cssi/SciSciGPT-SciSciNet huggingface dataset:
Does Industry Timing Ability of Hedge Funds Predict Their Future Performance, Survival, and Fund Flows?

This paper investigates hedge funds’ ability to time industry-specific returns and shows that funds’ timing ability in the manufacturing industry improves their future performance, probability of survival, and ability to attract more capital. The results indicate that the best industry-timing hedge funds in the manufacturing sector have the highest return exposure to earnings surprises. This, together with persistently sticky earnings surprises, transparent information environment in regards to earnings releases, and large post-earnings-announcement drift in the manufacturing industry, explain to a great extent why best-timing hedge funds can generate significantly larger future returns compared to worst-timing hedge funds.
THE CASE AGAINST FEDERALIZING TRADE SECRECY

RADE secrecy is unique among the major intellectual property (“IP”) doctrines because it is governed primarily by state law. Recently, however, a number of influential actors—including legislators, academics, high-technology firms, and organizations representing IP attorneys and owners—have supported the creation of a private civil cause of action for trade secret misappropriation under federal law. Proponents assert that federalizing trade secrecy would provide numerous benefits, including substantive uniformity, the availability of a federal forum for misappropriation litigation, and the creation of a unified national regime for IP rights. This Article engages in the first systematic critique of the claim that federalizing trade secrecy is normatively desirable. Ultimately, it concludes that there are multiple reasons for trade secrecy to remain primarily the province of state law, including preservation of the states’ ability to engage in limited experimentation regarding the scope of trade secret protection and federalization’s potential negative impact on the disclosure of patenteligible inventions. Finally, it proposes an alternative approach—a modest expansion of federal courts’ jurisdiction over state law trade secret claims—that can help address the issue of trade secret theft without requiring outright federalization.
Making, breaking, and (partially) remaking markets: State regulation and photovoltaic electricity in New Jersey

This paper describes the development of the U.S. state of New Jersey’s policy to accelerate the growth of photovoltaic electricity generating capacity over the past ten years. It provides insights that may be of use to scholars and policy-makers who seek to understand how markets for photovoltaics and other renewable energy technologies may be created and sustained, and it adds to the growing set of detailed historical case studies on these issues. Aggressive state policy measures have put New Jersey second to California among the U.S. states in installed photovoltaic capacity. That growth was achieved in a series of stages. New Jersey initially experienced a boom and bust as generous up-front rebates catalyzed rapid growth in demand and exhausted the program’s budget. A shift in 2007 to a policy that emphasized Solar Renewable Energy Certificates failed to sustain the growth in capacity. In response, the state began to require regulated transmission and distribution utilities to provide up-front financing for photovoltaic systems. This approach has restarted the momentum of the market, but it shifts the policy’s costs into the future, while empowering a new set of players with uncertain interests over the long term.
Building a Tower of Babel or Building a Discipline? Talking About Legal Writing

This article examines the language legal writing professors use and create to teach legal writing. It concludes the benefits of creating a legal writing language outweigh the risk of confusion, because legal writing professors are actually creating the emerging discipline as they create new language.
Corporate control and credible commitment

Abstract The separation of control and ownership – the ability of a small group effectively to control a company though holding a minority of its cash flow rights – is common throughout the world, but also is commonly decried. The control group, it is thought, will use its position to consume excessive amounts of the firm's returns, and this injures minority shareholders in two ways: there is less money and the controllers are not maximizing firm value. To the contrary, we argue here that there is an optimal share of the firm that induces the control group to maximize shareholder value while inducing investors to fund the firm's projects. The concern that controlled companies raise is that the controlling group cannot easily commit not to consume more than the optimal share. A firm's cost of capital is increasing in the controllers’ inability to solve this credibility problem because potential investors will charge the firm for later consuming private benefits. Today, accurate judicial review of controlled transactions under mandatory fiduciary rules helps controllers to commit not to take more than the optimal share. Private contracting and judicial review are strong complements, however. Therefore, our principal normative recommendation is to open up the contracting space by reducing the fiduciary rules to defaults. This reform would be less helpful in developing countries that lack effective legal systems. The public corporations in such countries, however, also have controlling shareholders. We explore various non-legal methods by which these shareholders credibly commit to cap private benefits, although we also show that these methods are less efficient in a mature legal system than contracting would be.
Proton-transfer dynamics in ionized water chains using real-time time-dependent density functional theory

The authors investigate nonadiabatic proton-transfer dynamics in ionized hydrogen-bonded water clusters, describe its connection to hole-localization, and show that the proton transfer probability increases with the length of the hydrogen-bonded chain.
A Deep Q-learning based Path Planning and Navigation System for Firefighting Environments.

Live fire creates a dynamic, rapidly changing environment that presents a worthy challenge for deep learning and artificial intelligence methodologies to assist firefighters with scene comprehension in maintaining their situational awareness, tracking and relay of important features necessary for key decisions as they tackle these catastrophic events. We propose a deep Q-learning based agent who is immune to stress induced disorientation and anxiety and thus able to make clear decisions for navigation based on the observed and stored facts in live fire environments. As a proof of concept, we imitate structural fire in a gaming engine called Unreal Engine which enables the interaction of the agent with the environment. The agent is trained with a deep Q-learning algorithm based on a set of rewards and penalties as per its actions on the environment. We exploit experience replay to accelerate the learning process and augment the learning of the agent with human-derived experiences. The agent trained under this deep Q-learning approach outperforms agents trained through alternative path planning systems and demonstrates this methodology as a promising foundation on which to build a path planning navigation assistant capable of safely guiding fire fighters through live fire environments.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System

Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr
Bra-ket wormholes in gravitationally prepared states

We consider two dimensional CFT states that are produced by a gravitational path integral. As a first case, we consider a state produced by Euclidean AdS2 evolution followed by flat space evolution. We use the fine grained entropy formula to explore the nature of the state. We find that the naive hyperbolic space geometry leads to a paradox. This is solved if we include a geometry that connects the bra with the ket, a bra-ket wormhole. The semiclassical Lorentzian interpretation leads to CFT state entangled with an expanding and collapsing Friedmann cosmology. As a second case, we consider a state produced by Lorentzian dS2 evolution, again followed by flat space evolution. The most naive geometry also leads to a similar paradox. We explore several possible bra-ket wormholes. The most obvious one leads to a badly divergent temperature. The most promising one also leads to a divergent temperature but by making a projection onto low energy states we find that it has features that look similar to the previous Euclidean case. In particular, the maximum entropy of an interval in the future is set by the de Sitter entropy.
BioScript: programming safe chemistry on laboratories-on-a-chip

This paper introduces BioScript, a domain-specific language (DSL) for programmable biochemistry that executes on emerging microfluidic platforms. The goal of this research is to provide a simple, intuitive, and type-safe DSL that is accessible to life science practitioners. The novel feature of the language is its syntax, which aims to optimize human readability; the technical contribution of the paper is the BioScript type system. The type system ensures that certain types of errors, specific to biochemistry, do not occur, such as the interaction of chemicals that may be unsafe. Results are obtained using a custom-built compiler that implements the BioScript language and type system.
Exploration versus Exploitation in Reinforcement Learning: A Stochastic Control Approach

We consider reinforcement learning (RL) in continuous time and study the problem of achieving the best trade-off between exploration of a black box environment and exploitation of current knowledge. We propose an entropy-regularized reward function involving the differential entropy of the distributions of actions, and motivate and devise an exploratory formulation for the feature dynamics that captures repetitive learning under exploration. The resulting optimization problem is a revitalization of the classical relaxed stochastic control. We carry out a complete analysis of the problem in the linear--quadratic (LQ) setting and deduce that the optimal control distribution for balancing exploitation and exploration is Gaussian. This in turn interprets and justifies the widely adopted Gaussian exploration in RL, beyond its simplicity for sampling. Moreover, the exploitation and exploration are captured, respectively and mutual-exclusively, by the mean and variance of the Gaussian distribution. We also find that a more random environment contains more learning opportunities in the sense that less exploration is needed, other things being equal. As the weight of exploration decays to zero, we prove the convergence of the solution to the entropy-regularized LQ problem to that of the classical LQ problem. Finally, we characterize the cost of exploration, which is shown to be proportional to the entropy regularization weight and inversely proportional to the discount rate in the LQ case.
Enabling Opportunistic Low-cost Smart Cities By Using Tactical Edge Node Placement

Smart city projects aim to enhance the management of city infrastructure by enabling government entities to monitor, control and maintain infrastructure efficiently through the deployment of Internet-of-things (IoT) devices. However, the financial burden associated with smart city projects is a detriment to prospective smart cities. A noteworthy factor that impacts the cost and sustainability of smart city projects is providing cellular Internet connectivity to IoT devices. In response to this problem, this paper explores the use of public transportation network nodes and mules, such as bus-stops as buses, to facilitate connectivity via device-to-device communication in order to reduce cellular connectivity costs within a smart city. The data mules convey non-urgent data from IoT devices to edge computing hardware, where data can be processed or sent to the cloud. Consequently, this paper focuses on edge node placement in smart cities that opportunistically leverage public transit networks for reducing reliance on and thus costs of cellular connectivity. We introduce an algorithm that selects a set of edge nodes that provides maximal sensor coverage and explore another that selects a set of edge nodes that provide minimal delivery delay within a budget. The algorithms are evaluated for two public transit network data-sets: Chapel Hill, North Carolina and Louisville, Kentucky. Results show that our algorithms consistently outperform edge node placement strategies that rely on traditional centrality metrics (betweenness and in-degree centrality) by over 77% reduction in coverage budget and over 20 minutes reduction in latency.
Entanglement Entropies of Equilibrated Pure States in Quantum Many-Body Systems and Gravity

We develop a universal approximation for the Renyi entropies of a pure state at late times in a non-integrable many-body system, which macroscopically resembles an equilibrium density matrix. The resulting expressions are fully determined by properties of the associated equilibrium density matrix, and are hence independent of the details of the initial state, while also being manifestly consistent with unitary time-evolution. For equilibrated pure states in gravity systems, such as those involving black holes, this approximation gives a prescription for calculating entanglement entropies using Euclidean path integrals which is consistent with unitarity and hence can be used to address the information loss paradox of Hawking. Applied to recent models of evaporating black holes and eternal black holes coupled to baths, it provides a derivation of replica wormholes, and elucidates their mathematical and physical origins. In particular, it shows that replica wormholes can arise in a system with a fixed Hamiltonian, without the need for ensemble averages.
On Multi-Cascade Influence Maximization: Model, Hardness and Algorithmic Framework

This paper studies the multi-cascade influence maximization problem, which explores the strategies for launching one information cascade in a social network in which multiple cascades can spread concurrently. We leverage the concept of resolution function for defining the correlations between different cascades and propose the independent multi-cascade model. Based on the proposed model, we study the multi-cascade influence maximization problem and provide approximation hardness under common complexity assumptions. Given the hardness results, we build a framework for designing seed selection algorithms with a data-dependent approximation ratio that can be computed in real-time. The main contribution is a novel sampling method for computing the upper and lower bounds that reveal the key combinatorial structure behind the multi-cascade influence maximization problem. The performance of the proposed algorithm is theoretically analyzed and practically evaluated through extensive simulations. The superiority of the proposed solution is supported by encouraging experimental results in terms of effectiveness and efficiency.
Isometry groups of infinite-genus hyperbolic surfaces

Given a 2-manifold, a fundamental question to ask is which groups can be realized as the isometry group of a Riemannian metric of constant curvature on the manifold. In this paper, we give a nearly complete classification of such groups for infinite-genus 2-manifolds with no planar ends. Surprisingly, we show there is an uncountable class of such 2-manifolds where every countable group can be realized as an isometry group (namely, those with self-similar end spaces). We apply this result to obtain obstructions to standard group theoretic properties for the groups of homeomorphisms, diffeomorphisms, and the mapping class groups of such 2-manifolds. For example, none of these groups satisfy the Tits Alternative; are coherent; are linear; are cyclically or linearly orderable; or are residually finite. As a second application, we give an algebraic rigidity result for mapping class groups.
Modeling covid-19 for lifting non-pharmaceutical interventions

As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to slow the spread of the disease Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19 We apply the model to the 24 county-equivalents of Maryland and illustrate that different NPI strategies can be employed in different parts of the state Our objective is to outline a modeling process that combines the critical disease factors and factors relevant to decision-makers who must balance the health of the population with the health of the economy © 2021, University of Surrey All rights reserved
Partisanship, health behavior, and policy attitudes in the early stages of the COVID-19 pandemic.

Objective To study the U.S. public's health behaviors, attitudes, and policy opinions about COVID-19 in the earliest weeks of the national health crisis (March 20-23, 2020). Method We designed and fielded an original representative survey of 3,000 American adults between March 20-23, 2020 to collect data on a battery of 38 health-related behaviors, government policy preferences on COVID-19 response and worries about the pandemic. We test for partisan differences COVID-19 related policy attitudes and behaviors, measured in three different ways: party affiliation, intended 2020 Presidential vote, and self-placed ideological positioning. Our multivariate approach adjusts for a wide range of individual demographic and geographic characteristics that might confound the relationship between partisanship and health behaviors, attitudes, and preferences. Results We find that partisanship-measured as party identification, support for President Trump, or left-right ideological positioning-explains differences in Americans across a wide range of health behaviors and policy preferences. We find no consistent evidence that controlling for individual news consumption, the local policy environment, and local pandemic-related deaths erases the observed partisan differences in health behaviors, beliefs, and attitudes. In further analyses, we use a LASSO regression approach to select predictors, and find that a partisanship indicator is the most commonly selected predictor across the 38 dependent variables that we study. Conclusion Our analysis of individual self-reported behavior, attitudes, and policy preferences in response to COVID-19 reveals that partisanship played a central role in shaping individual responses in the earliest months of the COVID-19 pandemic. These results indicate that partisan differences in responding to a national public health emergency were entrenched from the earliest days of the pandemic.
Application of Yolo on Mask Detection Task

2020 has been a year marked by the COVID-19 pandemic. This event has caused disruptions to many aspects of normal life. An important aspect in reducing the impact of the pandemic is to control its spread. Studies have shown that one effective method in reducing the transmission of COVID-19 is to wear masks. Strict mask-wearing policies have been met with not only public sensation but also practical difficulty. We cannot hope to manually check if everyone on a street is wearing a mask properly. Existing technology to help automate mask checking uses deep learning models on real-time surveillance camera footages. The current dominant method to perform real-time mask detection uses Mask-R-CNN with ResNet as backbone. While giving good detection results, this method is computationally intensive and its efficiency in real-time face mask detection is not ideal. Our research proposes a new approach to the mask detection by replacing Mask-R-CNN with a more efficient model "YOLO" to increase the processing speed of real-time mask detection and not compromise on accuracy. Besides, given the small volume as well as extreme imbalance of the mask detection datasets, we adopt a latest progress made in few-shot visual classification, simple CNAPs, to improve the classification performance.
Online Boosting with Bandit Feedback.

We consider the problem of online boosting for regression tasks, when only limited information is available to the learner. We give an efficient regret minimization method that has two implications: an online boosting algorithm with noisy multi-point bandit feedback, and a new projection-free online convex optimization algorithm with stochastic gradient, that improves state-of-the-art guarantees in terms of efficiency.
Parameterized Concurrent Multi-Party Session Types

Session types have been proposed as a means of statically verifying implementations of communication protocols. Although prior work has been successful in verifying some classes of protocols, it does not cope well with parameterized, multi-actor scenarios with inherent asynchrony. For example, the sliding window protocol is inexpressible in previously proposed session type systems. This paper describes System-A, a new typing language which overcomes many of the expressiveness limitations of prior work. System-A explicitly supports asynchrony and parallelism, as well as multiple forms of parameterization. We define System-A and show how it can be used for the static verification of a large class of asynchronous communication protocols.
Abstinence-only Adolescent Education: Ineffective, Unpopular and Unconstitutional

Abstinence-only and abstinence-only-until-marriage education programs, ostensibly designed to prevent unwanted pregnancy and sexually transmitted disease ("STD") infection, are a waste of valuable public health resources of both time and money. These ideologically based interventions interfere with serious, effective public health education and must be dismantled. Not only are abstinence-only programs ineffective for their intended purpose, they are dangerous in that they fail to protect our nation's youth against serious and potentially deadly diseases. Furthermore, these programs unconstitutionally violate both the Establishment Clause and the unconstitutional conditions doctrine.Part I of this Article reviews the history of abstinence-only education funding legislation and explores some of the inconsistencies these laws inflict into the struggle to protect the health of this nations' adolescents. Part II discusses the effect of these abstinence-only programs on HIV/STD prevention. Part III addresses how these programs fail to address the needs of gay and lesbian students. Part IV addresses the constitutional deficiencies of current federal funding for abstinence-only education. Finally, Part V suggests ways of implementing comprehensive sexual education.
Game Over for First Sale

Video game companies have long considered secondhand game retailers a threat to their bottom lines. With the next generation of gaming consoles on the horizon, some companies are experimenting with technological tools to discourage and even prevent gamers from buying and selling used games. Most significantly, a recent patent application describes a system for suppressing secondhand sales by permanently identifying game discs with a single video game console. This technology flies in the face of copyright law’s “first sale” doctrine, which gives lawful purchasers the right to sell, lease, and lend DVDs, CDs, and other media. This Article answers a question posed by many industry analysts: whether it is legal to employ technology that restricts first sale rights. The answer hinges on two statutes: the Digital Millennium Copyright Act (DMCA) and the Sherman Antitrust Act. The DMCA broadly protects technological measures that restrict access to copyrighted material, which would likely include technologies aimed at suppressing secondhand video game sales. If gamers came up with a method for getting around these devices, they could potentially incur liability under the DMCA. However, some courts have interpreted the DMCA narrowly so as to allow circumvention that does not clearly lead to copyright infringement. Since accessing the content on a video game disc does not constitute infringement, this construction of the statute would most likely permit gamers to lawfully circumvent restrictive technology. Moreover, technology that abridges first sale rights may violate the Sherman Act, which prohibits monopolists from acquiring, maintaining, or extending their market power through predatory or anticompetitive means. Courts recognize the importance of secondhand markets, and have held that monopolists may not use technology to suppress competition. A Sherman Act challenge to antiused game technologies would therefore be plausible. However, the difficulty of demonstrating that any video game company possesses monopoly power could be fatal to the claim. Given these doctrinal complexities, it may ultimately devolve upon consumers to vote with their wallets and choose not to patronize video game companies that engage in anticompetitive business practices. Otherwise, it really could be “Game Over” for first sale.
On Simultaneous Long-Short Stock Trading Controllers with Cross-Coupling

Abstract The Simultaneous Long-Short (SLS) controller for trading a single stock is known to guarantee positive expected value of the resulting gain-loss function with respect to a large class of stock price dynamics. In the literature, this is known as the Robust Positive Expectation (RPE) property. An obvious way to extend this theory to the trading of two stocks is to trade each one of them using its own independent SLS controller. Motivated by the fact that such a scheme does not exploit any correlation between the two stocks, we study the case when the relative sign between the drifts of the two stocks is known. The main contributions of this paper are threefold: First, we put forward a novel architecture in which we cross-couple two SLS controllers for the two-stock case. Second, we derive a closed-form expression for the expected value of the gain-loss function. Third, we use this closed-form expression to prove that the RPE property is guaranteed with respect to a large class of stock-price dynamics. When more information over and above the relative sign is assumed, additional benefits of the new architecture are seen. For example, when bounds or precise values for the means and covariances of the stock returns are included in the model, numerical simulations suggest that our new controller can achieve lower trading risk than a pair of decoupled SLS controllers for the same level of expected trading gain.
Entropy, noncollapsing, and a gap theorem for ancient solutions to the Ricci flow

In this paper we discuss the asymptotic entropy for ancient solutions to the Ricci flow. We prove a gap theorem for ancient solutions, which could be regarded as an entropy counterpart of Yokota's work. In addition, we prove that under some assumptions on one time slice of a complete ancient solution with nonnegative curvature operator, finite asymptotic entropy implies kappa-noncollapsing on all scales. This provides an evidence for Perelman's more general assertion that on a complete ancient solution with nonnegative curvature operator, bounded entropy is equivalent to kappa-noncollapsing.
Site-to-site internet traffic control

Queues allow network operators to control traffic: where queues build, they can enforce scheduling and shaping policies. In the Internet today, however, there is a mismatch between where queues build and where control is most effectively enforced; queues build at bottleneck links that are often not under the control of the data sender. To resolve this mismatch, we propose a new kind of middlebox, called Bundler. Bundler uses a novel inner control loop between a sendbox (in the sender's site) and a receivebox (in the receiver's site) to determine the aggregate rate for the bundle, leaving the end-to-end connections and their control loops intact. Enforcing this sending rate ensures that bottleneck queues that would have built up from the bundle's packets now shift from the bottleneck to the sendbox. This enables the sendbox to exercise control over its traffic by scheduling packets according to any policy necessary to achieve the network operator's higher-level objectives. We have implemented Bundler in Linux and evaluated it with real-world and emulation experiments. We find that Bundler allows the sender-chosen policy to be effective: when configured to implement Stochastic Fairness Queueing (SFQ), it improves median flow completion time (FCT) by between 28% and 97% across various scenarios.
Second Derivatives Estimate of Suitable Solutions to the 3D Navier-Stokes Equations

We study the second spatial derivatives of suitable weak solutions to the incompressible Navier–Stokes equations in dimension three. We show that it is locally $$L ^{\frac{4}{3}, q}$$ for any $$q > \frac{4}{3}$$ , which improves from the current result of $$L ^{\frac{4}{3}, \infty }$$ . Similar improvements in Lorentz space are also obtained for higher derivatives of the vorticity for smooth solutions. We use a blow-up technique to obtain nonlinear bounds compatible with the scaling. The local study works on the vorticity equation and uses De Giorgi iteration. In this local study, we can obtain any regularity of the vorticity without any a priori knowledge of the pressure. The local-to-global step uses a recently constructed maximal function for transport equations.
Cycle-to-cycle queue length estimation from connected vehicles with filtering on primary parameters

Abstract Estimation models from connected vehicles often assume low level parameters such as arrival rates and market penetration rates as known or estimate them in real-time. At low market penetration rates, such parameter estimators produce large errors making estimated queue lengths inefficient for control or operations applications. In order to improve accuracy of low level parameter estimations, this study investigates the impact of connected vehicles information filtering on queue length estimation models. Filters are used as multilevel real-time estimators. Accuracy is tested against known arrival rate and market penetration rate scenarios using microsimulations. To understand the effectiveness for short-term or for dynamic processes, arrival rates, and market penetration rates are changed every 15 min. The results show that with Kalman and Particle filters, parameter estimators are able to find the true values within 15 min and meet and surpass the accuracy of known parameter scenarios especially for low market penetration rates. In addition, using last known estimated queue lengths when no connected vehicle is present performs better than inputting average estimated values. Moreover, the study shows that both filtering algorithms are suitable for real-time applications that require less than 0.1 second computational time.
How cells determine the number of polarity sites.

The diversity of cell morphologies arises, in part, through regulation of cell polarity by Rho-family GTPases. A poorly understood but fundamental question concerns the regulatory mechanisms by which different cells generate different numbers of polarity sites. Mass-conserved activator-substrate (MCAS) models that describe polarity circuits develop multiple initial polarity sites, but then those sites engage in competition, leaving a single winner. Theoretical analyses predicted that competition would slow dramatically as GTPase concentrations at different polarity sites increase towards a 'saturation point', allowing polarity sites to coexist. Here, we test this prediction using budding yeast cells, and confirm that increasing the amount of key polarity proteins results in multiple polarity sites and simultaneous budding. Further, we elucidate a novel design principle whereby cells can switch from competition to equalization among polarity sites. These findings provide insight into how cells with diverse morphologies may determine the number of polarity sites.
Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems

Recent research has demonstrated that ordinal comparison has fast convergence despite the possible presence of large estimation noise in the design of discrete event dynamic systems. In this paper, we address the fundamental problem of characterizing the convergence of ordinal comparison. To achieve this goal, an indicator process is formulated and its properties are examined. For several performance measures frequently used in simulation, the rate of convergence for the indicator process is proven to be exponential for regenerative simulations. Therefore, the fast convergence of ordinal comparison is supported and explained in a rigorous framework. Many performance measures of averaging type have asymptotic normal distributions. The results of this paper show that ordinal comparison converges monotonically in the case of averaging normal random variables. Such monotonicity is useful in simulation planning.
Understanding Bounding Functions in Safety-Critical UAV Software

Unmanned Aerial Vehicles (UAVs) are an emerging computation platform known for their safety-critical need. In this paper, we conduct an empirical study on a widely used open-source UAV software framework, Paparazzi, with the goal of understanding the safety-critical concerns of UAV software from a bottom-up developer-in-the-field perspective. We set our focus on the use of Bounding Functions (BFs), the runtime checks injected by Paparazzi developers on the range of variables. Through an in-depth analysis on BFs in the Paparazzi autopilot software, we found a large number of them (109 instances) are used to bound safety-critical variables essential to the cyber-physical nature of the UAV, such as its thrust, its speed, and its sensor values. The novel contributions of this study are two fold. First, we take a static approach to classify all BF instances, presenting a novel datatype-based 5-category taxonomy with fine-grained insight on the role of BFs in ensuring the safety of UAV systems. Second, we dynamically evaluate the impact of the BF uses through a differential approach, establishing the UAV behavioral difference with and without BFs. The two-pronged static and dynamic approach together illuminates a rarely studied design space of safety-critical UAV software systems.
Visualizing Missing Surfaces In Colonoscopy Videos Using Shared Latent Space Representations

Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has a high miss rate due to a number of factors, including the geometry of the colon (haustral fold and sharp bends occlusions), endoscopist inexperience or fatigue, endoscope field of view, etc. We present a framework to visualize the missed regions per-frame during the colonoscopy, and provides a workable clinical solution. Specifically, we make use of 3D reconstructed virtual colonoscopy (VC) data and the insight that VC and OC share the same underlying geometry but differ in color, texture and specular reflections, embedded in the OC domain. A lossy unpaired image-to-image translation model is introduced with enforced shared latent space for OC and VC. This shared latent space captures the geometric information while deferring the color, texture, and specular information creation to additional Gaussian noise input. This additional noise input can be utilized to generate one-to-many mappings from VC to OC and OC to OC.
Auto-Validate: Unsupervised Data Validation Using Data-Domain Patterns Inferred from Data Lakes

Complex data pipelines are increasingly common in diverse applications such as BI reporting and ML modeling. These pipelines often recur regularly (e.g., daily or weekly), as BI reports need to be refreshed, and ML models need to be retrained. However, it is widely reported that in complex production pipelines, upstream data feeds can change in unexpected ways, causing downstream applications to break silently that are expensive to resolve. Data validation has thus become an important topic, as evidenced by notable recent efforts from Google and Amazon, where the objective is to catch data quality issues early as they arise in the pipelines. Our experience on production data suggests, however, that on string-valued data, these existing approaches yield high false-positive rates and frequently require human intervention. In this work, we develop a corpus-driven approach to auto-validate machine-generated data by inferring suitable data-validation "patterns'' that accurately describe the underlying data-domain, which minimizes false-positives while maximizing data quality issues caught. Evaluations using production data from real data lakes suggest that \sj is substantially more effective than existing methods. Part of this technology ships as an Auto-Tag feature in Microsoft Azure Purview.
Atomic Oxygen Treatment and Its Effect on a Variety of Artist’s Media

Atomic oxygen treatment has been investigated as an unconventional option for art restoration where conventional methods have not been effective. Exposure of surfaces to atomic oxygen was first performed to investigate the durability of materials in the low Earth orbit environment of space. The use of the ground based environmental simulation chambers, developed for atomic oxygen exposure testing, has been investigated in collaboration with conservators at a variety of institutions, as a method to clean the surfaces of works of art. The atomic oxygen treatment technique has been evaluated as a method to remove soot and char from the surface of oil paint (both varnished and unvarnished), watercolors, acrylic paint, and fabric as well as the removal of graffiti and other marks from surfaces which are too porous to lend themselves to conventional solvent removal techniques. This paper will discuss the treatment of these surfaces giving an example of each and a discussion of the treatment results.
Distinct in vivo Dynamics of Excitatory Synapses Onto Cortical Pyramidal Neurons and Inhibitory Interneurons

Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons, which cannot be easily identified morphologically. Here, we fluorescently visualize the excitatory postsynaptic marker PSD-95 at endogenous levels as a proxy for excitatory synapses onto layer 2/3 pyramidal neurons and parvalbumin-positive (PV+) inhibitory interneurons in the mouse barrel cortex. Longitudinal in vivo imaging reveals that, while synaptic weights in both neuronal types are log-normally distributed, synapses onto PV+ neurons are less heterogeneous and more stable. Markov-model analyses suggest that the synaptic weight distribution is set intrinsically by ongoing cell type-specific dynamics, and substantial changes are due to accumulated gradual changes. Synaptic weight dynamics are multiplicative, i.e., changes scale with weights, though PV+ synapses also exhibit an additive component. These results reveal that cell type-specific processes govern cortical synaptic strengths and dynamics.
Thermally tolerant symbionts may explain Caribbean octocoral resilience to heat stress

Coral reef ecosystems are under threat from the frequent and severe impacts of anthropogenic climate change, particularly rising sea surface temperatures. The effects of thermal stress may be ameliorated by adaptation and/or acclimation of the host, symbiont, or holobiont (host + symbiont) to increased temperatures. We examined the role of the symbiont in promoting thermal tolerance of the holobiont, using Antillogorgia bipinnata (octocoral host) and Breviolum antillogorgium (symbiont) as a model system. We identified five distinct genotypes of B. antillogorgium from symbiont populations isolated from Antillogorgia colonies in the Florida Keys. Three symbiont genotypes were cultured and maintained at 26 °C (ambient historical temperature), and two were cultured and maintained at 30 °C (elevated historical temperature) for 2 yrs. We analyzed the growth rate and carrying capacity of each symbiont genotype at both ambient and elevated temperatures in culture (in vitro). All genotypes grew well at both temperatures, indicating that thermal tolerance exists among these B. antillogorgium cultures. However, a history of long-term growth at 30 °C did not yield better performance for B. antillogorgium at 30 °C (as compared to 26 °C), suggesting that prior culturing at the elevated temperature did not result in increased thermal tolerance. We then inoculated juvenile A. bipinnata polyps with each of the five symbiont genotypes and reared these polyps at both ambient and elevated temperatures (in hospite experiment). All genotypes established symbioses with polyps in both temperature treatments. Survivorship of polyps at 30 °C was significantly lower than survivorship at 26 °C, but all treatments had surviving polyps at 56 d post-infection. Our results suggest broad thermal tolerance in B. antillogorgium, which may play a part in the increased resilience of Caribbean octocorals during heat stress events.
A methodological overview to defining and measuring “digital” financial literacy

Researchers and financial practitioners alike recognize the importance of defining and measuring financial literacy (FL) to better understand its relationship to financial behavior and decision-making. Despite many efforts, there is still no widely accepted definition or methodological approach for measuring FL. The rapid expansion of digital financial services (DFS), which promises to enhance financial inclusion and improve personal financial management, has brought to light a new challenge: linking FL to digital literacy (DL) and assessing their dual effect on financial outcomes. Recent research has even proposed a framework to operationalize the emerging concept of digital financial literacy (DFL), as traditional FL definitions and metrics have become insufficient to capture the specificities of financial services within a digital context. This survey article discusses empirical research techniques being used to assess FL, DL, and most recently DFL. It highlights the characteristics and limitations of these approaches and suggests ways to address some of the challenges related to the construction, testing, weighting, and standardization of multidimensional measures, as well as methodological issues related to modeling and estimation. The article is a helpful guide to researchers and practitioners interested in FL in general and in the emerging concept of “digital” financial literacy.
Permutable SOS (symmetry operational similarity)

Based on symmetry consideration, quasi-one-dimensional (1D) objects, relevant to numerous observables or phenomena, can be classified into eight different types. We provide various examples of each 1D type and discuss their symmetry operational similarity (SOS) relationships, which are often permutable. A number of recent experimental observations, including current-induced magnetization in polar or chiral conductors, non-linear Hall effect in polar conductors, spin-polarization of tunneling current to chiral conductors, and ferro-rotational domain imaging with linear gyration are discussed in terms of (permutable) SOS. In addition, based on (permutable) SOS, we predict a large number of new phenomena in low symmetry materials that can be experimentally verified in the future.
Tight conditional lower bounds for approximating diameter in directed graphs

Among the most fundamental graph parameters is the Diameter, the largest distance between any pair of vertices in a graph. Computing the Diameter of a graph with m edges requires m2−o(1) time under the Strong Exponential Time Hypothesis (SETH), which can be prohibitive for very large graphs, so efficient approximation algorithms for Diameter are desired. There is a folklore algorithm that gives a 2-approximation for Diameter in O(m) time (where O notation suppresses logarithmic factors). Additionally, a line of work [SODA’96, STOC’13, SODA’14] concludes with a 3/2-approximation algorithm for Diameter in weighted directed graphs that runs in O(m3/2) time. For directed graphs, these are the only known approximation algorithms for Diameter. The 3/2-approximation algorithm is known to be tight under SETH: Roditty and Vassilevska W. [STOC’13] proved that under SETH any 3/2−e approximation algorithm for Diameter in undirected unweighted graphs requires m2−o(1) time, and then Backurs, Roditty, Segal, Vassilevska W., and Wein [STOC’18] and the follow-up work of Li proved that under SETH any 5/3−e approximation algorithm for Diameter in undirected unweighted graphs requires m3/2−o(1) time. Whether or not the folklore 2-approximation algorithm is tight, however, is unknown, and has been explicitly posed as an open problem in numerous papers. Towards this question, Bonnet recently proved that under SETH, any 7/4−e approximation requires m4/3−o(1), only for directed weighted graphs. We completely resolve this question for directed graphs by proving that the folklore 2-approximation algorithm is conditionally optimal. In doing so, we obtain a series of conditional lower bounds that together with prior work, give a complete time-accuracy trade-off that is tight with the three known algorithms for directed graphs. Specifically, we prove that under SETH for any δ>0, a (2k−1/k−δ)-approximation algorithm for Diameter on directed unweighted graphs requires mk/k−1−o(1) time.
Fair and Efficient Allocations with Limited Demands.

We study the fair division problem of allocating multiple resources among a set of agents with Leontief preferences that are each required to complete a finite amount of work, which we term "limited demands". We examine the behavior of the classic Dominant Resource Fairness (DRF) mechanism in this setting and show it is fair but only weakly Pareto optimal and inefficient in many natural examples. We propose as an alternative the Least Cost Product (LCP) mechanism, a natural adaptation of Maximum Nash Welfare to this setting. We characterize the structure of allocation of the LCP mechanism in this setting, show that it is Pareto efficient, and that it satisfies the relatively weak fairness property of sharing incentives. While we prove that it satisfies the stronger fairness property of (expected) envy freeness in some special cases, we provide a counterexample showing it does not do so in general, a striking contrast to the "unreasonable fairness" of Maximum Nash Welfare in other settings. Simulations suggest, however, that these violations of envy freeness are rare in randomly generated examples.