cssi/SciSciGPT-SciSciNet huggingface dataset:
Calmodulin-binding transcription activator AtSR1/CAMTA3 fine-tunes plant immune response by transcriptional regulation of the salicylate receptor NPR1.

Calcium (Ca2+ ) signaling regulates salicylic acid (SA)-mediated immune response through calmodulin-meditated transcriptional activators, AtSRs/CAMTAs, but its mechanism is not fully understood. Here, we report an AtSR1/CAMTA3-mediated regulatory mechanism involving the expression of the SA receptor, NPR1. Results indicate that the transcriptional expression of NPR1 was regulated by AtSR1 binding to a CGCG box in the NPR1 promotor. The atsr1 mutant exhibited resistance to the virulent strain of Pseudomonas syringae pv. tomato (Pst), however it was susceptible to an avirulent Pst strain carrying avrRpt2, due to the failure of the induction of hypersensitive responses. These resistant/susceptible phenotypes in the atsr1 mutant were reversed in the npr1 mutant background, suggesting that AtSR1 regulates NPR1 as a downstream target during plant immune response. The virulent Pst strain triggered a transient elevation in intracellular Ca2+ concentration, whereas the avirulent Pst strain triggered a prolonged change. The distinct Ca2+ signatures were decoded into the regulation of NPR1 expression through AtSR1's IQ motif binding with Ca2+ -free-CaM2, while AtSR1's calmodulin-binding domain with Ca2+ -bound-CaM2. These observations reveal a role for AtSR1 as a Ca2+ -mediated transcription regulator in controlling the NPR1-mediated plant immune response. This article is protected by copyright. All rights reserved.
Epidemiologic Evidence of Glyphosate’s Carcinogenic Potential

Worldwide use of glyphosate has exploded since it was introduced in 1974. Most official assessments have disagreed with IARC’s 2015 conclusion that glyphosate is probably carcinogenic to humans (gr...
Small cap decoupling inequalities: Bilinear methods

We obtain sharp small cap decoupling inequalities associated to the moment curve for certain range of exponents $p$. Our method is based on the bilinearization argument due to Bourgain and Bourgain-Demeter. Our result generalizes theirs to all higher dimensions.
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations.

Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users’ text input. There are three primary challenges in designing robust and accurate intent detection models. First, typical intent detection models require a large amount of labeled data to achieve high accuracy. Unfortunately, in practical scenarios it is more common to find small, unbalanced, and noisy datasets. Secondly, even with large training data, the intent detection models can see a different distribution of test data when being deployed in the real world, leading to poor accuracy. Finally, a practical intent detection model must be computationally efficient in both training and single query inference so that it can be used continuously and re-trained frequently. We benchmark intent detection methods on a variety of datasets. Our results show that Watson Assistant’s intent detection model outperforms other commercial solutions and is comparable to large pretrained language models while requiring only a fraction of computational resources and training data. Watson Assistant demonstrates a higher degree of robustness when the training and test distributions differ.
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning

With three complexes spread evenly across the Earth, NASA's Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozens of active missions around the world. A rapidly rising number of spacecraft and increasingly complex scientific instruments with higher bandwidth requirements have resulted in demand that exceeds the network's capacity across its 12 antennae. The existing DSN scheduling process operates on a rolling weekly basis and is time-consuming; for a given week, generation of the final baseline schedule of spacecraft tracking passes takes roughly 5 months from the initial requirements submission deadline, with several weeks of peer-to-peer negotiations in which mission planners and DSN personnel iteratively update their respective schedules based on inputs from the other. This paper proposes a deep reinforcement learning (RL) approach to generate candidate DSN schedules from mission requests and spacecraft view period data with demonstrated capability to address real-world operational constraints. A deep RL agent is developed that takes mission requests for a given week as input, and interacts with a DSN scheduling environment to allocate tracks such that its reward signal is maximized. A comparison is made between an agent trained using Proximal Policy Optimization and its random, untrained counterpart. The results represent a proof-of-concept that, given a well-shaped reward signal, a deep RL agent can learn the complex heuristics used by experts to schedule the DSN. A trained agent can potentially be used to generate candidate schedules to bootstrap the scheduling process and thus reduce the turnaround cycle for DSN scheduling.
First Multimessenger Observations of a Neutron Star Merger

We describe the first observations of the same celestial object with gravitational waves and light. ▪ GW170817 was the first detection of a neutron star merger with gravitational waves. ▪ The detec...
Cognitive functions of metaphor in the natural sciences

My interest in scientific metaphor focuses on its role in charting our search for knowledge of a world not yet known. How else is one to seek understanding of the new, of the not yet intelligible, ...
Trajectory Specification for Automation of Terminal Air Traffic Control

"Trajectory specification" is the explicit bounding and control of aircraft tra- jectories such that the position at each point in time is constrained to a precisely defined volume of space. The bounding space is defined by cross-track, along-track, and vertical tolerances relative to a reference trajectory that specifies position as a function of time. The tolerances are dynamic and will be based on the aircraft nav- igation capabilities and the current traffic situation. A standard language will be developed to represent these specifications and to communicate them by datalink. Assuming conformance, trajectory specification can guarantee safe separation for an arbitrary period of time even in the event of an air traffic control (ATC) sys- tem or datalink failure, hence it can help to achieve the high level of safety and reliability needed for ATC automation. As a more proactive form of ATC, it can also maximize airspace capacity and reduce the reliance on tactical backup systems during normal operation. It applies to both enroute airspace and the terminal area around airports, but this paper focuses on arrival spacing in the terminal area and presents ATC algorithms and software for achieving a specified delay of runway arrival time.
High-Accuracy Mesh-Free Quadrature for Trimmed Parametric Surfaces and Volumes

Abstract This work presents a high-accuracy, mesh-free, generalized Stokes theorem-based numerical quadrature scheme for integrating functions over trimmed parametric surfaces and volumes. The algorithm relies on two fundamental steps: (1) We iteratively reduce the dimensionality of integration using the generalized Stokes theorem to line integrals over trimming curves, and (2) we employ numerical antidifferentiation in the generalized Stokes theorem using high-order quadrature rules. The scheme achieves exponential convergence up to trimming curve approximation error and has applications to computation of geometric moments, immersogeometric analysis, conservative field transfer between high-order curvilinear meshes, and initialization of multi-material simulations. We compare the quadrature scheme to commonly-used quadrature schemes in the literature and show that our scheme is much more efficient in terms of number of quadrature points used. We provide an open-source implementation of the scheme in MATLAB as part of QuaHOG, a software package for Quadrature of High-Order Geometries.
Entropic Dynamics Yields Reciprocal Relations.

Entropic dynamics is a framework for defining dynamical systems that is aligned with the principles of information theory. In an entropic dynamics model for motion on a statistical manifold, we find that the rate of changes for expected values is linear with respect to the gradient of entropy with reciprocal (symmetric) coefficients. Reciprocity principles have been useful in physics since Onsager. Here we show how the entropic dynamics reciprocity is a consequence of the information geometric structure of the exponential family, hence it is a general property that can be extended to a broader class of dynamical models.
Using Machine Learning to Develop a Fully Automated Soybean Nodule Acquisition Pipeline (SNAP).

Nodules form on plant roots through the symbiotic relationship between soybean (Glycine max L. Merr.) roots and bacteria (Bradyrhizobium japonicum) and are an important structure where atmospheric nitrogen (N2) is fixed into bioavailable ammonia (NH3) for plant growth and development. Nodule quantification on soybean roots is a laborious and tedious task; therefore, assessment is frequently done on a numerical scale that allows for rapid phenotyping, but is less informative and suffers from subjectivity. We report the Soybean Nodule Acquisition Pipeline (SNAP) for nodule quantification that combines RetinaNet and UNet deep learning architectures for object (i.e., nodule) detection and segmentation. SNAP was built using data from 691 unique roots from diverse soybean genotypes, vegetative growth stages, and field locations and has a good model fit ( ). SNAP reduces the human labor and inconsistencies of counting nodules, while acquiring quantifiable traits related to nodule growth, location, and distribution on roots. The ability of SNAP to phenotype nodules on soybean roots at a higher throughput enables researchers to assess the genetic and environmental factors, and their interactions on nodulation from an early development stage. The application of SNAP in research and breeding pipelines may lead to more nitrogen use efficiency for soybean and other legume species cultivars, as well as enhanced insight into the plant-Bradyrhizobium relationship.
Structural Iterative Rounding for Generalized k-Median Problems.

This paper considers approximation algorithms for generalized $k$-median problems. This class of problems can be informally described as $k$-median with a constant number of extra constraints, and includes $k$-median with outliers, and knapsack median. Our first contribution is a pseudo-approximation algorithm for generalized $k$-median that outputs a $6.387$-approximate solution, with a constant number of fractional variables. The algorithm builds on the iterative rounding framework introduced by Krishnaswamy, Li, and Sandeep for $k$-median with outliers. The main technical innovation is allowing richer constraint sets in the iterative rounding and taking advantage of the structure of the resulting extreme points. Using our pseudo-approximation algorithm, we give improved approximation algorithms for $k$-median with outliers and knapsack median. This involves combining our pseudo-approximation with pre- and post-processing steps to round a constant number of fractional variables at a small increase in cost. Our algorithms achieve approximation ratios $6.994 + \epsilon$ and $6.387 + \epsilon$ for $k$-median with outliers and knapsack median, respectively. These improve on the best-known approximation ratio $7.081 + \epsilon$ for both problems \cite{DBLP:conf/stoc/KrishnaswamyLS18}.
Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins.

Abstract Computational prediction of enzyme mechanism and protein function requires accurate physics-based models and suitable sampling. We discuss recent advances in large-scale quantum mechanical (QM) modeling of biochemical systems that have reduced the cost of high-accuracy models. Tradeoffs between sampling and accuracy have motivated modeling with molecular mechanics (MM) in a multiscale QM/MM or iterative approach. Limitations to both conventional density-functional theory and classical MM force fields remain for describing noncovalent interactions in comparison to experiment or wavefunction theory. Because predictions of enzyme action (i.e. electrostatics), free energy barriers, and mechanisms are sensitive to the protocol and embedding method in QM/MM, convergence tests and systematic methods for quantifying QM-level interactions are a needed, active area of development.
Hypernetwork Science: From Multidimensional Networks to Computational Topology

As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other that of higher-order mathematical abstractions from algebra, lattice theory, and topology. They are able to represent complex systems interactions more faithfully than graphs and networks, while also being some of the simplest classes of systems representing topological structures as collections of multidimensional objects connected in a particular pattern. In this paper we discuss the role of (undirected) hypergraphs in the science of complex networks, and provide a mathematical overview of the core concepts needed for hypernetwork modeling, including duality and the relationship to bicolored graphs, quantitative adjacency and incidence, the nature of walks in hypergraphs, and available topological relationships and properties. We close with a brief discussion of two example applications: biomedical databases for disease analysis, and domain-name system (DNS) analysis of cyber data.
Vortex Solution in Elliptic Coordinates

Vortices (flows with closed elliptic streamlines) are exact nonlinear solutions to the compressible Euler equation. In this contribution, we use differential geometry to derive the transformations between Cartesian and elliptic coordinates, and show that in elliptic coordinates a constant vorticity flow reduces to $\dot{\mu}=0$ and $\dot{\nu}={\rm const}$ along the streamline $\mu_0$ that matches the vortex eccentricity.
Axion-Like Particles at the ILC Giga-Z

Axion-Like Particles (ALPs) are a generic, calculable, and well motivated extension of the Standard Model with far reaching phenomenology. ALPs that couple only to hypercharge represent one subset of such models, coupling the ALP to both photons and the $Z$ boson. We examine the current constraints on this class of models with an ALP mass in the 100 MeV to 100 GeV range, paying particular attention to the region between 100 MeV to 10 GeV, a portion of parameter space which is ill constrained by current experiments. We show that the more than $10^{9}$ $Z$ bosons produced in the Giga-Z mode of the future ILC experiment, combined with the highly granular nature of its detectors, will allow for ALPs coupled to hypercharge to be discovered with couplings down to nearly $10^{-5}\,\rm{GeV^{-1}}$ over a range of masses from 0.4 to 50 GeV.
If U.S. Citizens Pose a Risk to International Health, Can the Federal Government Prevent Them from Traveling Abroad?

While states normally controlling public health emergencies, this article discusses the current Centers for Disease Control and Prevention regulations to determine whether the federal agency has the authority to stop ill U.S. citizens from traveling abroad in the event of a public health emergency. The article compares state, federal, and international laws. It also provides an in-depth history of federal quarantine and health recommendations and compares that prior history with the current COVID-19 pandemic.
Grassmannian codes from paired difference sets

An equiangular tight frame (ETF) is a sequence of vectors in a Hilbert space that achieves equality in the Welch bound and so has minimal coherence. More generally, an equichordal tight fusion frame (ECTFF) is a sequence of equi-dimensional subspaces of a Hilbert space that achieves equality in Conway, Hardin and Sloane’s simplex bound. Every ECTFF is a type of optimal Grassmannian code, that is, an optimal packing of equi-dimensional subspaces of a Hilbert space. We construct ECTFFs by exploiting new relationships between known ETFs. Harmonic ETFs equate to difference sets for finite abelian groups. We say that a difference set for such a group is “paired” with a difference set for its Pontryagin dual when the corresponding subsequence of its harmonic ETF happens to be an ETF for its span. We show that every such pair yields an ECTFF. We moreover construct an infinite family of paired difference sets using quadratic forms over the field of two elements. Together this yields two infinite families of real ECTFFs.
Collective Synchronization of Undulatory Movement through Contact

Physical contact among simple robots with undulatory gaits leads to rich collective dynamics including synchronization, thus showing how some populations in nature sync their movements when in close proximity.
A note on distance-preserving graph sparsification

Abstract We consider problems of the following type: given a graph G, how many edges are needed in the worst case for a sparse subgraph H that approximately preserves distances between a given set of node pairs P? Examples include pairwise spanners, distance preservers, reachability preservers, etc. There has been a trend in the area of simple constructions based on the hitting set technique, followed by somewhat more complicated constructions that improve over the bounds obtained from hitting sets by roughly a log factor. In this note, we point out that the simpler constructions based on hitting sets don't actually need an extra log factor in the first place. This simplifies and unifies a few proofs in the area, and it improves the size of the +4 pairwise spanner from O ˜ ( n p 2 / 7 ) (Kavitha (2017) [13] ) to O ( n p 2 / 7 ) .
Impact of COVID-19 on gastroenterology fellowship training: a multicenter analysis of endoscopy volumes.

Background and study aims The COVID-19 pandemic has had a profound impact on gastroenterology training programs. We aimed to objectively evaluate procedural training volume and impact of COVID-19 on gastroenterology fellowship programs in the United States. Methods This was a retrospective, multicenter study. Procedure volume data on upper and lower endoscopies performed by gastroenterology fellows was abstracted directly from the electronic medical record. The study period was stratified into 2 time periods: Study Period 1, SP1 (03/15/2020 to 06/30/2020) and Study Period 2, SP2 (07/01/2020 to 12/15/2020). Procedure volumes during SP1 and SP2 were compared to Historic Period 1 (HP1) (03/15/2019 to 06/30/2019) and Historic Period 2 (HP2) (07/01/2019 to 12/15/2019) as historical reference. Results Data from 23 gastroenterology fellowship programs (total procedures = 127,958) with a median of 284 fellows (range 273–289; representing 17.8 % of all trainees in the United States) were collected. Compared to HP1, fellows performed 53.6 % less procedures in SP1 (total volume: 28,808 vs 13,378; mean 105.52 ± 71.94 vs 47.61 ± 41.43 per fellow; P Conclusions Although there was a significant reduction in fellows’ endoscopy volume in the initial stages of the pandemic, adaptive mechanisms have resulted in a return of procedure volume to near baseline without ongoing impact on endoscopy training.
Home equity lending, credit constraints and small business in the US

We use Texas's constitutional amendment in 1997 that expanded the scope of home equity loans as a source of exogenous variation to estimate the effects of relaxing credit constraints on small businesses. We find, using standard panel data methods and restricted-use micro-data from the US Census Bureau, that the Texas amendment increased the use of home equity finance by small businesses, increased new business and job creation and reduced establishment exit and job loss. The effects are larger and significant for businesses with fewer than ten employees.
Semantically Robust Unpaired Image Translation for Data With Unmatched Semantics Statistics

Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations. Unaware of the inherently unmatched semantics distributions between source and target domains, existing distribution matching methods (i.e., GAN-based) can give undesired solutions. In particular, although producing visually reasonable outputs, the learned models usually flip the semantics of the inputs. To tackle this without using extra supervision, we propose to enforce the translated outputs to be semantically invariant w.r.t. small perceptual variations of the inputs, a property we call "semantic robustness". By optimizing a robustness loss w.r.t. multi-scale feature space perturbations of the inputs, our method effectively reduces semantics flipping and produces translations that outperform existing methods both quantitatively and qualitatively.
Effect of Dry Eye Disease on the Kinetics of Lacrimal Gland Dendritic Cells as Visualized by Intravital Multi-Photon Microscopy

The lacrimal gland (LG) is the main source of the tear film aqueous layer and its dysfunction results in dry eye disease (DED), a chronic immune-mediated disorder of the ocular surface. The desiccating stress (DS) murine model that mimics human DED, results in LG dysfunction, immune cell infiltration, and consequently insufficient tear production. To date, the immune cell kinetics in DED are poorly understood. The purpose of this study was to develop a murine model of intravital multi-photon microscopy (IV-MPM) for the LG, and to investigate the migratory kinetics and 3D morphological properties of conventional dendritic cells (cDCs), the professional antigen presenting cells of the ocular surface, in DED. Mice were placed in a controlled environmental chamber with low humidity and increased airflow rate for 2 and 4 weeks to induce DED, while control naive transgenic mice were housed under standard conditions. DED mice had significantly decreased tear secretion and increased fluorescein staining (p < 0.01) compared to naive controls. Histological analysis of the LG exhibited infiltrating mononuclear and polymorphonuclear cells (p < 0.05), as well as increased LG swelling (p < 0.001) in DED mice compared to controls. Immunofluorescence staining revealed increased density of cDCs in DED mice (p < 0.001). IV-MPM of the LG demonstrated increased density of cDCs in the LGs of DED mice, compared with controls (p < 0.001). cDCs were more spherical in DED at both time points compared to controls (p < 0.001); however, differences in surface area were found at 2 weeks in DED compared with naive controls (p < 0.001). Similarly, 3D cell volume was significantly lower at 2 weeks in DED vs. the naive controls (p < 0.001). 3D instantaneous velocity and mean track speed were significantly higher in DED compared to naive mice (p < 0.001). Finally, the meandering index, an index for directionality, was significant increased at 4 weeks after DED compared with controls and 2 weeks of DED (p < 0.001). Our IV-MPM study sheds light into the 3D morphological alterations and cDC kinetics in the LG during DED. While in naive LGs, cDCs exhibit a more dendritic morphology and are less motile, they became more spherical with enhanced motility during DED. This study shows that IV-MPM represents a robust tool to study immune cell trafficking and kinetics in the LG, which might elucidate cellular alterations in immunological diseases, such as DED.
Tight distance-dependent estimators for screening two-center and three-center short-range Coulomb integrals over Gaussian basis functions.

We derive distance-dependent estimators for two-center and three-center electron repulsion integrals over a short-range Coulomb potential, erfc(ωr12)/r12. These estimators are much tighter than the ones based on the Schwarz inequality and can be viewed as a complement to the distance-dependent estimators for four-center short-range Coulomb integrals and for two-center and three-center full Coulomb integrals previously reported. Because the short-range Coulomb potential is commonly used in solid-state calculations, including those with the Heyd–Scuseria–Ernzerhof functional and with our recently introduced range-separated periodic Gaussian density fitting, we test our estimators on a diverse set of periodic systems using a wide range of the range-separation parameter ω. These tests demonstrate the robust tightness of our estimators, which are then used with integral screening to calculate periodic three-center short-range Coulomb integrals with linear scaling in system size.
Sums of Powers in Large Finite Fields: A Mix of Methods

Can any element in a sufficiently large finite field be represented as a sum of two dth powers in the field? In this article, we recount some of the history of this problem, touching on cyclotomy, ...
OpenBot: Turning Smartphones into Robots

Current robots are either expensive or make significant compromises on sensory richness, computational power, and communication capabilities. We propose to leverage smartphones to equip robots with extensive sensor suites, powerful computational abilities, state-of-the-art communication channels, and access to a thriving software ecosystem. We design a small electric vehicle that costs $50 and serves as a robot body for standard Android smartphones. We develop a software stack that allows smartphones to use this body for mobile operation and demonstrate that the system is sufficiently powerful to support advanced robotics workloads such as person following and real-time autonomous navigation in unstructured environments. Controlled experiments demonstrate that the presented approach is robust across different smartphones and robot bodies.
Universality in Kinetic Models of Circadian Rhythms in Arabidopsis thaliana

Biological evolution has endowed the plant Arabidopsis thaliana with genetically regulated circadian rhythms. A number of authors have published kinetic models for these oscillating chemical reactions based on a network of interacting genes. To investigate the hypothesis that the Arabidopsis circadian dynamical system is poised near a Hopf bifurcation like some other biological oscillators, we varied the kinetic parameters in the models and searched for bifurcations. Finding that each model does exhibit a supercritical Hopf bifurcation, we performed a weakly nonlinear analysis near the bifurcation points to derive the Stuart-Landau amplitude equation. To illustrate a common dynamical structure, we scaled the numerical solutions to the models with the asymptotic solutions to the Stuart-Landau equation to collapse the circadian oscillations onto two universal curves-one for amplitude, and one for frequency. However, some models are close to bifurcation while others are far, some models are post-bifurcation while others are pre-bifurcation, and kinetic parameters that lead to a bifurcation in some models do not lead to a bifurcation in others. Future kinetic modeling can make use of our analysis to ensure models are consistent with each other and with the dynamics of the Arabidopsis circadian rhythm.
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics.

Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differential Equations, a recently introduced family of continuous-depth, differentiable networks (Chen et al 2018), as a way to propagate latent-space dynamics in reduced order models. We compare their behavior with two classical non-intrusive methods based on proper orthogonal decomposition and radial basis function interpolation as well as dynamic mode decomposition. The test problems we consider include incompressible flow around a cylinder as well as real-world applications of shallow water hydrodynamics in riverine and estuarine systems. Our findings indicate that Neural ODEs provide an elegant framework for stable and accurate evolution of latent-space dynamics with a promising potential of extrapolatory predictions. However, in order to facilitate their widespread adoption for large-scale systems, significant effort needs to be directed at accelerating their training times. This will enable a more comprehensive exploration of the hyperparameter space for building generalizable Neural ODE approximations over a wide range of system dynamics.
On minimal presentations of shifted affine semigroups with few generators

An affine semigroup is a finitely generated subsemigroup of $(\mathbb Z_{\ge 0}^d, +)$, and a numerical semigroup is an affine semigroup with $d = 1$. A growing body of recent work examines shifted families of numerical semigroups, that is, families of numerical semigroups of the form $M_n = \langle n + r_1, \ldots, n + r_k \rangle$ for fixed $r_1, \ldots, r_k$, with one semigroup for each value of the shift parameter $n$. It has been shown that within any shifted family of numerical semigroups, the size of any minimal presentation is bounded (in fact, this size is eventually periodic in $n$). In this paper, we consider shifted families of affine semigroups, and demonstrate that some, but not all, shifted families of 4-generated affine semigroups have arbitrarily large minimal presentations.
Input-distribution-aware parallel decoding of block codes

Many channel decoders rely on parallel decoding attempts to achieve good performance with acceptable latency. However, most of the time fewer attempts than the foreseen maximum are sufficient for successful decoding. Input-distribution-aware (IDA) decoding allows to determine the parallelism of polar code list decoders by observing the distribution of channel information. In this work, IDA decoding is proven to be effective with different codes and decoding algorithms as well. Two techniques, M-IDA and MD-IDA, are proposed: they exploit the sampling of the input distribution inherent to particular decoding algorithms to perform low-cost IDA decoding. Simulation results on the decoding of BCH codes via the Chase and ORBGRAND algorithms show that they perform at least as well as the original IDA decoding, allowing to reduce run-time complexity down to 17% and 67% with minimal error correction degradation.
Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components

Minimizing a sum of simple submodular functions of limited support is a special case of general submodular function minimization that has seen numerous applications in machine learning. We develop fast techniques for instances where components in the sum are cardinality-based, meaning they depend only on the size of the input set. This variant is one of the most widely applied in practice, encompassing, e.g., common energy functions arising in image segmentation and recent generalized hypergraph cut functions. We develop the first approximation algorithms for this problem, where the approximations can be quickly computed via reduction to a sparse graph cut problem, with graph sparsity controlled by the desired approximation factor. Our method relies on a new connection between sparse graph reduction techniques and piecewise linear approximations to concave functions. Our sparse reduction technique leads to significant improvements in theoretical runtimes, as well as substantial practical gains in problems ranging from benchmark image segmentation tasks to hypergraph clustering problems.
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers

Recurrent neural networks (RNNs), temporal convolutions, and neural differential equations (NDEs) are popular families of deep learning models for time-series data, each with unique strengths and tradeoffs in modeling power and computational efficiency. We introduce a simple sequence model inspired by control systems that generalizes these approaches while addressing their shortcomings. The Linear State-Space Layer (LSSL) maps a sequence $u \mapsto y$ by simply simulating a linear continuous-time state-space representation $\dot{x} = Ax + Bu, y = Cx + Du$. Theoretically, we show that LSSL models are closely related to the three aforementioned families of models and inherit their strengths. For example, they generalize convolutions to continuous-time, explain common RNN heuristics, and share features of NDEs such as time-scale adaptation. We then incorporate and generalize recent theory on continuous-time memorization to introduce a trainable subset of structured matrices $A$ that endow LSSLs with long-range memory. Empirically, stacking LSSL layers into a simple deep neural network obtains state-of-the-art results across time series benchmarks for long dependencies in sequential image classification, real-world healthcare regression tasks, and speech. On a difficult speech classification task with length-16000 sequences, LSSL outperforms prior approaches by 24 accuracy points, and even outperforms baselines that use hand-crafted features on 100x shorter sequences.
Magic in twisted transition metal dichalcogenide bilayers.

The long-wavelength moire superlattices in twisted 2D structures have emerged as a highly tunable platform for strongly correlated electron physics. We study the moire bands in twisted transition metal dichalcogenide homobilayers, focusing on WSe2, at small twist angles using a combination of first principles density functional theory, continuum modeling, and Hartree-Fock approximation. We reveal the rich physics at small twist angles θ < 4∘, and identify a particular magic angle at which the top valence moire band achieves almost perfect flatness. In the vicinity of this magic angle, we predict the realization of a generalized Kane-Mele model with a topological flat band, interaction-driven Haldane insulator, and Mott insulators at the filling of one hole per moire unit cell. The combination of flat dispersion and uniformity of Berry curvature near the magic angle holds promise for realizing fractional quantum anomalous Hall effect at fractional filling. We also identify twist angles favorable for quantum spin Hall insulators and interaction-induced quantum anomalous Hall insulators at other integer fillings. The e moire superlattice in twisted 2D structures becomes a highly tunable platform of strongly correlated electron systems. Here, the authors predict rich physics at small twist angles in twisted transition metal dichalcogenide bilayers, including a magic angle for flat band, interaction-driven Haldane insulator, fractional quantum anomalous Hall effect and quantum spin Hall insulators.
Does Nonprofit Ownership Matter for Firm Performance? Financial Distress and Ownership Conversion of Nursing Homes

In the past two decades, many nursing homes converted their ownership status from nonprofit to for-profit (NP-to-FP). These conversions have drawn public scrutiny and triggered a debate about the i...
Anomaly resolution via decomposition

In this paper we apply decomposition to orbifolds with quantum symmetries to resolve anomalies. Briefly, it has been argued by e.g. Wang-Wen-Witten, Tachikawa that an anomalous orbifold can sometimes be resolved by enlarging the orbifold group so that the pullback of the anomaly to the larger group is trivial. For this procedure to resolve the anomaly, one must specify a set of phases in the larger orbifold, whose form is implicit in the extension construction. There are multiple choices of consistent phases, which give rise to physically distinct resolutions. We apply decomposition, and find that theories with enlarged orbifold groups are equivalent to (disjoint unions of copies of) orbifolds by nonanomalous subgroups of the original orbifold group. In effect, decomposition implies that enlarging the orbifold group is equivalent to making it smaller. We provide a general conjecture for such descriptions, which we check in a number of examples.
The False Strategy Theorem: A Financial Application of Experimental Mathematics

The late Jonathan M. Borwein excelled at a wide range of mathematical fields. He is perhaps best known for his work in experimental mathematics and optimization. But his interests extended far beyo...
Simultaneous Evaluation of Heat Capacity and In-plane Thermal Conductivity of Nanocrystalline Diamond Thin Films

As wide bandgap electronic devices have continued to advance in both size reduction and power handling capabilities, heat dissipation has become a significant concern. To mitigate this, chemical vapor deposited (CVD) diamond has been demonstrated as an effective solution for thermal management of these devices by directly growing onto the transistor substrate. A key aspect of power and radio frequency (RF) electronic devices involves transient switching behavior, which highlights the importance of understanding the temperature dependence of the heat capacity and thermal conductivity when modeling and predicting device electrothermal response. Due to the complicated microstructure near the interface between CVD diamond and electronics, it is difficult to measure both properties simultaneously. In this work, we use time domain thermoreflectance (TDTR) to simultaneously measure the in plane thermal conductivity and heat capacity of a 1 um thick CVD diamond film, and also use the pump as an effective heater to perform temperature dependent measurements. The results show that the in plane thermal conductivity varied slightly with an average of 103 W per meter per K over a temperature range of 302 to 327 K, while the specific heat capacity has a strong temperature dependence over the same range and matches with heat capacity data of natural diamond in literature.
How Do Consortia Organize Collaborative R&D? Evidence from the National Cooperative Research Act

This paper uses contract-level data to present stylized facts about how firms organize collaborative R&D agreements. It finds several dominant modes of collaboration, including outsourcing R&D, specialization and separation of R&D activities, and more integrated learning approaches. It also finds that when consortia participants are direct competitors in existing product markets, they are more likely to outsource their collaborative R&D. The results suggest that many consortia organize to avoid spillovers, and therefore do not achieve the cross-pollination of know-how the National Cooperative Research Act was designed to achieve.
Sampling in Parallel-Beam Tomography

We present Shannon sampling theory for functions defined on \(T \times \mathbb{R} \) × ℝ, where T denotes the circle group, prove a new estimate for the aliasing error, and apply the result to parallel-beam diffraction tomography. The class of admissible sampling lattices is characterized and general sampling conditions are derived which lead to the identification of new efficient sampling schemes. Corresponding results for x-ray tomography are obtained in the high-frequency limit.
Comparative buccal anatomy in Helisoma (mollusca, pulmonata, basommatophora).

Dissections were performed to document buccal anatomy in three species of the pulmonate genus Helisoma Swainson, 1840. The 28 muscles which are responsible for radular feeding in these animals are organized in three concentric and integrated envelopes. The deepest of these includes muscles which manipulate the radula about the odontophoral cartilage. Elements of the middle envelope direct movements of the cartilage within the buccal cavity, and muscles of the outer envelope control movements of the buccal mass within the cephalic haemocoel. Motion analysis by videomicrography showed that muscles of the middle and outer envelopes contribute to the action of radular feeding by acting as antagonists to other muscles and to hydrostatic elements of the buccal apparatus. Observations of radular dentition showed that although each of the three species examined has a unique radula, especially with regard to the specific details of tooth shape, all resemble a radula characteristic of the Planorbidae with regard to other, more general, aspects of ribbon architecture.
Differences in antibody repertoires for (4-hydroxy-3-nitrophenyl)acetyl (NP) in splenic vs immature bone marrow precursor cells.

To evaluate the contribution of environmental regulatory mechanisms in fashioning the primary B cell repertoire, we have compared the repertoire of (4-hydroxy-3-nitrophenyl)acetyl (NP)-specific primary splenic B cells with that of precursor cells present as surface immunoglobulin-negative (sIg-) cells in adult bone marrow of C.B20 (Ighb) mice. Previous analyses using a variety of antigens have led to the conclusion that the antibody repertoire expressed in the spleen is similar to that expressed in newly generated B cell precursors with respect to both repertoire diversity and the representation of various predominant clonotypes. However, in the response to NP of C.B20 precursor cells, two marked disparities have been identified between the repertoire of sIg- bone marrow cells vs splenic precursor cells. The first concerns precursor cells that give rise to lambda-bearing NP-specific antibodies with heteroclitic fine specificity. Such antibodies normally dominate the primary response of Ighb mice; however, the representation of precursor cells giving rise to lambda-bearing antibodies is disproportionately low in the sIg- bone marrow cell population of C.B20 mice. Thus, during the maturation of these cells, post-sIg receptor expression, there is an apparent increase in the proportionate representation of lambda-bearing NP-specific cells. The second disparity concerns precursor cells whose antibody products bear kappa-light chains and exhibit high affinity and homoclitic binding for the NP haptenic determinant. Such precursor cells are poorly represented in the spleen, but represent a sizeable proportion of the sIg- NP-specific precursor cell population. Thus, there seems to be a selective elimination of high affinity, kappa-homoclitic anti-NP antibody-bearing cells as they acquire their sIg receptors. The elimination of this cell population could partially account for the dominance of lambda-heteroclitic antibodies in the serum responses to NP of C.B20 mice.
On the Thermal Decomposition and Residue Formation of Plant Oils

Plant oils are considered viable replacement fuels for diesel engines. However, in order to become successful, diesel fuel substitutes, problems associated with the formation of lacquer and carbon deposits on engine components must be resolved, else truly long-term engine reliability will not be possible. This paper reports some basic experiments into the formation of residues due to liquid phase reactions of a number of plant oils as a function of temperature.
Boosting crowdsourcing with expert labels: Local vs. global effects

Crowdsourcing provides a cheap but efficient approach for large-scale data and information collection. However, human judgments are inherently noisy, ambiguous and sometimes biased, and should be calibrated by additional (usually much more expensive) expert or true labels. In this work, we study the optimal allocation of the true labels to best calibrate the crowdsourced labels. We frame the problem as a submodular optimization, and propose a greedy allocation strategy that exhibits an interesting trade-off between a local effect, which encourages acquiring true labels for the most uncertain items, and a global effect, which favors the true labels of the most “influential” items, whose information can propagate to help the prediction of other items. We show that exploiting and monitoring the global effect yields a significantly better selection strategy, and also provides potentially valuable information for other tasks such as designing stopping rules.
Chapter 23 Central Pattern Generators and Interphyletic Awareness

Publisher Summary Central pattern generators (CPGs) in both invertebrates and vertebrates were extensively studied in the last four decades of the 20th century. The ’60s were marked by key experimental support for the CPG concept in invertebrate motor neuroscience. An important 1965 Society for Experimental Biology conference emphasized invertebrate contributions; its conference volume introduced new preparations to a wider audience. Breakthroughs with major impact on CPG research occurred in the mammalian motor neuroscience in the ’60s. During that decade, however, papers on invertebrate preparations rarely cited important mammalian work and vice versa. In the ’70s, key international conferences provided a venue for investigators working with one species to meet each other and be aware of the important contributions of those working on other species. Contributions from many investigators from a set of conferences and their volumes serve as benchmarks. Changes are noted in the points of view of the investigators in the field of CPGs. This chapter focuses on interphyletic awareness—in particular, the influences of experimental results obtained in one preparation on hypotheses and experimental designs in other preparations. These influences are important for many investigators.
Room-Temperature Ferroelectricity in Lithium Hydrazinium Sulfate, Li ( N 2 H 5 )S O 4

Li(${\mathrm{N}}_{2}$${\mathrm{H}}_{5}$)S${\mathrm{O}}_{4}$ is ferroelectric over the temperature range from below \char22{}15\ifmmode^\circ\else\textdegree\fi{}C to above 80\ifmmode^\circ\else\textdegree\fi{}C, and shows no dielectric peaks between \char22{}196\ifmmode^\circ\else\textdegree\fi{}C and 140\ifmmode^\circ\else\textdegree\fi{}C. No thermal anomalies are observed. The crystals are orthorhombic, space group $\mathrm{Pbn}{2}_{1}$, with $a={8.96}_{9}$ A, $b={9.91}_{3}$ A, $c={5.17}_{8}$ A, and the ferroelectric axis is, of course, along $c$; $\ensuremath{\rho}=1.966$ g/cc, and there are 4 molecules per cell. The coercive field is about 320 volts/cm at room temperature, and the spontaneous polarization is 0.30 microcoulomb/${\mathrm{cm}}^{2}$. Beautiful large crystals can be grown from water by a variety of methods. After electroding, the crystal plates should be protected by silicone oil. Without such protection, reaction with normal atmosphere for prolonged time results in excessive conductivity.
Effects of maternal nutritional status and maternal energy supplementation on length of postpartum amenorrhea among Guatemalan women.

To investigate the extent to which better maternal nutrition leads to reduction in length of postpartum amenorrhea multivariate-logistic and linear-regression analyses were applied to data on 339 mother-infant pairs from the longitudinal Guatemalan Four Village Study 1969-1977. Maternal triceps skinfolds thickness was negatively associated with length of amenorrhea when infant supplementation (a proxy for reduced suckling) was accounted for. However its effect was small: amenorrhea was only 0.5 months shorter among women at the 75th percentile than among those at the 25th equivalent to less than even 1 additional child during the womens reproductive years. Maternal supplementation was not associated with length of amenorrhea when infant supplementation was controlled. This is in contrast to previous studies in which breast-feeding or infant supplementation was not controlled. These results suggest that infant not maternal supplementation influences length of postpartum amenorrhea and that maternal nutritional status has minimal influence. (authors)
Technology assessment of decision-analysis methodologies

For the development of a set of metrics to assess the effectiveness of tools, techniques, and procedures to aid in group decision-making, a technology assessment methodology is outlined. This paper addresses a set of considerations and criteria to generate the advantages and limitations inherent to the technologies involved with the purpose of avoiding some of the pitfalls and negative secondary repercussions that can stem from their use. Software tools that are intended to facilitate group decision-making include a set of techniques for the internal processing of information. They call for data and information given a particular world view which may or may not fit the organizational structure.