cssi/SciSciGPT-SciSciNet huggingface dataset:
The SEC's New Regulation CE Exemption: Federal-State Coordination Run Rampant

In 1996, the SEC adopted Regulation CE, an exemption from the registration requirements of the Securities Act of 1933 for offers and sales of securities that satisfy the conditions of a California state exemption, section 25102(n) of the California Corporations Code. Section 25102(n), in turn, exempts offerings to "qualified purchasers" and includes a "test-the-waters" provision allowing securities issuers to solicit purchasers. Regulation CE is the first SEC exemption that defers to a state exemption. I argue that, although the substantive provisions of the exemption make sense, this type of rule is problematic. Regulation CE's delegation of authority to California state officials probably exceeds the SEC's regulatory authority and, if the underlying California exemption is amended, the rule faces serious problems under the federal Administrative Procedure Act. Regulation CE probably also conflicts with the recent changes to the preemption provisions of section 18 of the Securities Act. Finally, the incorporation of state law presents some difficult interpretive questions that could have been avoided through the SEC's direct enactment of its own exemption similar to the California provision.
A Reentry-Centered Vision of Criminal Justice

In recent years, record numbers of individuals have been released from U.S. correctional facilities and have reentered their communities. At present, approximately 650,000 individuals are released annually from U.S. federal and state prisons, while an estimated additional 7 million are released from its jails. In addition, the number of individuals with criminal records - whether or not they were incarcerated - continues to climb. At present, approximately 20 percent of adults in the United States have criminal records. Part I [of this article] details the shortcomings of current reentry practice. Part II sets forth a reentry-centered vision of criminal justice that recasts the roles of defense attorneys, prosecutors, and judges. Part III sets out a couple of ways in which the reentry-centered model differs from models that, at first blush, appear to be similar and then explains that broader reforms are necessary to fully realize the reentry-centered vision.
Are Budget Deficits Used Strategically

This paper tests empirically the strategic explanation of budget deficits suggested by Tabellini and Alesina [13] and Persson and Svensson [12]. Tabellini and Alesina suggest that governments with different political orientation provide different public goods. The model predicts that: a) public good provision follows a political pattern; b) the incumbent that anticipates her defeat at the next election runs budget deficits to tie the hands of the future government. Persson and Svensson suggest that liberal governments prefer more public good provision than conservative ones. The model predicts that: a) the conservative (liberal) incumbent that anticipates her defeat at the next election runs budget deficits (surpluses); b) budget imbalances have a political color. Using U.S. and pooled data for sixteen OECD countries, we find little evidence that the incumbent’s probability of being voted out of office explains budget deficits, that the provision of public goods follows a political pattern or that budget imbalances have a political color.
The effects of student composition on teacher turnover: Evidence from an admission reform

Abstract This paper examines the effects of student ability on teacher turnover using data from Stockholm high schools and an admission reform that led to the exogenous reshuffling of pupils. The results indicate that a 10-percentile-point increase in student credentials decreases the probability of a job separation by up to 10 percentage points. These effects vary somewhat across different groups of teachers and are found mainly for mobility between schools rather than out of the profession. Teachers react most strongly to direct measures of student ability, grades from compulsory school, rather than to other correlated characteristics such as immigrant origin or parental income.
Commercial lending concentration and bank expertise: Evidence from borrower financial statements

Abstract Lending concentration features prominently in models of information acquisition by banks, but empirical evidence on its role is limited. Using bank-level loan exposures, we find banks are less likely to collect audited financial statements from firms in industries and regions in which they have more exposure. These findings are stronger in settings in which adverse selection is acute and muted when the bank lacks experience with an exposure. Our results offer novel evidence on how bank characteristics are related to the type of financial information they use and support theoretical predictions suggesting portfolio concentration reveals a bank's relative expertise.
Tales of the (Virtual) City: Governing Property Disputes in Virtual Worlds

As virtual worlds grow in size and acceptance, increasing amounts of time and energy are spent by their users in the pursuit and creation of virtual property. This paper argues that the current governing regimes of these worlds, end user licensing agreements, are insufficient to deal with these interests. The author examines several of these worlds, the conflicts within them, and the inadequacy of EULAs to govern them.
Which voting rule is most likely to choose the “best” candidate?

One criterion for evaluating voting rules is the frequency with which they select the best candidate. Using a spatial model of voting that is capable of simulating data with the same statistical structure as data from actual elections, we simulate elections for which we can define the best candidate. We use these simulated data to investigate the frequencies with which 14 voting rules chose this candidate as their winner. We find that the Black rule tends to perform better than the other rules, especially in elections with few voters. The Bucklin rule, the plurality rule, and the anti-plurality rule tend to perform worse than the other 11 rules, especially in elections with many voters.
“UNE CHOSE PUBLIQUE”? THE AUTHOR’S DOMAIN AND THE PUBLIC DOMAIN IN EARLY BRITISH, FRENCH AND US COPYRIGHT LAW

THE public domain is all the rage. It is invoked to breach copyright’s encroaching enclosure of what one might grandiloquently call the cultural commons of the mind. The heralds of our “remix culture” deploy the public domain to smash that icon of the entertainment–industrial complex, the Romantic Author. But even before the Author became Romantic, he still served as a shill for concentrated industry, then the printing–bookselling complex. Authors’ moral claims of labourious entitlement merely masked the power grab of the printers. If we speak of a grab, we imply that copyright was seised from somewhere. So whence, in this account, was copyright wrested? From the public domain.
The French Prosecutor in Question

Both the pre-trial and dispositive roles of the French prosecutor have continued to expand over the last decades with a resulting shift in power away from the trial judge and the juge d’instruction. The recommendations of the Leger Commission in 2009 went beyond the redistribution of authority and proposed the abolition of the juge d’instruction, placing the prosecutor in charge of all criminal investigations, even the most serious, complex, and sensitive. At the same time, the prosecutor’s role and status has been challenged in a number of ways—in particular concerning her function as judicial supervisor of the detention and interrogation of suspects in the garde a vue. The case of Medvedyev v. France called into question the prosecutor’s status as a judge and the string of cases beginning with Salduz v. Turkey has caused several jurisdictions, including France, to reconsider the provision made for custodial legal advice. There is a real tension between the direction of reforms proposed within France and the pressure from Europe to ensure more effective due process safeguards. As a result of domestic litigation and constitutional challenge, the French government is slowly relenting and allowing lawyers a greater role. It has yet to grasp the nettle of the independence of the prosecutor, however. The ECtHR has made it clear that a judge must be independent of the executive and of the case parties—both of which are contested in relation to the French prosecutor. Within a procedural model in which defense rights are secondary to the supposed truth-seeking ideology of the judicial supervisor, the independence of the prosecutor is crucial.
Political Strategy and Market Capabilities: Evidence from the Chinese Private Sector

In this article, I argue that in emerging markets firms' market capabilities are positively related to the political strategies that they employ to reduce the risks of expropriation by public and private entities. I focus specifically on the moderating conditions, including institutional environments that have direct constraint on governmental power, supportive policies that promote private sector growth, and developed legal systems. My empirical analysis utilizes data on privately owned Chinese enterprises. The results show that firms' market capabilities – as indicated by their asset turnover ratios and R&D intensity – are positively related to their likelihood of participating in key policymaking political organizations but this relationship is weaker in provinces that have more effective constraints on governmental power, more supportive policies for private sector growth, and more developed legal systems.
Aggregation and stabilization policy in a multi-contract economy

Abstract This paper presents a model of a multi-sector economy in which each sector is characterized by a different type of wage or price stickness. The various sectors experience the same exogenous shocks and have the same money supply. The analysis shows that demand shocks pose no serious problems for stabilization policy. In contrast, supply shocks may force the policymaker to choose between stability in one sector and stability in another. The analysis also shows that the economist as to the economy's underlying structure and obscures the tradeoffs the policymaker must confront. In particular, a feedback rule chosen on the basis of an aggregate model could be either better or worse than a passive policy.
Automorphisms of the compression body graph

When $S$ is a closed, orientable surface with genus $g(S) \geq 2$, we show that the automorphism group of the compression body graph $\mathcal{CB}(S)$ is the mapping class group. Here, vertices are compression bodies with exterior boundary $S$, and edges connect pairs of compression bodies where one contains the other.
Fragmentation of Small-Cell Lung Cancer Regulatory States in Heterotypic Microenvironments

Small-cell lung cancers derive from pulmonary neuroendocrine cells, which have stem-like properties to reprogram into other cell types upon lung injury. It is difficult to uncouple transcriptional plasticity of these transformed cells from genetic changes that evolve in primary tumors or secondary metastases. Profiling of single cells is also problematic if the required sample dissociation activates injury-like signaling and reprogramming. Here we defined cell state heterogeneities in situ through laser capture microdissection-based 10-cell transcriptomics coupled with stochastic-profiling fluctuation analysis. In labeled cells from a small-cell lung cancer mouse model initiated by neuroendocrine deletion of Rb1-Trp53, variations in transcript abundance revealed cell-to-cell differences in regulatory state in vitro and in vivo. Fluctuating transcripts in spheroid culture were partly shared among Rb1-Trp53-null models, and heterogeneities increased considerably when cells were delivered intravenously to colonize the liver. Colonization of immunocompromised animals drove a fractional appearance of alveolar type II-like markers and poised cells for paracrine stimulation from immune cells and hepatocytes. Immunocompetency further exaggerated the fragmentation of tumor states in the liver, yielding mixed stromal signatures evident in bulk sequencing from autochthonous tumors and metastases. Dozens of transcript heterogeneities recurred irrespective of biological context; their mapped orthologs brought together observations of murine and human small-cell lung cancer. Candidate heterogeneities recurrent in the liver also stratified primary human tumors into discrete groups not readily explained by molecular subtype but with prognostic relevance. These data suggest that heterotypic interactions in the liver and lung are an accelerant for intratumor heterogeneity in small-cell lung cancer.
KinPred: A unified and sustainable approach for harnessing proteome-level human kinase-substrate predictions

Tyrosine and serine/threonine kinases are essential regulators of cell processes and are important targets for human therapies. Unfortunately, very little is known about specific kinase-substrate relationships, making it difficult to infer meaning from dysregulated phosphoproteomic datasets or for researchers to identify possible kinases that regulate specific or novel phosphorylation sites. The last two decades have seen an explosion in algorithms to extrapolate from what little is known into the larger unknown-predicting kinase relationships with site-specific substrates using a variety of approaches that include the sequence-specificity of kinase catalytic domains and various other factors, such as evolutionary relationships, co-expression, and protein-protein interaction networks. Unfortunately, a number of limitations prevent researchers from easily harnessing these resources, such as loss of resource accessibility, limited information in publishing that results in a poor mapping to a human reference, and not being updated to match the growth of the human phosphoproteome. Here, we propose a methodological framework for publishing predictions in a unified way, which entails ensuring predictions have been run on a current reference proteome, mapping the same substrates and kinases across resources to a common reference, filtering for the human phosphoproteome, and providing methods for updating the resource easily in the future. We applied this framework on three currently available resources, published in the last decade, which provide kinase-specific predictions in the human proteome. Using the unified datasets, we then explore the role of study bias, the emergent network properties of these predictive algorithms, and comparisons within and between predictive algorithms. The combination of the code for unification and analysis, as well as the unified predictions are available under the resource we named KinPred. We believe this resource will be useful for a wide range of applications and establishes best practices for long-term usability and sustainability for new and existing predictive algorithms.
A theorem of Besicovitch and a generalization of the Birkhoff Ergodic Theorem

A remarkable theorem of Besicovitch is that an integrable function $f$ on $\mathbb{R}^2$ is strongly differentiable if and only if its associated strong maximal function $M_S f$ is finite a.e. We provide an analogue of Besicovitch's result in the context of ergodic theory that provides a generalization of Birkhoff's Ergodic Theorem. In particular, we show that if $f$ is a measurable function on a standard probability space and $T$ is an invertible measure-preserving transformation on that space, then the ergodic averages of $f$ with respect to $T$ converge a.e. if and only if the associated ergodic maximal function $T^*f$ is finite a.e.
Portfolios of actively managed mutual funds

Investors should focus on the performance of portfolios of active funds, not on the performance of individual active funds. Taking this portfolio approach with respect to active U.S. equity mutual funds, I build an optimized portfolio of funds that subsequently has low idiosyncratic volatility and a large, positive, statistically significant alpha. Consistent with a Berk and Green (2004) equilibrium, that outperformance is short lived if the optimized portfolio is not rebalanced often, as investors allocate substantial capital—in excess of that expected based on past performance—to the funds with a large weight in the optimized portfolio.
Modeling the Second Player in Distributionally Robust Optimization

Distributionally robust optimization (DRO) provides a framework for training machine learning models that are able to perform well on a collection of related data distributions (the "uncertainty set"). This is done by solving a min-max game: the model is trained to minimize its maximum expected loss among all distributions in the uncertainty set. While careful design of the uncertainty set is critical to the success of the DRO procedure, previous work has been limited to relatively simple alternatives that keep the min-max optimization problem exactly tractable, such as f-divergence balls. In this paper, we argue instead for the use of neural generative models to characterize the worst-case distribution, allowing for more flexible and problem-specific selection of the uncertainty set. However, while simple conceptually, this approach poses a number of implementation and optimization challenges. To circumvent these issues, we propose a relaxation of the KL-constrained inner maximization objective that makes the DRO problem more amenable to gradient-based optimization of large scale generative models, and develop model selection heuristics to guide hyper-parameter search. On both toy settings and realistic NLP tasks, we find that the proposed approach yields models that are more robust than comparable baselines.
Spatial Indexing for System-Level Evaluation of 5G Heterogeneous Cellular Networks

System level simulations of large 5G networks are essential to evaluate and design algorithms related to network issues such as scheduling, mobility management, interference management, and cell planning. In this paper, we look back to the idea of spatial indexing and its advantages, applications, and future potentials in accelerating large 5G network simulations. We introduce a multi-level inheritance based architecture which is used to index all elements of a heterogeneous network (HetNet) on a single geometry tree. Then, we define spatial queries to accelerate searches in distance, azimuth, and elevation. We demonstrate that spatial indexing can accelerate location-based searches by 3 orders of magnitude. Further, the proposed design is implemented as an open source platform freely available to all.
Computer-aided Abnormality Detection in Chest Radiographs in a Clinical Setting via Domain-adaptation.

Deep learning (DL) models are being deployed at medical centers to aid radiologists for diagnosis of lung conditions from chest radiographs. Such models are often trained on a large volume of publicly available labeled radiographs. These pre-trained DL models' ability to generalize in clinical settings is poor because of the changes in data distributions between publicly available and privately held radiographs. In chest radiographs, the heterogeneity in distributions arises from the diverse conditions in X-ray equipment and their configurations used for generating the images. In the machine learning community, the challenges posed by the heterogeneity in the data generation source is known as domain shift, which is a mode shift in the generative model. In this work, we introduce a domain-shift detection and removal method to overcome this problem. Our experimental results show the proposed method's effectiveness in deploying a pre-trained DL model for abnormality detection in chest radiographs in a clinical setting.
A universal allosteric mechanism for G protein activation

G proteins play a central role in signal transduction and pharmacology. Signaling is initiated by cell-surface receptors, which promote guanosine triphosphate (GTP) binding and dissociation of Gα from the Gβγ subunits. Structural studies have revealed the molecular basis of subunit association with receptors, RGS proteins, and downstream effectors. In contrast, the mechanism of subunit dissociation is poorly understood. We use cell signaling assays, molecular dynamics (MD) simulations, and biochemistry and structural analyses to identify a conserved network of amino acids that dictates subunit release. In the presence of the terminal phosphate of GTP, a glycine forms a polar network with an arginine and glutamate, putting torsional strain on the subunit binding interface. This "G-R-E motif" secures GTP and, through an allosteric link, discharges the Gβγ dimer. Replacement of network residues prevents subunit dissociation regardless of agonist or GTP binding. These findings reveal the molecular basis of the final committed step of G protein activation.
Writing While Quarantined: A Personal Interpretation of Contemporary Comparative Constitutional Law

This Essay is a personal reflection on the state of scholarship in the field of comparative constitutional law. I draw parallels between the development of and reaction to “critical perspectives” on domestic US constitutional law in the 1970s and 1980s and the development and reaction to similar perspectives on comparative constitutional law today. I argue that the parallels have similar political roots, in concern that critical perspectives undermine the ability of constitutional law, whether domestic or comparative, to resist conservative and anti-liberal tendencies. I conclude with some speculations about the source of the political commitments by scholars of comparative constitutional law, and in particular about the way the field’s overall cosmopolitanism affects scholarship on anti-cosmopolitan populisms.
Cryo-ePDF: Overcoming Electron Beam Damage to Study the Local Atomic Structure of Amorphous ALD Aluminum Oxide Thin Films within a TEM.

Atomic layer deposition (ALD) provides uniform and conformal thin films that are of interest for a range of applications. To better understand the properties of amorphous ALD films, we need an improved understanding of their local atomic structure. Previous work demonstrated measurement of how the local atomic structure of ALD-grown aluminum oxide (AlO x ) evolves in operando during growth by employing synchrotron high-energy X-ray diffraction (HE-XRD). In this work, we report on efforts to employ electron diffraction pair distribution function (ePDF) measurements using more broadly available transmission electron microscope (TEM) instrumentation to study the atomic structure of amorphous ALD-AlO x . We observe electron beam damage in the ALD-coated samples during ePDF at ambient temperature and successfully mitigate this beam damage using ePDF at cryogenic temperatures (cryo-ePDF). We employ cryo-ePDF and reverse Monte Carlo (RMC) modeling to obtain structural models of ALD-AlO x coatings formed at a range of deposition temperatures from 150 to 332 °C. From these model structures, we derive structural metrics including stoichiometry, pair distances, and coordination environments in the ALD-AlO x films as a function of deposition temperature. The structural variations we observe with growth temperature are consistent with temperature-dependent changes in the surface hydroxyl density on the growth surface. The sample preparation and cryo-ePDF procedures we report here can be used for the routine measurement of ALD-grown amorphous thin films to improve our understanding of the atomic structure of these materials, establish structure-property relationships, and help accelerate the timescale for the application of ALD to address technological needs.
Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

Reinforcement Learning (RL) is an area of machine learning concerned with enabling an agent to navigate an environment with uncertainty in order to maximize some notion of cumulative long-term reward. In this paper, we implement and analyze two different RL techniques, Sarsa and Deep Q-Learning, on OpenAI Gym’s LunarLander-v2 environment. We then introduce additional uncertainty to the original problem to test the robustness of the mentioned techniques. With our best models, we are able to achieve average rewards of 170+ with the Sarsa agent and 200+ with the Deep Q-Learning agent on the original problem. We also show that these techniques are able to overcome the additional uncertainities and achieve positive average rewards of 100+ with both agents. We then perform a comparative analysis of the two techniques to conclude which agent performs better.
A precise determination of the HVP contribution to the muon anomalous magnetic moment from lattice QCD

In this talk I present the current status of a precise first-principles calculation of the quark connected, quark disconnected, and leading QED and strong isospin-breaking contributions to the leading-order hadronic vacuum polarization by the RBC and UKQCD collaborations. The lattice data is also combined with experimental e + e − scattering data, consistency between the two datasets is checked, and a combined result with smaller error than the lattice data and e + e − scattering data individually is presented.
Re-crowning The Queen: Membership, Age, and Rotation Periods for the Open Cluster Coma Berenices

Coma Berenices (Coma Ber), an open cluster about the same age as Praesepe and the Hyades (700–800 Myr) is, despite being only 85 pc away, less well studied than its famous cousins. This is due principally to its sparseness and low proper motion, which together made Coma Ber's membership challenging to establish pre-Gaia. We have curated a new list of its members based on Gaia DR2 astrometry, derived its metallicity and interstellar reddening using LAMOST data, and inferred the cluster's age by fitting PARSEC isochrones to its color–magnitude diagram. We then measured rotation periods for Coma Ber's low-mass members using TESS and ZTF photometry. Our isochrone fitting and the TESS- and ZTF-derived rotation periods confirm that Coma Ber is coeval with the Hyades and Praesepe. This work is the first step toward re-establishing Coma Ber as another valuable benchmark cluster for age–rotation–activity studies.
Combinatorial Multi-armed Bandits for Resource Allocation

We study the sequential resource allocation problem where a decision maker repeatedly allocates budgets between resources. Motivating examples include allocating limited computing time or wireless spectrum bands to multiple users (i.e., resources). At each timestep, the decision maker should distribute its available budgets among different resources to maximize the expected reward, or equivalently to minimize the cumulative regret. In doing so, the decision maker should learn the value of the resources allocated for each user from feedback on each user's received reward. For example, users may send messages of different urgency over wireless spectrum bands; the reward generated by allocating spectrum to a user then depends on the message's urgency. We assume each user's reward follows a random process that is initially unknown. We design combinatorial multi-armed bandit algorithms to solve this problem with discrete or continuous budgets. We prove the proposed algorithms achieve logarithmic regrets under semi-bandit feedback.
DNA barcodes using a double nanopore system.

The potential of a double nanopore system to determine DNA barcodes has been demonstrated experimentally. By carrying out Brownian dynamics simulation on a coarse-grained model DNA with protein tag (barcodes) at known locations along the chain backbone, we demonstrate that due to large variation of velocities of the chain segments between the tags, it is inevitable to under/overestimate the genetic lengths from the experimental current blockade and time of flight data. We demonstrate that it is the tension propagation along the chain’s backbone that governs the motion of the entire chain and is the key element to explain the non uniformity and disparate velocities of the tags and DNA monomers under translocation that introduce errors in measurement of the length segments between protein tags. Using simulation data we further demonstrate that it is important to consider the dynamics of the entire chain and suggest methods to accurately decipher barcodes. We introduce and validate an interpolation scheme using simulation data for a broad distribution of tag separations and suggest how to implement the scheme experimentally.
Defining Kinetic Roles of Transcriptional Activators in the Early Drosophila Embryo

Most animal transcription factors are categorized as activators or repressors without specifying their mechanisms of action. Defining their specific roles is critical for deciphering the logic of transcriptional regulation and predicting the function of regulatory sequences. Here, we define the kinetic roles of three activating transcription factors in the Drosophila embryo—Zelda, Bicoid and Stat92E—by introducing their binding sites into the even skipped stripe 2 enhancer and measuring transcriptional output with live imaging. We find that these transcription factors act on different sets of kinetic parameters, and these sets can change over the course of nuclear cycle (NC) 14. These transcription factors all increase the fraction of transcriptionally active nuclei. Zelda dramatically shortens the time interval between the start of NC 14 and initial activation, and Stat92E increases the duration of active transcription intervals throughout NC 14. Zelda also decreases the time intervals between instances of active transcription early in NC 14, while Stat92E does so later. Different transcription factors therefore play distinct kinetic roles in activating transcription; this has consequences for understanding both regulatory DNA sequences as well as the biochemical function of transcription factors.
Dynamic State Estimation for Radial Microgrid Protection

Microgrids are localized electrical grids with control capability that are able to disconnect from the traditional grid to operate autonomously. They strengthen grid resilience, help mitigate grid disturbances, and support a flexible grid by enabling the integration of distributed energy resources. Given the likely presence of critical loads, the proper protection of microgrids is of vital importance; however, this is complicated in the case of inverter-interfaced microgrids where low fault currents preclude the use of conventional time-overcurrent protection. This paper introduces and investigates the application of dynamic state estimation, a generalization of differential protection, for the protection of radial portions of microgrids (or distribution networks); both phasor-based and dynamic approaches are investigated for protection. It is demonstrated through experiments on three case-study systems that dynamic state estimation is capable of correctly identifying model parameters for both normal and faulted operation.
A diffeomorphism-invariant metric on the space of vector-valued one-forms

In this article we introduce a diffeomorphism-invariant Riemannian metric on the space of vector valued one-forms. The particular choice of metric is motivated by potential future applications in the field of functional data and shape analysis and by connections to the Ebin metric on the space of all Riemannian metrics. In the present work we calculate the geodesic equations and obtain an explicit formula for the solutions to the corresponding initial value problem. Using this we show that it is a geodesically and metrically incomplete space and study the existence of totally geodesic subspaces. Furthermore, we calculate the sectional curvature and observe that, depending on the dimension of the base manifold and the target space, it either has a semidefinite sign or admits both signs.
Using infographics to improve trust in science: a randomized pilot test

This study describes the iterative process of selecting an infographic for use in a large, randomized trial related to trust in science, COVID-19 misinformation, and behavioral intentions for non-pharmaceutical prevenive behaviors. Five separate concepts were developed based on underlying subcomponents of ‘trust in science and scientists’ and were turned into infographics by media experts and digital artists. Study participants (n = 100) were recruited from Amazon’s Mechanical Turk and randomized to five different arms. Each arm viewed a different infographic and provided both quantitative (narrative believability scale and trust in science and scientists inventory) and qualitative data to assist the research team in identifying the infographic most likely to be successful in a larger study. Data indicated that all infographics were perceived to be believable, with means ranging from 5.27 to 5.97 on a scale from one to seven. No iatrogenic outcomes were observed for within-group changes in trust in science. Given equivocal believability outcomes, and after examining confidence intervals for data on trust in science and then the qualitative responses, we selected infographic 3, which addressed issues of credibility and consensus by illustrating changing narratives on butter and margarine, as the best candidate for use in the full study.
The-Square-and-Add Markov Chain

Squaring and adding $\pm 1$ mod p generates a curiously intractable random walk. A similar process over the finite field $\mathbf{F}_q$ (with $q=2^d$) leads to novel connections between elementary Galois theory and probability.
Correlating the force network evolution and dynamics in slider experiments

The experiments involving a slider moving on top of granular media consisting of photoelastic particles in two dimensions have uncovered elaborate dynamics that may vary from continuous motion to crackling, periodic motion, and stick-slip type of behavior. We establish that there is a clear correlation between the slider dynamics and the response of the force network that spontaneously develop in the granular system. This correlation is established by application of the persistence homology that allows for formulation of objective measures for quantification of time-dependent force networks. We find that correlation between the slider dynamics and the force network properties is particularly strong in the dynamical regime characterized by well-defined stick-slip type of dynamics.
MUSER: MUltimodal Stress detection using Emotion Recognition as an Auxiliary Task

The capability to automatically detect human stress can benefit artificial intelligent agents involved in affective computing and human-computer interaction. Stress and emotion are both human affective states, and stress has proven to have important implications on the regulation and expression of emotion. Although a series of methods have been established for multimodal stress detection, limited steps have been taken to explore the underlying inter-dependence between stress and emotion. In this work, we investigate the value of emotion recognition as an auxiliary task to improve stress detection. We propose MUSER – a transformer-based model architecture and a novel multi-task learning algorithm with speed-based dynamic sampling strategy. Evaluation on the Multimodal Stressed Emotion (MuSE) dataset shows that our model is effective for stress detection with both internal and external auxiliary tasks, and achieves state-of-the-art results.
Strong-Field Cyclotron Scattering. I. Scattering Amplitudes and Natural Line Width

The introduction of resonance line width into the QED cyclotron scattering amplitudes is considered. It is shown that the width arises from loop corrections to the electron propagator, which also bring about shifts in the Landau energy levels. A formalism is developed that allows the dressed electron propagator to be derived. It is shown that the states of Herold et al. (1982) and of Sokolov and Ternov (1968), which diagonalize the component of the magnetic moment operator parallel to the external magnetic field, are appropriate for calculation of the scattering amplitudes, whereas the states of Johnson and Lippmann (1949) are not. In addition, it is shown that the Breit-Wigner broadening approximation E tends to E - i(Gamma)/2 is consistent with the perturbation-theoretic order of the calculation, if the former basis states are chosen, but not the latter.
Graph Learning for Inverse Landscape Genetics.

The problem of inferring unknown graph edges from numerical data at a graph's nodes appears in many forms across machine learning. We study a version of this problem that arises in the field of landscape genetics, where genetic similarity between organisms living in a heterogeneous landscape is explained by a weighted graph that encodes the ease of dispersal through that landscape. Our main contribution is an efficient algorithm for inverse landscape genetics, which is the task of inferring this graph from measurements of genetic similarity at different locations (graph nodes). Inverse landscape genetics is important in discovering impediments to species dispersal that threaten biodiversity and long-term species survival. In particular, it is widely used to study the effects of climate change and human development. Drawing on influential work that models organism dispersal using graph effective resistances (McRae 2006), we reduce the inverse landscape genetics problem to that of inferring graph edges from noisy measurements of these resistances, which can be obtained from genetic similarity data. Building on the NeurIPS 2018 work of Hoskins et al. (2018) on learning edges in social networks, we develop an efficient first-order optimization method for solving this problem. Despite its non-convex nature, experiments on synthetic and real genetic data establish that our method provides fast and reliable convergence, significantly outperforming existing heuristics used in the field. By providing researchers with a powerful, general purpose algorithmic tool, we hope our work will have a positive impact on accelerating work on landscape genetics.
Automatic Discovery of Political Meme Genres with Diverse Appearances

Forms of human communication are not static --- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events. A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.
A supervised machine learning approach for accelerating the design of particulate composites: Application to thermal conductivity

Abstract A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material’s properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
The Urinary Microbiome in Postmenopausal Women with Recurrent Urinary Tract Infections.

Purpose:The etiology of postmenopausal recurrent urinary tract infection (UTI) is not completely known, but the urinary microbiome is thought to be implicated. We compared the urinary microbiome in...
On the Origins of the CMB: Insight from the COBE, WMAP, and Relikt-1 Satellites

The powerful “Cosmic Microwave Background (CMB)” signal currently associated with the origins of the Universe is examined from a historical perspective and relative to the experimental context in which it was measured. Results from the COBE satellite are reviewed, with particular emphasis on the systematic error observed in determining the CMB temperature. The nature of the microwave signal emanating from the oceans is also discussed. From this analysis, it is demonstrated that it is improper for the COBE team to model the Earth as a 285 K blackbody source. The assignment of temperatures to objects that fail to meet the requirements set forth in Kirchhoff’s law constitutes a serious overextension of the laws of thermal emission. Using this evidence, and the general rule that powerful signals are associated with proximal sources, the CMB monopole signal is reassigned to the oceans. In turn, through the analysis of COBE, WMAP, and Relikt-1 data, the dipole signal is attributed to motion through a much weaker microwave field present both at the position of the Earth and at the second Lagrange point.
Unconventional translation initiation factor EIF2A is required for Drosophila spermatogenesis.

BACKGROUND EIF2A is an unconventional translation factor required for initiation of protein synthesis from non-AUG codons from a variety of transcripts, including oncogenes and stress related transcripts in mammalian cells. Its function in multicellular organisms has not been reported. RESULTS Here, we identify and characterize mutant alleles of the CG7414 gene, which encodes the Drosophila EIF2A ortholog. We identified that CG7414 undergoes sex-specific splicing that regulates its male-specific expression. We characterized a Mi{Mic} transposon insertion that disrupts the coding regions of all predicted isoforms and is a likely null allele, and a PBac transposon insertion into an intron, which is a hypomorph. The Mi{Mic} allele is homozygous lethal, while the viable progeny from the hypomorphic PiggyBac allele are male sterile and female fertile. In dEIF2A mutant flies, sperm failed to individualize due to defects in F-actin cones and failure to form and maintain cystic bulges, ultimately leading to sterility. CONCLUSIONS These results demonstrate that EIF2A is essential in a multicellular organism, both for normal development and spermatogenesis, and provide an entree into the elucidation of the role of EIF2A and unconventional translation in vivo.
Unsupervised learning of sequence-specific aggregation behavior for a model copolymer

We apply a recently developed unsupervised machine learning scheme for local environments [Reinhart, Comput. Mater. Sci., 2021, 196, 110511] to characterize large-scale, disordered aggregates formed by sequence-defined macromolecules. This method provides new insight into the structure of these disordered, dilute aggregates, which has proven difficult to understand using collective variables manually derived from expert knowledge [Statt et al., J. Chem. Phys., 2020, 152, 075101]. In contrast to such conventional order parameters, we are able to classify the global aggregate structure directly using descriptions of the local environments. The resulting characterization provides a deeper understanding of the range of possible self-assembled structures and their relationships to each other. We also provide detailed analysis of the effects of finite system size, stochasticity, and kinetics of these aggregates based on the learned collective variables. Interestingly, we find that the spatiotemporal evolution of systems in the learned latent space is smooth and continuous, despite being derived from only a single snapshot from each of about 1000 monomer sequences. These results demonstrate the insight which can be gained by applying unsupervised machine learning to soft matter systems, especially when suitable order parameters are not known.
Avis: In-Situ Model Checking for Unmanned Aerial Vehicles

Control firmware in unmanned aerial vehicles (UAVs) uses sensors to model and manage flight operations, from takeoff to landing to flying between waypoints. However, sensors can fail at any time during a flight. If control firmware mishandles sensor failures, UAVs can crash, fly away, or suffer other unsafe conditions. In-situ model checking finds sensor failures that could lead to unsafe conditions by systematically failing sensors. However, the type of sensor failure and its timing within a flight affect its manifestation, creating a large search space. We propose Avis, an in-situ model checker to quickly uncover UAV sensor failures that lead to unsafe conditions. Avis exploits operating modes, i.e., a label that maps software execution to corresponding flight operations. Widely used control firmware already support operating modes. Avis injects sensor failures as the control firmware transitions between modes – a key execution point where mishandled software exceptions can trigger unsafe conditions. We implemented Avis and applied it to ArduPilot and PX4. Avis found unsafe conditions 2.4X faster than Bayesian Fault Injection, the leading, state-of-theart approach. Within the current code base of ArduPilot and PX4, Avis discovered 10 previously unknown software bugs that lead to unsafe conditions. Additionally, we reinserted 5 known bugs that caused serious, unsafe conditions and Avis correctly reported all of them.