cssi/SciSciGPT-SciSciNet huggingface dataset:
On the non-diffusive Magneto-Geostrophic equation

Motivated by an equation arising in magnetohydrodynamics, we address the well-posedness theroy for the non-diffusive magneto-geostrophic equation. Namely, an active scalar equation in which the divergence-free drift velocity is one derivative more singular than the active scalar. In Friedlander and Vicol (Nonlinearity 24(11)::3019–3042, 2011), the authors prove that the non-diffusive equation is ill-posed in the sense of Hadamard in Sobolev spaces, but locally well posed in spaces of analytic functions. Here, we give an example of a steady state that is nonlinearly stable for periodic perturbations with initial data localized in frequency straight lines crossing the origin. For such well-prepared data, the local existence and uniqueness of solutions can be obtained in Sobolev spaces and the global existence holds under a size condition over the $$H^{5/2^{+}}({{\mathbb {T}}}^3)$$ norm of the perturbation.
A (2+ε)-Approximation Scheme for Minimum Domination on Circle Graphs

The main result of this paper is a (2+?)-approximation scheme for the minimum dominating set problem on circle graphs. We first present an O(n2) time 8-approximation algorithm for this problem and then extend it to an O(n3+6?n?+1?m) time (2+?)-approximation scheme for this problem. Here n and m are the number of vertices and the number of edges of the circle graph. We then present simple modifications to this algorithm that yield (3+?)-approximation schemes for the minimum connected and the minimum total dominating set problems on circle graphs. Keil (1993, Discrete Appl. Math.42, 51?63) shows that these problems are NP-complete for circle graphs and leaves open the problem of devising approximation algorithms for them. These are the first O(1)-approximation algorithms for domination problems on circle graphs.
A Happy-Go-Lucky Story: The American Supreme Court and Overload Problems

Nowadays, the U.S. Supreme Court controls its own docket thanks to the discretion it enjoys to select cases based on the writ of certiorari. As a result, the current court has no serious problems of overload and, in fact, the number of cases has decreased by half compared to previous generations. Furthermore, the reduced caseload has not diminished the prominent role that the Supreme Court plays in American society today. But it was not always so. In its beginnings, the Supreme Court was a ‘feeble institution,’ with justices riding circuit, with almost no cases and with not enough justices showing up anyway. After the Civil War, the federal courts and the Supreme Court gained more jurisdiction on ‘federal questions’ and, consequently, more cases to resolve. This increase in powers was not accompanied by changes in the judicial structure—which produced a case overload problem—until 1891. In that year, the U.S. Congress abolished the task of riding circuit for Supreme Court justices and created the intermediate circuit courts of appeals. This reform, however, brought only temporary relief. Litigants kept an automatic right to appeal to the Supreme Court, increasing its caseload pressures again. In the Judiciary Act of 1925, combined with other reforms in a similar direction, Congress redefined the role of the Supreme Court by giving it near-complete control of its docket. This contribution recounts the long journey through which the U.S. Supreme Court became its own master and how case overload problems are now long forgotten.
Optimal policy for attention-modulated decisions explains human fixation behavior

Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.
China, the G20 and the International Investment Regime

China has become a major home country for outward foreign direct investment (OFDI) flows. As a result, the country is increasingly concerned with protecting its OFDI and facilitating the operations of its firms investing abroad and creating a strong universal international investment law and policy regime. This article briefly reviews the emergence of China as an outward investor. It continues with an analysis of some policy issues related to the rise of FDI from emerging markets. A brief discussion of issues central to the future of the international investment law and policy regime follows, before focusing on several outcomes that could be pursued under China's G20 leadership: nonbinding shared principles that could outline the architecture of a universal framework on international investment; an international support program for sustainable investment facilitation; and the creation of an additional intergovernmental platform that would allow for a continued systematic intergovernmental process to discuss the range of issues related to the governance of international investment, preferably paralleled by an informal, inclusive and result-oriented consensus-building process that takes place outside intergovernmental settings.
An Open Source Platform for Presenting Dynamic Visual Stimuli.

Operant behavior procedures often rely on visual stimuli to cue the initiation or secession of a response, and to provide a means for discriminating between two or more simultaneously available responses. While primate and human studies typically use LCD or OLED monitors and touch screens, rodent studies use a variety of methods to present visual cues ranging from traditional incandescent light bulbs, single LEDs, and, more recently, touch screen monitors. Commercially available systems for visual stimulus presentation are costly, challenging to customize, and are typically closed source. We developed an open-source, highly-modifiable visual stimulus presentation platform that can be combined with a 3D-printed operant response device. The device uses an eight by eight matrix of LEDs, and can be expanded to control much larger LED matrices. Implementing the platform is low-cost ( Significance Statement The design of an open source and low cost device for presenting visual stimuli is described. It is capable of presenting complex visual patterns and dynamically changing stimuli. A practical demonstration of the device is also reported, from an experiment in which rats performed a luminance based visual discrimination. The device has utility for studying visual processing, psychophysics, and decision making in a variety of species.
FPGA-based tracklet approach to Level-1 track finding at CMS for the HL-LHC

During the High Luminosity LHC, the CMS detector will need charged particle tracking at the hardware trigger level to maintain a manageable trigger rate and achieve its physics goals. The tracklet approach is a track-finding algorithm based on a road-search algorithm that has been implemented on commercially available FPGA technology. The tracklet algorithm has achieved high performance in track-finding and completes tracking within 3.4 μ s on a Xilinx Virtex-7 FPGA. An overview of the algorithm and its implementation on an FPGA is given, results are shown from a demonstrator test stand and system performance studies are presented.
IL‐10 lentivirus‐laden hydrogel tubes increase spinal progenitor survival and neuronal differentiation after spinal cord injury

A complex cellular cascade characterizes the pathophysiological response following spinal cord injury (SCI) limiting regeneration. Biomaterial and stem cell combination therapies together have shown synergistic effects, compared to the independent benefits of each intervention, and represent a promising approach towards regaining function after injury. In this study, we combine our polyethylene glycol (PEG) cell delivery platform with lentiviral-mediated overexpression of the anti-inflammatory cytokine interleukin (IL)-10 to improve mouse embryonic Day 14 (E14) spinal progenitor transplant survival. Immediately following injury in a mouse SCI hemisection model, five PEG tubes were implanted followed by direct injection into the tubes of lentivirus encoding for IL-10. Two weeks after tube implantation, mouse E14 spinal progenitors were injected directly into the integrated tubes, which served as a soft substrate for cell transplantation. Together, the tubes with the IL-10 encoding lentivirus improved E14 spinal progenitor survival, assessed at 2 weeks posttransplantation (4 weeks postinjury). On average, 8.1% of E14 spinal progenitors survived in mice receiving IL-10 lentivirus-laden tubes compared with 0.7% in mice receiving transplants without tubes, an 11.5-fold difference. Surviving E14 spinal progenitors gave rise to neurons when injected into tubes. Axon elongation and remyelination were observed, in addition to a significant increase in functional recovery in mice receiving IL-10 lentivirus-laden tubes with E14 spinal progenitor delivery compared to the injury only control by 4 weeks postinjury. All other conditions did not exhibit increased stepping until 8 or 12 weeks postinjury. This system affords increased control over the transplantation microenvironment, offering the potential to improve stem cell-mediated tissue regeneration.
Scouting the Path to a Million-Client Server

To keep up with demand, servers will scale up to handle hundreds of thousands of clients simultaneously. Much of the focus of the community has been on scaling servers in terms of aggregate traffic intensity (packets transmitted per second). However, bottlenecks caused by the increasing number of concurrent clients, resulting in a large number of concurrent flows, have received little attention. In this work, we focus on identifying such bottlenecks. In particular, we define two broad categories of problems; namely, admitting more packets into the network stack than can be handled efficiently, and increasing per-packet overhead within the stack. We show that these problems contribute to high CPU usage and network performance degradation in terms of aggregate throughput and RTT. Our measurement and analysis are performed in the context of the Linux networking stack, the most widely used publicly available networking stack. Further, we discuss the relevance of our findings to other network stacks. The goal of our work is to highlight considerations required in the design of future networking stacks to enable efficient handling of large numbers of clients and flows.
Structure of normally and finitely non-co-Hopfian groups

A group G is (finitely) co-Hopfian if it does not contain any proper (finite-index) subgroups isomorphic to itself. We study finitely generated groups G that admit a descending chain of proper normal finite-index subgroups, each of which is isomorphic to G. We prove that up to finite index, these are always obtained by pulling back a chain of subgroups from a free abelian quotient. We give two applications: First, we show any characteristic proper finite-index subgroup isomorphic to G arises by pulling back a finite-index subgroup of the abelianization, and secondly, we prove special cases (for normal subgroups) of conjectures of Benjamini and Nekrashevych--Pete regarding the classification of scale-invariant groups.
Friedman Versus Hayek on Private Outside Monies: New Evidence for the Debate

Friedrich Hayek is often credited with the resurgence of interest in alternative monetary systems. His own proposal, however, received sharp criticism from Milton Friedman, Stanley Fischer, and others at the outset and never gained much support among academic economists or the wider population. According to Friedman, Hayek erred in believing that the mere admission of competing private currencies will spontaneously generate a more stable monetary system. In Friedman’s view, network effects and switching costs discourage an alternative system from emerging in general and prevent Hayek’s system from functioning as desired in particular. I offer new evidence provided by recent events in Somalia as support for Friedman’s initial doubts.
Novel Newt Regeneration Genes Regulate Wingless Signaling to Restore Patterning in Drosophila Eye

A fundamental process of regeneration, which varies among animals, recruits conserved signaling pathways to restore missing parts. Only a few animals like newts can repeatedly regenerate lost body parts throughout their lifespan that can be attributed to strategic regulation of conserved signaling pathways by newt’s regeneration tool-kit genes. Lack of genetic tools pose challenges to understand the molecular genetic mechanism of regeneration response of these genes. Here we report use of genetically tractable Drosophila eye model to demonstrate the regeneration potential of a group of unique protein(s) from newt (Notophthalmus viridescens), which when ectopically expressed can significantly rescue missing photoreceptor cells in a Drosophila eye mutant. These newt proteins with signal peptides motifs exhibit non-cell-autonomous rescue properties and their regeneration potential even extends into later stages of fly development. Ectopic expression of these newt genes can rescue eye mutant phenotype by promoting cell proliferation and blocking cell death. These novel newt genes downregulate evolutionarily conserved Wingless (Wg)/Wnt signaling pathway to promote rescue. Modulation of Wg/Wnt signaling levels by using antagonists or agonists of Wg/Wnt signaling pathway in eye mutant background where newt gene(s) is ectopically expressed suggests that Wg signaling acts downstream of newt genes. Our data highlights the regeneration potential of novel newt proteins that regulate conserved pathways to trigger a robust regeneration response in Drosophila model with weak regeneration capability.
Reduced conformal symmetry

We construct field theories in 2 + 1 dimensions with multiple conformal symmetries acting on only one of the spatial directions. These can be considered a conformal extension to “subsystem scale invariances”, borrowing the language often used for fractons.
Strategic Network Formation Through an Intermediary

Settings in which independent self-interested agents form connections with each other are extremely common, and are usually modeled using network formation games. We study a natural extension of network formation games in which the nodes cannot form connections themselves, but instead must do it through an intermediary, and must pay the intermediary to form these connections. The price charged by the intermediary is assumed to be determined by its operating costs, which in turn depend on the total amount of connections it facilitates. We investigate the existence and worst-case efficiency (price of anarchy) of stable solutions in these settings, and especially when the intermediary uses common pricing schemes like proportional pricing or marginal cost pricing. For both these pricing schemes we prove existence of stable solutions and completely characterize their structure, as well as generalize these results to a large class of pricing schemes. Our main results are on bounding the price of anarchy in such settings: we show that while marginal cost pricing leads to an upper bound of only 2, i.e., stable solutions are always close to optimal, proportional pricing also performs reasonably well as long as the operating costs of the intermediary are not too convex.
County-level exposures to greenness and associations with COVID-19 incidence and mortality in the United States.

Abstract Background COVID-19 is an infectious disease that has killed more than 555,000 people in the US. During a time of social distancing measures and increasing social isolation, green spaces may be a crucial factor to maintain a physically and socially active lifestyle while not increasing risk of infection. Objectives We evaluated whether greenness was related to COVID-19 incidence and mortality in the US. Methods We downloaded data on COVID-19 cases and deaths for each US county up through June 7, 2020, from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. We used April–May 2020 Normalized Difference Vegetation Index (NDVI) data, to represent the greenness exposure during the initial COVID-19 outbreak in the US. We fitted negative binomial mixed models to evaluate associations of NDVI with COVID-19 incidence and mortality, adjusting for potential confounders such as county-level demographics, epidemic stage, and other environmental factors. We evaluated whether the associations were modified by population density, proportion of Black residents, median home value, and issuance of stay-at-home orders. Results An increase of 0.1 in NDVI was associated with a 6% (95% Confidence Interval: 3%, 10%) decrease in COVID-19 incidence rate after adjustment for potential confounders. Associations with COVID-19 incidence were stronger in counties with high population density and in counties with stay-at-home orders. Greenness was not associated with COVID-19 mortality in all counties; however, it was protective in counties with higher population density. Discussion Exposures to NDVI were associated with reduced county-level incidence of COVID-19 in the US as well as reduced county-level COVID-19 mortality rates in densely populated counties.
Modulating the voltage sensor of a cardiac potassium channel shows antiarrhythmic effects

Cardiac arrhythmias are the most common cause of sudden cardiac death worldwide. Lengthening the ventricular action potential duration (APD), either congenitally or via pathologic or pharmacologic means, predisposes to a life-threatening ventricular arrhythmia, Torsade de Pointes. IKs (KCNQ1+KCNE1), a slowly activating K+ current, plays a role in action potential repolarization. In this study, we screened a chemical library in silico by docking compounds to the voltage-sensing domain (VSD) of the IKs channel. Here, we show that C28 specifically shifted IKs VSD activation in ventricle to more negative voltages and reversed the drug-induced lengthening of APD. At the same dosage, C28 did not cause significant changes of the normal APD in either ventricle or atrium. This study provides evidence in support of a computational prediction of IKs VSD activation as a potential therapeutic approach for all forms of APD prolongation. This outcome could expand the therapeutic efficacy of a myriad of currently approved drugs that may trigger arrhythmias.
Unsupervised clustering and performance prediction of vortex wakes from bio-inspired propulsors.

An unsupervised machine learning strategy is developed to automatically cluster the vortex wakes of bio-inspired propulsors into groups of similar propulsive thrust and efficiency metrics. A pitching and heaving foil is simulated via computational fluid dynamics with 121 unique kinematics by varying the frequency, heaving amplitude, and pitching amplitude. A Reynolds averaged Navier-Stokes model is employed to simulate the flow over the oscillating foils at Re = 106, computing the propulsive efficiency, thrust coefficient and the unsteady vorticity wake signature. Using a pairwise Pearson correlation it is found that the Strouhal number most strongly influences the thrust coefficient, whereas the relative angle of attack, defined by both the mid-stroke and maximum have the most significant impact on propulsive efficiency. Next, the various kinematics are automatically clustered into distinct groups exclusively using the vorticity footprint in the wake. A convolutional autoencoder is developed to reduce vortex wake images to their most significant features, and ak-means++ algorithm performs the clustering. The results are assessed by comparing clusters to a thrust versus propulsive efficiency map, which confirms that wakes of similar performance metrics are successfully clustered together. This automated clustering has the potential to identify complex vorticity patterns in the wake and modes of propulsion not easily discerned from traditional classification methods.
Skip Blocks: Reusing Execution History to Accelerate Web Scripts

With more and more web scripting languages on offer, programmers have access to increasing language support for web scraping tasks. However, in our experiences collaborating with data scientists, we learned that two issues still plague long-running scraping scripts: i) When a network or website goes down mid-scrape, recovery sometimes requires restarting from the beginning, which users find frustratingly slow. ii) Websites do not offer atomic snapshots of their databases; they update their content so frequently that output data is cluttered with slight variations of the same information — \emph{e.g.}, a tweet from profile 1 that is retweeted on profile 2 and scraped from both profiles, once with 52 responses then later with 53 responses. We introduce the \emph{skip block}, a language construct that addresses both of these disparate problems. Programmers write lightweight annotations to indicate when the current object can be considered equivalent to a previously scraped object and direct the program to skip over the scraping actions in the block. The construct is hierarchical, so programs can skip over long or short script segments, allowing adaptive reuse of prior work. After network and server failures, skip blocks accelerate failure recovery by 7.9x on average. Even scripts that do not encounter failures benefit; because sites display redundant objects, skipping over them accelerates scraping by up to 2.1x. For longitudinal scraping tasks that aim to fetch only new objects, the second run exhibits an average speedup of 5.2x. Our small user study reveals that programmers can quickly produce skip block annotations.
Growing A Test Corpus with Bonsai Fuzzing

This paper presents a coverage-guided grammar-based fuzzing technique for automatically synthesizing a corpus of concise test inputs. We walk-through a case study of a compiler designed for education and the corresponding problem of generating meaningful test cases to provide to students. The prior state-of-the-art solution is a combination of fuzzing and test-case reduction techniques such as variants of delta-debugging. Our key insight is that instead of attempting to minimize convoluted fuzzer-generated test inputs, we can instead grow concise test inputs by construction using a form of iterative deepening. We call this approach bonsai fuzzing. Experimental results show that bonsai fuzzing can generate test corpora having inputs that are 16-45% smaller in size on average as compared to a fuzz-then-reduce approach, while achieving approximately the same code coverage and fault-detection capability.
Finite Element Reconstruction of Stiffness Images in MR Elastography Using Statistical Physical Forward Modeling and Proximal Optimization Methods

Quantitative characterization of tissue properties, known as elasticity imaging, can be cast as solving an ill-posed inverse problem. The finite element methods (FEMs) in magnetic resonance elastography (MRE) imaging are based on solving a constrained optimization problem consisting of a physical forward model and a regularizer as the data-fidelity term and the prior term, respectively. In existing formulation for the elasticity forward model, physical laws that arise from equilibrium equation of harmonic motion, indicate a deterministic relationship between MRE-measured data and unknown elasticity distribution which leads to the poor and unstable elasticity distribution estimation in the presence of noise. Toward this end, we propose an efficient statistical methodology for physical forward model refinement by formulating it as linear algebraic representation with respect to the unknown elasticity distribution and incorporating an analytical noise model. To solve the subsequent total variation regularized optimization task, we benefit from a fixed-point scheme involving proximal gradient methods. Simulation results of elasticity reconstruction in various SNR conditions verify the effectiveness of the proposed approach.
Rutting performance prediction using index-volumetrics relationships with stress sweep rutting test and Hamburg wheel-track test

Abstract This paper describes a methodology to develop index-volumetrics relationships (IVRs) for rutting of asphalt mixtures. These relationships are developed from rutting performance test results and the volumetric properties (In-place VMA and In-place VFA) of the mixture at so-called ‘four corners’ of the volumetric space. Two rutting tests, the stress sweep rutting (SSR) test and Hamburg wheel-track (HWT) test, were employed to characterize the rutting performance of two mixtures acquired from two field projects. The IVRs for the individual mixtures and test methods were developed and used to predict the mixtures’ rutting performance as a function of the two volumetric properties.
Cryptography from sublinear-time average-case hardness of time-bounded Kolmogorov complexity

Let MKtP[s] be the set of strings x such that Kt(x) ≤ s(|x|), where Kt(x) denotes the t-bounded Kolmogorov complexity of the truthtable described by x. Our main theorem shows that for an appropriate notion of mild average-case hardness, for every e>0, polynomial t(n) ≥ (1+e)n, and every “nice” class F of super-polynomial functions, the following are equivalent: (i) the existence of some function T ∈ F such that T-hard one-way functions (OWF) exists (with non-uniform security); (ii) the existence of some function T ∈ F such that MKtP[T−1] is mildly average-case hard with respect to sublinear-time non-uniform algorithms (with running-time nδ for some 0tP[poly logn] (resp. MKtP[2O(√logn))]) w.r.t. sublinear-time non-uniform algorithms. We additionally note that if we want to deduce T-hard OWFs where security holds w.r.t. uniform T-time probabilistic attackers (i.e., uniformly-secure OWFs), it suffices to assume sublinear time hardness of MKtP w.r.t. uniform probabilistic sublinear-time attackers. We complement this result by proving lower bounds that come surprisingly close to what is required to unconditionally deduce the existence of (uniformly-secure) OWFs: MKtP[polylogn] is worst-case hard w.r.t. uniform probabilistic sublinear-time algorithms, and MKtP[n−logn] is mildly average-case hard for all O(t(n)/n3)-time deterministic algorithms.
Optimized biasing of pump laser diodes in a highly reliable metrology source for long-duration space missions

Optical metrology system reliability during a prolonged space mission is often limited by the reliability of pump laser diodes. We developed a metrology laser pump module architecture that meets NASA SIM Lite instrument optical power and reliability requirements by combining the outputs of multiple single-mode pump diodes in a low-loss, high port count fiber coupler. We describe Monte-Carlo simulations used to calculate the reliability of the laser pump module and introduce a combined laser farm aging parameter that serves as a load-sharing optimization metric. Employing these tools, we select pump module architecture, operating conditions, biasing approach and perform parameter sensitivity studies to investigate the robustness of the obtained solution.
Crash energy absorbing composite sub-floor structure

Static crushing tests were conducted on four different beam concepts; honeycomb sandwich, sine-wave and two integrally stiffened designs. The sine-wave beams, depending upon specimen geometry, has the highest energy absorption potential of the four concepts evaluated. All beam designs produced a progressive crushing mode similar to tube specimens. The energy absorption capability of sine-wave beam specimens were predictable from results of circular cross section tubes. A comparison of energy absorption capability was made between integrally stiffened beams fabricated from graphite/epoxy, Kevlar-49/epoxy and aluminum. The energy absorption capability of the graphite/epoxy integrally stiffened beams exceeded both the Kevlar-49/epoxy and aluminum integrally stiffened beams. The energy absorption potential of composite structures is between five and ten times that of comparable metallic structure.
Entitled to Property: Inheritance Laws, Female Bargaining Power, and Child Health in India

Child height is a significant predictor of human capital and economic status throughout adulthood. Moreover, non-unitary household models of family behavior posit that an increase in women's bargaining power can influence child health. We study the effects of an inheritance policy change, the Hindu Succession Act Amendment (HSAA), which conferred enhanced inheritance rights to unmarried women, on child height. We find robust evidence that the HSAA improved the height and weight of children. In addition, we find evidence consistent with a channel that the policy improved the women's intrahousehold bargaining power within the household, leading to improved parental investments for children. These study findings are also compatible with the notion that children do better when their mothers control a more significant fraction of the family resources. Therefore, policies that empower women can have additional positive spillovers for children's human capital.
Incentive-Compatible Kidney Exchange in a Slightly Semi-Random Model

Motivated by kidney exchange, we study the following mechanism-design problem: On a directed graph (of transplant compatibilities among patient--donor pairs), the mechanism must select a simple path (a chain of transplantations) starting at a distinguished vertex (an altruistic donor) such that the total length of this path is as large as possible (a maximum number of patients receive a kidney). However, the mechanism does not have direct access to the graph. Instead, the vertices are partitioned over multiple players (hospitals), and each player reports a subset of her vertices to the mechanism. In particular, a player may strategically omit vertices to increase how many of her vertices lie on the path returned by the mechanism. Our objective is to find mechanisms that limit incentives for such manipulation while producing long paths. Unfortunately, in worst-case instances, competing with the overall longest path is impossible while incentivizing (approximate) truthfulness, i.e., requiring that hiding nodes cannot increase a player's utility by more than a factor of 1 + o(1). We therefore adopt a semi-random model where a small ($o(n)$) number of random edges are added to worst-case instances. While it remains impossible for truthful mechanisms to compete with the overall longest path, we give a truthful mechanism that competes with a weaker but non-trivial benchmark: the length of any path whose subpaths within each player have a minimum average length. In fact, our mechanism satisfies even a stronger notion of truthfulness, which we call matching-time incentive compatibility. This notion of truthfulness requires that each player not only reports her nodes truthfully but also does not stop the returned path at any of her nodes in order to divert it to a continuation inside her own subgraph.
Measuring the impact of influence on individuals: roadmap to quantifying attitude

Diffusion of information in social network has been the focus of intense research in the recent past decades due to its significant impact in shaping public discourse through group/individual influence. Existing research primarily models influence as a binary property of entities: influenced or not influenced. While this is a useful abstraction, it discards the notion of degree of influence, i.e., certain individuals may be influenced “more” than others. We introduce the notion of attitude, which, as described in social psychology, is the degree by which an entity is influenced by the information. Intuitively, attitude captures the number of distinct neighbors of an entity influencing the latter. We present an information diffusion model (AIC model) that quantifies the degree of influence, i.e., attitude of individuals, in a social network. With this model, we formulate and study attitude maximization problem. We prove that the function for computing attitude is monotonic and sub-modular, and the attitude maximization problem is NP-Hard. We present a greedy algorithm for maximization with an approximation guarantee of $$(1-1/e)$$ . In the context of AIC model, we study two problems, with the aim to investigate the scenarios where attaining individuals with high attitude is objectively more important than maximizing the attitude of the entire network. In the first problem, we introduce the notion of actionable attitude; intuitively, individuals with actionable attitude are likely to “act” on their attained attitude. We show that the function for computing actionable attitude, unlike that for computing attitude, is non-submodular and however is approximately submodular. We present approximation algorithm for maximizing actionable attitude in a network. In the second problem, we consider identifying the number of individuals in the network with attitude above a certain value, a threshold. In this context, the function for computing the number of individuals with attitude above a given threshold induced by a seed set is neither submodular nor supermodular. We present heuristics for realizing the solution to the problem. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in network such as spatial and value distribution of high attitude nodes.
Do Bots Modify the Workflow of GitHub Teams

The ever-increasing complexity of modern software engineering projects makes the usage of automated assistants imperative. Bots can be used to complete repetitive tasks during development and testing, as well as promoting communication between team members through issue reporting and documentation. Although the ultimate aim of these automated assistants is to speed taskwork completion, their inclusion into GitHub repositories may affect teamwork as well. This paper studies the question of how bots modify the team workflow. We examined the event sequences of repositories with bots and without bots using a contrast motif discovery method to detect subsequences that are more prevalent in one set of event sequences vs. the other. Our study reveals that teams with bots are more likely to intersperse comments throughout their coding activities, while not actually being more prolific commenters.
Betti Curves of Rank One Symmetric Matrices

Betti curves of symmetric matrices were introduced in [3] as a new class of matrix invariants that depend only on the relative ordering of matrix entries. These invariants are computed using persistent homology, and can be used to detect underlying structure in biological data that may otherwise be obscured by monotone nonlinearities. Here we prove three theorems that characterize the Betti curves of rank 1 symmetric matrices. We then illustrate how these Betti curve signatures arise in natural data obtained from calcium imaging of neural activity in zebrafish.
Correction to the effective refractive index and the confinement factor in waveguide modeling for quantum cascade lasers

The equations for the effective medium refractive index and for the confinement factor in the waveguide design for quantum cascade lasers are derived. Compared to equations used in prior literature, by applying rigorous perturbation theory and including the effect of the anisotropic optical gain and non-Hermitian properties of the waveguide structure and materials, a few percent correction should be made to the confinement factor and the effective gain. This result can easily be generalized to any optical devices with a layered structure.
Robust Gas Pipeline Network Expansion Planning to Support Power System Reliability

We examine the problem of optimal transport capacity expansion planning for a gas pipeline network to service the growing demand of gas-fired power plants that are increasingly used to provide base load, flexibility, and reserve generation for bulk electric system. The aim is to determine the minimal cost set of additional pipes and gas compressors that can be added to the network to provide the additional capacity to service future loads. This combinatorial optimization problem is initially formulated as a mixed-integer nonlinear program, which we then extend to account for the variability that is inherent to the demands of gas-fired electricity production and uncertainty in expected future loads. We consider here steady-state flow modeling while ensuring that the solution is feasible for all possible values of interval uncertainty in loads, which results in a challenging semi-infinite problem. We apply previously derived monotonicity properties that enable simplification of the problem to require constraint satisfaction in the two extremal scenarios only, and then formulate the robust gas pipeline network expansion planning problem using a mixed-integer second order cone formulation. We consider case studies on the Belgian network test case to examine the performance of the proposed approach.
Analysis of the mechanosensor channel functionality of TACAN

Mechanosensitive ion channels mediate transmembrane ion currents activated by mechanical forces. A mechanosensitive ion channel called TACAN was recently reported. We began to study TACAN with the intent to understand how it senses mechanical forces and functions as an ion channel. Using cellular patch-recording methods, we failed to identify mechanosensitive ion channel activity. Using membrane reconstitution methods, we found that TACAN, at high protein concentrations, produces heterogeneous conduction levels that are not mechanosensitive and are most consistent with disruptions of the lipid bilayer. We determined the structure of TACAN using single-particle cryo-electron microscopy and observed that it is a symmetrical dimeric transmembrane protein. Each protomer contains an intracellular-facing cleft with a coenzyme A cofactor, confirmed by mass spectrometry. The TACAN protomer is related in three-dimensional structure to a fatty acid elongase, ELOVL7. Whilst its physiological function remains unclear, we anticipate that TACAN is not a mechanosensitive ion channel.
The effect of option-implied skewness on delta- and vega-hedged option returns

Abstract We study the relation between option-implied skewness (IS) and the cross-section of option returns under daily hedging to better understand the pricing of skewness in isolation from lower moments. Creating portfolios of delta-hedged (D-hedged) and delta-vega-hedged (DV-hedged) options with daily rebalancing, we find that IS is negatively related to both D-hedged and DV-hedged call option returns, but has no significant relation to hedged put option returns. The negative relation observed is stronger when the underlying stock has a larger market beta and when the firm is more opaque. Our results suggest that investors’ skewness preference grows stronger with greater market risk and lower information quality.
The Corruption of Copyright and Returning It to Its Original Purposes

Since its inception, Copyright has had two purposes: the private interest of the author in being paid for her work and the public interest served by the dissemination of these works. Within the las...
Coupled large-strain mechanochemical theory for solid-state reaction with application to oxidation

Abstract Oxidation is still perceived as a significant challenge in various industrial applications, e.g., pressurized water reactors and gas-turbines. One of the hurdles in understanding oxidation is its coupled multi-physics nature, i.e., the coupling between mechanics, chemical reaction, change in the microstructure, and diffusion. Here, a general large-strain mechanochemical theory is developed to model the anisotropic reaction/compositional strain in the solid-state chemical reaction. This novel stress-relaxation is achieved by introducing a kinetic relationship between deviatoric reaction deformation rate and the deviatoric Cauchy stress. Also, a new chemical potential and the expression for the driving force for the chemical reaction, including the deviatoric stress, are derived. The new model shows that the deviatoric stress alters the chemical equilibrium constant and reaction rate through reaction-induced deviatoric stress. Finally, the developed mathematical framework is used to study the aluminum oxidation for model calibration and verification; results are in good agreement with existing experimental studies.
Generative Models for Automatic Chemical Design

Materials discovery is decisive for tackling urgent challenges related to energy, the environment, health care, and many others. In chemistry, conventional methodologies for innovation usually rely on expensive and incremental strategies to optimize properties from molecular structures. On the other hand, inverse approaches map properties to structures, thus expediting the design of novel useful compounds. In this chapter, we examine the way in which current deep generative models are addressing the inverse chemical discovery paradigm. We begin by revisiting early inverse design algorithms. Then, we introduce generative models for molecular systems and categorize them according to their architecture and molecular representation. Using this classification, we review the evolution and performance of important molecular generation schemes reported in the literature. Finally, we conclude highlighting the prospects and challenges of generative models as cutting edge tools in materials discovery.
On Kodaira fibrations with invariant cohomology

A Kodaira fibration is a compact, complex surface admitting a holomorphic submersion onto a complex curve such that the fibers have nonconstant moduli. We consider Kodaira fibrations X with nontrivial invariant ℚ–cohomology in degree 1, proving that if the dimension of the holomorphic invariants is 1 or 2, then X admits a branch covering over a product of curves inducing an isomorphism on rational cohomology in degree 1. We also study the class of Kodaira fibrations possessing a holomorphic section, and demonstrate that having a section imposes no restriction on possible monodromies.
QBism and Relational Quantum Mechanics compared

The subjective Bayesian interpretation of quantum mechanics (QBism) and Rovelli’s relational interpretation of quantum mechanics (RQM) are both notable for embracing the radical idea that measurement outcomes correspond to events whose occurrence (or not) is relative to an observer. Here we provide a detailed study of their similarities and especially their differences.
A Visibility Roadmap Sampling Approach for a Multi-Robot Visibility-Based Pursuit-Evasion Problem

Given a two-dimensional polygonal space, the multi-robot visibility-based pursuit-evasion problem tasks several pursuer robots with the goal of establishing visibility with an arbitrarily fast evader. The best known complete algorithm for this problem takes time doubly exponential in the number of robots. However, sampling-based techniques have shown promise in generating feasible solutions in these scenarios. One of the primary drawbacks to employing existing sampling-based methods is that existing algorithms have long execution times and high failure rates for complex environments. This paper addresses that limitation by proposing a new algorithm that takes an environment as its input and returns a joint motion strategy which ensures that the evader is captured by one of the pursuers. Starting with a single pursuer, we sequentially construct Sample-Generated Pursuit-Evasion Graphs to create such a joint motion strategy. This sequential graph structure ensures that our algorithm will always terminate with a solution, regardless of the complexity of the environment. We describe an implementation of this algorithm and present quantitative results that show significant improvement in comparison to the existing algorithm.
Adaptation to Team Composition Changes for Heterogeneous Multi-Robot Sensor Coverage

We consider the problem of multi-robot sensor coverage, which deals with deploying a multi-robot team in an environment and optimizing the sensing quality of the overall environment. As real-world environments involve a variety of sensory information, and individual robots are limited in their available number of sensors, successful multi-robot sensor coverage requires the deployment of robots in such a way that each individual team member’s sensing quality is maximized. Additionally, because individual robots have varying complements of sensors and both robots and sensors can fail, robots must be able to adapt and adjust how they value each sensing capability in order to obtain the most complete view of the environment, even through changes in team composition. We introduce a novel formulation for sensor coverage by multi-robot teams with heterogeneous sensing capabilities that maximizes each robot's sensing quality, balancing the varying sensing capabilities of individual robots based on the overall team composition. We propose a solution based on regularized optimization that uses sparsity-inducing terms to ensure a robot team focuses on all possible event types, and which we show is proven to converge to the optimal solution. Through extensive simulation, we show that our approach is able to effectively deploy a multi-robot team to maximize the sensing quality of an environment, responding to failures in the multi-robot team more robustly than non-adaptive approaches.
Information Inundation on Platforms and Implications

Information Inundation and PolarizationIn the paper “Information Inundation on Platforms and Implications,” Gad Allon, Kimon Drakopoulos, and Vahideh Manshadi study the process of learning and opin...
Microbial diversity in tropical marine sediments assessed using culture-dependent and culture-independent techniques

The microbial communities associated with marine sediments are critical for ecosystem function yet remain poorly characterized. While culture-independent (CI) techniques capture the broadest perspective on community composition, culture-dependent (CD) methods can select for low abundance taxa that are missed using CI approaches. This study aimed to assess microbial diversity in tropical marine sediments at five shallow-water sites in Belize using both CD and CI techniques. The CD methods captured approximately 3% of the >800 genera detected across all sites using the CI approach. Additionally, 39 genera were only detected in culture, revealing rare taxa that were missed with the CI approach. Significantly different communities were detected across sites, with rare taxa playing an important role in distinguishing among communities. This study provides important baseline data describing shallow-water sediment microbial communities, evidence that standard cultivation techniques may be more effective than previously recognized, and the first steps towards identifying new taxa that are amenable to agar plate cultivation.