cssi/SciSciGPT-SciSciNet huggingface dataset:
Antitrust Limits to Patent Settlements

This paper focuses on the class of legal rules that governs intellectual property rights: the antitrust limits imposed on patent settlements. The paper discusses the benefits and costs of settlements and explains why antitrust limits on settlements are needed to prevent abuse of the settlement process. A general rule for evaluating proposed settlements is developed. This paper explores a simple antitrust rule governing settlements of intellectural property disputes: a settlement cannot lead to lower expected consumer surplus than would have arisen from ongoing litigation. It argues that this rule respects intellectural property rights while encouraging efficient settlements. Under extremely general conditions, there exists a settlement that leaves consumers better off and raises the joint profits of the two firms engaged in the dispute. This general test is then applied to several types of settlements: mergers; agreements specifying the timing of entry; and patent pools.
The Endurance of Biological Connection: Heteronormativity, Same-Sex Parenting and the Lessons of Adoption

This paper traces the intersecting and diverging paths of adoption norms and the legal recognition of same sex parents. It compares adoption law's trajectory from replicating the modern family and erasing biological connection to its current embrace of biology with a similar movement among lesbian and gay families. While many of these postmodern families are replicating modern family forms, they are also heeding lessons learned in adoption about the endurance of biology, acknowledging and even embracing their children's biological families. The paper first reveals the tenacity of biological connection and its deep and wide significance in United States culture, history, and law. The next section explores lesbian and gay families with children, noting ways these families reflect heteronormativity through two, rather than plural, parent families and yet still value and honor biological connections by including reproductive partners, such as sperm donors and surrogates, into their family systems. The article concludes with lessons open-adoption law and practice might offer lesbian and gay families with children, particularly regarding the possible benefits of developing legal schemes regarding these family systems.
Justice Kennedy and Ecosystem Services: A Functional Approach to Clean Water Act Jurisdiction After Rapanos

Justice Kennedy’s “significant nexus” test may emerge as the proverbial silver lining of the U.S. Supreme Court’s June 2006 decision in Rapanos v. United States, at least so far as recognition of ecosystem services is concerned. The Court’s opinion in Rapanos was fractured. Nevertheless, it left no doubts that the Clean Water Act’s jurisdiction over “navigable waters” had been limited, drawing criticism for both its lack of clarity and its restriction of federal jurisdiction under the Act. The extent of that restriction, however, would depend on which of the three major opinion’s in the case – Justice Scalia’s plurality, Justice Kennedy’s concurrence, or Justice Stevens’ dissent – the lower courts chose to follow. Since the Rapanos decision, it has become clear that Justice Kennedy’s concurrence provides the controlling test or one of the controlling tests in every circuit where the Court of Appeals has addressed the issue. As such, Justice Kennedy’s significant nexus test is likely to guide Clean Water Act jurisdiction for the foreseeable future, unless Congress decides to act instead. By forcing lower courts to find and articulate a functional connection between the waters at issue and more traditionally “navigable” waters, Justice Kennedy’s test encourages the federal courts to articulate the ecosystem functions that these waters serve and to identify the ecosystem services that they provide to humans. As a result, this Article argues, Justice Kennedy’s significant nexus test may help to produce an ecosystem services rhetoric that will emphasize both the ecological and economic value of the nation’s waters, potentially improving both the public’s appreciation of water quality regulation and the overall quality of that regulation.
Unemployment Insurance and the Role of Self-Insurance

Abstract This paper employs a dynamic general equilibrium model to design and evaluate long-term unemployment insurance plans (plans that depend on workers' unemployment history) in economies with and without hidden savings. We show that optimal benefit schemes and welfare implications differ considerably in these two economies. Switching to long-term plans can improve welfare significantly in the absence of hidden savings. However, welfare gains are much lower when we consider hidden savings. Therefore, we argue that switching to long-term plans should not be a primary concern from a policy point of view. Journal of Economic Literature Classification Numbers: J65, D82.
The Real Costs of Financial Efficiency When Some Information Is Soft

This article shows that improving financial efficiency may reduce real efficiency. While the former depends on the total amount of information available, the latter depends on the relative amounts of hard and soft information. Disclosing more hard information (e.g., earnings) increases total information, raising financial efficiency and reducing the cost of capital. However, it induces the manager to prioritize hard information over soft by cutting intangible investment to boost earnings, lowering real efficiency. The optimal level of financial efficiency is non-monotonic in investment opportunities. Even if low financial efficiency is desirable to induce investment, the manager may be unable to commit to it. Optimal government policy may involve upper, not lower, bounds on financial efficiency.
Monochromatic Triangles, Triangle Listing and APSP

All-Pairs Shortest Paths (APSP) is one of the most basic problems in graph algorithms. Given an $n$ -node directed or undirected graph with integer weights in $\{-n^{c}, \ldots, n^{c}\}$ and no negative cycles, APSP asks to compute the shortest paths distance between every pair of vertices. The fastest known algorithm for APSP runs in $n^{3}/2^{\Theta(\sqrt{\log n})}$ time [Williams'14], and no truly subcubic time algorithms are known. One of the main hypotheses in fine-grained complexity is that APSP requires $n^{3-o(1)}$ time. Another famous hypothesis in fine-grained complexity is that the 3SUM problem for $n$ integers (which can be solved in $O(n^{2})$ time) requires $n^{2-o(1)}$ time. Although there are no direct reductions between 3SUM and APSP, it is known that they are related: the (min, +)-convolution problem reduces in a fine-grained way to both, and both fine-grained reduce to the Exact Triangle problem. In this paper we find more relationships between these two problems and other basic problems. Patrascu had shown that under the 3SUM hypothesis the All-Edges Sparse Triangle problem in $m$ -edge graphs requires $m^{4/3-o(1)}$ time. The latter problem asks to determine for every edge $e$ , whether $e$ is in a triangle. It is equivalent to the problem of listing $m$ triangles in an $m$ -edge graph where $m=-\tilde{O}(n^{1.5})$ , and can be solved in $O(m^{1.41})$ time [Alon et al.'97] with the current matrix multiplication bounds, and in $\tilde{O}(m^{4/3})$ time if $\omega=2$ . We show that one can reduce Exact Triangle to All-Edges Sparse Triangle, showing that All-Edges Sparse Triangle (and hence Triangle Listing) requires $m^{4/3-o(1)}$ time also assuming the APSP hypothesis. This allows us to provide APSP-hardness for many dynamic problems that were previously known to be hard under the 3SUM hypothesis. We also consider the All-Edges Monochromatic Triangle problem. Via work of [Lincoln et al.'20], our result on All-Edges Sparse Triangle implies that if the All-Edges Monochromatic Triangle problem has an $O(n^{2.5-\varepsilon})$ time algorithm for $\epsilon > 0$ , then both the APSP and 3SUM hypotheses are false. The fastest algorithm for All-Edges Monochromatic Triangle runs in $\tilde{O}(n^{(3+\omega)/2})$ time [Vassilevska et al.'06], and our new reduction shows that if $\omega=2$ , this algorithm is best possible, unless 3SUM or APSP can be solved faster. Besides 3SUM, previously the only problems known to be fine-grained reducible to All-Edges Monochromatic Triangle were the seemingly easier problems directed unweighted APSP and Min-Witness Product [Lincoln et al.'20]. Our reduction shows that this problem is much harder. We also connect the problem to other “intermediate” problems, whose runtimes are between $O(n^{\omega})$ and $O(n^{3})$ , such as the Max-Min product problem.
On Hallucinations in Tomographic Image Reconstruction

Tomographic image reconstruction is generally an ill-posed linear inverse problem. Such ill-posed inverse problems are typically regularized using prior knowledge of the sought-after object property. Recently, deep neural networks have been actively investigated for regularizing image reconstruction problems by learning a prior for the object properties from training images. However, an analysis of the prior information learned by these deep networks and their ability to generalize to data that may lie outside the training distribution is still being explored. An inaccurate prior might lead to false structures being hallucinated in the reconstructed image and that is a cause for serious concern in medical imaging. In this work, we propose to illustrate the effect of the prior imposed by a reconstruction method by decomposing the image estimate into generalized measurement and null components. The concept of a hallucination map is introduced for the general purpose of understanding the effect of the prior in regularized reconstruction methods. Numerical studies are conducted corresponding to a stylized tomographic imaging modality. The behavior of different reconstruction methods under the proposed formalism is discussed with the help of the numerical studies.
Tensor decomposition to compress convolutional layers in deep learning

Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control. Although many methods have bee...
Pervasive effects of Wolbachia on host activity.

Heritable symbionts have diverse effects on the physiology, reproduction and fitness of their hosts. Maternally transmitted Wolbachia are one of the most common endosymbionts in nature, infecting a...
Accelerating ultrafast spectroscopy with compressive sensing

A signal-processing algorithm called compressive sensing lets researchers characterize a sample with ultrafast spectroscopy using far fewer measurements than before.
Spatio-Temporal Tensor Sketching via Adaptive Sampling

Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor representation can be used to capture the correlation between space and time and simultaneously exploit the latent structure of the spatial and temporal patterns in an unsupervised fashion. However, the increasing volume of spatio-temporal data has made it prohibitively expensive to store and analyze using tensor factorization.
Convolutional neural network denoising in fluorescence lifetime imaging microscopy (FLIM)

Fluorescence lifetime imaging microscopy (FLIM) systems are limited by their slow processing speed, low signal- to-noise ratio (SNR), and expensive and challenging hardware setups. In this work, we demonstrate applying a denoising convolutional network to improve FLIM SNR. The network will integrated with an instant FLIM system with fast data acquisition based on analog signal processing, high SNR using high-efficiency pulse-modulation, and cost-effective implementation utilizing off-the-shelf radio-frequency components. Our instant FLIM system simultaneously provides the intensity, lifetime, and phasor plots in vivo and ex vivo. By integrating image de- noising using the trained deep learning model on the FLIM data, provide accurate FLIM phasor measurements are obtained. The enhanced phasor is then passed through the K-means clustering segmentation method, an unbiased and unsupervised machine learning technique to separate different fluorophores accurately. Our experimental in vivo mouse kidney results indicate that introducing the deep learning image denoising model before the segmentation effectively removes the noise in the phasor compared to existing methods and provides clearer segments. Hence, the proposed deep learning-based workflow provides fast and accurate automatic segmentation of fluorescence images using instant FLIM. The denoising operation is effective for the segmentation if the FLIM measurements are noisy. The clustering can effectively enhance the detection of biological structures of interest in biomedical imaging applications.
Vacuum magnetic fields with exact quasisymmetry near a flux surface. Part 1. Solutions near an axisymmetric surface

While several results have pointed to the existence of exactly quasisymmetric fields on a surface (Garren & Boozer, Phys. Fluids B, vol. 3, 1991, pp. 2805–2821; 2822–2834; Plunk & Helander, J. Plasma Phys., vol. 84, 2018, 905840205), we have obtained the first such solutions using a vacuum surface expansion formalism. We obtain a single nonlinear parabolic partial differential equation for a function . Closed-form solutions are obtained in cylindrical, slab and isodynamic geometries. Numerical solutions of the full nonlinear equations in general axisymmetric toroidal geometry are obtained, resulting in a class of quasihelical local vacuum equilibria near an axisymmetric surface. The analytic models provide additional insight into general features of the nonlinear solutions, such as localization of the surface perturbations on the inboard side. The local solutions thus obtained can be continued globally only for special initial surfaces.
Evaluating the benefits of kinetic Monte Carlo and microkinetic modeling for catalyst design studies in the presence of lateral interactions

Abstract Popular computational catalyst design strategies rely on the identification of reactivity descriptors, which can be used along with Bronsted−Evans−Polanyi (BEP) and scaling relations as input to a microkinetic model (MKM) to make predictions for activity or selectivity trends. The main benefit of this approach is related to the inherent dimensionality reduction of the large material space to just a few catalyst descriptors. Conversely, it is well documented that a small set of descriptors is insufficient to capture the intricacies and complexities of a real catalytic system. The inclusion of coverage effects through lateral adsorbate-adsorbate interactions can narrow the gap between simplified descriptor predictions and real systems, but mean-field MKMs cannot properly account for local coverage effects. This shortcoming of the mean-field approximation can be rectified by switching to a lattice-based kinetic Monte Carlo (kMC) method using cluster expansion representation of adsorbate−adsorbate lateral interactions. Using the prototypical CO oxidation reaction as an example, we critically evaluate the benefits of kMC over MKM in terms of trend predictions and computational cost when using only a small set of input parameters. After confirming that in the absence of lateral interactions the kMC and MKM approaches yield identical trends and mechanistic information, we observed substantial differences between the two kinetic models when lateral interactions were introduced. The mean-field implementation applies coverage corrections directly to the descriptors, causing an artificial overprediction of the activity of strongly binding metals. In contrast, the cluster expansion in kMC implementation can differentiate among the highly active metals but it is very sensitive to the set of included interaction parameters. Considering that computational screening relies on a minimal set of descriptors, for which MKM makes reasonable trend predictions at a ca. three orders of magnitude lower computational cost than kMC, the MKM approach does provide a better entry point for computational catalyst design.
Comparison of Short-Term Health and Performance Effects Related to Prophylactic Administration of Tulathromycin versus Tilmicosin in Long-Hauled, Highly Stressed Beef Stocker Calves

Bovine respiratory disease (BRD) is the most common disease condition in both the stocker and feedlot stages of beef production. This syndrome causes economic loss in the form of treatment costs, loss of growth performance, decreased quality grade, and higher mortality risk. Metaphylactic antimicrobial therapy upon arrival to a feedlot facility has been intensely studied and repeatedly shown to be a cost-effective practice in many production settings. Two approved metaphylactic drugs, tilmicosin and tulathromycin, are members of the macrolide class of antibiotics. The stocker segment of the beef industry is traditionally a grower phase for young, lightweight calves prior to their entry into a feedlot. However, stocker calves are on feed for a much shorter duration than calves in the finishing period in feedlots. Therefore, maintaining a high level of health and growth performance is imperative. The objective of this research was to determine if health and performance differences were present when comparing a longer-acting (tulathromycin) or shorter-acting (tilmicosin) antimicrobial administered to beef stocker calves at arrival.
Spin Berry points as crucial for ultrafast demagnetization

Laser-induced ultrafast demagnetization has puzzled researchers around the world for over two decades. Intrinsic complexity in electronic, magnetic and phononic subsystems is difficult to understan...
The Misguided Transformation of Loyalty into Contract

The law of unincorporated associations is engaged in a misguided march in transforming the duty of loyalty into a contractarian construct. This Article argues that these developments reflect doctrinal confusion, outworn economics, and weak policy. The Article begins by tracing the evolution of the duty of loyalty in the law of unincorporated associations. It begins with a discussion of the struggle between contractarianism and fiduciary duty in the uniform laws promulgated by the National Conference of Commissioners on Uniform State Laws (NCCUSL). It then shifts gears to the more squarely contractarian, and likely highly influential, Delaware statutes. The current state of the doctrine suggests that precious little is left of the duty of loyalty. The article then shifts to showing that the transformation is troublesome along three dimensions. First, the move conflates fiduciary with contractual duties, notably weak and nebulous notions of good faith. Second, it deploys outworn economic concepts reminiscent of the neoclassical Chicago School. The economic justifications for contractarianism are based on facile assumptions applied in a static manner; they do not represent real humans interacting in real institutions over time. Third, the move from loyalty to contract brings with it a host of public policy problems: it tries to toss out a well developed legal tradition, downplays the role of trust and morality, and ignores the role positive law can play in shaping norms. In the end, the rise of contractarianism reflects a step backward to nineteenth-century legal formalism and presents the risk that its faulty precepts may spread further into corporate law.
Design of reduced-order, H2 optimal controllers using a homotopy algorithm

The minimal dimension of a linear-quadratic-gaussian (LQG) compensator is almost always equal to the dimension of the design plant. This deficiency can lead to implementation problems when considering control design for high-order systems such as flexible structures and has led to the development of methodologies for the design of optimal (or near optimal) controllers whose dimension is less than that of the design plant. This paper presents a new (gradient-based) homotopy algorithm for the design of reduced-order, H2 optimal controllers. An important result is the development of an efficient methodology for computation of the cost functional hessian which is required by the algorithm. The optimal controller is represented by a parameter vector and various parametrizations of the optimal controller are considered to reduce the algorithm dimensionality. The algorithm has been implemented in MAT-LAB and the results are illustrated using a benchmark, non-collocated flexible structural control problem. It is ...
Profits-Only Partnership Interests

Profits-only partnership interests grant service-providing partners an interest in the profits of a partnership but not its capital. Such interests are a proverbial double-edged sword: they create economic arrangements needed in business, but provide opportunities for inequitable tax reductions. Business participants make economic decisions to use profits-only partnership interests to reduce agency costs and appropriable rents. The current law, however, empowers business participants to form partnerships that are equivalent to employment arrangements and use profits-only partnership interests to obtain long-term capital gains. Thus, with no economic consequences, they convert ordinary income (taxed at up to thirty-five percent) to long-term capital gain (taxed at fifteen percent). Commentators and lawmakers generally propose partnership disaggregation to eliminate the inequity. Partnership disaggregation changes the character of income (from capital gain to ordinary income) as it flows from the partnership to service-providing partners. It may enhance equity, but it ignores the nature of tax partnerships, threatens the partnership tax regime, and has other negative side effects. The Article suggests that partnership disregard is a better way to address the inequity profits-only partnership interests cause. Partnership disregard uses economic concepts to identify the policy-relevant differences between tax partnerships and disregarded arrangements, such as employment arrangements, leases, and loans. Partnership disregard distinguishes arrangements that should qualify for partnership tax treatment and those that should not. It eliminates inequity while preserving the integrity of partnership tax regime and other areas of the law.
Classical-to-topological transmission line couplers

Recent advances in topologically robust waveguiding for electromagnetic systems have presented opportunities for improving practical photonic and microwave devices. To bring this rich area of physics within the reach of application, it is critical for such systems to be interfaced with classical, continuous waveguiding, and transmission line technology. This Letter presents a compact, highly efficient transition from a classical metallic transmission line to a topologically nontrivial line wave emulating the quantum spin Hall effect. A zero-gap antipodal slot line is used as the starting transmission line, which is then coupled to the topological metasurface via a field matching procedure. Additional modifications to the interface between the two structures to eliminate unwanted edge coupling improve transmission further. A simulated loss analysis isolates the effect of the transitions from the rest of the structure, showing a loss contribution of only 2.1% per classical-to-topological conversion. Using the transition, a quantitative characterization of the robustness of common topologically protected devices is presented. This design lays the foundation to integrate topologically robust metasurface transmission lines to traditional systems, opening the door to future uses of such structures in systems.
HARVESTMAN: a framework for hierarchical feature learning and selection from whole genome sequencing data.

Supervised learning from high-throughput sequencing data presents many challenges. For one, the curse of dimensionality often leads to overfitting as well as issues with scalability. This can bring about inaccurate models or those that require extensive compute time and resources. Additionally, variant calls may not be the optimal encoding for a given learning task, which also contributes to poor predictive capabilities. To address these issues, we present Harvestman, a method that takes advantage of hierarchical relationships among the possible biological interpretations and representations of genomic variants to perform automatic feature learning, feature selection, and model building. We demonstrate that Harvestman scales to thousands of genomes comprising more than 84 million variants by processing phase 3 data from the 1000 Genomes Project, one of the largest publicly available collection of whole genome sequences. Using breast cancer data from The Cancer Genome Atlas, we show that Harvestman selects a rich combination of representations that are adapted to the learning task, and performs better than a binary representation of SNPs alone. We compare Harvestman to existing feature selection methods and demonstrate that our method is more parsimonious—it selects smaller and less redundant feature subsets while maintaining accuracy of the resulting classifier. Harvestman is a hierarchical feature selection approach for supervised model building from variant call data. By building a knowledge graph over genomic variants and solving an integer linear program , Harvestman automatically and optimally finds the right encoding for genomic variants. Compared to other hierarchical feature selection methods, Harvestman is faster and selects features more parsimoniously.
Semirigid GCD domains II

Let D be an integral domain with quotient field K, throughout. Call two elements x,y ∈ D∖{0}v-coprime if xD ∩ yD = xyD. Call a nonzero non-unit r of an integral domain D rigid if for all x,y|r we h...
Network structure and pricing in the FX market

Abstract We construct the network, centrality measures, and attributions of trading profits for a sample of CLS Bank settlement data that spans diverse currency pairs, participants, and execution platforms. We define an average centrality differential as the return to the more-central counterparty in the trade. Estimates imply that the more-central counterparty receives a higher return, and that this differential increases as the counterparties’ centralities diverge. These two results are consistent with a pervasive centrality premium. This premium may reflect bargaining power, but we also find evidence that the premium is partially offset by losses that central agents incur in supplying liquidity.
A Lesson from the James Webb Space Telescope: Early Engagement with Future Astrophysics Great Observatories Maximizes their Solar System Science

Astrophysics facilities have been of tremendous importance for planetary science. The flagship space observatory Hubble Space Telescope has produced ground-breaking Solar System science, but when launched it did not even have the capability to track moving targets. The next astrophysics flagship mission, the James Webb Space Telescope, included Solar System scientists in its science team from the earliest days, with the result that Webb will launch with a diverse program and capabilities for Solar System exploration. The New Great Observatories, as well as future ground-based facilities, offer the opportunity for a robust suite of observations that will complement, enhance, and enable future Solar System exploration. We encourage the Planetary Science and Astrobiology Decadal Survey to overtly acknowledge the prospects for excellent Solar System science with the next generation of astrophysics facilities. We hope the Planetary Decadal will further encourage these missions to continue to formally involve Solar System scientists in the science working groups and development teams.
Limit Theorems as Blessing of Dimensionality: Neural-Oriented Overview.

As a system becomes more complex, at first, its description and analysis becomes more complicated. However, a further increase in the system’s complexity often makes this analysis simpler. A classical example is Central Limit Theorem: when we have a few independent sources of uncertainty, the resulting uncertainty is very difficult to describe, but as the number of such sources increases, the resulting distribution gets close to an easy-to-analyze normal one—and indeed, normal distributions are ubiquitous. We show that such limit theorems often make analysis of complex systems easier—i.e., lead to blessing of dimensionality phenomenon—for all the aspects of these systems: the corresponding transformation, the system’s uncertainty, and the desired result of the system’s analysis.
Design of grasping methodologies for rotational assembly components

This article describes a design methodology for grasping assembly components. Three heuristics are developed. The first determines feasible grasping configurations based on component geometric information. The second heuristic determines feasible grasping configurations by including gripper functional attributes. The third heuristic generates the final set of grasping configurations by including the area available for grasping a component. An interactive program written in Fortran 77 is developed to capture the user inputs, and a sample application is described. The methodology does not assume an initial feeding state of the component to the robot. The grasping configurations generated (if more than one) provide the designer with alternate feasible feeding methods.
Regression Discontinuity Designs With a Continuous Treatment

The standard regression discontinuity (RD) design deals with a binary treatment. Many empirical applications of RD designs involve continuous treatments. This article establishes identification and...
Unfolded Deep Neural Network (UDNN) for High Mobility Channel Estimation

High mobility channel estimation is crucial for beyond 5G(B5G) or 6G wireless communication networks. This paper is concerned with channel estimation of high mobility OFDM communication systems. First, a two-dimensional compressed sensing problem is formulated by approximately linearizing the channel as a product of an overcomplete dictionary with a sparse vector, in which the Doppler effect caused by the high mobility channel is considered. To solve the problem that the traditional compressed sensing algorithms have too many iterations and are time consuming, we propose an unfolded deep neural network (UDNN) as the fast solver, which is inspired by the structure of iterative shrinkage-thresholding algorithm (ISTA). All the parameters in UDNN (e.g. nonlinear transforms, shrinkage thresholds, measurement matrices, etc.) are learned end-to-end, rather than being hand-crafted. Experiments demonstrate that the proposed UDNN performs better than ISTA for OFDM high mobility channel estimation, while maintaining extremely fast computational speed.
An enriched count of the bitangents to a smooth plane quartic curve

Recent work of Kass–Wickelgren gives an enriched count of the 27 lines on a smooth cubic surface over arbitrary fields. Their approach using $$\mathbb {A}^1$$ -enumerative geometry suggests that other classical enumerative problems should have similar enrichments, when the answer is computed as the degree of the Euler class of a relatively orientable vector bundle. Here, we consider the closely related problem of the 28 bitangents to a smooth plane quartic. However, it turns out that the relevant vector bundle is not relatively orientable and new ideas are needed to produce enriched counts. We introduce a fixed “line at infinity,” which leads to enriched counts of bitangents that depend on their geometry relative to the quartic and this distinguished line.
Urban land-use analysis using proximate sensing imagery: a survey

Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile dev...
Variable Selection Via Thompson Sampling

Abstract–Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning. The algorithm has a Bayesian spirit in the sense that it selec...
2-Segal objects and algebras in spans

We define a category parameterizing Calabi–Yau algebra objects in an infinity category of spans. Using this category, we prove that there are equivalences of infinity categories relating, firstly: 2-Segal simplicial objects in C to algebra objects in Span(C); and secondly: 2-Segal cyclic objects in C to Calabi–Yau algebra objects in Span(C).
On the competitiveness of on-line real-time task scheduling

With respect to on-line scheduling algorithms that must direct the service of sporadic task requests we quantify the benefit of clairvoyancy, i.e., the power of possessing knowledge of various task parameters of future events. Specifically, we consider the problem of preemptively sheduling sporadic task requests in both uni- and multi-processor environments. If a task request is successfuly scheduled to completion, a value equal to the task's execution time is obtained; otherwise no value is obtained. We prove that no on-line scheduling algorithm can guarantee a cumulative value greater than 1/4th the value obtainable by a clairvoyant scheduler; i.e., we prove a 1/4th upper bound on the competitive factor of on-line real-time schedulers. We present an online uniprocessor scheduling algorithm TD 1 that actually has a competitive factor of 1/4; this bound is thus shown to be tight. We further consider the effect of restricting the amount of overloading permitted (the loading factor), and quantify the relationship between the loading factor and the upper bound on the competitive factor. Other results of a similar nature deal with the effect of value densities (measuring the importance of type of a task). Generalizations to dual-processor on-line scheduling are also considered. For the dual-processor case, we prove an upper bound of 1/2 on the competitive factor. This bound is shown to be tight in the special case when all the tasks have the same density and zero laxity.
Religion and Informational Influence: Evidence from Individual Tax Behavior in the U.S.

Using individual tax behavior as a research setting, this paper seeks to understand whether and how religion plays a role in people’s information search and dissemination behavior in a non-religious context. Google search data show that the tax season in the U.S. is marked by a notably higher search interest in religious teachings on taxes. However, despite the commonly held view that religion and rationality are often at odds, we also find that people’s online search interest in tax loss strategies (which help minimize taxable income) and tendency to ask “why” questions about taxes are greater in states with higher religiosity. In parallel, users from highly versus less religious states include more interrogatives and less certainty words in their tax-related tweets posted during the 2019 U.S. federal government shutdown. Further, when giving advice about tax preparation software, people living in highly versus less religious states use certainty words less extensively.
Self-Supervised Physics-Guided Deep Learning Reconstruction for High-Resolution 3D LGE CMR

Late gadolinium enhancement (LGE) cardiac MRI (CMR) is the clinical standard for diagnosis of myocardial scar. 3D isotropic LGE CMR provides improved coverage and resolution compared to 2D imaging. However, image acceleration is required due to long scan times and contrast washout. Physics-guided deep learning (PG-DL) approaches have recently emerged as an improved accelerated MRI strategy. Training of PG-DL methods is typically performed in a supervised manner requiring fully-sampled data, which is challenging in 3D LGE CMR. Recently, a self-supervised learning approach was proposed to enable training PG-DL techniques without fully-sampled data. In this work, we extend this self-supervised learning approach to 3D imaging, while tackling challenges related to small training database sizes of 3D volumes. Results and a reader study on prospectively accelerated 3D LGE show that the proposed approach at 6-fold acceleration outperforms the clinically utilized compressed sensing approach at 3-fold acceleration.
Vorticity intensification and transition to turbulence in a three-dimensional shear flow

The evolution of a perturbed vortex tube in a triply periodic domain is studied by means of a second-order projection method for the incompressible Euler equations. We observe that, within the limits of grid resolution, the vorticity becomes unbounded. The onset of the apparent singularity is accompanied by a decay in the mean kinetic energy. In the post-singularity regime, we observe reconnection events, continued decay of the mean kinetic energy, and the emergence of a Kolmogorov (k{sup {minus}5/3}) range in the energy spectrum. 23 refs., 8 figs.
The broken window: An algorithm for quantifying and characterizing misleading trajectories in ecological processes

Abstract A core issue in temporal ecology is the concept of trajectory—that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create ‘broken windows,’ that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales. The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly ‘sample’ moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the ‘broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions.
A Deep Metric Learning Approach to Account Linking

We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and meta-data of the corresponding document streams. We focus on learning an embedding that maps variable-sized samples of user activity–ranging from single posts to entire months of activity–to a vector space, where samples by the same author map to nearby points. Our approach does not require human-annotated data for training purposes, which allows us to leverage large amounts of social media content. The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains. Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.
PAM: Understanding Product Images in Cross Product Category Attribute Extraction

Understanding product attributes plays an important role in improving online shopping experience for customers and serves asan integral part for constructing a product knowledge graph. Most existing methods focus on attribute extraction from text description or utilize visual information from product images such as shape and color. Compared to the inputs considered in prior works, a product image in fact contains more information, represented by a rich mixture of words and visual clues with a layout carefully designed to impress customers. This work proposes a more inclusive framework that fully utilizes these different modalities for attribute extraction.Inspired by recent works in visual question answering, we use a transformer based sequence to sequence model to fuse representations of product text, Optical Character Recognition (OCR) tokens and visual objects detected in the product image. The framework is further extended with the capability to extract attribute value across multiple product categories with a single model, by training the decoder to predict both product category and attribute value and conditioning its output on product category. The model provides a unified attribute extraction solution desirable at an e-commerce platform that offers numerous product categories with a diverse body of product attributes. We evaluated the model on two product attributes, one with many possible values and one with a small set of possible values, over 14 product categories and found the model could achieve 15% gain on the Recall and 10% gain on the F1 score compared to existing methods using text-only features.
A CRISPR/Cas9 Based Strategy to Manipulate the Alzheimer’s Amyloid Pathway

CRISPR/Cas9 guided gene-editing is emerging as a promising therapeutic tool, however application to neurodegenerative diseases has been limited. Moreover, conventional ‘mutation correction’ by gene-editing would only be relevant for the small fraction of neurodegenerative cases that are inherited, and fresh ideas are needed. Here we introduce a CRISPR/Cas9-based strategy to edit endogenous APP (amyloid precursor protein) at the extreme C-terminus and reciprocally manipulate the amyloid pathway – attenuating β-cleavage and Aβ production, while upregulating neuroprotective α-cleavage. APP N-terminus and compensatory APP-homologues remain intact; with no apparent effects on neurophysiology. Robust APP-editing is seen in a variety of cells – including iPSC-neurons and mouse brains – with no detectable off-target effects. Our strategy works by limiting APP and BACE-1 approximation, and we also delineate the underlying mechanism that abrogates APP/BACE-1 convergence in this setting. Our work offers conceptual proof for a ‘cut and silence’ gene-editing strategy that could be a new therapeutic paradigm for AD.
SG-PALM: a Fast Physically Interpretable Tensor Graphical Model

We propose a new graphical model inference procedure, called SG-PALM, for learning conditional dependency structure of high-dimensional tensor-variate data. Unlike most other tensor graphical models the proposed model is interpretable and computationally scalable to high dimension. Physical interpretability follows from the Sylvester generative (SG) model on which SG-PALM is based: the model is exact for any observation process that is a solution of a partial differential equation of Poisson type. Scalability follows from the fast proximal alternating linearized minimization (PALM) procedure that SG-PALM uses during training. We establish that SG-PALM converges linearly (i.e., geometric convergence rate) to a global optimum of its objective function. We demonstrate the scalability and accuracy of SG-PALM for an important but challenging climate prediction problem: spatio-temporal forecasting of solar flares from multimodal imaging data.
Heliogyro-Configured Small Spacecraft Solar Sail

In order to perform long term missions with multiple objectives using a single space vehicle, there is a need to develop a highly efficient propulsion-navigation system that enables multi-mission capabilities, point-to-point operation and an extended operational lifetime. The majority of space propulsion systems are fuel-based and require the vehicle to carry and consume fuel as part of the mission. Once the fuel is consumed, the mission is terminated. Alternatively, a method that derives its acceleration, velocity and direction from solar photon pressure using a solar sail to capture photon momentum would eliminate the requirement of fuel and all the fuel-based propulsion components. The most important factors that govern the solar sail spacecraft’s characteristic acceleration are the sail loading (how much total mass the solar sail has to carry) and the exposed sail area. This paper introduces several potential mission concepts that can be achieved using heliogyro-configured solar sail spacecraft. It then presents 30 potential configurations of heliogyro small spacecraft solar sail and design concepts, based on CubeSat-scale units from 6U to 48U (1U = a cube 10 cm on each side). The area of the sail and total CubeSat masses are used to calculate their characteristic accelerations, and these accelerations are equated to those of previous spacecraft using solar sail technologies; IKAROS, NanoSail-D and LightSail. The analyses in this paper predict that out of these 30 configurations, the 12U-4B(a) configuration has the maximum and the 45U-6B(a) configuration has the minimum characteristic accelerations of 190 and 70 times higher than the IKAROS, 49 and 18 times higher than the NanoSail-D, and 16 and 6 times higher than LightSail, respectively. Several blade deployment configurations, the “jelly roll”, and a hybrid heliogyro-jelly roll are introduced and compared to the standard “reel” configuration. The hybrid configurations are predicted to produce higher characteristic accelerations than the jelly roll configurations. The analyses of heliogyro configurations suggest that the amount of payload units (non-sail) when compared to the whole spacecraft allowable units should be less than 40% and the optimized amount, i.e. no empty payload units, is approximately 33% to produce characteristic accelerations > 0.7 mm/sq s. For the hybrid configuration, the results suggests that the number of payload units should be between 30 – 40% of the total units to produce a characteristic acceleration > 0.8 mm/sq s.
Building manifolds from quantum codes

We give a procedure for “reverse engineering" a closed, simply connected, Riemannian manifold with bounded local geometry from a sparse chain complex over $${\mathbb {Z}}$$ . Applying this procedure to chain complexes obtained by “lifting" recently developed quantum codes, which correspond to chain complexes over $${\mathbb {Z}}_2$$ , we construct the first examples of power law $${\mathbb {Z}}_2$$ systolic freedom. As a result that may be of independent interest in graph theory, we give an efficient randomized algorithm to construct a weakly fundamental cycle basis for a graph, such that each edge appears only polylogarithmically times in the basis. We use this result to trivialize the fundamental group of the manifold we construct.