cssi/SciSciGPT-SciSciNet huggingface dataset:
Resetting the Baseline of Ownership: Takings and Investor Expectations After the Bailouts

During the economic crisis that began in 2008, the federal government nationalized several of the nation’s most significant private companies as part of a broad effort to forestall a global depression. Shareholders in those companies later filed suit, alleging that the federal government in so doing — and in subsequent actions while in control of the firms — took their property without compensation in violation of the Fifth Amendment. To date, those claims have not succeeded. If these cases continue on their current trajectory, with courts rejecting arguments that the rescue of systematically important firms on the brink of collapse requires compensation for shareholders, that outcome will have a profound consequence. Any investor contemplating investment in such companies must now, in the calculus of the Takings Clause, understand the change in the reasonableness of their investment-backed expectations, and, potentially even more broadly, the “background principles” of property law that define such ownership, may now reflect those outcomes. In other words, the fact that federal bailouts in the recent crisis impaired (or even wiped out) shareholder value, and that those policy steps are surviving Fifth Amendment takings challenges, means we are resetting the baseline of expectation for shareholders. As a result, future regulators contemplating the need for swift intervention in the inevitable crises to come should worry far less about takings liability.
What Drives Equity Market Non-participation?

This paper produces endogenous equity market non-participation in an economy with uninsurable labor income risk and heterogeneous skill levels. Prudence and impatience generate stationary household wealth levels that depend on income. Skill, and therefore labor income, heterogeneity leads to wealth heterogeneity, with high skill households accumulating high wealth and low skill households accumulating low wealth. A HARA class utility with subsistence consumption requirement generates decreasing RRA with respect to household wealth. Consequently, low skill households also have significantly higher local RRA. In addition low skill households have less human capital and therefore have lower diversification demand for stocks. Low wealth, high RRA and low diversification demand predicts that low skill households do not hold stocks in the face of a moderate ownership cost. In addition, the model predicts a humped lifecycle wealth accumulation pattern and a humped lifecycle stock allocation pattern. I also find that stockholders exhibit a greater aggregate willingness to supply risky capital during the expansion phase of a business cycle, despite the lower conditional equity premium.
Interaction Between Endogenous Human Capital and Technological Change

This paper examines how interaction between endogenous human capital accumulation and technological change affects relative wages and economic growth. Private incentives to invest in human capital finance the employment of skilled labour in the education sector, while non-rival technology is a by-product of the education process. The absorption of new technologies into production is skill intensive, creates skill-biased labour demand, and increases the relative wage of skilled to unskilled labour. In contrast to recent models of endogenous growth, higher rates of technological change and growth may be accompanied by a higher relative wage but lower relative supply of skilled labour. Thus the model provides a theoretical foundation for the empirically observed relation between technological change and relative demand, supply and wages of skilled labour. I. INTRODUCTION Rapid change in technology in the 1980's was associated with a sharp rise in the relative wage and a decline in the relative supply of skilled labour. Models of endogenous technological change imply, however, that higher rates of innovation should be associated with more skilled labour and lower relative wages.' These models focus on private incentives to innovate, but assume that human capital is either exogenous or accidental (learning by doing).2 Thus, the literature provides few insights into how endogenous technological change influences private incentives to accumulate human capital through relative wage movements. This paper has two key elements that allow for interaction between endogenous technological change and the relative supply, demand, and wage of skilled labour. First, the two production sectors in the model differ not only in their skill-intensity, but also in technological sophistication. This generates strong skill-biased labour demand and higher relative wages in response to higher rates of technological change. Second, skilled labour is assumed to be an essential input in education, research, and in the absorption of innovations into production. The absorption of bursts in technological change then requires the withdrawal of skilled labour from research and education which subsequently increases the costs of both human capital investment and innovation. We term the resulting leveraging of the future rate of technological change and human capital the absorption effect.
New Goods and the Transition to a New Economy

The U.S. went through a remarkable structural transformation between 1800 and 2000. A precipitous decline in the importance of agricultural goods in the economy was matched by the rapid ascent of a plethora of new non-agricultural goods and services. A competitive model is presented here where consumption evolves along the extensive margin. This lessens the need to rely on satiation points, subsistence levels of consumption, and the like to explain agriculture’s demise. The analysis suggests that between 1800 and 2000 economic welfare grew by at least 1.5% a year, and may be as much as 10% annually, the exact number depending upon the metric preferred.
The Anomaly of Entrapment

Now in our second decade after 9/11, we are firmly in the prevention era of law enforcement. Faced with the unacceptable consequences of identifying threats too late, government agents are moving aggressively to identify potential terrorists before they strike. Undercover agents and confidential informants necessarily play a large role in such efforts. As a result of such operations, we have seen a number of cases brought to trial in the federal courts in which defendants have asserted the entrapment defense. To date, the defense has not succeeded. However, as a consequence of these cases, the United States Supreme Court may be required to reconsider the defense for the first time in over twenty years. Thus, now is a good time to re-examine the entrapment defense that the Supreme Court first recognized eighty years ago. This Article argues that the federal entrapment defense represents a doctrinal anomaly that straddles the line between criminal procedure and criminal substance. Understanding how and why the entrapment defense evolved as it did may engender greater sympathy for this much-maligned corner of the criminal law. It could also lead to reforms in the way the defense is administered that would better serve the interests that animate the defense—some sounding in the traditional concerns of substantive criminal law (culpability and dangerousness) and others in the traditional concerns of criminal procedure (deterring overzealous and unwarranted intrusions by government agents).  Assistant Professor of Law, Benjamin N. Cardozo School of Law. Thanks to Daniel Richman, Alex Stein, Max Minzner, Clifford Fishman and participants in the Cardozo School of Law Junior Faculty Workshop for helpful comments and suggestions, as well as participants in the AALS Criminal Justice Section June 2013 mid-year meeting in San Diego, CA, where this paper was presented as a work-in-progress. Thanks also to Jil Simon and Yan Slavinskiy for excellent research assistance. Washington University Open Scholarship 980 WASHINGTON UNIVERSITY LAW REVIEW [VOL. 91:979
Optimal Team Size and Monitoring in Organizations

We formulate and analyze a model of team structure and monitoring within a Linear‐Exponential‐Normal (LEN) agency framework. We incorporate three key instruments in the internal design of an organization involving team production: team size, monitoring activities, and incentive contracts. We show that the complex tradeoffs among these instruments lead to surprisingly simple implications. One such result is that the equilibrium level of pay‐for‐performance for workers is attenuated and is, at times, invariant to most environmental variables of interest. As such, our model helps explain the empirical puzzle of the lack of a trade‐off for risk/incentives shown in standard agency models. Our work also demonstrates the presence of complementarities between team size and monitoring, and between worker talent and managerial monitoring ability. Finally, we derive predictions about the impact of environmental variables on the choice of optimal team size, incentives, and employee quality, even in the presence of an...
Could have tethered: predicting the proportion of scoliosis patients most appropriate for thoracic anterior spinal tethering

Posterior spinal fusion (PSF) has proven to be a safe, reliable technique to treat spinal deformities in adolescents. In recent early reports, vertebral body tethering (VBT) is showing promise as a method to modulate growth, driving scoliosis correction, while offering the potential added benefit of maintaining some flexibility in the instrumented segment. With recent FDA humanitarian device exemption (HDE) approval, VBT is poised to become more widely available as a treatment for a subset of current PSF candidates. Our aim was to use approved criteria from a recent FDA IDE to determine who could have been tethered in the years preceding approval. A retrospective analysis was performed of patients with idiopathic scoliosis treated with PSF or VBT at a large pediatric spine center from 1/1/2016 to 6/25/2019. Tethering indications followed the criteria outlined by an ongoing FDA IDE: age 8–16, Sanders bone age ≤ 4, primary thoracic curve between 35° and 60°, and lumbar curve 34°, 9 (2.5%) had a main thoracic curve out of range, and 1 had a proximal thoracic curve > 40°. 81 patients (22.6%) had multiple exclusionary criteria. After decades with a successful treatment for AIS (PSF), we are at an inflection point: VBT is conditionally approved by the FDA as an HDE device, unleashing more widespread use. Many pediatric spine surgeons will want to know what proportion of PSFs will someday be VBTs. If FDA IDE criteria are used to ensure that a VBT candidate has an appropriate maturity stage and scoliosis deformity pattern, 20.9% of our 359 surgical range patients would have qualified for thoracic VBT. Level III.
Excited state dynamics of 7-deazaguanosine and guanosine 5'-monophosphate.

Minor structural modifications to the DNA and RNA nucleobases have a significant effect on their excited state dynamics and electronic relaxation pathways. In this study, the excited state dynamics of 7-deazaguanosine and guanosine 5′-monophosphate are investigated in aqueous solution and in a mixture of methanol and water using femtosecond broadband transient absorption spectroscopy following excitation at 267 nm. The transient spectra are collected using photon densities that ensure no parasitic multiphoton-induced signal from solvated electrons. The data can be fit satisfactorily using a two- or three-component kinetic model. By analyzing the results from steady-state, time-resolved, computational calculations, and the methanol–water mixture, the following general relaxation mechanism is proposed for both molecules, Lb → La → 1πσ*(ICT) → S0, where the 1πσ*(ICT) stands for an intramolecular charge transfer excited singlet state with significant πσ* character. In general, longer lifetimes for internal conversion are obtained for 7-deazaguanosine compared to guanosine 5′-monophosphate. Internal conversion of the 1πσ*(ICT) state to the ground state occurs on a similar time scale of a few picoseconds in both molecules. Collectively, the results demonstrate that substitution of a single nitrogen atom for a methine (C–H) group at position seven of the guanine moiety stabilizes the 1ππ* Lb and La states and alters the topology of their potential energy surfaces in such a way that the relaxation dynamics in 7-deazaguanosine are slowed down compared to those in guanosine 5′-monophosphate but not for the internal conversion of 1πσ*(ICT) state to the ground state.
Accuracy and precision of citizen scientist animal counts from drone imagery.

Repeated counts of animal abundance can reveal changes in local ecosystem health and inform conservation strategies. Unmanned aircraft systems (UAS), also known as drones, are commonly used to photograph animals in remote locations; however, counting animals in images is a laborious task. Crowd-sourcing can reduce the time required to conduct these censuses considerably, but must first be validated against expert counts to measure sources of error. Our objectives were to assess the accuracy and precision of citizen science counts and make recommendations for future citizen science projects. We uploaded drone imagery from Ano Nuevo Island (California, USA) to a curated Zooniverse website that instructed citizen scientists to count seals and sea lions. Across 212 days, over 1,500 volunteers counted animals in 90,000 photographs. We quantified the error associated with several descriptive statistics to extract a single citizen science count per photograph from the 15 repeat counts and then compared the resulting citizen science counts to expert counts. Although proportional error was relatively low (9% for sea lions and 5% for seals during the breeding seasons) and improved with repeat sampling, the 12+ volunteers required to reduce error was prohibitively slow, taking on average 6 weeks to estimate animals from a single drone flight covering 25 acres, despite strong public outreach efforts. The single best algorithm was 'Median without the lowest two values', demonstrating that citizen scientists tended to under-estimate the number of animals present. Citizen scientists accurately counted adult seals, but accuracy was lower when sea lions were present during the summer and could be confused for seals. We underscore the importance of validation efforts and careful project design for researchers hoping to combine citizen science with imagery from drones, occupied aircraft, and/or remote cameras.
Price gouging, non-worseness, and distributive justice

This paper develops my position on the ethics of price gouging in response to Jeremy Snyder's article, "What's the Matter with Price Gouging." First, it explains how the "nonworseness claim" supports the moral permissibility of price gouging, even if it does not show that price gougers are morally virtuous agents. Second, it argues that questions about price gouging and distributive justice must be answered in light of the relevant possible institutional alternatives, and that Snyder's proposed alternatives to price gouging fare worse on the dimension of justice than a system in which goods are allocated by a system of market prices.
Material Mapping in Unknown Environments using Tapping Sound

In this paper, we propose an autonomous exploration and a tapping mechanism-based material mapping system for a mobile robot in unknown environments. The goal of the proposed system is to integrate simultaneous localization and mapping (SLAM) modules and sound-based material classification to enable a mobile robot to explore an unknown environment autonomously and at the same time identify the various objects and materials in the environment. This creates a material map that localizes the various materials in the environment which has potential applications for search and rescue scenarios. A tapping mechanism and tapping audio signal processing based on machine learning techniques are exploited for a robot to identify the objects and materials. We demonstrate the proposed system through experiments using a mobile robot platform installed with Velodyne LiDAR, a linear solenoid, and microphones in an exploration-like scenario with various materials. Experiment results demonstrate that the proposed system can create useful material maps in unknown environments.
The x-dependence of hadronic parton distributions: A review on the progress of lattice QCD

In this article, we review recent lattice calculations on the x-dependence of parton distributions, with the latter providing information on hadron structure. These calculations are based on matrix elements of boosted hadrons coupled to non-local operators and can be related to the standard, light-cone distribution functions via an appropriate factorization formalism. There is significant progress in several directions, including calculations of flavor-singlet parton distribution functions (PDFs), first calculations of generalized parton distributions (GPDs), as well as the implementation of some of the approaches for the transverse-momentum-dependent PDFs (TMD PDFs). This new direction of lattice calculations is particularly interesting for phenomenological fits on experimental data sets, as the lattice results can help improve the constraints on the distribution functions.
A synthetic small molecule stalls pre-mRNA splicing by promoting an early-stage U2AF2-RNA complex

Dysregulated pre-mRNA splicing is an emerging Achilles heel of cancers and myelodysplasias. To expand the currently limited portfolio of small-molecule drug leads, we screened for chemical modulators of the U2AF complex, which nucleates spliceosome assembly and is mutated in myelodysplasias. A hit compound specifically enhances RNA binding by a U2AF2 subunit. Remarkably, the compound inhibits splicing of representative substrates and stalls spliceosome assembly at the stage of U2AF function. Computational docking, together with structure-guided mutagenesis, indicates that the compound bridges the tandem U2AF2 RNA recognition motifs via hydrophobic and electrostatic moieties. Cells expressing a cancer-associated U2AF1 mutant are preferentially killed by treatment with the compound. Altogether, our results highlight the potential of trapping early spliceosome assembly as an effective pharmacological means to manipulate pre-mRNA splicing. By extension, we suggest that stabilizing assembly intermediates may offer a useful approach for small-molecule inhibition of macromolecular machines.
A grid-free approach for simulating sweep and cyclic voltammetry.

We present a computational approach to simulate linear sweep and cyclic voltammetry experiments that does not require a discretized grid in space to quantify diffusion. By using a Green’s function solution coupled to a standard implicit ordinary differential equation solver, we are able to simulate current and redox species concentrations using only a small grid in time. As a result, where benchmarking is possible, we find that the current method is faster than (and quantitatively identical to) established techniques. The present algorithm should help open the door for studying adsorption effects in inner sphere electrochemistry.
Quantum Foundations of Classical Reversible Computing

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible digital paradigm. However, to date, the essential rationale for, and analysis of, classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer’s Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.
Mapping the organization and dynamics of the posterior medial network during movie watching

Brain regions within a posterior medial network (PMN) are characterized by sensitivity to episodic tasks, and they also demonstrate strong functional connectivity as part of the default network. Despite its cohesive structure, delineating the intranetwork organization and functional diversity of the PMN is crucial for understanding its contributions to multidimensional event cognition. Here, we probed functional connectivity of the PMN during movie watching to identify its pattern of connections and subnetwork functions in a split-sample replication of 136 participants. Consistent with prior findings of default network fractionation, we identified distinct PMN subsystems: a Ventral PM subsystem (retrosplenial cortex, parahippocampal cortex, posterior angular gyrus) and a Dorsal PM subsystem (medial prefrontal cortex, hippocampus, precuneus, posterior cingulate cortex, anterior angular gyrus). Ventral and Dorsal PM subsystems were differentiated by functional connectivity with parahippocampal cortex and precuneus and integrated by retrosplenial cortex and posterior cingulate cortex, respectively. Finally, the distinction between PMN subsystems is functionally relevant: whereas both Dorsal and Ventral PM connectivity tracked the movie content, only Ventral PM connections increased in strength at event transitions and appeared sensitive to episodic memory. Overall, these findings reveal PMN functional pathways and the distinct functional roles of intranetwork subsystems during event cognition.
Measuring the Impact of COVID-19 Induced Campus Closure on Student Self-Regulated Learning in Physics Online Learning Modules

This paper examines the impact of COVID-19 induced campus closure on university students’ self-regulated learning behavior by analyzing click-stream data collected from student interactions with 70 online learning modules in a university physics course. To do so, we compared the trend of six types of actions related to the three phases of self-regulated learning before and after campus closure and between two semesters. We found that campus closure changed students’ planning and goal setting strategies for completing the assignments, but didn’t have a detectable impact on the outcome or the time of completion, nor did it change students’ self-reflection behavior. The results suggest that most students still manage to complete assignments on time during the pandemic, while the design of online learning modules might have provided the flexibility and support for them to do so.
Accelerating high-throughput virtual screening through molecular pool-based active learning

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 108 molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure–property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking datasets and assess the impact of surrogate model architecture, acquisition function, and acquisition batch size on optimization performance. We observe significant reductions in computational costs; for example, using a directed-message passing neural network we can identify 94.8% or 89.3% of the top-50 000 ligands in a 100M member library after testing only 2.4% of candidate ligands using an upper confidence bound or greedy acquisition strategy, respectively. Such model-guided searches mitigate the increasing computational costs of screening increasingly large virtual libraries and can accelerate high-throughput virtual screening campaigns with applications beyond docking.
Unified k-space theory of optical coherence tomography

We present a general theory of optical coherence tomography (OCT), which synthesizes the fundamental concepts and implementations of OCT under a common 3D k-space framework. At the heart of this analysis is the Fourier diffraction theorem, which relates the coherent interaction between a sample and plane wave to the Ewald sphere in the 3D k-space representation of the sample. While only the axial dimension of OCT is typically analyzed in k-space, we show that embracing a fully 3D k-space formalism allows explanation of nearly every fundamental physical phenomenon or property of OCT, including contrast mechanism, resolution, dispersion, aberration, limited depth of focus, and speckle. The theory also unifies diffraction tomography, confocal microscopy, point-scanning OCT, line-field OCT, full-field OCT, Bessel beam OCT, transillumination OCT, interferometric synthetic aperture microscopy (ISAM), and optical coherence refraction tomography (OCRT), among others. Our unified theory not only enables clear understanding of existing techniques but also suggests new research directions to continue advancing the field of OCT.
Detecting sparse heterogeneous mixtures in a two-sample problem

We consider the problem of detecting sparse heterogeneous mixtures in a two-sample setting from a nonparametric perspective, where the effect manifests itself as a positive shift. We suggest a two-...
On a rank-unimodality conjecture of Morier-Genoud and Ovsienko

Abstract Let α = ( a , b , … ) be a composition. Consider the associated poset F ( α ) , called a fence, whose covering relations are x 1 ◁ x 2 ◁ … ◁ x a + 1 ▷ x a + 2 ▷ … ▷ x a + b + 1 ◁ x a + b + 2 ◁ … . We study the associated distributive lattice L ( α ) consisting of all lower order ideals of F ( α ) . These lattices are important in the theory of cluster algebras and their rank generating functions can be used to define q-analogues of rational numbers. In particular, we make progress on a recent conjecture of Morier-Genoud and Ovsienko that L ( α ) is rank unimodal. We show that if one of the parts of α is greater than the sum of the others, then the conjecture is true. We conjecture that L ( α ) enjoys the stronger properties of having a nested chain decomposition and having a rank sequence which is either top or bottom interlacing, the latter being a recently defined property of sequences. We verify that these properties hold for compositions with at most three parts and for what we call d-divided posets, generalizing work of Claussen and simplifying a construction of Gansner.
Knowledge Transfers from Federally Funded Research and Development Centers

The findings from a study of the relationship between postdoctoral students and R&D at Federally Funded Research and Development Centers (FFRDCs) are presented in this paper. The goal of the study is not only to understand better knowledge transfers from publicly supported R&D but also to estimate the returns to R&D conducted in federally funded laboratories. Using public domain data related to FFRDCs, published by the US National Science Foundation, the R&D elasticity of doctoral students is estimated over the years 2010–2019 to be about 0.85. This estimate compares well to previous studies of the returns to publicly funded R&D.
An Experimental Study of Roughness-Induced Instabilities in a Supersonic Boundary Layer

Progress on an experimental study of laminar-to-turbulent transition induced by an isolated roughness element in a supersonic laminar boundary layer is reported in this paper. Here, the primary focus is on the effects of roughness planform shape on the instability and transition characteristics. Four different roughness planform shapes were considered (a diamond, a circle, a right triangle, and a 45 degree fence) and the height and width of each one was held fixed so that a consistent frontal area was presented to the oncoming boundary layer. The nominal roughness Reynolds number was 462 and the ratio of the roughness height to the boundary layer thickness was 0.48. Detailed flow- field surveys in the wake of each geometry were performed via hot-wire anemometry. High- and low-speed streaks were observed in the wake of each roughness geometry, and the modified mean flow associated with these streak structures was found to support a single dominant convective instability mode. For the symmetric planform shapes - the diamond and circular planforms - the instability characteristics (mode shapes, growth rates, and frequencies) were found to be similar. For the asymmetric planform shapes - the right-triangle and 45 degree fence planforms - the mode shapes were asymmetrically distributed about the roughness-wake centerline. The instability growth rates for the asymmetric planforms were lower than those for the symmetric planforms and therefore, transition onset was delayed relative to the symmetric planforms.
One-Shot Variable-Length Secret Key Agreement Approaching Mutual Information

This paper studies an information-theoretic one-shot variable-length secret key agreement problem with public discussion. Let $X$ and $Y$ be jointly distributed random variables, each taking values in some measurable space. Alice and Bob observe $X$ and $Y$ respectively, can communicate interactively through a public noiseless channel, and want to agree on a key length and a key that is approximately uniformly distributed over all bit sequences with the agreed key length. The public discussion is observed by an eavesdropper, Eve. The key should be approximately independent of the public discussion, conditional on the key length. We show that the optimal expected key length is close to the mutual information $I(X;Y)$ within a logarithmic gap. Moreover, an upper bound and a lower bound on the optimal expected key length can be written down in terms of $I(X;Y)$ only. This means that the optimal one-shot performance is always within a small gap of the optimal asymptotic performance regardless of the distribution of the pair $(X,Y)$ . This one-shot result may find applications in situations where the components of an i.i.d. pair source $(X^{n},Y^{n})$ are observed sequentially and the key is output bit by bit, or in situations where the random source is not an i.i.d. or ergodic process.
How useful is Active Learning for Image-based Plant Phenotyping?

Deep learning models have been successfully deployed for a diverse array of image-based plant phenotyping applications including disease detection and classification. However, successful deployment of supervised deep learning models requires large amount of labeled data, which is a significant challenge in plant science (and most biological) domains due to the inherent complexity. Specifically, data annotation is costly, laborious, time consuming and needs domain expertise for phenotyping tasks, especially for diseases. To overcome this challenge, active learning algorithms have been proposed that reduce the amount of labeling needed by deep learning models to achieve good predictive performance. Active learning methods adaptively select samples to annotate using an acquisition function to achieve maximum (classification) performance under a fixed labeling budget. We report the performance of four different active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy, (3) Least Confidence, and (4) Coreset, with conventional random sampling-based annotation for two different image-based classification datasets. The first image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to eight different soybean stresses and a healthy class, and the second consists of nine different weed species from the field. For a fixed labeling budget, we observed that the classification performance of deep learning models with active learning-based acquisition strategies is better than random sampling-based acquisition for both datasets. The integration of active learning strategies for data annotation can help mitigate labelling challenges in the plant sciences applications particularly where deep domain knowledge is required.
Acceleration via Fractal Learning Rate Schedules

In practical applications of iterative first-order optimization, the learning rate schedule remains notoriously difficult to understand and expensive to tune. We demonstrate the presence of these subtleties even in the innocuous case when the objective is a convex quadratic. We reinterpret an iterative algorithm from the numerical analysis literature as what we call the Chebyshev learning rate schedule for accelerating vanilla gradient descent, and show that the problem of mitigating instability leads to a fractal ordering of step sizes. We provide some experiments to challenge conventional beliefs about stable learning rates in deep learning: the fractal schedule enables training to converge with locally unstable updates which make negative progress on the objective.
Islamic Law from the Internal Point of View

Haider Hamoudi notes the different perspectives lawyers and historians employ in making sense of the law. Invoking H.L.A. Hart’s famous distinction between “internal” and “external” points of view with respect to law and legal rules, Hamoudi describes lawyers as primarily adopting the former, and historians, the latter point of view. This is not to suggest that lawyers do not take history into consideration, but rather to mean that when they do, their focus is results oriented in that they use history to understand the ultimate endpoint, the contemporaneous meaning of a legal rule or institution. Hamoudi observes two consequences emanating from lawyers’ adoption of the internal view that puts lawyers somewhat at odds with the demands of historical method and meaning. While deliberately omitting discussion on the normative desirability of either method, Hamoudi concludes by observing value in merely pointing out the differences between the internal and external viewpoints of law and history, respectively, to help expose “our own biases and assumptions.”
Measuring Robustness in Deep Learning Based Compressive Sensing

Deep neural networks give state-of-the-art accuracy for reconstructing images from few and noisy measurements, a problem arising for example in accelerated magnetic resonance imaging (MRI). However, recent works have raised concerns that deep-learning-based image reconstruction methods are sensitive to perturbations and are less robust than traditional methods: Neural networks (i) may be sensitive to small, yet adversarially-selected perturbations, (ii) may perform poorly under distribution shifts, and (iii) may fail to recover small but important features in an image. In order to understand the sensitivity to such perturbations, in this work, we measure the robustness of different approaches for image reconstruction including trained and un-trained neural networks as well as traditional sparsity-based methods. We find, contrary to prior works, that both trained and un-trained methods are vulnerable to adversarial perturbations. Moreover, both trained and un-trained methods tuned for a particular dataset suffer very similarly from distribution shifts. Finally, we demonstrate that an image reconstruction method that achieves higher reconstruction quality, also performs better in terms of accurately recovering fine details. Our results indicate that the state-of-the-art deep-learning-based image reconstruction methods provide improved performance than traditional methods without compromising robustness.
Agnostic Proper Learning of Halfspaces under Gaussian Marginals

We study the problem of agnostically learning halfspaces under the Gaussian distribution. Our main result is the {\em first proper} learning algorithm for this problem whose running time qualitatively matches that of the best known improper agnostic learner. Building on this result, we also obtain the first proper polynomial time approximation scheme (PTAS) for agnostically learning homogeneous halfspaces. Our techniques naturally extend to agnostically learning linear models with respect to other activation functions, yielding the first proper agnostic algorithm for ReLU regression.
Design Patterns and Trade-Offs in Responsive Visualization for Communication

Increased access to mobile devices motivates the need to design communicative visualizations that are responsive to varying screen sizes. However, relatively little design guidance or tooling is currently available to authors. We contribute a detailed characterization of responsive visualization strategies in communication-oriented visualizations, identifying 76 total strategies by analyzing 378 pairs of large screen (LS) and small screen (SS) visualizations from online articles and reports. Our analysis distinguishes between the Targets of responsive visualization, referring to what elements of a design are changed and Actions representing how targets are changed. We identify key trade-offs related to authors' need to maintain graphical density, referring to the amount of information per pixel, while also maintaining the "message" or intended takeaways for users of a visualization. We discuss implications of our findings for future visualization tool design to support responsive transformation of visualization designs, including requirements for automated recommenders for communication-oriented responsive visualizations.
HOT-VAE: Learning High-Order Label Correlation for Multi-Label Classification via Attention-Based Variational Autoencoders

Understanding how environmental characteristics affect biodiversity patterns, from individual species to communities of species, is critical for mitigating effects of global change. A central goal for conservation planning and monitoring is the ability to accurately predict the occurrence of species communities and how these communities change over space and time. This in turn leads to a challenging and long-standing problem in the field of computer science - how to perform accurate multi-label classification with hundreds of labels? The key challenge of this problem is its exponential-sized output space with regards to the number of labels to be predicted. Therefore, it is essential to facilitate the learning process by exploiting correlations (or dependency) among labels. Previous methods mostly focus on modelling the correlation on label pairs; however, complex relations between real-world objects often go beyond second order. In this paper, we propose a novel framework for multi-label classification, High-order Tie-in Variational Autoencoder (HOT-VAE), which performs adaptive high-order label correlation learning. We experimentally verify that our model outperforms the existing state-of-the-art approaches on a bird distribution dataset on both conventional F1 scores and a variety of ecological metrics. To show our method is general, we also perform empirical analysis on seven other public real-world datasets in several application domains, and Hot-VAE exhibits superior performance to previous methods.
Convexification numerical algorithm for a 2D inverse scattering problem with backscatter data

This paper is concerned with the inverse scattering problem which aims to determine the spatially distributed dielectric constant coefficient of the 2D Helmholtz equation from multifrequency backsc...
Traction force microscopy by deep learning.

Abstract Cells interact mechanically with their surroundings by exerting and sensing forces. Traction force microscopy (TFM), purported to map cell-generated forces or stresses, represents an important tool that has powered the rapid advances in mechanobiology. However, to solve the ill-posed mathematical problem, conventional TFM involved compromises in accuracy and/or resolution. Here, we applied neural network-based deep learning as an alternative approach for TFM. We modified a neural network designed for image processing to predict the vector field of stress from displacements. Furthermore, we adapted a mathematical model for cell migration to generate large sets of simulated stresses and displacements for training and testing the neural network. We found that deep learning-based TFM yielded results that resemble those using conventional TFM but at a higher accuracy than several conventional implementations tested. In addition, a trained neural network is appliable to a wide range of conditions, including cell size, shape, substrate stiffness, and traction output. The performance of deep learning-based TFM makes it an appealing alternative to conventional methods for characterizing mechanical interactions between adherent cells and the environment.
Industry tournament incentives and corporate hedging policies

This paper examines how a tournament among CEOs to progress within the CEO labor market influences their corporate hedging policies. We employ a textual analysis of 10-Ks to generate corporate hedging proxies, finding that the likelihood and intensity of hedging grow as the CEO labor-market tournament prizes increase. We also explore the mitigating impact of corporate hedging on the adverse effects of risk-inducing industry tournament incentives (ITIs) on the cost of debt and stock price crash risk, noting that these could be possible reasons behind the relation. Additionally, we observe that the relationship between ITIs and corporate hedging is less pronounced for firms that demonstrate more financial distress and for firms whose CEOs are the founders of the company or are of retirement age. We identify a causal relation between ITIs and corporate hedging using an instrumental variable approach and an exogenous shock sourced from changes in the enforceability of non-competition agreements across states.
Medium-energy neutrino physics

A selection of opportunities for neutrino physics from a source generated from pions that decay in flight and at rest is described. The present source at LAMPF has a duty factor of about 6%; improvements in opportunities that emerge from a source using a pulse 0.25 μsec long from a proton storage ring are also described.
Energy and flow effects of optimal automated driving in mixed traffic: Vehicle-in-the-loop experimental results

Abstract This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We implement a Vehicle-in-the-Loop (VIL) testing environment in which experimental CAVs driven on a track interact with surrounding virtual traffic in real-time. We explore the energy savings when following city and highway drive cycles, as well as in emergent highway traffic created from microsimulations. Model predictive control handles high level velocity planning and benefits from communicated intentions of a preceding CAV or estimated probable motion of a preceding human driven vehicle. A combination of classical feedback control and data-driven nonlinear feedforward control of pedals achieve acceleration tracking at the low level. The controllers are implemented in ROS and energy is measured via calibrated OBD-II readings. We report up to 30 % improved energy economy compared to realistically calibrated human driver car-following without sacrificing following headway.
Supervised deep learning of elastic SRV distances on the shape space of curves

Motivated by applications from computer vision to bioinformatics, the field of shape analysis deals with problems where one wants to analyze geometric objects, such as curves, while ignoring actions that preserve their shape, such as translations, rotations, scalings, or reparametrizations. Mathematical tools have been developed to define notions of distances, averages, and optimal deformations for geometric objects. One such framework, which has proven to be successful in many applications, is based on the square root velocity (SRV) transform, which allows one to define a computable distance between spatial curves regardless of how they are parametrized. This paper introduces a supervised deep learning framework for the direct computation of SRV distances between curves, which usually requires an optimization over the group of reparametrizations that act on the curves. The benefits of our approach in terms of computational speed and accuracy are illustrated via several numerical experiments on both synthetic and real data.
Krüppel-like factor gene function in the ctenophore Mnemiopsis leidyi assessed by CRISPR/Cas9-mediated genome editing.

The Kruppel-like factor (Klf) gene family encodes transcription factors that play an important role in the regulation of stem cell proliferation, cell differentiation and development in bilaterians. Although Klf genes have been shown to specify functionally various cell types in non-bilaterian animals, their role in early-diverging animal lineages has not been assessed. Thus, the ancestral activity of these transcription factors in animal development is not well understood. The ctenophore Mnemiopsis leidyi has emerged as an important non-bilaterian model system for understanding early animal evolution. Here, we characterize the expression and functional role of Klf genes during M. leidyi embryogenesis. Zygotic Klf gene function was assessed with both CRISPR/Cas9-mediated genome editing and splice-blocking morpholino oligonucleotide knockdown approaches. Abrogation of zygotic Klf expression during M. leidyi embryogenesis resulted in abnormal development of several organs, including the pharynx, tentacle bulbs and apical organ. Our data suggest an ancient role for Klf genes in regulating endodermal patterning, possibly through regulation of cell proliferation.
Efficient generation of optical bottle beams

Optical bottle beams can be used to trap atoms and small low-index particles. We introduce a figure of merit for optical bottle beams, specifically in the context of optical traps, and use it to compare optical bottle-beam traps obtained by three different methods. Using this figure of merit and an optimization algorithm, we identified optical bottle-beam traps based on a Gaussian beam illuminating a metasurface that are superior in terms of power efficiency than existing approaches. We numerically demonstrate a silicon metasurface for creating an optical bottle-beam trap.