cssi/SciSciGPT-SciSciNet huggingface dataset:
Effects of neighborhood environments on perceived risk of self-driving: evidence from the 2015 and 2017 Puget Sound Travel Surveys

Autonomous vehicles (AVs), with an expectation of improving road safety, are closer to becoming a reality. A large number of people are still concerned about how AVs would operate in real-life driving environments. The present paper investigates the factors that affect people’s views of the interactions between AVs and other road users based on a large sample from the 2015 and 2017 Puget Sound Travel Surveys. We specifically highlight the effects of the neighborhood environment and road infrastructure. We estimate a generalized ordered logit model to demonstrate the extent to which certain neighborhood environment and road infrastructure features affect individuals’ safety perceptions of AVs, controlling for demographics, daily travel patterns, and general interest in riding AVs. The results reveal that designated bicycle facilities are positively associated with individuals’ safety perceptions related to AVs. We find that residents from neighborhoods with more pedestrian facilities are more likely to express higher levels of concern on AVs’ capabilities to react to the environment. Our results also suggest that people living in mixed-use neighborhoods are more confident in sharing the road with AVs. The findings provide useful implications for effective policy interventions and infrastructure provisions that may affect the market penetration rates of AVs while keeping up the standards for other road users, such as bicyclists and pedestrians.
Controlling and measuring dynamic odorant stimuli in the laboratory

ABSTRACT Animals experience complex odorant stimuli that vary widely in composition, intensity and temporal properties. However, stimuli used to study olfaction in the laboratory are much simpler. This mismatch arises from the challenges in measuring and controlling them precisely and accurately. Even simple pulses can have diverse kinetics that depend on their molecular identity. Here, we introduce a model that describes how stimulus kinetics depend on the molecular identity of the odorant and the geometry of the delivery system. We describe methods to deliver dynamic odorant stimuli of several types, including broadly distributed stimuli that reproduce some of the statistics of naturalistic plumes, in a reproducible and precise manner. Finally, we introduce a method to calibrate a photo-ionization detector to any odorant it can detect, using no additional components. Our approaches are affordable and flexible and can be used to advance our understanding of how olfactory neurons encode real-world odor signals.
Remittances, health insurance, and pension contributions: Evidence from Colombia

Abstract Pension contributions and health insurance guarantee that people withdraw from the labor market without falling into poverty and protect individuals and their families against adverse health shocks. However, many workers with informal jobs in developing countries lack access to these two components of the social security system. This study addresses whether international remittance income increases participation in the pension and health schemes for individuals working in informal jobs. To identify the causal impact of remittances, I use a nationally-representative household data from Colombia and an instrumental variable (IV) approach that relies on unemployment shocks in the main destination countries of Colombian migrants arising from the 2008 financial crisis. My study finds that those informal workers who live in a recipient household are more likely to enroll in the contributive plans than those in non-recipient households. Moreover, relative to their mean participation shares, I find that for workers at their prime working-age, the impact is more pronounced for women, and for informal workers at schooling age, the impact is slightly larger for boys. Finally, I also find that remittances decrease the likelihood of having an informal job and increase the probability of having a job where the employer share the contribution to the social security system. As part of a special issue on Sustainable Development Goals, this article provides evidence that remittances are an important source of income that increases contribution to pension and health, two essential components of the social security system to improve well-being and fight against poverty.
Crowdsourced PAC Learning under Classification Noise

In this paper, we analyze PAC learnability from labels produced by crowdsourcing. In our setting, unlabeled examples are drawn from a distribution and labels are crowdsourced from workers who operate under classification noise, each with their own noise parameter. We develop an end-to-end crowdsourced PAC learning algorithm that takes unlabeled data points as input and outputs a trained classifier. Our three-step algorithm incorporates majority voting, pure-exploration bandits, and noisy-PAC learning. We prove several guarantees on the number of tasks labeled by workers for PAC learning in this setting and show that our algorithm improves upon the baseline by reducing the total number of tasks given to workers. We demonstrate the robustness of our algorithm by exploring its application to additional realistic crowdsourcing settings.
Modeling avian full annual cycle distribution and population trends with citizen science data

Information on species' distributions, abundances, and how they change over time is central to the study of the ecology and conservation of animal populations. This information is challenging to obtain at landscape scales across range-wide extents for two main reasons. First, landscape-scale processes that affect populations vary throughout the year and across species' ranges, requiring high-resolution, year-round data across broad, sometimes hemispheric, spatial extents. Second, while citizen science projects can collect data at these resolutions and extents, using these data requires appropriate analysis to address known sources of bias. Here, we present an analytical framework to address these challenges and generate year-round, range-wide distributional information using citizen science data. To illustrate this approach, we apply the framework to Wood Thrush (Hylocichla mustelina), a long-distance Neotropical migrant and species of conservation concern, using data from the citizen science project eBird. We estimate occurrence and abundance across a range of spatial scales throughout the annual cycle. Additionally, we generate intra-annual estimates of the range, intra-annual estimates of the associations between species and characteristics of the landscape, and interannual trends in abundance for breeding and non-breeding seasons. The range-wide population trajectories for Wood Thrush show a close correspondence between breeding and non-breeding seasons with steep declines between 2010 and 2013 followed by shallower rates of decline from 2013 to 2016. The breeding season range-wide population trajectory based on the independently collected and analyzed North American Breeding Bird Survey data also shows this pattern. The information provided here fills important knowledge gaps for Wood Thrush, especially during the less studied migration and non-breeding periods. More generally, the modeling framework presented here can be used to accurately capture landscape scale intra- and interannual distributional dynamics for broadly distributed, highly mobile species.
Voluntary Disclosure and Personalized Pricing

A concern central to the economics of privacy is that firms may use consumer data to price discriminate. A common response is that consumers should have control over their data and the ability to choose how firms access it. Since firms draw inferences based on both the data seen as well as the consumer's disclosure choices, the strategic implications of this proposal are unclear. We investigate whether such measures improve consumer welfare in monopolistic and competitive environments. We find that consumer control can guarantee gains for every consumer type relative to both perfect price discrimination and no personalized pricing. This result is driven by two ideas. First, consumers can use disclosure to amplify competition between firms. Second, consumers can share information that induces a seller---even a monopolist---to make price concessions. Furthermore, whether consumer control improves consumer surplus depends on both the technology of disclosure and the competitiveness of the marketplace. In a competitive market, simple disclosure technologies such as "track / do-not-track'' suffice for guaranteeing gains in consumer welfare. However, in a monopolistic market, welfare gains require richer forms of disclosure technology whereby consumers can decide how much information they would like to convey.
On the remarkable fracture toughness of 90 to 97W-NiFe alloys revealing powerful new ductile phase toughening mechanisms

Abstract Tungsten is generally too brittle to serve a robust structural function. Here, we explore the fracture toughness of 90–97 wt%W Ni-Fe liquid phase sintered tungsten heavy alloys (WHAs). The room temperature (RT) maximum load fracture toughness (KJm ≈ 69–107 MPa√m) of the WHA, containing only 3–10 wt% of a Ni–Fe ductile phase (DP), is ≈ 9–13 times higher than KIc typical of monolithic W (≈ 8 MPa√m). All the WHAs show extensive stable crack growth, and increasing blunting line toughness averaging ≈ 170 MPa√m, prior to significant crack extension. In contrast to classical ductile phase toughening, that is primarily due to macrocrack bridging, the WHA toughness increase mainly involves new mechanisms associated with arrest, blunting and bridging of numerous dilatational shielding process zone microcracks in the macrocrack process zone. Tests down to –196 °C, to partially emulate irradiation hardening, show decreasing toughness and a transition to elastic fracture at a temperature of –150 °C for 90W to –25 °C for 97W. However, even at –196 °C, the leanest DP 97W WHA KIc is ≈ 3 times higher than that of monolithic W at RT. Possible effects of the small specimen size used in this study are briefly summarized.
Predictions of the structure of turbulent, particle-laden round jets

Models of gas and particle motion in turbulent, particle-laden, round jets were evaluated using existing measurements of flow structure. Three models were considered: (1) a locally homogeneous flow model, where velocities and turbulent mixing properties of both phases were assumed to be equal, (2) a deterministic separated flow model, where interphase slip was considered but effects of turbulent dispersion were ignored, and (3) a stochastic separated flow model where effects of interphase slip and turbulent dispersion were considered using random sampling techniques. In all three cases, mean and turbulent properties of the continuous phase were found with a well-calibrated k-epsilon model. The locally homogeneous flow and deterministic separated flow models over- and underestimated particle spread and flow development rates, respectively. The stochastic separated flow model, however, yielded satisfactory predictions of flow structure - except at the high particle loadings. Uncertainties in initial conditions for the measurements and possible effects of turbulence modulation by the particles are proposed as the reason for these errors.
Increasing growth rate slows adaptation when genotypes compete for diffusing resources.

The rate at which a species responds to natural selection is a central predictor of the species’ ability to adapt to environmental change. It is well-known that spatially-structured environments slow the rate of adaptation due to increased intra-genotype competition. Here, we show that this effect magnifies over time as a species becomes better adapted and grows faster. Using a reaction-diffusion model, we demonstrate that growth rates are inextricably coupled with effective spatial scales, such that higher growth rates cause more localized competition. This has two effects: selection requires more generations for beneficial mutations to fix, and spatially-caused genetic drift increases. Together, these effects diminish the value of additional growth rate mutations in structured environments.
Committee Spaces and the Random Column–Row Property

A committee space is a Hilbert space of power series, perhaps in several or noncommuting variables, such that $$\Vert z^\alpha \Vert \Vert z^\beta \Vert \ge \Vert z^{\alpha +\beta }\Vert .$$ Such a space satisfies the true column–row property when ever the map transposing a column multiplier to a row multiplier is contractive. We describe a model for random multipliers and show that such random multipliers satisfy the true column–row property. We also show that the column–row property holds asymptotically for large random Nevanlinna–Pick interpolation problems where the nodes are chosen identically and independently. These results suggest that finding a violation of the true column–row property for the Drury–Arveson space via naive random search is unlikely.
Measurement of Prostate Volume with MRI (A Guide for the Perplexed): Biproximate Method with Analysis of Precision and Accuracy.

To review the anatomic basis of prostate boundary selection on T2-weighted magnetic resonance imaging (MRI). To introduce an alternative 3D ellipsoid measuring technique that maximizes precision, report the intra- and inter-observer reliability, and to advocate it’s use for research involving multiple observers. We demonstrate prostate boundary anatomy using gross pathology and MRI examples. This provides background for selecting key boundary marks when measuring prostate volume. An alternative ellipsoid volume method is then proposed using these boundaries in an attempt to improve inter-observer precision. An IRB approved retrospective study of 140 patients with elevated serum prostate specific antigen levels and/or abnormal digital rectal examinations was done with T2-weighted MRI applying a new (Biproximate) technique. Measurements were made by 2 examiners, correlated with each other for inter-observer precision and correlated with an expert observer for accuracy. Correlation statistics, linear regression analysis, and tests of means were applied using p ≤ 0.05 as the threshold for significance. Inter-observer correlation (precision) was 0.95 between observers. Correlation between these observers and the expert (accuracy) was 0.94 and 0.97 respectively. Intra-observer correlation for the expert was 0.98. Means for inter-rater reliability and accuracy were all the same (p = 0.001). We conclude that using more precise reproducible landmarks with biproximate technique, precision and accuracy of total prostate volume is found to be demonstrated.
Failure to detect synergy between variants in transferrin and hemochromatosis and Alzheimer's disease in large cohort.

Abstract Alzheimer's disease (AD) is the most common cause of dementia and, despite decades of effort, there is no effective treatment. In the last decade, many association studies have identified genetic markers that are associated with AD status. Two of these studies suggest that an epistatic interaction between variants rs1049296 in the transferrin (TF) gene and rs1800562 in the homeostatic iron regulator (HFE) gene, commonly known as hemochromatosis, is in genetic association with AD. TF and HFE are involved in the transport and regulation of iron in the brain, and disrupting these processes exacerbates AD pathology through increased neurodegeneration and oxidative stress. However, by using a significantly larger data set from the Alzheimer's Disease Genetics Consortium, we fail to detect an association between TF rs1049296 or HFE rs1800562 with AD risk (TF rs1049296 p = 0.38 and HFE rs1800562 p = 0.40). In addition, logistic regression with an interaction term and a synergy factor analysis both failed to detect epistasis between TF rs1049296 and HFE rs1800562 (SF = 0.94; p = 0.48) in AD cases. Each of these analyses had sufficient statistical power (power > 0.99), suggesting that previously reported associations may be the result of more complex epistatic interactions, genetic heterogeneity, or false-positive associations because of limited sample sizes.
The Kobayashi–Royden metric on punctured spheres

This paper gives an explicit formula of the asymptotic expansion of the Kobayashi-Royden metric on the punctured sphere $\mathbb{CP}^1\backslash\{0,1,\infty\}$ in terms of the exponential Bell polynomials. We prove a local quantitative version of the Little Picard's theorem as an application of the asymptotic expansion. Meanwhile, the approach in the paper leads to the conclusion that the coefficients in the asymptotic expansion are rational numbers. Furthermore, the explicit metric formula and the conclusion regarding the coefficients apply to a more general case as well, the metric on $\mathbb{CP}^1\backslash\{0,\frac{1}{3},-\frac{1}{6}\pm\frac{\sqrt{3}}{6}i\}$ will be given as a concrete example of our results.
Finite Sample Analysis of Stochastic System Identification

In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalman filter gain, given a single trajectory of output measurements over a finite horizon of length N. Based on a subspace identification algorithm and a finite number of N output samples, we provide non-asymptotic high-probability upper bounds for the system parameter estimation errors. Our analysis uses recent results from random matrix theory, self-normalized martingales and SVD robustness, in order to show that with high probability the estimation errors decrease with a rate of $1/\sqrt N$ up to logarithmic terms. Our non-asymptotic bounds not only agree with classical asymptotic results, but are also valid even when the system is marginally stable.
Rand-OFDM: A Secured Wireless Signal

Wireless communication has been a broadcast system since its inception, which violates security and privacy issues at the physical layer between the intended transmit and receive pairs. As we move towards advanced spectrum sharing methodologies involving billions of devices connected over wireless networks, it is essential to secure the wireless signal such that only the intended receiver can realize the properties of the signal. In this paper, we propose Rand-OFDM, a new waveform, secured with a shared secret key, where the signal properties can be recovered only at the expected receiver. We achieve the signal level security by modifying the OFDM signal in time-domain, thus erasing the OFDM properties and in turn obfuscating the signal properties to an eavesdropper. We introduce a key-based secured training signal for channel estimation, which can be used only at the intended receiver with prior knowledge of the key. As a final step for recovery of the signal, we use a clustering based technique to correct the phase of the received signal. Our cryptanalysis shows that Rand-OFDM is especially useful in future wide band signals. Extensive simulation and over-the-air experiments show that the performance of Rand-OFDM is comparable to legacy OFDM and SNR penalty due to the secured waveform varies between ≈ 1-4dB. In all these scenarios, Rand-OFDM remains unrecognized by the adversary even at the highest possible SNR.
Substrate dependent resistive switching in amorphous-HfOx memristors: an experimental and computational investigation

While two-terminal HfOx (x < 2) memristor devices have been studied for ion transport and current evolution, there have been limited reports on the effect of the long-range thermal environment on their performance. In this work, amorphous-HfOx based memristor devices on two different substrates, microscopic glass (∼1 mm) and thin SiO2 (280 nm)/Si, with different thermal conductivities in the range from 1.2 to 138 W m−1 K−1 were fabricated. Devices on glass substrates exhibit lower reset voltage, wider memory window and, in turn, a higher performance window. In addition, the devices on glass show better endurance than the devices on the SiO2/Si substrate. These devices also show non-volatile multi-level resistances at relatively low operating voltages which is critical for neuromorphic computing applications. A multiphysics COMSOL computational model is presented that describes the transport of heat, ions and electrons in these structures. The combined experimental and COMSOL simulation results indicate that the long-range thermal environment can have a significant impact on the operation of HfOx-based memristors and that substrates with low thermal conductivity can enhance switching performance.
Effect of Thomas Rotation on the Lorentz Transformation of Electromagnetic fields

A relativistic particle undergoing successive boosts which are non collinear will experience a rotation of its coordinate axes with respect to the boosted frame. This rotation of coordinate axes is caused by a relativistic phenomenon called Thomas Rotation. We assess the importance of Thomas rotation in the calculation of physical quantities like electromagnetic fields in the relativistic regime. We calculate the electromagnetic field tensor for general three dimensional successive boosts in the particle’s rest frame as well as the laboratory frame. We then compare the electromagnetic field tensors obtained by a direct boost $$\overrightarrow{\beta }+\delta \overrightarrow{\beta }$$ and successive boosts $$\overrightarrow{\beta }$$ and $$\Delta \overrightarrow{\beta }$$ and check their consistency with Thomas rotation. This framework might be important to situations such as the calculation of frequency shifts for relativistic spin-1/2 particles undergoing Larmor precession in electromagnetic fields with small field non-uniformities.
Contributions of the Conserved Insect Carbon Dioxide Receptor Subunits to Odor Detection

The CO2 receptor in mosquitoes is broadly tuned to detect many diverse odorants. The receptor consists of three subunits (Gr1, Gr2, and Gr3) in mosquitoes but only two subunits in Drosophila: Gr21a (Gr1 ortholog) and Gr63a (Gr3 ortholog). We demonstrate that Gr21a is required for CO2 responses in Drosophila, as has been shown for Gr63a. Next, we generate a Drosophila double mutant for Gr21a and Gr63a, and in this background, we functionally express combinations of Aedes Gr1, Gr2, and Gr3 genes in the CO2 empty neuron. Only two subunits, Gr2 and Gr3, suffice for response to CO2. Addition of Gr1 increases sensitivity to CO2, whereas it decreases the response to pyridine. The inhibitory effect of the antagonist isobutyric acid is observed upon addition of Gr1. Gr1 therefore increases the diversity of ligands of the receptor and modulates the response of the receptor complex.
Corporate Leadership and Mass Atrocity

With the last Holocaust survivors quietly passing away, one might also expect to see accountability debates slowing to a trickle. Surprisingly, however, recent years show an upswing in corporate World War II-related atonement debates. Interest in corporate participation in mass atrocity has expanded worldwide; yet what constitutes ethical corporate behavior during and after war remains understudied. This article considers these questions through a study of the French National Railways’ (SNCF) roles during the German occupation and its more recent struggle to make amends. This study demonstrates that ethical business leadership requires taking responsibility for past as well as current decisions. Most executives grappling with complex corporate histories work in isolation, in part because the scholarship on business ethics fails to provide guidance. Without such guidance, corporations often respond to accusations about their pasts with carefully crafted statements and legal strategies rather than deep expressions of moral leadership. To assist in remedying this tendency, this paper simultaneously encourages companies to engage in deeper reflection on corporate history, while urging scholars to help guide corporations through critical ethical conversations.
Unintended costs of a dual regulatory environment: Evidence from state-level cannabis legalization and bank audit fees

Abstract Since 2014, a number of U.S. states have legalized business activities related to the production, distribution, and use of recreational cannabis. These activities remain illegal at the U.S. federal level, creating a dual regulatory environment. The uncertainty related to the enforcement of federal cannabis laws affects businesses located in legalizing states, particularly federally-insured banks. Applying a difference-in-differences approach to a matched sample of banks in legalizing and non-legalizing states, we document an increase in audit fees incurred by banks located in legalizing states after cannabis legalization. This finding is consistent with increased auditor effort and engagement risk being an unintended consequence of state-level recreational cannabis legalization. In supplemental analysis, we find that the relation between banks’ audit fees and cannabis legalization was greater for banks having larger increases in banking activity, suggesting that audit fees increased primarily for banks that may be engaging in relationships with cannabis-related businesses.
Cortical processing of chemosensory and hedonic features of taste in active licking mice

In the last two decades, a considerable amount of work has been devoted to investigating the neural processing and dynamics of the primary taste cortex of rats. Surprisingly, much less information is available on cortical taste electrophysiology in awake mice, an animal model that is taking on a more prominent role in taste research. Here we present electrophysiological evidence demonstrating how the gustatory cortex (GC) encodes the basic taste qualities (sweet, salty, sour, and bitter) and water when stimuli are actively sampled through licking, the stereotyped behavior by which mice control the access of fluids in the mouth. Mice were trained to receive each stimulus on a fixed ratio schedule in which they had to lick a dry spout six times to receive a tastant on the seventh lick. Electrophysiological recordings confirmed that GC neurons encode both chemosensory and hedonic aspects of actively sampled tastants. In addition, our data revealed two other main findings: GC neurons rapidly encode information about taste qualities in as little as 120 ms, and nearly half of the recorded neurons exhibit spiking activity entrained to licking at rates up to 8 Hz. Overall, our results highlight how the GC of active licking mice rapidly encodes information about taste qualities as well as ongoing sampling behavior, expanding our knowledge on cortical taste processing.NEW & NOTEWORTHY Relatively little information is available on the neural dynamics of taste processing in the mouse gustatory cortex (GC). In this study we investigate how the GC encodes chemosensory and palatability features of a wide panel of gustatory stimuli when actively sampled through licking. Our results show that GC neurons broadly encode basic taste qualities but also process taste hedonics and licking information in a temporally dynamic manner.
Insurance Pricing/Nonlife†

The essentials of nonlife insurance pricing are based on the same fundamental principles as applied in pricing life insurance. However, distinct characteristics of nonlife risks (e.g., shorter contract duration, more complicated claim structure, and the possibility of multiple occurrences of an event within the contract period) have inspired development of actuarial/financial pricing techniques specific to nonlife risk area, where insurers cope with a more regulated yet highly competitive market. This article serves as a brief introduction to various aspects of nonlife insurance pricing. Manual rates, individual risk rating, and classification are first introduced. Credibility theory and its use in determining rates for individual risks are discussed. Important premium principles and properties of these premium principles are also presented. In addition to traditional actuarial approaches, we ultimately discuss financial models that are widely employed in this area, including insurance capital asset pricing models (CAPMs), discounted cash flow models, and option pricing models. We conclude with a remark on the integration of insurance and financial pricing. Keywords: nonlife insurance pricing; premium principles; credibility theory; distortion function; financial pricing; manual rates; insurance CAPM
Beating the Generator-Enumeration Bound for Solvable-Group Isomorphism

We consider the isomorphism problem for groups specified by their multiplication tables. Until recently, the best published bound for the worst-case was achieved by the nlogp n + O(1) generator-enumeration algorithm where n is the order of the group and p is the smallest prime divisor of n. In previous work with Fabian Wagner, we showed an n(1 / 2) logp n + O(log n / log log n) -time algorithm for testing isomorphism of p-groups by building graphs with degree bounded by p + O(1) that represent composition series for the groups and applying Luks’ algorithm for testing isomorphism of bounded-degree graphs. In this work, we extend this improvement to the more general class of solvable groups to obtain an n(1 / 2) logp n + O(log n / log log n) -time algorithm. In the case of solvable groups, the composition factors can be large which prevents previous methods from outperforming the generator-enumeration algorithm. Using Hall’s theory of Sylow bases, we define a new object that generalizes the notion of a composition series with small factors but exists even when the composition factors are large. By constructing graphs that represent these objects and running Luks’ algorithm, we obtain our algorithm for solvable-group isomorphism. We also extend our algorithm to compute canonical forms of solvable groups while retaining the same complexity.
Fight or flight: managing stigma in executive careers

We examine the labor market consequences borne by executives who remain at financially distressed firms relative to those who flee to another employer to avoid the stigma of failure. Our study makes two contributions. First, we document an ex ante dimension of executive labor markets unaccounted for by ex post settling up models. Specifically, we show that executives who ‘jump ship’—change employers in the two years prior to the failure—suffer fewer labor market consequences than their counterparts who remain with the failing firm. Second, we extend the study of bankruptcy stigma to examine how stigma might be managed by jumping ship. Copyright © 2007 John Wiley & Sons, Ltd.
The OECD Anti-Bribery Convention: Changing the Currents of Trade

This paper examines how criminalizing the act of bribing a foreign public official affects international trade flows using a watershed global anti-corruption initiative – the 1997 OECD Anti-Bribery Convention. This multilateral agreement criminalized foreign bribery (previously illegal only for U.S. firms) in countries that represent over 75% of world exports. I exploit temporal variation in the implementation of the Convention along with variation in the level of corruption of importing countries to quantify the effects of the Convention on bilateral exports. In contrast to previous work on U.S. efforts to criminalize foreign bribery, I use a large panel of exporters and importers to control for a broad range of confounding global and national trends and shocks. I find that, on average, the Convention caused a reduction in bilateral exports from signatory countries to high corruption importing countries relative to low corruption importing countries. In particular, we observe a 5.6% relative decline in bilateral exports to importing countries that lie one standard deviation lower on the Worldwide Governance Indicators index of corruption. This suggests that by creating large penalties for foreign bribery, the Convention indirectly increased transaction costs between importing countries with high levels of corruption and exporting countries that criminalized foreign bribery. The Convention may have induced OECD firms to divert their exports to less corrupt countries; while at the same time, non-OECD firms that are not bound by the Convention may have increased their exports to corrupt countries. I also find evidence that the effects of the Convention differed across product categories.
Detecting Medicare Abuse

This paper identifies which types of patients and hospitals have abusive Medicare billings that are responsive to law enforcement. For a 20% random sample of elderly Medicare beneficiaries hospitalized from 1994 to 1998 with one or more of six illnesses that are prone to abuse, we obtain longitudinal claims data linked with social security death records, hospital characteristics, and state/year-level anti-fraud enforcement efforts. We show that increased enforcement leads certain types of types of patients and hospitals to have lower billings, without adverse consequences for patients' health outcomes.
Corner accuracy in direct ink writing with support material

Abstract 3D printing methods which enable control over the position and orientation of embedded particles have promising applications in cell patterning and composite scaffolds. Extrusion-based additive manufacturing techniques such as fused deposition modeling and direct ink writing can experience particle patterning defects at corners which could hinder cell survival at corners and create unintended property gradients. Here, we propose models which predict the behavior of deposited lines at corners for moderate viscosity inks which are impacted by both capillarity and viscous dissipation. Using direct ink writing with acoustophoresis and a Carbopol-based support gel, we write polygons out of dental resin-based composite inks containing a narrow distribution of microparticles at the center of the filament. A Laplace pressure differential between the inner and outer surfaces of the corner drives corner smoothing, wherein the inner radius of the corner increases. Double deposition, or printing on the same area twice, drives corner swelling, wherein excess ink is diverted to the outer edge of the corner. Fast turns at corners produce ringing, wherein vibrations in the stage manifest in oscillations in the print path. Swelling and ringing effects are apparent in the particle distributions at corners immediately after deposition, while smoothing effects are apparent after the printed structure has had time to relax. When the nozzle returns to write a neighboring line, it imposes shear stresses which mitigate inconsistencies in microstructure at corners by erasing defects which appeared during relaxation. Using a support bath instead of layer-by-layer support suppresses microstructural corner defects.
Synaptic Partner Assignment Using Attentional Voxel Association Networks

Connectomics aims to recover a complete set of synaptic connections within a dataset imaged by volume electron microscopy. Many systems have been proposed for locating synapses, and recent research has included a way to identify the synaptic partners that communicate at a synaptic cleft. We reframe the problem of identifying synaptic partners as directly generating the mask of the synaptic partners from a given cleft. We train a convolutional network to perform this task. The network takes the local image context and a binary mask representing a single cleft as input. It is trained to produce two binary output masks: one which labels the voxels of the presynaptic partner within the input image, and another similar labeling for the postsynaptic partner. The cleft mask acts as an attentional gating signal for the network. We find that an implementation of this approach performs well on a dataset of mouse somatosensory cortex, and evaluate it as part of a combined system to predict both clefts and connections.
ConvoKit: A Toolkit for the Analysis of Conversations.

This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis.
Embodied Multimodal Multitask Learning

Recent efforts on training visual navigation agents conditioned on language using deep reinforcement learning have been successful in learning policies for different multimodal tasks, such as semantic goal navigation and embodied question answering. In this paper, we propose a multitask model capable of jointly learning these multimodal tasks, and transferring knowledge of words and their grounding in visual objects across the tasks. The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual concepts in the visual representations, and align them with each other. This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks. We show that the proposed model outperforms a range of baselines on both tasks in simulated 3D environments. We also show that this disentanglement of representations makes our model modular, interpretable, and allows for transfer to instructions containing new words by leveraging object detectors.
Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization

Various types of parameter restart schemes have been proposed for accelerated gradient algorithms to facilitate their practical convergence in convex optimization. However, the convergence properties of accelerated gradient algorithms under parameter restart remain obscure in nonconvex optimization. In this paper, we propose a novel accelerated proximal gradient algorithm with parameter restart (named APG-restart) for solving nonconvex and nonsmooth problems. Our APG-restart is designed to 1) allow for adopting flexible parameter restart schemes that cover many existing ones; 2) have a global sub-linear convergence rate in nonconvex and nonsmooth optimization; and 3) have guaranteed convergence to a critical point and have various types of asymptotic convergence rates depending on the parameterization of local geometry in nonconvex and nonsmooth optimization. Numerical experiments demonstrate the effectiveness of our proposed algorithm.
Feature Selection using Stochastic Gates

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the $\ell_0$ norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an information-theoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications.
Deep Radar Waveform Design for Efficient Automotive Radar Sensing

In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiring sophisticated a priori knowledge of environmental parameters which may be difficult to obtain in real-time scenarios. In this paper, we propose a novel hybrid model-driven and data-driven architecture that adapts to the ever changing environment and allows for adaptive unimodular waveform design. In particular, the approach lays the groundwork for developing extremely low-cost waveform design and processing frameworks for radar systems deployed in autonomous vehicles. The proposed model-based deep architecture imitates a wellknown unimodular signal design algorithm in its structure, and can quickly infer statistical information from the environment using the observed data. Our numerical experiments portray the advantages of using the proposed method for efficient radar waveform design in time-varying environments.
Contact graphs of ball packings

Abstract A contact graph of a packing of closed balls is a graph with balls as vertices and pairs of tangent balls as edges. We prove that the average degree of the contact graph of a packing of balls (with possibly different radii) in R 3 is not greater than 13.92. We also find new upper bounds for the average degree of contact graphs in R 4 and R 5 .
Fluorogenic Probe Using a Mislow–Evans Rearrangement for Real‐Time Imaging of Hydrogen Peroxide

Hydrogen peroxide (H2 O2 ) mediates the biology of wound healing, apoptosis, inflammation, etc. H2 O2 has been fluorometrically imaged with protein- or small-molecule-based probes. However, only protein-based probes have afforded temporal insights within seconds. Small-molecule-based electrophilic probes for H2 O2 require many minutes for a sufficient response in biological systems. Here, we report a fluorogenic probe that selectively undergoes a [2,3]-sigmatropic rearrangement (seleno-Mislow-Evans rearrangement) with H2 O2 , followed by acetal hydrolysis, to produce a green fluorescent molecule in seconds. Unlike other electrophilic probes, the current probe acts as a nucleophile. The fast kinetics enabled real-time imaging of H2 O2 produced in endothelial cells in 8 seconds (much earlier than previously shown) and H2 O2 in a zebrafish wound healing model. This work may provide a platform for endogenous H2 O2 detection in real time with chemical probes.
MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform

We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation. We unify existing text generation algorithms in a shared codebase and further adapt earlier algorithms for constrained generation. To borrow advantages from different models, we implement strategies for cross-model integration, from the token probability level to the latent space level. An interface to external knowledge is provided via a module that retrieves, on-the-fly, relevant knowledge from passages on the web or a document collection. A user interface for local development, remote webpage access, and a RESTful API are provided to make it simple for users to build their own demos.
Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks

Virtual screenings can accelerate and reduce the cost of discovering metal-organic frameworks (MOFs) for their applications in gas storage, separation, and sensing. In molecular simulations of gas ...
Truthful Computation Offloading Mechanisms for Edge Computing

Edge computing (EC) is a promising paradigm providing a distributed computing solution for users at the edge of the network. Preserving satisfactory quality of experience (QoE) for users when offloading their computation to EC is a non-trivial problem. Computation offloading in EC requires jointly optimizing access points (APs) allocation and edge service placement for users, which is computationally intractable due to its combinatorial nature. Moreover, users are self-interested, and they can misreport their preferences leading to inefficient resource allocation and network congestion. In this paper, we tackle this problem and design a novel mechanism based on algorithmic mechanism design to implement a system equilibrium. Our mechanism assigns a proper pair of AP and edge server along with a service price for each new joining user maximizing the instant social surplus while satisfying all users’ preferences in the EC system. Declaring true preferences is a weakly dominant strategy for the users. The experimental results show that our mechanism outperforms user equilibrium and random selection strategies in terms of the experienced end-to-end latency.
Fiber-optic sensors

This paper reviews the theory of fiber-optic sensor technology and presents some of the current applications. The future potential for applications of fiber-optic sensors is almost unlimited since there appear to be few parameters that cannot be measured with this technology.
Global Weak Solutions to Compressible Navier–Stokes–Vlasov–Boltzmann Systems for Spray Dynamics

This work focus on the construction of weak solutions to a kinetic-fluid system of partial differential–integral equations modeling the evolution of particles droplets in a compressible fluid. The system is given by a coupling between the standard isentropic compressible Navier–Stokes equations for the macroscopic description of a gas fluid flow, and a Vlasov–Boltzmann type equation governing the evolution of spray droplets modeled as particles with varying radius. We establish the existence of global weak solutions with finite energy, whose density of gas satisfies the renormalized mass equation. The proof combines techniques inspired by the work of Feireisl et al. (J Math Fluid Mech 3:358–392, 2001) on the weak solutions of the compressible Navier–Stokes equations in a coupled system to the kinetic problem for the spray droplets by extending techniques of Leger and Vasseur (J Hyperbolic Differ Equ 6(1):185–206, 2009) developed for the incompressible fluid-kinetic system.
Linear Mixed Models with Endogenous Covariates: Modeling Sequential Treatment Effects with Application to a Mobile Health Study

Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT, the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous—that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity.
Mutual Funds as Venture Capitalists? Evidence from Unicorns

Using novel contract-level data, we study the recent trend in open-end mutual funds investing in unicorns—highly valued, privately held start-ups—and the consequences of these investments for corporate governance provisions. Larger funds and those with more stable funding are more likely to invest in unicorns. Compared to venture capital groups (VCs), mutual funds have weaker cash flow rights and are less involved in terms of corporate governance, being particularly underrepresented on boards of directors. Having to carefully manage their own liquidity pushes mutual funds to require stronger redemption rights, suggesting contractual choices consistent with mutual funds’ short-term capital sources.