cssi/SciSciGPT-SciSciNet huggingface dataset:
Jet Noise Scaling in Dual Stream Nozzles

Power spectral laws in dual stream jets are studied by considering such flows a superposition of appropriate single-stream coaxial jets. Noise generation in each mixing region is modeled using spectral power laws developed earlier for single stream jets as a function of jet temperature and observer angle. Similarity arguments indicate that jet noise in dual stream nozzles may be considered as a composite of four single stream jets representing primary/secondary, secondary/ambient, transition, and fully mixed zones. Frequency filter are designed to highlight spectral contribution from each jet. Predictions are provided at an area ratio of 2.0--bypass ratio from 0.80 to 3.40, and are compared with measurements within a wide range of velocity and temperature ratios. These models suggest that the low frequency noise in unheated jets is dominated by the fully mixed region at all velocity ratios, while the high frequency noise is dominated by the secondary when the velocity ratio is larger than 0.80. Transition and fully mixed jets equally dominate the low frequency noise in heated jets. At velocity ratios less than 0.50, the high frequency noise from primary/bypass becomes a significant contributing factor similar to that in the secondary/ambient jet.
Improving Users' Mental Model with Attention-directed Counterfactual Edits

In the domain of Visual Question Answering (VQA), studies have shown improvement in users’ mental model of the VQA system when they are exposed to examples of how these systems answer certain Image-Question (IQ) pairs. In this work, we show that showing controlled counterfactual image-question examples are more effective at improving the mental model of users as compared to simply showing random examples. We compare a generative approach and a retrieval-based approach to show counterfactual examples. We use recent advances in generative adversarial networks (GANs) to generate counterfactual images by deleting and inpainting certain regions of interest in the image. We then expose users to changes in the VQA system’s answer on those altered images. To select the region of interest for inpainting, we experiment with using both human-annotated attention maps and a fully automatic method that uses the VQA system’s attention values. Finally, we test the user’s mental model by asking them to predict the model’s performance on a test counterfactual image. We note an overall improvement in users’ accuracy to predict answer change when shown counterfactual explanations. While realistic retrieved counterfactuals obviously are the most effective at improving the mental model, we show that a generative approach can also be equally effective.
Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale

We present a distributed-memory algorithm for sparse matrix-matrix multiplication (SpGEMM) of extremely large matrices where the generated output is larger than the aggregated memory of a target supercomputer. We address this challenge by splitting the computation into batches with each batch generating a set of output columns. We developed a distributed symbolic step to understand the memory requirement and determine the number of batches beforehand. We integrated the multiplication in each batch with an existing communication avoiding techniques to reduce the communication overhead while multiplying matrices in a 3-D process grid. Furthermore, we made the in-node computations faster by designing a sort-free SpGEMM and merging algorithm. Incorporating all the proposed approaches, our SpGEMM scales for large protein-similarity networks using 262,144 cores on a Cray XC40 supercomputer while achieving a 10x speedup using 16x more nodes. Our code is available as part of the Combinatorial BLAS library (https://github.com/PASSIONLab/CombBLAS).
TopoKnit: A Process-Oriented Representation for Modeling the Topology of Yarns in Weft-Knitted Textiles

Abstract Machine knitted textiles are complex multi-scale material structures increasingly important in many industries, including consumer products, architecture, composites, medical, and military. Computational modeling, simulation, and design of industrial fabrics require efficient representations of the spatial, material, and physical properties of such structures. We propose a process-oriented representation, TopoKnit, that defines a foundational data structure for representing the topology of weft-knitted textiles at the yarn scale. Process space serves as an intermediary between the machine and fabric spaces, and supports a concise, computationally efficient evaluation approach based on on-demand, near constant-time queries. In this paper, we define the properties of the process space, and design a data structure to represent it and algorithms to evaluate it. We demonstrate the effectiveness of the representation scheme by providing results of evaluations of the data structure in support of common topological operations in the fabric space.
Surface acoustic wave induced modulation of tunneling magnetoresistance in magnetic tunnel junctions

We show that a surface acoustic wave (SAW) applied across the terminals of a magnetic tunnel junction (MTJ) decreases both the (time-averaged) parallel and antiparallel resistances of the MTJ, with the latter decreasing much more than the former. This results in a decrease in the tunneling magnetoresistance ratio. The coercivities of the free and fixed layers of the MTJ, however, are not affected significantly, suggesting that the SAW does not cause large-angle magnetization rotation in the magnetic layers through the inverse magnetostriction (Villari) effect at the power levels used. This study sheds light on the dynamical behavior of an MTJ under periodic compressive and tensile strain.
New results on Nyldon words and Nyldon-like sets

Abstract Grinberg defined Nyldon words as those words which cannot be factorized into a sequence of lexicographically nondecreasing smaller Nyldon words. He was inspired by Lyndon words, defined the same way except with “nondecreasing” replaced by “nonincreasing.” Charlier, Philibert, and Stipulanti proved that, like Lyndon words, any word has a unique nondecreasing factorization into Nyldon words. They also show that the Nyldon words form a right Lazard set, and equivalently, a right Hall set. In this paper, we provide a new proof of unique factorization into Nyldon words related to Hall set theory and resolve several questions of Charlier, Philibert, and Stipulanti. In particular, we prove that Nyldon words of a fixed length form a circular code, we prove a result on factorizing powers of words into Nyldon words, and we investigate the Lazard procedure for generating Nyldon words. We show that these results generalize to a new class of Hall sets, of which Nyldon words are an example, that we name “Nyldon-like sets”, and show how to generate these sets easily.
A characterization of Johnson and Hamming graphs and proof of Babai's conjecture

Abstract One of the central results in the representation theory of distance-regular graphs classifies distance-regular graphs with μ ≥ 2 and second largest eigenvalue θ 1 = b 1 − 1 . In this paper we give a classification under the (weaker) approximate eigenvalue constraint θ 1 ≥ ( 1 − e ) b 1 for the class of geometric distance-regular graphs. As an application, we confirm Babai's conjecture on the minimal degree of the automorphism group of distance-regular graphs of bounded diameter. This conjecture asserts that if X is a primitive distance-regular graph with n vertices, and X is not a Hamming graph or a Johnson graph, then the automorphism group Aut ( X ) has minimal degree ≥ c n for some constant c > 0 . It follows that Aut ( X ) satisfies strong structural constraints.
Restricted color $n$-color compositions

Agarwal introduced $n$-color compositions in 2000 and most subsequent research has focused on restricting which parts are allowed. Here we focus instead on restricting allowed colors. After three general results, giving recurrence formulas for the cases of given allowed colors, given prohibited colors, and colors satisfying modular conditions, we consider several more specific conditions, establishing direct formulas and connections to other combinatorial objects. Proofs are combinatorial, mostly using the notion of spotted tilings introduced by the first named author in 2012.
Model checking via testing for direct effects in Mendelian Randomization and transcriptome-wide association studies.

It is of great interest and potential to discover causal relationships between pairs of exposures and outcomes using genetic variants as instrumental variables (IVs) to deal with hidden confounding in observational studies. Two most popular approaches are Mendelian randomization (MR), which usually use independent genetic variants/SNPs across the genome, and transcriptome-wide association studies (TWAS) (or their generalizations) using cis-SNPs local to a gene (or some genome-wide and likely dependent SNPs), as IVs. In spite of their many promising applications, both approaches face a major challenge: the validity of their causal conclusions depends on three critical assumptions on valid IVs, and more generally on other modeling assumptions, which however may not hold in practice. The most likely as well as challenging situation is due to the wide-spread horizontal pleiotropy, leading to two of the three IV assumptions being violated and thus to biased statistical inference. More generally, we’d like to conduct a goodness-of-fit (GOF) test to check the model being used. Although some methods have been proposed as being robust to various degrees to the violation of some modeling assumptions, they often give different and even conflicting results due to their own modeling assumptions and possibly lower statistical efficiency, imposing difficulties to the practitioner in choosing and interpreting varying results across different methods. Hence, it would help to directly test whether any assumption is violated or not. In particular, there is a lack of such tests for TWAS. We propose a new and general GOF test, called TEDE (TEsting Direct Effects), applicable to both correlated and independent SNPs/IVs (as commonly used in TWAS and MR respectively). Through simulation studies and real data examples, we demonstrate high statistical power and advantages of our new method, while confirming the frequent violation of modeling (including valid IV) assumptions in practice and thus the importance of model checking by applying such a test in MR/TWAS analysis.
Exact closed-form analytic wave functions in two dimensions: Contact-interacting fermionic spinful ultracold atoms in a rapidly rotating trap

This paper presents a set of solutions for two-dimensional spinful hard-core-repelling fermions under fractional quantum-Hall conditions using computer symbolic language and numerical diagonalization.
Cable manipulation with a tactile-reactive gripper:

Cables are complex, high-dimensional, and dynamic objects. Standard approaches to manipulate them often rely on conservative strategies that involve long series of very slow and incremental deforma...
Radar Cross Section Based Statistical Recognition of UAVs at Microwave Frequencies

This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable.
Precise Punishment: Why Precise Punitive Damage Requests Result in Higher Awards Than Round Requests

Imagine a setting in which someone asks two people what the temperature is outside. The first person says it is 80°, while the second person says it is 78.7°. Research regarding precise versus round cognitive anchoring suggests that the second person is more likely to be believed. This is because it is human nature to assume that if someone gives a precise answer, he must have good reason for doing so. This principle remains constant in a variety of settings, including used car negotiations, eBay transactions, and estimating the field goal percentage of a basketball player. This Article reports the findings of a first-of-its-kind study designed to measure if this same principle applies to punitive damage requests from plaintiffs’ attorneys. In other words, can a plaintiff’s attorney increase the punitive damages awarded simply by requesting $497,000 instead of $500,000. The stark differences produced from such a subtle and costless change provides a valuable strategy for plaintiffs’ attorneys, a cautionary warning for civil defense attorneys, and constructive insight into the subjective nature of juror decision-making.
System design and control of an apple harvesting robot

Abstract There is a growing need for robotic apple harvesting due to decreasing availability and rising cost in labor. Towards the goal of developing a viable robotic system for apple harvesting, this paper presents synergistic mechatronic design and motion control of a robotic apple harvesting prototype, which lays a critical foundation for future advancements. Specifically, we develop a deep learning-based fruit detection and localization system using a RGB-D camera. A three degree-of-freedom manipulator is designed with a hybrid pneumatic/motor actuation mechanism to achieve dexterous movements. A vacuum-based end-effector is used for apple detaching. These three components are integrated into a robotic apple harvesting prototype with simplicity, compactness, and robustness. Moreover, a nonlinear control scheme is developed for the manipulator to achieve accurate and agile motion control. Field experiments are conducted to demonstrate the performance of the developed apple harvesting robot.
Shortest closed curve to inspect a sphere

We show that in Euclidean 3-space any closed curve which lies outside the unit sphere and contains the sphere within its convex hull has length at least $4\pi$. Equality holds only when the curve is composed of $4$ semicircles of length $\pi$, arranged in the shape of a baseball seam, as conjectured by V. A. Zalgaller in 1996.
Randomization Tests in Observational Studies with Staggered Adoption of Treatment

This paper considers the problem of inference in observational studies with time-varying adoption of treatment. In addition to an unconfoundedness assumption that the potential outcomes are indepen...
Fields of locally compact quantum groups: Continuity and pushouts

We prove that discrete compact quantum groups (or more generally locally compact, under additional hypotheses) with coamenable dual are continuous fields over their central closed quantum subgroups...
Who Should Own the League? Transaction Costs and the Structure of NBA Ownership:

Like many professional sports leagues worldwide, the National Basketball Association is organized as a cooperative of team owners. We argue that league ownership structures must balance two types o...
Dynamic CT Reconstruction From Limited Views With Implicit Neural Representations and Parametric Motion Fields

Reconstructing dynamic, time-varying scenes with computed tomography (4D-CT) is a challenging and ill-posed problem common to industrial and medical settings. Existing 4D-CT reconstructions are designed for sparse sampling schemes that require fast CT scanners to capture multiple, rapid revolutions around the scene in order to generate high quality results. However, if the scene is moving too fast, then the sampling occurs along a limited view and is difficult to reconstruct due to spatiotemporal ambiguities. In this work, we design a reconstruction pipeline using implicit neural representations coupled with a novel parametric motion field warping to perform limited view 4D-CT reconstruction of rapidly deforming scenes. Importantly, we utilize a differentiable analysis-by-synthesis approach to compare with captured x-ray sinogram data in a self-supervised fashion. Thus, our resulting optimization method requires no training data to reconstruct the scene. We demonstrate that our proposed system robustly reconstructs scenes containing deformable and periodic motion and validate against state-of-the-art baselines. Further, we demonstrate an ability to reconstruct continuous spatiotemporal representations of our scenes and upsample them to arbitrary volumes and frame rates post-optimization. This research opens a new avenue for implicit neural representations in computed tomography reconstruction in general.
Cannabinoid tetrad effects of oral Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) in male and female rats: sex, dose-effects and time course evaluations

Rationale The legalization of medicinal use of Cannabis sativa in most US states and the removal of hemp from the Drug Enforcement Agency (DEA) controlled substances act has resulted in a proliferation of products containing Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) for oral consumption (e.g., edibles, oils, and tinctures) that are being used for recreational and medicinal purposes. Objective This study examined the effects of cannabinoids THC and CBD when administered orally on measures of pain sensitivity, body temperature, locomotor activity, and catalepsy (i.e., cannabinoid tetrad) in male and female Sprague Dawley rats. Methods Rats (N = 24, 6 per sex/drug group) were administered THC (1-20 mg/kg), CBD (3-30 mg/kg), or sesame oil via oral gavage. Thermal and mechanical pain sensitivity (tail flick assay, von Frey test), rectal measurements for body temperature, locomotor activity, and the bar-test of catalepsy were completed. A separate group of rats (N = 8/4 per sex) was administered morphine (5-20 mg/kg; intraperitoneal, IP) and evaluated for pain sensitivity as a positive control. Results We observed classic tetrad effects of antinociception, hypothermia, hyper- and hypolocomotion, and catalepsy after oral administration of THC that were long lasting (> 7 h). CBD modestly increased mechanical pain sensitivity and produced sex-dependent effects on body temperature and locomotor activity. Conclusions Oral THC and CBD produced long lasting effects that differed in magnitude and time course when compared with other routes of administration. Examination of cannabinoid effects administered via different routes of administration, species, and in both males and females is critical to enhance translation.
Mapping the corticoreticular pathway from cortex-wide anterograde axonal tracing in the mouse.

The corticoreticular pathway (CRP) has been implicated as an important mediator of motor recovery and rehabilitation after central nervous system damage. However, its origins, trajectory and laterality are not well understood. This study mapped the mouse CRP in comparison with the corticospinal tract (CST). We systematically searched the Allen Mouse Brain Connectivity Atlas (© 2011 Allen Institute for Brain Science) for experiments that used anterograde tracer injections into the right isocortex in mice. For each eligible experiment (N = 607), CRP and CST projection strength were quantified by the tracer volume reaching the reticular formation motor nuclei (RFmotor ) and pyramids, respectively. Tracer density in each brain voxel was also correlated with RFmotor versus pyramids projection strength to explore the relative trajectories of the CRP and CST. We found significant CRP projections originating from the primary and secondary motor cortices, anterior cingulate, primary somatosensory cortex, and medial prefrontal cortex. Compared with the CST, the CRP had stronger projections from each region except the primary somatosensory cortex. Ipsilateral projections were stronger than contralateral for both tracts (above the pyramidal decussation), but the CRP projected more bilaterally than the CST. The estimated CRP trajectory was anteromedial to the CST in the internal capsule and dorsal to the CST in the brainstem. Our findings reveal a widespread distribution of CRP origins and confirm strong bilateral CRP projections, theoretically increasing the potential for partial sparing after brain lesions and contralesional compensation after unilateral injury.
Gapped Boundary Theories in Three Dimensions

We prove a theorem in 3-dimensional topological field theory: a Reshetikhin–Turaev theory admits a nonzero boundary theory iff it is a Turaev–Viro theory. The proof immediately implies a characterization of fusion categories in terms of dualizability. Our results rely on a (special case of) the cobordism hypothesis with singularities. The main theorem applies to physics, where it implies an obstruction to a gapped 3-dimensional quantum system admitting a gapped boundary theory. Appendices on bordism multicategories, on 2-dualizable categories, and on internal duals may be of independent interest.
Deep Affordance Foresight: Planning Through What Can Be Done in the Future

Planning in realistic environments requires searching in large planning spaces. Affordances are a powerful concept to simplify this search, because they model what actions can be successful in a given situation. However, the classical notion of affordance is not suitable for long horizon planning because it only informs the robot about the immediate outcome of actions instead of what actions are best for achieving a long-term goal. In this paper, we introduce a new affordance representation that enables the robot to reason about the longterm effects of actions through modeling what actions are afforded in the future. Based on the new representation, we develop a learning-to-plan method, Deep Affordance Foresight (DAF), that learns partial environment models of affordances of parameterized motor skills through trial-and-error. We evaluate DAF on two challenging manipulation domains and show that it can effectively learn to carry out multi-step tasks, share learned affordance representations among different tasks, and learn to plan with high-dimensional image inputs.
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval

Pre-trained contextualized representations offer great success for many downstream tasks, including document ranking. The multilingual versions of such pre-trained representations provide a possibility of jointly learning many languages with the same model. Although it is expected to gain big with such joint training, in the case of cross-lingual information retrieval (CLIR), the models under a multilingual setting are not achieving the same level of performance as those under a monolingual setting. We hypothesize that the performance drop is due to thetranslation gap between query and documents. In the monolingual retrieval task, because of the same lexical inputs, it is easier for model to identify the query terms that occurred in documents. However, in the multilingual pre-trained models that the words in different languages are projected into the same hyperspace, the model tends to "translate" query terms into related terms - i.e., terms that appear in a similar context - in addition to or sometimes rather than synonyms in the target language. This property is creating difficulties for the model to connect terms that co-occur in both query and document. To address this issue, we propose a novel Mixed Attention Transformer (MAT) that incorporates external word-level knowledge, such as a dictionary or translation table. We design a sandwich-like architecture to embed MAT into the recent transformer-based deep neural models. By encoding the translation knowledge into an attention matrix, the model with MAT is able to focus on the mutually translated words in the input sequence. Experimental results demonstrate the effectiveness of the external knowledge and the significant improvement of MAT-embedded neural reranking model on CLIR task.
Structure and Evolutionary History of the Solar System

In this fourth and last part of our analysis, the first section (14) contains a study of the chemical composition of the planets and satellites. A sharp distinction is made between the large quantity of speculations about the interiors of the bodies and the rather meagerfacts known with a reasonable degree of certainty. It is shown, however, that the latter are sufficient todisprove the old concept of a Laplacian disc of homogeneous chemical composition. There is asystematic variations in the chemical composition of planets (and probably also of satellites) so that heavy elements are more abundant in the outermost and in the innermost regions of the systems.
A taxonomy of grain boundary migration mechanisms via displacement texture characterization

Abstract Atomistic simulations provide the most detailed picture of grain boundary (GB) migration currently available. Nevertheless, extracting unit mechanisms from atomistic simulation data is difficult because of the zoo of competing, geometrically complex 3D atomic rearrangement processes. In this work, we introduce the displacement texture characterization framework for analyzing atomic rearrangement events during GB migration, combining ideas from slip vector analysis, bicrystallography and optimal transportation. Two types of decompositions of displacement data are described: the shear-shuffle and min-shuffle decomposition. The former is used to extract shuffling patterns from shear coupled migration trajectories and the latter is used to analyze temperature dependent shuffling mechanisms. As an application of the displacement texture framework, we characterize the GB geometry dependence of shuffling mechanisms for a crystallographically diverse set of mobile GBs in FCC Ni bicrystals. Two scientific contributions from this analysis include 1) an explanation of the boundary plane dependence of shuffling patterns via metastable GB geometry and 2) a taxonomy of multimodal constrained GB migration mechanisms which may include multiple competing shuffling patterns, period doubling effects, distinct sliding and shear coupling events, and GB self diffusion.
A Redundancy-Guided Approach for the Hazard Analysis of Digital Instrumentation and Control Systems in Advanced Nuclear Power Plants

Digital instrumentation and control (I&C) upgrades are a vital research area for nuclear industry. Despite their performance benefits, deployment of digital I&C in nuclear power plants (NPPs) has been limited. Digital I&C systems exhibit complex failure modes including common cause failures (CCFs) which can be difficult to identify. This paper describes the development of a redundancy-guided application of the Systems-Theoretic Process Analysis (STPA) and Fault Tree Analysis (FTA) for the hazard analysis of digital I&C in advanced NPPs. The resulting Redundancy-guided System-theoretic Hazard Analysis (RESHA) is applied for the case study of a representative state-of-the-art digital reactor trip system. The analysis qualitatively and systematically identifies the most critical CCFs and other hazards of digital I&C systems. Ultimately, RESHA can help researchers make informed decisions for how, and to what degree, defensive measures such as redundancy, diversity, and defense-in-depth can be used to mitigate or eliminate the potential hazards of digital I&C systems.
Universal Compiling and (No-)Free-Lunch Theorems for Continuous-Variable Quantum Learning

Continuous variable algorithms for quantum compilation are developed, establishing a connection with quantum learning theory through a collection of no-free lunch theorems.
Evaluation and resolution of many challenges of neural spike-sorting: a new sorter.

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds a...
Mass-concentration of low-regularity blow-up solutions to the focusing 2D modified Zakharov–Kuznetsov equation

We consider the focusing modified Zakharov–Kuznetsov (mZK) equation in two space dimensions. We prove that solutions which blow up in finite time in the $$H^1(\mathbb {R}^{2})$$ norm have the property that they concentrate a non-trivial portion of their mass (more precisely, at least the amount equal to the mass of the ground state) at the blow-up time. For finite-time blow-up solutions in the $$H^s(\mathbb {R}^2)$$ norm for $$\frac{17}{18}< s < 1$$ , we prove a slightly weaker result. Moreover, we prove that the stronger concentration result can be extended to the range $$ \frac{17}{18} < s \le 1$$ under an additional assumption on the upper bound of the blow-up rate of the solution. The main tools used here are the I-method and a profile decomposition theorem for a bounded family of $$H^1(\mathbb {R}^{2})$$ functions.
An Ultra-Fast Method for Simulation of Realistic Ultrasound Images

Convolutional neural networks (CNNs) have attracted a rapidly growing interest in a variety of different processing tasks in the medical ultrasound community. However, the performance of CNNs is highly reliant on both the amount and fidelity of the training data. Therefore, scarce data is almost always a concern, particularly in the medical field, where clinical data is not easily accessible. The utilization of synthetic data is a popular approach to address this challenge. However, simulating a large number of images using packages such as Field II is time-consuming, and the distribution of simulated images is far from that of the real images. Herein, we introduce a novel ultra-fast ultrasound image simulation method based on the Fourier transform and evaluate its performance in a lesion segmentation task. We demonstrate that data augmentation using the images generated by the proposed method substantially outperforms Field II in terms of Dice similarity coefficient, while the simulation is almost 36000 times faster (both on CPU).
Spatiotemporal Dynamics of Stemphylium Leaf Blight and Potential Inoculum Sources in New York Onion Fields.

Stemphylium leaf blight (SLB) caused by Stemphylium vesicarium is the dominant foliar disease affecting large-scale onion production in New York. The disease is managed by fungicides, but control failures are prevalent and recently attributed to fungicide resistance. Little is known of the relative role of inoculum sources in initiation and spread of SLB epidemics. Plate testing of 28 commercially available organic onion seedlots from 2016 and 2017 did not detect S. vesicarium. This finding suggests that although S. vesicarium has been reported as seed transmitted, this is unlikely to be a significant inoculum source in commercially available organic seed lots and even less so in fungicide-treated seed used to establish conventional fields. The spatial and spatiotemporal dynamics of SLB epidemics in six onion fields were evaluated along linear transects in 2017 and 2018. Average SLB incidence increased from 0 to 100% throughout the cropping seasons with an average final lesion length of 28.3 cm. Disease progress was typical of a polycyclic epidemic and the logistic model provided the best fit to 83.3% of the datasets. Spatial patterns were better described by the beta-binomial than binomial distribution in half of the datasets (50%) and random patterns were more frequently observed by the index of dispersion (59%). Geostatistical analyses also found a low frequency of datasets with aggregation (60%). Spatiotemporal analysis of epidemics detected that the aggregation was influenced by disease incidence. However, diseased units were not frequently associated with the previous time period according to the spatiotemporal association function of SADIE. Variable spatial patterns suggested mixed inoculum sources dependent upon location, and likely an external inoculum source at the sampling scale used in this study. A small-plot replicated trial was also conducted in each of two years to quantify the effect of S. vesicarium-infested onion residue on SLB epidemics in a field isolated from other onion fields. SLB incidence was significantly reduced in plots without residue compared to those in which residue remained on the soil surface. Burial of infested residue also significantly reduced epidemic progress in one year. The effect of infested onion residue on SLB epidemics in the subsequent onion crop suggests rotation or residue management may have a substantial effect on epidemics. However, the presence of an inoculum source external to fields in onion production regions as indicated by a lack of spatial aggregation may reduce the efficacy of in-field management techniques.
DiscASP: A Graph-based ASP System for Finding Relevant Consistent Concepts with Applications to Conversational Socialbots.

We consider the problem of finding relevant consistent concepts in a conversational AI system, particularly, for realizing a conversational socialbot. Commonsense knowledge about various topics can be represented as an answer set program. However, to advance the conversation, we need to solve the problem of finding relevant consistent concepts, i.e., find consistent knowledge in the "neighborhood" of the current topic being discussed that can be used to advance the conversation. Traditional ASP solvers will generate the whole answer set which is stripped of all the associations between the various atoms (concepts) and thus cannot be used to find relevant consistent concepts. Similarly, goal-directed implementations of ASP will only find concepts directly relevant to a query. We present the DiscASP system that will find the partial consistent model that is relevant to a given topic in a manner similar to how a human will find it. DiscASP is based on a novel graph-based algorithm for finding stable models of an answer set program. We present the DiscASP algorithm, its implementation, and its application to developing a conversational socialbot.
Detecting dark matter subhalos with the Nancy Grace Roman Space Telescope

The dark matter subhalo mass function is a promising way of distinguishing between dark matter models. While cold dark matter predicts halos down to Earth-sized masses, other dark matter models typically predict a cut-off in the subhalo mass function. Thus a definitive detection or limits on the existence of subhalos at small masses can give us insight into the nature of dark matter. If these subhalos exist in the Milky Way, they would produce weak lensing signatures, such as modified apparent positions, on background stars. These signatures would generate correlations in the apparent velocities and accelerations of these stars, which could be observable given sufficient astrometric resolution and cadence. The Nancy Grace Roman Space Telescope's Exoplanet Microlensing Survey will be perfectly suited to measuring the acceleration signatures of these halos. Here we forward model these acceleration signatures and explore the Roman Space Telescope's future constraints on lens profiles and populations. We find that the Roman Space Telescope could place competitive bounds on point source, Gaussian, and Navarro-Frenk-White (NFW) profile lenses that are complementary to other proposed methods. In particular, it could place 95% upper limits on the NFW concentration, $c_{200} < 10^{2.5}$. We discuss possible systematic effects that could hinder these efforts, but argue they should not prevent the Roman Space Telescope from placing strong limits. We also discuss further analysis methods for improving these constraints.
Parallel Lotteries: Insights from Alaskan Hunting Permit Allocation

We analyze the parallel lottery that is used to allocate hunting permits in the state of Alaska. Each participant is given tickets to distribute among lotteries for different types of items. Partic...
Estimating Substitution and Basket Effects in Retail Stores: Implications for Assortment Planning

Many retailers are reducing store footprint and downsizing their assortments accordingly to improve store productivity. Some of the revenue for items removed from the assortment may be recouped by ...
Periods of Modular Forms

In this chapter we develop the tools needed to describe the subgroup of H1(X(Γ);ℚ/ℤ) corresponding to the cuspidal group C(Γ).
Optimization of delay tracking loops for binary modulated systems

Optimal performance of delay tracking loops in a binary linearly-modulated system is investigated, which is compared with the Cramer-Rao lower bound and the performance of a tracking loop suggested by the maximum-likelihood timing estimation.
Microprocessors in control education

Microprocessors have played an important role in realization and applications of automatic control in a variety of real life systems. This paper presents issues and concerns regarding the requirements in teaching microprocessors for control implementation as these devices experience rapid upgrades with improvements in micro-electronic technology. While fundamentals are very important to teach, at the same time students should be up to date with technology. As an approach to resolve this problem a two-course sequence in teaching microprocessors is proposed. The first course would cover the basics of microprocessor architectures, programming, and interfacing, while the second course would cover advanced microprocessors, low level device drivers, complete system integration and issues relating to implementation of the microprocessor-based control systems.
Association of pathogenesis-related proteins and activities of peroxidase, β-1,3-glucanase and chitinase with systemic induced resistance to blue mould of tobacco but not to systemic tobacco mosaic virus

Abstract Inoculation of three leaves of tobacco cv. Ky 14 with tobacco mosaic virus and incubation at 23 °C for 3–12 days caused localized necrosis on the inoculated leaves whereas plants held at 28 °C after inoculation developed systemic mosaic symptoms. Pathogenesis-related (PR) proteins and activities of peroxidase, β-1,3-glucanase and chitinase systemically increased in the inoculated plants at 23 °C. When TMV-inoculated leaves were removed 12 days after inoculation at 23 °C and plants were challenged with TMV or Peronospora tabacina and held at 23 °C, induced resistance to P. tabacina and TMV was apparent. However, resistance to blue mould but not TMV was apparent when the procedure was repeated, except that plants were transferred to 28 °C one day after challenge with TMV or P. tabacina. PR proteins and activities of peroxidase, β-1,3-glucanase and chitinase were further increased after challenge with P. tabacina or TMV in induced plants at both temperatures. Tn 86, a cultivar systemically susceptible to TMV, was protected against blue mould but not against TMV by stem inoculation with P. tabacina. PR proteins were induced and the activities of peroxidase, β-1,3-glucanase and chitinase were greatly increased in the induced plants.
Vibrotactile amplitude and frequency discrimination in temporomandibular disorders

The purpose of this study was to determine whether the elevation in vibrotactile detection threshold, found in many individuals with temporomandibular disorders (TMD), is paralleled by suprathreshold impairments. Participants with TMD were compared with pain-free control subjects in their ability to discriminate on the basis of differences in amplitude and frequency between vibratory stimuli delivered to the face. The TMD group was significantly impaired with respect to frequency discrimination, but not amplitude discrimination. This dissociation suggests that the cortical processing of vibrotactile signals may be affected in TMD patients. TMD participants' estimates of the intensity of their spontaneous and palpation-evoked pain did not significantly correlate with performance on either discrimination task; this finding makes it unlikely that impaired vibrotaction in TMD is primarily the result of a pain-dependent gating of tactile signals.
Pathogenic and molecular characterization of three Phomopsis isolates from peach, plum, and Asian pear

Three isolates of Phomopsis, causing shoot blight of peach, shoot tissue necrosis of plum, or bud death of Hosui Asian pear, respectively, were evaluated for their pathogenicity on apple, pear, peach, and plum. Current year's shoots of 1-year-old Stayman Winesap apple, Barlett pear, Babygold-7 peach, and Bruce plum trees were inoculated with each isolate by wounding a bud and applying agar blocks bearing young hyphae. The length of cankers on shoots was measured 10, 17, and 24 days after inoculation. Cankers developed on shoots of all hosts inoculated with the peach isolate and on peach shoots inoculated with plum and Asian pear isolates. No cankers developed on apple, pear, or plum shoots inoculated with plum and Asian pear isolates. In the first experiment, 10 days after inoculation, the length of cankers on apple trees (56.0 mm) inoculated with the peach isolate was not significantly different (P ≥ 0.05) from that on peach (42.8 mm), but was significantly greater than that on plum (25.3 mm) and pear (13.1 mm). The cankers on peach were significantly longer than those on pear, but not on plum. Cankers on all four hosts were significantly different from one another 17 and 24 days after inoculation. There was no significant difference between the length of cankers on peach shoots inoculated with plum and Asian pear isolates, and they were significantly smaller than those inoculated with the peach isolate. None of the control trees developed cankers. The three isolates differed in colony morphology, and appearance of conidiomata, conidiogenous cells, and α-conidia on potato-dextrose agar. None of the isolates produced β-conidia in culture. Multi-locus DNA fingerprint analysis and internal transcribed spacer sequence comparisons revealed similarities between the plum and Asian pear isolates but a significant difference between these two and the peach isolate. The results indicate that the Phomopsis sp. that causes shoot blight of peach has the potential to cause disease on other stone and pome fruits, and peach may also be susceptible to isolates of Phomopsis from different tree fruit hosts.
Neurological Correction of Lysosomal Storage in a Mucopolysaccharidosis IIIB Mouse Model by Adeno-associated Virus-Mediated Gene Delivery

Abstract Mucopolysaccharidosis (MPS) IIIB is characterized by mild somatic features and severe neurological diseases leading to premature death. No definite treatment is available for MPS IIIB patients. We constructed two recombinant adeno-associated virus (rAAV) vectors containing the human α-N-acetylglucosaminidase (NaGlu) cDNA driven by either a CMV or a neuron-specific enolase (NSE) promoter. In vitro, these rAAV vectors mediated efficient expression of recombinant NaGlu in human MPS IIIB fibroblasts and mouse MPS IIIB somatic and brain primary cell cultures. The secreted rNaGlu was taken up by both human and mouse MPS IIIB cells in culture and degraded the accumulated glycosaminoglycans (GAG). A direct microinjection (107 viral particles, 1 μl/10 minutes per injection) of vectors containing the NSE promoter resulted in long-term (6 months, the duration of the experiments) expression of rNaGlu in multiple brain structures/areas of adult MPS IIIB mice. Consistent with previous studies, the main target cells were neurons. However, while vector typically transduced an area of 400–500 μm surrounding the infusion sites, the correction of GAG storage involved neurons of a much broader area (1.5 mm) in a 6-month duration of experiments. These results provide a basis for the development of a treatment for neurological disease in MPS IIIB patients using AAV vectors.
Spatial and temporal stress drop variations in small earthquakes near Parkfield, California

[1] We estimate source parameters from spectra of 42367 earthquakes between 1984 and 2005 that occurred in the Parkfield segment of the San Andreas Fault in central California. We use a method that isolates the source term of the displacement spectra based on a convolutional model and correct the observed P wave source spectra with a spatially varying empirical Green's function (EGF). Our Brune-type stress drop estimates vary from 0.1 to over 100 MPa with a median value of 6.75 MPa, which is nearly constant with moment, implying self-similarity over the ML = 0.5 to 3.0 range of our data. The corner frequency decreases for earthquakes at shallower depths, consistent with slower rupture velocities and reduced shear wave velocities in local velocity models. The estimated median stress drops show significant lateral variations: we find lower stress drops in the Middle Mountain asperity and along the creeping fault section, and higher stress drops in the hypocentral region of the 2004 M6.0 Parkfield earthquake. The main shock did not alter the overall pattern of high and low stress drop regions. However, a statistical test reveals areas with significant changes in computed stress drops after the main shock, which we compare to estimated absolute shear stress changes from a main shock slip model. By calculating Δt* from the spectral EGF ratio, we also identify areas with increased attenuation after the main shock, and we are able to distinguish source effects and near-source attenuation effects in the spectral analysis. These results are confirmed independently from spectral ratios of repeating microearthquake clusters.
Detection of multiple small plasmoids in the magnetotail

Magnetic field lines in the Earth's dayside magnetic field can be broken down and swept to the Earth's nightside by the solar wind. The magnetic field lines reconnect in the magnetotail, in some cases triggering a magnetic substorm. During a substorm, plasma trapped in the magnetotail is sent flowing toward the Earth, and the magnetotail current sheet—the region where magnetic field lines from the Earth's North and South Poles come together—gets thinner.
Sulfonates: novel electron acceptors in anaerobic respiration

The enrichment and isolation in pure culture of a bacterium, identified as a strain of Desulfovibrio, able to release and reduce the sulfur of isethionate (2-hydroxyethanesulfonate) and other sulfonates to support anaerobic respiratory growth, is described. The sulfonate moiety was the source of sulfur that served as the terminal electron acceptor, while the carbon skeleton of isethionate functioned as an accessory electron donor for the reduction of sulfite. Cysteate (alanine-3-sulfonate) and sulfoacetaldehyde (acetaldehyde-2-sulfonate) could also be used for anaerobic respiration, but many other sulfonates could not. A survey of known sulfate-reducing bacteria revealed that some, but not all, strains tested could utilize the sulfur of some sulfonates as terminal electron acceptor. Isethionate-grown cells of Desulfovibrio strain IC1 reduced sulfonate-sulfur in preference to that of sulfate; however, sulfate-grown cells reduced sulfate-sulfur in preference to that of sulfonate.