cssi/SciSciGPT-SciSciNet huggingface dataset:
Originalism and the Good Constitution

Originalism holds that the U.S. Constitution should be interpreted according to its meaning at the time it was enacted. In their innovative defense of originalism, John McGinnis and Michael Rappaport maintain that the text of the Constitution should be adhered to by the Supreme Court because it was enacted by supermajorities--both its original enactment under Article VII and subsequent Amendments under Article V. A text approved by supermajorities has special value in a democracy because it has unusually wide support and thus tends to maximize the welfare of the greatest number.The authors recognize and respond to many possible objections. Does originalism perpetuate the dead hand of the past? How can following the original meaning be justified, given that African Americans and women were excluded from the enactment of the Constitution in 1787 and many of its subsequent Amendments? What is originalism's place in interpretation of the Constitution, when after two hundred years there is so much non-originalist precedent?A fascinating counterfactual they pose is this: had the Supreme Court not interpreted the Constitution so freely, perhaps the nation would have resorted to the Article V amendment process more often and with greater effect. Their book will be an important contribution to the literature on originalism, which is now the most prominent theory of constitutional interpretation.
Improving reconstructive surgery design using Gaussian process surrogates to capture material behavior uncertainty.

Abstract To produce functional, aesthetically natural results, reconstructive surgeries must be planned to minimize stress as excessive loads near wounds have been shown to produce pathological scarring and other complications (Gurtner et al., 2011). Presently, stress cannot easily be measured in the operating room. Consequently, surgeons rely on intuition and experience (Paul et al., 2016; Buchanan et al., 2016). Predictive computational tools are ideal candidates for surgery planning. Finite element (FE) simulations have shown promise in predicting stress fields on large skin patches and in complex cases, helping to identify potential regions of complication. Unfortunately, these simulations are computationally expensive and deterministic (Lee et al., 2018a). However, running a few, well selected FE simulations allows us to create Gaussian process (GP) surrogate models of local cutaneous flaps that are computationally efficient and able to predict stress and strain for arbitrary material parameters. Here, we create GP surrogates for the advancement, rotation, and transposition flaps. We then use the predictive capability of these surrogates to perform a global sensitivity analysis, ultimately showing that fiber direction has the most significant impact on strain field variations. We then perform an optimization to determine the optimal fiber direction for each flap for three different objectives driven by clinical guidelines (Leedy et al., 2005; Rohrer and Bhatia, 2005). While material properties are not controlled by the surgeon and are actually a source of uncertainty, the surgeon can in fact control the orientation of the flap with respect to the skin's relaxed tension lines, which are associated with the underlying fiber orientation (Borges, 1984). Therefore, fiber direction is the only material parameter that can be optimized clinically. The optimization task relies on the efficiency of the GP surrogates to calculate the expected cost of different strategies when the uncertainty of other material parameters is included. We propose optimal flap orientations for the three cost functions and that can help in reducing stress resulting from the surgery and ultimately reduce complications associated with excessive mechanical loading near wounds.
Trioxatriangulenium (TOTA+) as a robust carbon-based Lewis acid in frustrated Lewis pair chemistry.

We report the reactivity between the water stable Lewis acidic trioxatriangulenium ion (TOTA+) and a series of Lewis bases such as phosphines and N-heterocyclic carbene (NHC). The nature of the Lewis acid–base interaction was analyzed via variable temperature (VT) NMR spectroscopy, single-crystal X-ray diffraction, UV-visible spectroscopy, and DFT calculations. While small and strongly nucleophilic phosphines, such as PMe3, led to the formation of a Lewis acid–base adduct, frustrated Lewis pairs (FLPs) were observed for sterically hindered bases such as P(tBu)3. The TOTA+–P(tBu)3 FLP was characterized as an encounter complex, and found to promote the heterolytic cleavage of disulfide bonds, formaldehyde fixation, dehydrogenation of 1,4-cyclohexadiene, heterolytic cleavage of the C–Br bonds, and interception of Staudinger reaction intermediates. Moreover, TOTA+ and NHC were found to first undergo single-electron transfer (SET) to form [TOTA]·[NHC]˙+, which was confirmed via electron paramagnetic resonance (EPR) spectroscopy, and subsequently form a [TOTA–NHC]+ adduct or a mixture of products depending the reaction conditions used.
How to bridge East and West

Today, the field of public administration has two problems that can be fixed with one solution. The first problem is fixation on a limited set of “middle-level” topics in public administration. The...
THH and TC are (very) far from being homotopy functors

Abstract We compute the A 1 -localization of several invariants of schemes namely, topological Hochschild homology (THH), topological cyclic homology (TC) and topological periodic cyclic homology (TP). This procedure is quite brutal and kills the completed versions of most of these invariants. The main ingredient for the vanishing statements is the vanishing of A 1 -localization of de Rham cohomology (and, eventually, crystalline cohomology) in positive characteristics.
On Measuring Two-Party Partisan Bias in Unbalanced States

Assuming that partisan fairness and responsiveness are important aspects of redistricting, it is important to measure them. Many measures of partisan bias are satisfactory for states that are balan...
Modeling of a Rotaxane-Based Molecular Device

A computational procedure is presented for investigating photoinduced switchable rotaxanes and demonstrated for a known system. This procedure starts with the generation of > 10 4 chemically reasonable rotaxane coconformations based on an empirical intramolecular potential-energy function. Each of the structures is then assigned by its gross structural features (coiled or extended) and by the position of the ring along the shaft. Single-point energy calculations at the semiempirical (AM 1) level are then carried out for each structure in the singlet (ground), triplet, and anionic doublet states. The structural features are then correlated with energy for each state. What emerges is a profile of the structure-energy relationship that captures the salient features of the system that endow it with devicelike character. The full geometry optimization of a subset of the coconformations demonstrates that the procedure based on single-point calculations is sufficient to obtain a profile of the relationship of the structural features to energy that is consistent with experiments at a greatly reduced computational cost.
Idiosyncratic variation in the fitness costs of tetracycline‐resistance mutations in Escherichia coli

A bacterium's fitness relative to its competitors, both in the presence and absence of antibiotics, plays a key role in its ecological success and clinical impact. In this study, we examine whether tetracycline-resistant mutants are less fit in the absence of the drug than their sensitive parents, and whether the fitness cost of resistance is constant or variable across independently derived lines. Tetracycline-resistant lines suffered, on average, a reduction in fitness of almost 8%. There was substantial among-line variation in the fitness cost. This variation was not associated with the level of resistance conferred by the mutations, nor did it vary significantly across several genetic backgrounds. The two resistant lines with the most extreme fitness costs involved functionally unrelated mutations on different genetic backgrounds. However, there was also significant variation in the fitness costs for mutations affecting the same pathway and even different alleles of the same gene. Our findings demonstrate that the fitness costs of antibiotic resistance do not always correlate with the phenotypic level of resistance or the underlying genetic changes. Instead, these costs reflect the idiosyncratic effects of particular resistance mutations and the genetic backgrounds in which they occur.
Activation of G-protein coupled estradiol receptor 1 in the dorsolateral striatum attenuates preference for cocaine and saccharin in male but not female rats.

Abstract There are sex differences in the response to psychomotor stimulants, where females exhibit a greater response than males, due to the presence of the gonadal hormone estradiol (E2). Extensive research has shown that E2 enhances drug-seeking and the rewarding properties of cocaine for females. The role of E2 in male drug-seeking, however, is not well understood. The current study investigated pharmacological manipulation of E2 receptors in the dorsolateral striatum (DLS) on preference for cocaine in gonad-intact male and female rats. In males, activation of G-protein coupled E2 receptor 1 (GPER1), via administration of ICI 182,780 or G1, attenuated conditioned place preference for 10 mg/kg cocaine, while inhibition of GPER1, via G15, enhanced preference at a 5 mg/kg cocaine dose. Similarly, GPER1 activation, via G1, prevented males from forming a preference for 0.1% saccharin (SACC) versus plain water. Surprisingly, activation of GPER1 did not alter preference for cocaine or SACC in females. These studies also examined the quantity of E2 receptor mRNA in the dorsal striatum, using qPCR. No sex differences in relative mRNA expression of ERα, ERβ, and GPER1 were observed. However, there was greater GPER1 mRNA, relative to ERα and ERβ, in both males and females. The results presented here indicate that E2, acting via GPER1, may be protective against drug preference in male rats.
Crop Height and Plot Estimation for Phenotyping from Unmanned Aerial Vehicles using 3D LiDAR

We present techniques to measure crop heights using a 3D Light Detection and Ranging (LiDAR) sensor mounted on an Unmanned Aerial Vehicle (UAV). Knowing the height of plants is crucial to monitor their overall health and growth cycles, especially for high-throughput plant phenotyping. We present a methodology for extracting plant heights from 3D LiDAR point clouds, specifically focusing on plot-based phenotyping environments. We also present a toolchain that can be used to create phenotyping farms for use in Gazebo simulations. The tool creates a randomized farm with realistic 3D plant and terrain models. We conducted a series of simulations and hardware experiments in controlled and natural settings. Our algorithm was able to estimate the plant heights in a field with 112 plots with a root mean square error (RMSE) of 6.1 cm. This is the first such dataset for 3D LiDAR from an airborne robot over a wheat field. The developed simulation toolchain, algorithmic implementation, and datasets can be found on our GitHub repository.1
Model Hierarchy Predictive Control of Robotic Systems

This letter presents a new predictive control architecture for high-dimensional robotic systems. As opposed to a conventional Model Predictive Control (MPC) approach to locomotion that formulates a hierarchical sequence of optimization problems, the proposed work formulates a single optimization problem posed over a hierarchy of models, and is thus named Model Hierarchy Predictive Control (MHPC). MHPC is formulated as a multi-phase receding-horizon Trajectory Optimization (TO) problem, and can be implemented using any general multi-phase TO solver. MHPC is benchmarked in simulation on a quadruped, a biped, and a quadrotor, demonstrating control performance on par or exceeding whole-body MPC while maintaining a lower computational cost in each case. A preliminary gap jumping experiment is conducted on the MIT Mini Cheetah with the control policy generated offline, demonstrating the physical validity of the generated trajectories and motivating online MHPC in future work.
Initialization and Regularization of Factorized Neural Layers

Factorized layers---operations parameterized by products of two or more matrices---occur in a variety of deep learning contexts, including compressed model training, certain types of knowledge distillation, and multi-head self-attention architectures. We study how to initialize and regularize deep nets containing such layers, examining two simple, understudied schemes, spectral initialization and Frobenius decay, for improving their performance. The guiding insight is to design optimization routines for these networks that are as close as possible to that of their well-tuned, non-decomposed counterparts; we back this intuition with an analysis of how the initialization and regularization schemes impact training with gradient descent, drawing on modern attempts to understand the interplay of weight-decay and batch-normalization. Empirically, we highlight the benefits of spectral initialization and Frobenius across a variety of settings. In model compression, we show that they enable low-rank methods to significantly outperform both unstructured sparsity and tensor methods on the task of training low-memory residual networks; analogs of the schemes also improve the performance of tensor decomposition techniques. For knowledge distillation, Frobenius decay enables a simple, self-taught baseline that yields a compact model from over-parameterized training without requiring retraining with or pruning a teacher network. Finally, we show how both schemes applied to multi-head attention lead to improved performance on both translation and unsupervised pre-training.
Immunoglobulin superfamily receptor Junctional adhesion molecule 3 (Jam3) requirement for melanophore survival and patterning during formation of zebrafish stripes.

Abstract Adhesive interactions are essential for tissue patterning and morphogenesis yet difficult to study owing to functional redundancies across genes and gene families. A useful system in which to dissect roles for cell adhesion and adhesion-dependent signaling is the pattern formed by pigment cells in skin of adult zebrafish, in which stripes represent the arrangement of neural crest derived melanophores, cells homologous to melanocytes. In a forward genetic screen for adult pattern defects, we isolated the pissarro (psr) mutant, having a variegated phenotype of spots, as well as defects in adult fin and lens. We show that psr corresponds to junctional adhesion protein 3b (jam3b) encoding a zebrafish orthologue of the two immunoglobulin-like domain receptor JAM3 (JAM-C), known for roles in adhesion and signaling in other developing tissues, and for promoting metastatic behavior of human and murine melanoma cells. We found that zebrafish jam3b is expressed post-embryonically in a variety of cells including melanophores, and that jam3b mutants have defects in melanophore survival. Jam3b supported aggregation of cells in vitro and was required autonomously by melanophores for an adherent phenotype in vivo. Genetic analyses further indicated both overlapping and non-overlapping functions with the related receptor, Immunoglobulin superfamily 11 (Igsf11) and Kit receptor tyrosine kinase. These findings suggest a model for Jam3b function in zebrafish melanophores and hint at the complexity of adhesive interactions underlying pattern formation.
The multi-field, rapid-turn inflationary solution

There are well-known criteria on the potential and field-space geometry for determining if slow-roll, slow-turn, multi-field inflation is possible. However, even though it has been a topic of much recent interest, slow-roll, rapid-turn inflation only has such criteria in the restriction to two fields. In this work, we generalize the two-field, rapid-turn inflationary attractor to an arbitrary number of fields. We quantify a limit, which we dub extreme turning, in which rapid-turn solutions may be found efficiently and develop methods to do so. In particular, simple results arise when the covariant Hessian of the potential has an eigenvector in close alignment with the gradient — a situation we find to be common and we prove generic in two-field hyperbolic geometries. We verify our methods on several known rapid-turn models and search two type-IIA constructions for rapid-turn trajectories. For the first time, we are able to efficiently search for these solutions and even exclude slow-roll, rapid-turn inflation from one potential.
Histologic evaluation of therapeutic responses in ischemic myocardium elicited by dual growth factor delivery from composite glycosaminoglycan hydrogels.

Abstract In this project, the ability of dual growth factor-preloaded, silk-reinforced, composite hyaluronic acid-based hydrogels to elicit advantageous histologic responses when secured to ischemic myocardium was evaluated in vivo. Reinforced hydrogels containing both Vascular Endothelial Growth Factor (VEGF) and Platelet-derived Growth Factor (PDGF) were prepared by crosslinking chemically modified hyaluronic acid and heparin with poly(ethylene glycol)-diacrylate around a reinforcing silk mesh. Composite patches were sutured to the ventricular surface of ischemic myocardium in Sprague-Dawley rats, and the resulting angiogenic response was followed for 28 days. The gross appearance of treated hearts showed significantly reduced ischemic area and fibrous deposition compared to untreated control hearts. Histologic evaluation showed growth factor delivery to restore myofiber orientation to pre-surgical levels and to significantly increase elicited microvessel density and maturity by day 28 in infarcted myocardial tissue (p
Discovery of RSV-Induced BRD4 Protein Interactions Using Native Immunoprecipitation and Parallel Accumulation-Serial Fragmentation (PASEF) Mass Spectrometry.

Respiratory Syncytial Virus (RSV) causes severe inflammation and airway pathology in children and the elderly by infecting the epithelial cells of the upper and lower respiratory tract. RSV replication is sensed by intracellular pattern recognition receptors upstream of the IRF and NF-κB transcription factors. These proteins coordinate an innate inflammatory response via Bromodomain-containing protein 4 (BRD4), a protein that functions as a scaffold for unknown transcriptional regulators. To better understand the pleiotropic regulatory function of BRD4, we examine the BRD4 interactome and identify how RSV infection dynamically alters it. To accomplish these goals, we leverage native immunoprecipitation and Parallel Accumulation—Serial Fragmentation (PASEF) mass spectrometry to examine BRD4 complexes isolated from human alveolar epithelial cells in the absence or presence of RSV infection. In addition, we explore the role of BRD4’s acetyl-lysine binding bromodomains in mediating these interactions by using a highly selective competitive bromodomain inhibitor. We identify 101 proteins that are significantly enriched in the BRD4 complex and are responsive to both RSV-infection and BRD4 inhibition. These proteins are highly enriched in transcription factors and transcriptional coactivators. Among them, we identify members of the AP1 transcription factor complex, a complex important in innate signaling and cell stress responses. We independently confirm the BRD4/AP1 interaction in primary human small airway epithelial cells. We conclude that BRD4 recruits multiple transcription factors during RSV infection in a manner dependent on acetyl-lysine binding domain interactions. This data suggests that BRD4 recruits transcription factors to target its RNA processing complex to regulate gene expression in innate immunity and inflammation.
Base Station and Passive Reflectors Placement for Urban mmWave Networks

The use of millimeter-wave (mmWave) bands in 5G networks introduces a new set of challenges to network planning. Vulnerability to blockages and high path loss at mmWave frequencies require careful planning of the network to achieve a desired service quality. In this paper, we propose a novel 3D geometry-based framework for deploying mmWave base stations (gNBs) in urban environments by considering first-order reflection effects. We also provide a solution for the optimum deployment of passive metallic reflectors (PMRs) to extend radio coverage to non-line-of-sight (NLoS) areas. In particular, we perform visibility analysis to find the direct and indirect visibility regions, and using these, we derive a geometry-and-blockage-aided path loss model . We then formulate the network planning problem as two independent optimization problems, placement of gNB(s) and PMRs, to maximize the coverage area, minimize the deployment cost, and maintain a desired quality-of-service level. We test the efficacy of our proposed approach using a generic map and compare our simulation results with the ray tracing solution. Our simulation results show that considering the first-order reflections in planning the mmWave network helps reduce the number of PMRs required to cover the NLoS area. Moreover, the gNB placement aided with PMRs require fewer gNBs to cover the same area, which in turn reduces the deployment cost.
Management Practices and Mergers and Acquisitions

Using a novel data set of establishment-level management practices from the U.S. Census Bureau, we show that firms with more specific, formal, frequent, or explicit (i.e., “structured”) management ...
Truthful aggregation of budget proposals

Abstract We consider a participatory budgeting problem in which each voter submits a proposal for how to divide a single divisible resource (such as money or time) among several possible alternatives (such as public projects or activities) and these proposals must be aggregated into a single aggregate division. Under l 1 preferences—for which a voter's disutility is given by the l 1 distance between the aggregate division and the division he or she most prefers—the social welfare-maximizing mechanism, which minimizes the average l 1 distance between the outcome and each voter's proposal, is incentive compatible ( Goel et al., 2019 ). However, it fails to satisfy a natural fairness notion of proportionality, placing too much weight on majority preferences. Leveraging a connection between market prices and the generalized median rules of Moulin (1980) , we introduce the independent markets mechanism, which is both incentive compatible and proportional. We unify the social welfare-maximizing mechanism and the independent markets mechanism by defining a broad class of moving phantom mechanisms that includes both. We show that every moving phantom mechanism is incentive compatible. Finally, we characterize the social welfare-maximizing mechanism as the unique Pareto-optimal mechanism in this class, suggesting an inherent tradeoff between Pareto optimality and proportionality.
Quantification of the weight of fingerprint evidence using a ROC-based Approximate Bayesian Computation algorithm for model selection

For more than a century, fingerprints have been used with considerable success to identify criminals or verify the identity of individuals. The categorical conclusion scheme used by fingerprint examiners, and more generally the inference process followed by forensic scientists, have been heavily criticised in the scientific and legal literature. Instead, scholars have proposed to characterise the weight of forensic evidence using the Bayes factor as the key element of the inference process. In forensic science, quantifying the magnitude of support is equally as important as determining which model is supported. Unfortunately, the complexity of fingerprint patterns render likelihood-based inference impossible. In this paper, we use an Approximate Bayesian Computation (ABC) model selection algorithm to quantify the weight of fingerprint evidence. We supplement the original ABC model selection algorithm using a Receiver Operating Characteristic curve to mitigate its known shortcomings with respect to the choice of a suitable threshold and the curse of dimensionality. In a sense, we offer an alternative to other methods that have tried to address the same issues. Our method is straightforward to implement, computationally efficient, and visually intuitive for lay individuals (i.e., jurors). We apply our method to quantify the weight of fingerprint evidence in forensic science, but we note that it can be applied to any other forensic pattern evidence.
THE SUPPLY AND DEMAND FOR SAFE ASSETS

Abstract Safe assets are demanded as stores of value (to smooth consumption inter-temporally) and as collateral (to facilitate credit intra-temporally). Some are supplied publicly (government bonds) and some privately (asset-backed securities). Private assets are heterogeneous in quality, and information about their quality reduces their safety properties. We show that government bonds discourage both production of (crowding quantity out) and information about (crowding safety in) private assets. Hence, the optimal supply of government bonds need to take into account their dual roles and their impact on the quantity and informational content of private assets.
Quantum Theory of Parametric Amplification. I

The amplification of electromagnetic fields is analyzed in a quantum-mechanical context by discussing the behavior of a simple theoretical model of the parametric amplifier. The statistical properties of the amplifier fields are described by means of the time-dependent density operator for the system. In doing this, extensive use is made of the coherent states and the $P$ representation of the density operator, which provide a quantum-mechanical description of the fields closely resembling their classical description. Explicit solutions are obtained for the density operator for either of the two field modes for a variety of initial states of the modes. Initial states considered include combinations of coherent states, chaotic mixtures, and $n$-quantum states. Particular attention is given the behavior of the amplifier fields in the limit of large amplification. The conditions are established under which the amplification process leads in this limit to the existence of a non-negative $P$ representation for the density operator for a single mode of oscillation.
Are Foreclosure Spillover Effects Universal? Variation Over Space and Time

Government intervention in the housing market in response to the 2007–2010 mortgage crisis was driven in part by research showing that foreclosures lower neighboring housing values and thus increas...
An Economic Analysis of Agricultural Support Prices in Developing Economies

The goals of the guaranteed support price (GSP) scheme, adopted by several developing countries to support their farmers and the underprivileged population, are threefold: (a) as a supply‐side incentive, to ensure high output from the farmers, (b) as a demand‐side provisioning tool, to subsidize the consumption needs of the poor, and (c) to maintain an adequate amount of foodgrains as reserve stock, to mitigate the adverse effects of yield uncertainty (food security). We offer analytically supported insights on the fundamental aspects of this scheme by analyzing a Stackelberg game between a homogenous population of small farmers and a social planner. We model the strategic behavior of the farmers and the consuming population (Above‐ and Below‐Poverty‐Line consumers), and compare the equilibrium outcome with that under the direct benefit transfer (DBT) scheme, where the social planner simply distributes the budget among the BPL consumers. The comparison of the social planner's surplus depends on the marginal value from maintaining a reserve stock (i.e., the significance of food security). If this value is high, then the surplus under the GSP scheme strictly dominates that under DBT; otherwise, the surplus is identical. The comparison of the production by the farmers depends on two economic forces—the poorness of the BPL consumers and yield uncertainty. If the poorness is extreme, then the two schemes lead to identical production. If yield uncertainty is dominant, then DBT is ineffective in improving production while the GSP scheme can induce a strictly higher production by strategically choosing the reserve stock.
Mid-infrared radiative emission from bright hot plasmons in graphene

Carrier excitation and decay processes in graphene are of broad interest since relaxation pathways that are not present in conventional materials are enabled by a gapless Dirac electronic band structure. Here, we report that a previously unobserved decay pathway—hot plasmon emission—results in Fermi-level-dependent mid-infrared emission in graphene. Our observations of non-thermal contributions to Fermi-level-dependent radiation are an experimental demonstration of hot plasmon emission arising from a photo-inverted carrier distribution in graphene achieved via ultrafast optical excitation. Our calculations indicate that the reported plasmon emission process can be several orders of magnitude brighter than Planckian emission mechanisms in the mid-infrared spectral range. Both the use of gold nanodisks to promote scattering and localized plasmon excitation and polarization-dependent excitation measurements provide further evidence for bright hot plasmon emission. These findings define an approach for future work on ultrafast and ultrabright graphene emission processes and mid-infrared light source applications. The optical emission of graphene under pumping with femtosecond laser pulses contains a strong component linked to plasmon emission from the hot electrons in the system.
Compiler-driven FPGA virtualization with SYNERGY

FPGAs are increasingly common in modern applications, and cloud providers now support on-demand FPGA acceleration in data centers. Applications in data centers run on virtual infrastructure, where consolidation, multi-tenancy, and workload migration enable economies of scale that are fundamental to the provider’s business. However, a general strategy for virtualizing FPGAs has yet to emerge. While manufacturers struggle with hardware-based approaches, we propose a compiler/runtime-based solution called Synergy. We show a compiler transformation for Verilog programs that produces code able to yield control to software at sub-clock-tick granularity according to the semantics of the original program. Synergy uses this property to efficiently support core virtualization primitives: suspend and resume, program migration, and spatial/temporal multiplexing, on hardware which is available today. We use Synergy to virtualize FPGA workloads across a cluster of Altera SoCs and Xilinx FPGAs on Amazon F1. The workloads require no modification, run within 3−4× of unvirtualized performance, and incur a modest increase in FPGA fabric utilization.
MadDog: A Web-based System for Acronym Identification and Disambiguation

Acronyms and abbreviations are the short-form of longer phrases and they are ubiquitously employed in various types of writing. Despite their usefulness to save space in writing and reader’s time in reading, they also provide challenges for understanding the text especially if the acronym is not defined in the text or if it is used far from its definition in long texts. To alleviate this issue, there are considerable efforts both from the research community and software developers to build systems for identifying acronyms and finding their correct meanings in the text. However, none of the existing works provide a unified solution capable of processing acronyms in various domains and to be publicly available. Thus, we provide the first web-based acronym identification and disambiguation system which can process acronyms from various domains including scientific, biomedical, and general domains. The web-based system is publicly available at http://iq.cs.uoregon.edu:5000 and a demo video is available at https://youtu.be/IkSh7LqI42M. The system source code is also available at https://github.com/amirveyseh/MadDog.
New Way of Generating Electromagnetic Waves

This article presents a new method for generating low-frequency electromagnetic waves for navigation and communication in challenging environments, such as underwater and underground. The key concept is to disturb the magnetic energy stored around a permanent magnet in a time-variant fashion. The magnetic reluctance of the medium around the permanent magnet is modulated to alter the magnetic flux intensity and direction (disturb the stored energy) in order to achieve this goal. The nonlinear properties of the surrounding magnetic material are a critical phenomenon for efficient and effective modulation. Since the proposed way of generating the electromagnetic field is not based on a second-order system (resonant structure), the bandwidth of any modulation schema is not limited to the overall system quality factor. A transmitter is prototyped as a proof of concept, and the generated field is measured. Compared with the rotating magnet, the prototyped transmitter can modulate up to 50% of the permanent magnet’s stored energy with much lower power consumption.
FaNDOM: Fast nested distance-based seeding of optical maps.

Summary Optical mapping (OM) provides single-molecule readouts of fluorescently labeled sequence motifs on long fragments of DNA, resolved to nucleotide-level coordinates. With the advent of microfluidic technologies for analysis of DNA molecules, it is possible to inexpensively generate long OM data ( > 150 kbp) at high coverage. In addition to scaffolding for de novo assembly, OM data can be aligned to a reference genome for identification of genomic structural variants. We introduce FaNDOM (Fast Nested Distance Seeding of Optical Maps)—an optical map alignment tool that greatly reduces the search space of the alignment process. On four benchmark human datasets, FaNDOM was significantly (4–14×) faster than competing tools while maintaining comparable sensitivity and specificity. We used FaNDOM to map variants in three cancer cell lines and identified many biologically interesting structural variants, including deletions, duplications, gene fusions and gene-disrupting rearrangements. FaNDOM is publicly available at https://github.com/jluebeck/FaNDOM .
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios. In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers. To train our model, we formulate subjective risk assessment as a supervised scene classification problem. We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers. We show that our approach achieves a higher classification accuracy than the state-of-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small datasets. We also show that our model trained on a lane-changing synthesized dataset achieves an average accuracy of 87.8% when tested on a real-driving lane-changing dataset. In comparison, the state-of-the-art model trained on the same synthesized dataset only achieved 70.3% accuracy when tested on the real-driving dataset, showing that our approach can transfer knowledge more effectively. Moreover, we demonstrate that the addition of spatial and temporal attention layers improves our model's performance and explainability. Finally, our results illustrate that our model can assess the risk of various driving maneuvers more accurately than the state-of-the-art model (86.5% vs. 58.4%, respectively).
Low entropy and the mean curvature flow with surgery

In this article, we extend the mean curvature flow with surgery to mean convex hypersurfaces with entropy less than $$\varLambda _{n-2}$$ . In particular, 2-convexity is not assumed. Next we show the surgery flow with just the initial convexity assumption $$H - \frac{\langle x, \nu \rangle }{2} > 0$$ is possible and as an application we use the surgery flow to show that smooth n-dimensional closed self shrinkers with entropy less than $$\varLambda _{n-2}$$ are isotopic to the round n-sphere.
On the derivation of exact eigenstates of the generalized squeezing operator

We construct the states that are invariant under the action of the generalized squeezing operator $\exp{(z{a^{\dagger k}}-z^*a^k)}$ for arbitrary positive integer $k$. The states are given explicitly in the number representation. We find that for a given value of $k$ there are $k$ such states. We show that the states behave as $n^{-k/4}$ when occupation number $n\to\infty$. This implies that for any $k\geq3$ the states are normalizable. For a given $k$, the expectation values of operators of the form $(a^{\dagger} a)^j$ are finite for positive integer $j < (k/2-1)$ but diverge for integer $j\geq (k/2-1)$. For $k=3$ we also give an explicit form of these states in the momentum representation in terms of Bessel functions.
LAMMPS implementation of rapid artificial neural network derived interatomic potentials

Abstract While machine learning approaches have been successfully used to represent interatomic potentials, their speed has typically lagged behind conventional formalisms. This is often due to the complexity of the structural fingerprints used to describe the local atomic environment and the large cutoff radii and neighbor lists used in the calculation of these fingerprints. Even recent machine learned methods are at least 10 times slower than traditional formalisms. An implementation of a rapid artificial neural network (RANN) style potential in the LAMMPS molecular dynamics package is presented here which utilizes angular screening to reduce computational complexity without reducing accuracy. For the smallest neural network architectures, this formalism rivals the modified embedded atom method (MEAM) for speed and accuracy, while the networks approximately one third as fast as MEAM were capable of reproducing the training database with chemical accuracy. The numerical accuracy of the LAMMPS implementation is assessed by verifying conservation of energy and agreement between calculated forces and pressures and the observed derivatives of the energy as well as by assessing the stability of the potential in dynamic simulation. The potential style is tested using a force field for magnesium and the computational efficiency for a variety of architectures is compared to a traditional potential models as well as alternative ANN formalisms. The predictive accuracy is found to rival that of slower methods. Additionally, the transferability of the formalism is demonstrated by correctly predicting the Mg phase diagram include the pressure dependence on melting temperature and the presence of a high pressure BCC phase.
Acquisition and Online Display of High-Resolution Backscattered Electron and X-Ray Maps of Meteorite Sections

Analyses of meteorites in thin or thick section begins with a detailed mineralogic/petrologic study of the sample. Backscattered electron and x-ray imaging in a secondary electron microscope is cri...
Earlier and more robust sensorimotor discrimination of ASL signs in deaf signers during imitation

Prior research suggests that the amount of experience an individual has with an action influences the degree to which the sensorimotor systems of their brain are involved in the subsequent percepti...
FEbeam: Cavity and electron emission data conversion, processing, and analysis. A freeware toolkit for rf injectors.

FEbeam is an all-in-one field emission data processing interface with the capability to analyze the field emission cathode performance in an rf injector by extracting the field enhancement factor, local field, and effective emission area from the Fowler–Nordheim equations. It also has the capability of processing beam imaging micrographs using its sister software, FEpic. The current version of FEbeam was designed for the Argonne Cathode Test-stand of the Argonne Wakefield Accelerator facility switch yard. With slight modifications, FEbeam could work for many rf field emission injectors. This software is open-source and can be found at GitHub.
Detection of Covid-19 Through the Analysis of Vocal Fold Oscillations

Phonation, or the vibration of the vocal folds, is the primary source of vocalization in the production of voiced sounds by humans. It is a complex bio-mechanical process that is highly sensitive to changes in the speaker's respiratory parameters. Since most symptomatic cases of COVID-19 present with moderate to severe impairment of respiratory functions, we hypothesize that signatures of COVID-19 may be observable by examining the vibrations of the vocal folds. Our goal is to validate this hypothesis, and to quantitatively characterize the changes observed to enable the detection of COVID-19 from voice. For this, we use a dynamical system model for the oscillation of the vocal folds, and solve it using our recently developed ADLES algorithm to yield vocal fold oscillation patterns directly from recorded speech. Experimental results on a clinically curated dataset of COVID-19 positive and negative subjects reveal characteristic patterns of vocal fold oscillations that are correlated with COVID-19. We show that these are prominent and discriminative enough that even simple classifiers such as logistic regression yields high detection accuracies using just the recordings of isolated extended vowels.
Stiffness modeling for near field acoustic levitation bearings

The dynamic characteristics of near field levitation bearings have been investigated in this study. Through theoretical analysis, two different types of system stiffness are defined and derived analytically. The dynamic stiffness relates the excitation amplitude to the dynamic force amplitude, while the effective stiffness governs the time-averaged force–displacement relationship. The results indicate two non-linear and asymmetric spring constants that can effectively predict levitation force and height. The models are verified with a carefully designed experimental setup to eliminate the structural resonance effect. Besides, some unique dynamic behaviors are investigated and predicted based on the proposed stiffness model.
D-VDAMP: Denoising-Based Approximate Message Passing for Compressive MRI

Plug and play (P&P) algorithms iteratively apply highly optimized image denoisers to impose priors and solve computational image reconstruction problems, to great effect. However, in general the "effective noise", that is the difference between the true signal and the intermediate solution, within the iterations of P&P algorithms is neither Gaussian nor white. This fact makes existing denoising algorithms suboptimal.In this work, we propose a CNN architecture for removing colored Gaussian noise and combine it with the recently proposed VDAMP algorithm, whose effective noise follows a predictable colored Gaussian distribution. We apply the resulting denoising-based VDAMP (D-VDAMP) algorithm to variable density sampled compressive MRI where it substantially outperforms existing techniques.
RNase L limits host and viral protein synthesis via inhibition of mRNA export

RNase L is widely thought to limit viral protein synthesis by cleaving host rRNA and viral mRNA, resulting in translation arrest and viral mRNA degradation. Here, we show that the mRNAs of dengue virus and influenza A virus largely escape RNase L-mediated mRNA decay, and this permits viral protein production. However, activation of RNase L arrests nuclear mRNA export, which strongly inhibits influenza A virus protein synthesis and reduces cytokine production. The heterogeneous and temporal nature of the mRNA export block in individual cells permits sufficient production of antiviral cytokines from transcriptionally induced host mRNAs. This defines RNase L-mediated arrest of mRNA export as a key antiviral shutoff and cytokine regulatory pathway.
Behavioral and Neural Variability of Naturalistic Arm Movements.

Motor behaviors are central to many functions and dysfunctions of the brain, and understanding their neural basis has consequently been a major focus in neuroscience. However, most studies of motor behaviors have been restricted to artificial, repetitive paradigms, far removed from natural movements performed “in the wild.” Here, we leveraged recent advances in machine learning and computer vision to analyze intracranial recordings from 12 human subjects during thousands of spontaneous, unstructured arm reach movements, observed over several days for each subject. These naturalistic movements elicited cortical spectral power patterns consistent with findings from controlled paradigms, but with considerable neural variability across subjects and events. We modeled interevent variability using 10 behavioral and environmental features; the most important features explaining this variability were reach angle and day of recording. Our work is among the first studies connecting behavioral and neural variability across cortex in humans during unstructured movements and contributes to our understanding of long-term naturalistic behavior. Significance Statement Understanding the neural basis of human movement has long been a key focus in neuroscience. However, researchers often study constrained, monotonous tasks that differ greatly from the rich and diverse natural movements we actually make. Here, we use data-intensive computational approaches to reveal patterns in neural activity underlying naturalistic human arm movements. While such movements match previous experimental findings on average, there is substantial neural variability from one movement to the next. We partially explain this variability by aspects of the movement observed, though much variability remains unaccounted for. Our study sheds light on how the brain generates natural arm movements and emphasizes the critical need to study brain activity in more unstructured settings that mimic daily life.
Structure of the Martian atmosphere from Epsilon Gem occultation observations

Information about Martian atmospheric scale heights derived from observations of the occultation of Epsilon Gem by Mars on April 8, 1976 has been collected. The observations give data in the altitude range of about 50 to 80 km. A rough, unweighted average of results so far available yields a temperatue of approximately 165 K. Excursions of about + or - 4 K about this mean may be present as a function of both altitude and areographic coordinates.
Child Undernutrition following the Introduction of a Large-Scale Toilet Construction Campaign in India.

BACKGROUND Lack of toilets and the widespread practice of open defecation may contribute to India's large burden of child undernutrition. OBJECTIVES We examine whether a large national sanitation campaign launched in 2014, the Swachh Bharat Mission (SBM), precedes a reduction in stunting and wasting among under 5-y-old (u5) children in India. METHODS In this observational study, we used district-level data from before (2013-2014) and after (2015-2016) SBM from 3 national surveys to derive, as our outcomes, the percentage of u5 children per district who are stunted and wasted. We defined our exposures as 1) binary indicator of SBM and 2) percentage of households with toilets per district. Our analytic sample comprised nearly all 640 Indian districts (with ∼1200 rural/urban divisions per district per time point). Linear regression analyses controlled for baseline differences in districts, linear time trends by state, and relevant covariates. RESULTS Relative to pre-SBM, u5 stunting declines by 0.06% (95% CI: -0.10, -0.01; P = 0.009) with every percentage increase in households with toilets post-SBM. Rural regions and districts with higher pre-SBM toilet availability show greater decline in u5 stunting post-SBM. CONCLUSIONS An increase in toilet availability on a national scale, precipitated by the SBM sanitation campaign, is associated with a reduction in undernutrition among u5 children in India over the early phase of the campaign.