A. We do so in terms of economic changes, turnover, and regulatory characteristics. We use a sample of 1142 municipal MD&A disclosures from fiscal year 2011 to 2015 to calculate a difference score based on the degree to which municipal MD&As change from the previous year. Our empirical analysis highlights that MD&A content changes vary directly with changes in the unemployment rate, fund balance from governmental funds, and auditor turnover. Furthermore, we find evidence that MD&A content changes might be more likely in states without formal GAAP mandates, possibly implying that municipal managers increase transparency by complementing reported financial information with additional qualitative commentary in narrative disclosures when states do not centralize fiscal control over municipalities. Overall, our analysis provides insight on the use of qualitative disclosure by municipal managers, and highlights a need for enhanced commentary on certain items (such as debt and capital expenditures) in order to create greater credibility with and accountability to citizens and other financial statement users. This research is especially timely as GASB re-examines the disclosure mandates of GASB 34.
An open-source and wearable system for measuring 3D human motion in real-time.

Objective: Analyzing human motion is essential for diagnosing movement disorders and guiding rehabilitation for conditions like osteoarthritis, stroke, and Parkinson's disease. Optical motion capture systems are the standard for estimating kinematics but require expensive equipment located in a predefined space. While wearable sensor systems can estimate kinematics in any environment, existing systems are generally less accurate than optical motion capture. Many wearable sensor systems require a computer in close proximity and use proprietary software, limiting experimental reproducibility. Methods: Here, we present OpenSenseRT, an open-source and wearable system that estimates upper and lower extremity kinematics in real time by using inertial measurement units and a portable microcontroller. Results: We compared the OpenSenseRT system to optical motion capture and found an average RMSE of 4.4 degrees across 5 lower-limb joint angles during three minutes of walking and an average RMSE of 5.6 degrees across 8 upper extremity joint angles during a Fugl-Meyer task. The open-source software and hardware are scalable, tracking 1 to 14 body segments, with one sensor per segment. A musculoskeletal model and inverse kinematics solver estimate Kinematics in real-time. The computation frequency depends on the number of tracked segments, but is sufficient for real-time measurement for many tasks of interest; for example, the system can track 7 segments at 30 Hz in real-time. The system uses off-the-shelf parts costing approximately $100 USD plus $ 20 for each tracked segment. Significance: The OpenSenseRT system is validated against optical motion capture, low-cost, and simple to replicate, enabling movement analysis in clinics, homes, and free-living settings.
The Impact of Sports Stadiums on Localized Commercial Activity: Evidence from a Business Improvement District

Local government funding of sports stadiums is frequently justified as stimulating economic activity despite consensus contrary findings in the academic literature. Though some studies have identified positive neighborhood effects from stadiums on nearby residential property, scant research exists regarding impacts on highly-localized commercial activity that is hypothesized to spur further local economic development. This analysis exploits the recent relocation of a professional baseball team from downtown Atlanta to a pre-existing Georgia Business Improvement District (Community Improvement District or CID) in suburban Cobb County to estimate the impact of the stadium development on commercial property values. The existence of multiple CIDs in the metro-Atlanta area provides the opportunity to estimate a counterfactual comparison absent the stadium to draw causal inference regarding the stadium’s impact on economic activity using the synthetic control method. Estimates do not identify increased commercial property values in the district following the stadium opening, which is consistent with past studies that find little to no positive impact on economic activity. The results indicate that previous findings of positive impacts on residential property may not be applicable to commercial property.
The myth of the flat start-up: Reconsidering the organizational structure of start-ups

There has been an ongoing debate over whether start-ups should be "flat" with minimal hierarchical layers. To reconcile this debate, this paper distinguishes between creative and commercial success (i.e., product novelty vs. profitability), and examines how these outcomes are variously influenced by a start-up’s hierarchy. This study suggests that while a flatter hierarchy can improve ideation and creative success, it can result in haphazard execution and commercial failure by overwhelming managers with the burden of direction and causing subordinates to drift into power struggles and aimless idea explorations. I find empirical support for this trade-off using a large sample of game development start-ups. These findings offer one resolution to the debate by sorting out the conditions under which hierarchy can be conducive or detrimental to start-ups.
Reframing Jet Physics with New Computational Methods

We reframe common tasks in jet physics in probabilistic terms, including jet reconstruction, Monte Carlo tuning, matrix element – parton shower matching for large jet multiplicity, and efficient event generation of jets in complex, signal-like regions of phase space. We also introduce Ginkgo, a simplified, generative model for jets, that facilitates research into these tasks with techniques from statistics, machine learning, and combinatorial optimization. We also review some of the recent research in this direction that has been enabled with Ginkgo. We show how probabilistic programming can be used to efficiently sample the showering process, how a novel trellis algorithm can be used to efficiently marginalize over the enormous number of clustering histories for the same observed particles, and how the dynamic programming and reinforcement learning can be used to find the maximum likelihood clusterinng in this enormous search space. This work builds bridges with work in hierarchical clustering, statistics, combinatorial optmization, and reinforcement learning.