cssi/SciSciGPT-SciSciNet huggingface dataset:
Decoys and Dilution: The Impact of Incompetent Hosts on Prevalence of Chagas Disease

Biodiversity is commonly believed to reduce risk of vector-borne zoonoses. However, researchers already showed that the effect of biodiversity on disease transmission is not that straightforward. This study focuses on the effect of biodiversity, specifically on the effect of the decoy process (additional hosts distracting vectors from their focal host), on reducing infections of vector-borne diseases in humans. Here, we consider the specific case of Chagas disease and use mathematical population models to observe the impact on human infection of the proximity of chickens, which are incompetent hosts for the parasite but serve as a preferred food source for vectors. We consider three cases as the distance between the two host populations varies: short (when farmers bring chickens inside the home to protect them from predators), intermediate (close enough for vectors with one host to detect the presence of the other host type), and far (separate enclosed buildings such as a home and hen-house). Our analysis shows that the presence of chickens reduces parasite prevalence in humans only at an intermediate distance under the condition that the vector birth rate from feeding on chickens is sufficiently low.
A Brain-Inspired Framework for Evolutionary Artificial General Intelligence

From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this article demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called “neuroembryogenesis.” This framework shares some of the key behavioral aspects of the biological brain, such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of this article demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open source, and the code is shared with the scientific community at http://www.feagi.org .
Sparse Hopsets in Congested Clique.

We give the first Congested Clique algorithm that computes a sparse hopset with polylogarithmic hopbound in polylogarithmic time. Given a graph $G=(V,E)$, a $(\beta,\epsilon)$-hopset $H$ with "hopbound" $\beta$, is a set of edges added to $G$ such that for any pair of nodes $u$ and $v$ in $G$ there is a path with at most $\beta$ hops in $G \cup H$ with length within $(1+\epsilon)$ of the shortest path between $u$ and $v$ in $G$. Our hopsets are significantly sparser than the recent construction of Censor-Hillel et al. [6], that constructs a hopset of size $\tilde{O}(n^{3/2})$, but with a smaller polylogarithmic hopbound. On the other hand, the previously known constructions of sparse hopsets with polylogarithmic hopbound in the Congested Clique model, proposed by Elkin and Neiman [10],[11],[12], all require polynomial rounds. One tool that we use is an efficient algorithm that constructs an $\ell$-limited neighborhood cover, that may be of independent interest. Finally, as a side result, we also give a hopset construction in a variant of the low-memory Massively Parallel Computation model, with improved running time over existing algorithms.
On the Stein framing number of a knot

For an integer $n$, write $X_n(K)$ for the 4-manifold obtained by attaching a 2-handle to the 4-ball along the knot $K\subset S^3$ with framing $n$. It is known that if $n< \overline{\text{tb}}(K)$, then $X_n(K)$ admits the structure of a Stein domain, and moreover the adjunction inequality implies there is an upper bound on the value of $n$ such that $X_n(K)$ is Stein. We provide examples of knots $K$ and integers $n\geq \overline{\text{tb}}(K)$ for which $X_n(K)$ is Stein, answering an open question in the field. In fact, our family of examples shows that the largest framing such that the manifold $X_n(K)$ admits a Stein structure can be arbitrarily larger than $\overline{\text{tb}}(K)$. We also provide an upper bound on the Stein framings for $K$ that is typically stronger than that coming from the adjunction inequality.
High-resolution cryo-EM reconstructions in the presence of substantial aberrations

Here, an analysis is performed of how uncorrected antisymmetric aberrations, such as coma and trefoil, affect cryo-EM single-particle reconstruction (SPR) results, and an analytical formula quantifying information loss owing to their presence is inferred that explains why Fourier-shell coefficient-based statistics may report significantly overestimated resolution if these aberrations are not fully corrected. The analysis is validated with reference-based aberration refinement for two cryo-EM SPR data sets acquired with a 200 kV microscope in the presence of coma exceeding 40 µm, and 2.3 and 2.7 A reconstructions for 144 and 173 kDa particles, respectively, were obtained. The results provide a description of an efficient approach for assessing information loss in cryo-EM SPR data acquired in the presence of higher order aberrations, and address inconsistent guidelines regarding the level of aberrations that is acceptable in cryo-EM SPR experiments.
Sumoylation of NaV1.2 Channels Mediates the Early Response to Acute Hypoxia in Central Neurons

The mechanism for the earliest response of central neurons to hypoxia-an increase in voltage-gated sodium current (INa)-has been unknown. Here, we show that hypoxia activates the Small Ubiquitin-like Modifier (SUMO) pathway in rat cerebellar granule neurons (CGN) and that SUMOylation of NaV1.2 channels increases INa. The time-course for SUMOylation of single NaV1.2 channels at the cell surface and changes in INa coincide, and both are prevented by mutation of NaV1.2-Lys38 or application of a deSUMOylating enzyme. Within 40 s, hypoxia-induced linkage of SUMO1 to the channels is complete, shifting the voltage-dependence of channel activation so that depolarizing steps evoke larger sodium currents. Given the recognized role of INa in hypoxic brain damage, the SUMO pathway and NaV1.2 are identified as potential targets for neuroprotective interventions.
A time-domain view of charge carriers in semiconductor nanocrystal solids

The movement of charge carriers within semiconductor nanocrystal solids is fundamental to the operation of nanocrystal devices, including solar cells, LEDs, lasers, photodetectors, and thermoelectric modules. In this perspective, we explain how recent advances in the measurement and simulation of charge carrier dynamics in nanocrystal solids have led to a more complete picture of mesoscale interactions. Specifically, we show how time-resolved optical spectroscopy and transient photocurrent techniques can be used to track both equilibrium and non-equilibrium dynamics in nanocrystal solids. We discuss the central role of energetic disorder, the impact of trap states, and how these critical parameters are influenced by chemical modification of the nanocrystal surface. Finally, we close with a forward-looking assessment of emerging nanocrystal systems, including anisotropic nanocrystals, such as nanoplatelets, and colloidal lead halide perovskites.
How large is the bias in self-reported disability?

A pervasive concern with the use of self-reported health measures in behavioural models is that individuals tend to exaggerate the severity of health problems in order to rationalize their decisions regarding labour force participation, application for disability benefits, etc. We re-examine this issue using a self-reported indicator of disability status from the Health and Retirement Study. We study a subsample of individuals who applied for disability benefits from the Social Security Administration (SSA), for whom we can also observe the SSA's decision. Using a battery of tests, we are unable to reject the hypothesis that self-reported disability is an unbiased indicator of the SSA's decision. Copyright © 2004 John Wiley & Sons, Ltd.
On the fluctuation spectrum of plasma

Abstract The spectrum of electron phase space density fluctuations of a plasma is calculated by a novel method that parallels conventional calculations of the partition function in statistical physics. Expressions for the electric field fluctuations and the closely related form factor agree with existing results. The method clears up ambiguities about equipartition and provides a new expression for the spectrum of electrostatic phase space density fluctuations about stable non-Maxwellian equilibria.
Cities and Warfare: The Impact of Terrorism on Urban Form

What impact will terrorism have on America's cities? Historically, large-scale violence has impacted cities in three ways. First, concentrations of people have an advantage in defending themselves from attackers, making cities more appealing in times of violence. Second, cities often make attractive targets for violence, which creates an incentive for people to disperse. Finally, since warfare and terrorism often specifically target means of transportation, violence can increase the effective cost of transportation, which will usually increase the demand for density. Evidence on war and cities in the 20th century suggests that the effect of wars on urban form can be large (for example, Berlin in World War II), but more commonly neither terrorism nor wars have significantly altered urban form. As such, across America the effect of terrorism on cities is likely to be small. The only exception to this is downtown New York which, absent large-scale subsidies, will probably not be fully rebuilt. Furthermore, such subsidies make little sense to us.
Neural coding of natural stimuli: information at sub-millisecond resolution

Our knowledge of the sensory world is encoded by neurons in sequences of discrete, identical pulses termed action potentials or spikes. There is persistent controversy about the extent to which the precise timing of these spikes is relevant to the function of the brain. We revisit this issue, using the motion – sensitive neurons of the fly visual system as a test case. New experimental methods allow us to deliver more nearly natural visual stimuli, comparable to those which flies encounter in free, acrobatic flight, and new mathematical methods allow us to draw more reliable conclusions about the information content of neural responses even when the set of possible responses is very large. We find that significant amounts of visual information are represented by details of the spike train at millisecond and sub-millisecond precision, even though the sensory input has a correlation time of ~60 ms; different patterns of spike timing represent distinct motion trajectories, and the absolute timing of spikes points to particular features of these trajectories with high precision. Under these naturalistic conditions, the system's information transmission rate still increases with higher photon flux, even though individual photoreceptors are counting more than one million photons per second. Further, exploiting the relatively slow dynamics of the stimulus, the system removes redundancy and so generates a more efficient neural code.
High redshift supernovae: Cosmological implications

We review the findings for the values of the cosmological parameters as derived from high-redshift SNIa measurements. The most recent results confirm the picture of a non-empty inflationary Universe that is consistent with a cosmological constant Ω A ≃ 0.7. This implies that the expansion of the Universe is currently accelerated by the action of some mysterious dark energy. We also discuss the possibility and the consequences of the fact that SNIa may not be perfect standard candles, in the sense of having properties in the early Universe that are systematically different from those they have at the present times.
The Predicted RNA-Binding Protein ETR-1/CELF1 Acts in Muscles To Regulate Neuroblast Migration in Caenorhabditis elegans

Neuroblast migration is a critical aspect of nervous system development (e.g, neural crest migration). In an unbiased forward genetic screen, we identified a novel player in neuroblast migration, the ETR-1/CELF1 RNA binding protein. CELF1 RNA binding proteins are involved in multiple aspects of RNA processing including alternative splicing, stability, and translation. We find that a specific mutation in alternatively-spliced exon 8 results in migration defects of the AQR and PQR neurons, and not the embryonic lethality and body wall muscle defects of complete knockdown of the locus. Surprisingly, ETR-1 was required in body wall muscle cells for AQR/PQR migration (i.e., it acts cell non-autonomously). Genetic interactions indicate that ETR-1 acts with Wnt signaling, either in the Wnt pathway or in a parallel pathway. Possibly, ETR-1 is involved in the production of a Wnt signal or a parallel signal by the body wall muscles that controls AQR and PQR neuronal migration. In humans, CELF1 is involved in a number of neuromuscular disorders. If the role of ETR-1/CELF1 is conserved, these disorders might also involve cell or neuronal migration. Finally, we describe a technique of amplicon sequencing to detect rare, cell-specific genome edits by CRISPR/Cas9 in vivo (CRISPR-seq) as an alternative to the T7E1 assay.
SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control:

We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends Sequential Action Control [1] to stochastic belief dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approach in an active sensing scenario and a model-based Bayesian reinforcement learning problem. In these challenging problems, we show that the algorithm significantly outperforms other existing solution techniques including approximate dynamic programming and local trajectory optimization.
Two machine indexing projects at the Catholic University of America

This paper describes an application of Keyword-in-Context/KWIC/Keyword-Out-of-Context/KWOC programs (IBM 1401-CR-02X) to indexing masters' theses and doctoral dissertations at the Catholic University of America. Two recent (1970) computer-produced indexes, one covering 5,458 masters' theses and doctoral dissertations accepted in all schools between 1961 and 1967, inclusive, and the other covering 975 masters' theses in nursing accepted between 1932 and 1961, are described. The machine configuration, programs, general system and costs are discussed.
Geometric progressions in syndetic sets

An open problem about finite geometric progressions in syndetic sets leads to a family of diophantine equations related to the commutativity of translation and multiplication by squares.
The FAST Algorithm for Submodular Maximization

In this paper we describe a new algorithm called Fast Adaptive Sequencing Technique (FAST) for maximizing a monotone submodular function under a cardinality constraint $k$ whose approximation ratio is arbitrarily close to $1-1/e$, is $O(\log(n) \log^2(\log k))$ adaptive, and uses a total of $O(n \log\log(k))$ queries. Recent algorithms have comparable guarantees in terms of asymptotic worst case analysis, but their actual number of rounds and query complexity depend on very large constants and polynomials in terms of precision and confidence, making them impractical for large data sets. Our main contribution is a design that is extremely efficient both in terms of its non-asymptotic worst case query complexity and number of rounds, and in terms of its practical runtime. We show that this algorithm outperforms any algorithm for submodular maximization we are aware of, including hyper-optimized parallel versions of state-of-the-art serial algorithms, by running experiments on large data sets. These experiments show FAST is orders of magnitude faster than the state-of-the-art.
Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset

Machine reading comprehension has made great progress in recent years owing to large-scale annotated datasets. In the clinical domain, however, creating such datasets is quite difficult due to the domain expertise required for annotation. Recently, Pampari et al. (EMNLP’18) tackled this issue by using expert-annotated question templates and existing i2b2 annotations to create emrQA, the first large-scale dataset for question answering (QA) based on clinical notes. In this paper, we provide an in-depth analysis of this dataset and the clinical reading comprehension (CliniRC) task. From our qualitative analysis, we find that (i) emrQA answers are often incomplete, and (ii) emrQA questions are often answerable without using domain knowledge. From our quantitative experiments, surprising results include that (iii) using a small sampled subset (5%-20%), we can obtain roughly equal performance compared to the model trained on the entire dataset, (iv) this performance is close to human expert’s performance, and (v) BERT models do not beat the best performing base model. Following our analysis of the emrQA, we further explore two desired aspects of CliniRC systems: the ability to utilize clinical domain knowledge and to generalize to unseen questions and contexts. We argue that both should be considered when creating future datasets.
Characterization of the Pareto social choice correspondence

Abstract Independent, necessary and sufficient conditions are derived for a social choice correspondence to be the one that selects exactly the Pareto optimal alternatives.
B Cell Depletion Eliminates FVIII Memory B Cells and Enhances AAV8-coF8 Immune Tolerance Induction When Combined With Rapamycin

Hemophilia A is an inherited coagulation disorder resulting in the loss of functional clotting factor VIII (FVIII). Presently, the most effective treatment is prophylactic protein replacement therapy. However, this requires frequent life-long intravenous infusions of plasma derived or recombinant clotting factors and is not a cure. A major complication is the development of inhibitory antibodies that nullify the replacement factor. Immune tolerance induction (ITI) therapy to reverse inhibitors can last from months to years, requires daily or every other day infusions of supraphysiological levels of FVIII and is effective in only up to 70% of hemophilia A patients. Preclinical and recent clinical studies have shown that gene replacement therapy with AAV vectors can effectively cure hemophilia A patients. However, it is unclear how hemophilia patients with high risk inhibitor F8 mutations or with established inhibitors will respond to gene therapy, as these patients have been excluded from ongoing clinical trials. AAV8-coF8 gene transfer in naive BALB/c-F8e16 -/Y mice (BALB/c-HA) results in anti-FVIII IgG1 inhibitors following gene transfer, which can be prevented by transient immune modulation with anti-mCD20 (18B12) and oral rapamycin. We investigated if we could improve ITI in inhibitor positive mice by combining anti-mCD20 and rapamycin with AAV8-coF8 gene therapy. Our hypothesis was that continuous expression of FVIII protein from gene transfer compared to transient FVIII from weekly protein therapy, would enhance regulatory T cell induction and promote deletion of FVIII reactive B cells, following reconstitution. Mice that received anti-CD20 had a sharp decline in inhibitors, which corresponded to FVIII memory B (Bmem) cell deletion. Importantly, only mice receiving both anti-mCD20 and rapamycin failed to increase inhibitors following rechallenge with intravenous FVIII protein therapy. Our data show that B and T cell immune modulation complements AAV8-coF8 gene therapy in naive and inhibitor positive hemophilia A mice and suggest that such protocols should be considered for AAV gene therapy in high risk or inhibitor positive hemophilia patients.
Single Nucleus RNAseq Profiling of Mouse Lung: Reduced Dissociation Bias and Improved Detection of Rare Cell Types Compared with Single Cell RNAseq

Abstract RATIONALE Single cell RNA-sequencing (scRNASeq) has led to multiple recent advances in our understanding of lung biology and pathophysiology, but utility is limited by the need for fresh samples, loss of cell types due to death or inadequate dissociation, and the induction of transcriptional stress responses during tissue digestion. Single nucleus RNASeq (snRNASeq) has addressed these deficiencies in some other tissues, but no protocol exists for lung. We sought to develop such a protocol and compare its results with scRNA-seq. METHODS Single nucleus suspensions were prepared rapidly (45 min) from two mouse lungs in lysis buffer on ice while a single cell suspension from an additional mouse lung was generated using a combination of enzymatic and mechanical dissociation (1.5 h). Cells and nuclei were processed using the 10x Genomics platform, and following sequencing of cDNA libraries single cell data was analyzed by Seurat. RESULTS 16,656 single nucleus and 11,934 single cell transcriptomes were generated. Despite reduced mRNA levels in nuclei vs. cells, gene detection rates were equivalent in snRNASeq and scRNASeq (∼1,750 genes and 3,000 UMI per cell) when mapping intronic and exonic reads. snRNASeq identified a much greater proportion of epithelial cells than scRNASeq (46% vs 2% of total), including basal and neuroendocrine cells, while reducing immune cells from 54% to 15%. snRNASeq transcripts are enriched for transcription factors and signaling proteins, with reduced detection of housekeeping genes, mitochondrial genes, and artifactual stress response genes. Both techniques improved mesenchymal cell detection over previous studies, and analysis of fibroblast diversity showed two transcriptionally distinct populations of Col13a1+ cells, termed Bmper+ and Brinp1+ fibroblasts. To define homeostatic signaling relationships among cell types, receptor-ligand mapping of was performed for alveolar compartment cells using snRNASeq data, revealing complex interplay among epithelial, mesenchymal, and capillary endothelial cells. CONCLUSION Single nucleus RNASeq can be readily applied to snap frozen, archival murine lung samples, improves dissociation bias, eliminates artifactual gene expression and provides similar gene detection compared to scRNASeq.
Distinct conformational states of SARS-CoV-2 spike protein

Intervention strategies are urgently needed to control the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The trimeric viral spike (S) protein catalyzes fusion between viral and target cell membranes to initiate infection. Here, we report two cryo-electron microscopy structures derived from a preparation of the full-length S protein, representing its prefusion (2.9-angstrom resolution) and postfusion (3.0-angstrom resolution) conformations, respectively. The spontaneous transition to the postfusion state is independent of target cells. The prefusion trimer has three receptor-binding domains clamped down by a segment adjacent to the fusion peptide. The postfusion structure is strategically decorated by N-linked glycans, suggesting possible protective roles against host immune responses and harsh external conditions. These findings advance our understanding of SARS-CoV-2 entry and may guide the development of vaccines and therapeutics.
Ingredients for successful badges: evidence from a field experiment in bike commuting

Despite the popularity of badges in gamification applications, there is a lack of research on how to design badges to increase target behaviour. Motivated by this gap, we conduct a large-scale fiel...
Secure GDoF of the Z-channel with Finite Precision CSIT: How Robust are Structured Codes?

Under the assumption of perfect channel state information at the transmitters (CSIT), it is known that structured codes offer significant advantages in an interference network, e.g., lattice alignment allows a receiver to directly decode the sum of aligned interfering codewords at higher power levels even though individual codewords are not resolvable, subtract the aggregate interference, and then proceed to decode desired codewords at lower power levels. To what extent are such benefits of structured codes fundamentally limited by uncertainty in CSIT? To answer this question, we explore what is perhaps the simplest setting where the question presents itself — a Z interference channel with secure communication. Using sum-set inequalities based on Aligned Images bounds we prove that the GDoF benefits of structured codes are lost completely under finite precision CSIT. The secure GDoF region of the Z interference channel is obtained as a byproduct of the analysis.
Microwave characteristics of Al x Ga 1-x N/In x Ga 1-x N/GaN-based HEMT using propagation delay model

A new GaN-based High Electron Mobility Transistor (HEMT) structure is proposed to study DC, RF and microwave characteristics using TCAD tool and propagation delay model. The proposed device is embedded into the circuit and its circuit characteristics are also studied and presented in this paper. Apart from DC and small-signal characteristics, we have investigated various microwave parameters such as Maximum Allowable Gain (MAG), Maximum Stable Gain (MSG), Rollet stability factor (K), Mason's Unilateral Gain (MUG), Cutoff frequency (f T ), Maximum frequency of oscillation (f max ), two port network parameters such as Input/Output reflection parameters (S 11 , S 22 ) followed by Smith chart. We have determined the range of frequency where proposed HEMT will be used as stable amplifier/oscillator. The cut-off frequency of such structure is found to be 32GHz and noteworthy.
The index of Lie poset algebras

Abstract We provide general closed-form formulas for the index of type-A Lie poset algebras corresponding to posets of restricted height. Furthermore, we provide a combinatorial recipe for constructing all posets corresponding to type-A Frobenius Lie poset algebras of heights zero, one, and two. A discrete Morse theory argument establishes that the simplicial realizations of such posets are contractible. It then follows, from a recent theorem of Coll and Gerstenhaber, that the second Lie cohomology group of the corresponding Lie poset algebra with coefficients in itself is zero. Consequently, such a Lie poset algebra is absolutely rigid and cannot be deformed. We also provide matrix representations for Lie poset algebras in the other classical types.
A scalable matrix-free spectral element approach for unsteady PDE constrained optimization using PETSc/TAO

Abstract We provide a new approach for the efficient matrix-free application of the transpose of the Jacobian for the spectral element method for the adjoint-based solution of partial differential equation (PDE) constrained optimization. This results in optimizations of nonlinear PDEs using explicit integrators where the integration of the adjoint problem is not more expensive than the forward simulation. Solving PDE constrained optimization problems entails combining expertise from multiple areas, including simulation, computation of derivatives, and optimization. The Portable, Extensible Toolkit for Scientific computation (PETSc) together with its companion package, the Toolkit for Advanced Optimization (TAO), is an integrated numerical software library that contains an algorithmic/software stack for solving linear systems, nonlinear systems, ordinary differential equations, differential algebraic equations, and large-scale optimization problems and, as such, is an ideal tool for performing PDE-constrained optimization. This paper describes an efficient approach in which the software stack provided by PETSc/TAO can be used for large-scale nonlinear time-dependent problems. Time integration can involve a range of high-order methods, both implicit and explicit. The PDE-constrained optimization algorithm used is gradient-based and seamlessly integrated with the simulation of the physical problem.
Growth‐Equity Trade‐offs in Structural Reforms

Do structural reforms that aim to boost potential output also change the distribution of income? We shed light on this question by looking at the broad patterns in the cross‐country data covering advanced, emerging‐market, and low‐income countries. Our main finding is that there is indeed evidence of a growth‐equity trade‐off for some important reforms. Financial and capital account liberalization seem to increase both growth and inequality, as do some measures of liberalization of current account transactions. Reforms aimed at strengthening the impartiality of and adherence to the legal system seem to entail no growth‐equity trade‐off—such reforms are good for growth and do not worsen inequality. The results for our index of network reforms as well as our measure of the decentralization of collective labor bargaining are the weakest and least robust, potentially due to data limitations. We also ask: If some structural reforms worsen inequality, to what degree does this offset the growth gains from the reforms themselves? While higher inequality does dampen the growth benefits, the net effect on growth remains positive for most reform indicators.
Are Regulators Effective at Unraveling Accounting Manipulation? Evidence from Public Utility Commissions

Prior research proposes that a monopolist with private information inflates its reported costs under rate regulation to extract an informational rent. Using a sample of U.S. electric utilities from...
Extending the Morphological Hit-or-Miss Transform to Deep Neural Networks

While most deep learning architectures are built on convolution, alternative foundations such as morphology are being explored for purposes such as interpretability and its connection to the analysis and processing of geometric structures. The morphological hit-or-miss operation has the advantage that it considers both foreground information and background information when evaluating the target shape in an image. In this article, we identify limitations in the existing hit-or-miss neural definitions and formulate an optimization problem to learn the transform relative to deeper architectures. To this end, we model the semantically important condition that the intersection of the hit and miss structuring elements (SEs) should be empty and present a way to express Don’t Care (DNC), which is important for denoting regions of an SE that are not relevant to detecting a target pattern. Our analysis shows that convolution, in fact, acts like a hit-to-miss transform through semantic interpretation of its filter differences. On these premises, we introduce an extension that outperforms conventional convolution on benchmark data. Quantitative experiments are provided on synthetic and benchmark data, showing that the direct encoding hit-or-miss transform provides better interpretability on learned shapes consistent with objects, whereas our morphologically inspired generalized convolution yields higher classification accuracy. Finally, qualitative hit and miss filter visualizations are provided relative to single morphological layer.
Development and use of a toolkit to facilitate implementation of an evidence-based intervention: a descriptive case study

Implementation of evidence-based clinical interventions in real-world settings becomes a futile effort when effective strategies to foster adoption are not used. A toolkit, or a collection of adaptable documents to inform and facilitate implementation, can increase the use of evidence-based interventions. Most available toolkits provide resources about the intervention but lack guidance for adaptation to different contexts or strategies to support implementation. This paper describes the development and use of a toolkit to guide the implementation of an evidence-based intervention to identify and intervene for people with risky substance use. A descriptive case study describes the development and use of a toolkit throughout a two-year study. Investigators and site coordinators from 14 acute care hospitals developed tools and engaged external stakeholders as they prepared for implementation, integrated the clinical intervention into practice, and reflected on implementation. The final toolkit included 54 different tools selected or created to define the intervention, engage and communicate with stakeholders, assess for readiness and plan for implementation, train clinical nurses and other stakeholders, evaluate training and implementation effectiveness, create policies and procedures for different contexts, and identify opportunities for reimbursement. Each tool corresponds to one or more implementation strategies. The approach used to develop this implementation toolkit may be used to create resources for the implementation of other evidence-based interventions.
Improving Human Performance Using Mixed Granularity of Control in Multi-Human Multi-Robot Interaction

Due to the potentially large number of units involved, the interaction with a multi-robot system is likely to exceed the limits of the span of apprehension of any individual human operator. In previous work, we studied how this issue can be tackled by interacting with the robots in two modalities — environment-oriented and robot-oriented. In this paper, we study how this concept can be applied to the case in which multiple human operators perform supervisory control on a multirobot system. While the presence of extra operators suggests that more complex tasks could be accomplished, little research exists on how this could be achieved efficiently. In particular, one challenge arises — the out-of-the-loop performance problem caused by a lack of engagement in the task, awareness of its state, and trust in the system and in the other operators. Through a user study involving 28 human operators and 8 real robots, we study how the concept of mixed granularity in multi-human multi-robot interaction affects user engagement, awareness, and trust while balancing the workload between multiple operators.
Impact of prior and projected climate change on US Lyme disease incidence

Lyme disease is the most common vector-borne disease in temperate zones and a growing public health threat in the United States (US). The life cycles of the tick vectors and spirochete pathogen are highly sensitive to climate, but determining the impact of climate change on Lyme disease burden has been challenging due to the complex ecology of the disease and the presence of multiple, interacting drivers of transmission. Here we incorporated 18 years of annual, county-level Lyme disease case data in a panel data statistical model to investigate prior effects of climate variation on disease incidence while controlling for other putative drivers. We then used these climate-disease relationships to project Lyme disease cases using CMIP5 global climate models and two potential climate scenarios (RCP4.5 and RCP8.5). We find that interannual variation in Lyme disease incidence is associated with climate variation in all US regions encompassing the range of the primary vector species. In all regions, the climate predictors explained less of the variation in Lyme disease incidence than unobserved county-level heterogeneity, but the strongest climate-disease association detected was between warming annual temperatures and increasing incidence in the Northeast. Lyme disease projections indicate that cases in the Northeast will increase significantly by 2050 (23,619 ± 21,607 additional cases), but only under RCP8.5, and with large uncertainty around this projected increase. Significant case changes are not projected for any other region under either climate scenario. The results demonstrate a regionally variable and nuanced relationship between climate change and Lyme disease, indicating possible nonlinear responses of vector ticks and transmission dynamics to projected climate change. Moreover, our results highlight the need for improved preparedness and public health interventions in endemic regions to minimize the impact of further climate change-induced increases in Lyme disease burden.
Yielding and bifurcated aging in nanofibrillar networks

Although phenomenologically simple to define, the yield stress is often difficult to quantify unambiguously in practice, especially for thixotropic materials with complex shear histories. Here, we identify a stress-controlled bifurcation in the yielding response of cellulose nanofibril gels, which we show can rigorously localize the yield stress in disordered materials with time-dependent behavior. After an initial yielding event, the fibrillar networks subsequently yield faster and at lower magnitudes of stress. For low stresses, the time to yielding increases with waiting time ${t}_{w}$ and diverges once the network has restored sufficient entanglement density to support the stress. For higher stresses, the yield time instead plateaus at a finite value because the developed network density is insufficient to support the applied stress. We quantitatively relate the yielding and aging behavior of the network to the competition between stress-induced disentanglement and dynamic fluctuations of the fibrils rebuilding the network. The critical stress ${\ensuremath{\sigma}}_{c}$ that bifurcates the response of the material between these two states identifies the intrinsic yield stress in these disordered materials, independent of aging, thixotropic effects, or shear history.
Classifying 5d SCFTs via 6d SCFTs: rank one

Following a recent proposal, we delineate a general procedure to classify 5d SCFTs via compactifications of 6d SCFTs on a circle (possibly with a twist by a discrete global symmetry). The path from 6d SCFTs to 5d SCFTs can be divided into two steps. The first step involves computing the Coulomb branch data of the 5d KK theory obtained by compactifying a 6d SCFT on a circle of finite radius. The second step involves computing the limit of the KK theory when the inverse radius along with some other mass parameters is sent to infinity. Under this RG flow, the KK theory reduces to a 5d SCFT. We illustrate these ideas in the case of untwisted compactifications of rank one 6d SCFTs that can be constructed in F-theory without frozen singularities. The data of the corresponding KK theory can be packaged in the geometry of a Calabi-Yau threefold that we explicitly compute for every case. The RG flows correspond to flopping a collection of curves in the threefold and we formulate a concrete set of criteria which can be used to determine which collection of curves can induce the relevant RG flows, and, in principle, to determine the Calabi-Yau geometries describing the endpoints of these flows. We also comment on how to generalize our results to arbitrary rank.
Exploiting DRAM Latency Variations for Generating True Random Numbers

True random number generator (TRNG) plays a vital role in a variety of security applications and protocols. The security and privacy of an asset rely on the encryption, which solely depends on the quality of random numbers. Memory chips are widely used for generating random numbers because of their prevalence in modern electronic systems. Unfortunately, existing Dynamic Random-access Memory (DRAM)-based TRNGs produce random numbers with either limited entropy or poor throughput. In this paper, we propose a DRAM-latency based TRNG that generates high-quality random numbers. The silicon results from Samsung and Micron DDR3 DRAM modules show that our proposed DRAM-latency based TRNG is robust (against different operating conditions and environmental variations) and acceptably fast.
THE CHEMICAL COMPOSITION OF PRAESEPE (M44)

Star clusters have long been used to illuminate both stellar evolution and Galactic evolution. They also hold clues to the chemical and nucleosynthetic processes throughout the history of the Galaxy. We have taken high signal-to-noise (S/N), high-resolution spectra of 11 solar-type stars in the Praesepe open cluster to determine the chemical abundances of 16 elements: Li, C, O, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Fe, Ni, Y, and Ba. We have determined Fe from Fe I and Fe II lines and find [Fe/H] = +0.12 ±0.04. We find that Li decreases with temperature due to increasing Li depletion in cooler stars; it matches the Li-temperature pattern found in the Hyades. The [C/Fe] and [O/Fe] abundances are below solar and lower than the field star samples due to the younger age of Praesepe (0.7 Gyr) than the field stars. The alpha-elements, Mg, Si, Ca, and Ti, have solar ratios with respect to Fe, and are also lower than the field star samples. The Fe-peak elements, Cr and Ni, track Fe and have solar values. The neutron capture element [Y/Fe] is found to be solar, but [Ba/Fe] is enhanced relative to solar and to the field stars. Three Praesepe giants were studied by Carrera and Pancino; they are apparently enhanced in Na, Mg, and Ba relative to the Praesepe dwarfs. The Na enhancement may indicate proton-capture nucleosynthesis in the Ne → Na cycling with dredge-up into the atmospheres of the red giants.
Nonlinear stability and ergodicity of ensemble based Kalman filters

The ensemble Kalman filter (EnKF) and ensemble square root filter (ESRF) are data assimilation methods used to combine high dimensional, nonlinear dynamical models with observed data. Despite their widespread usage in climate science and oil reservoir simulation, very little is known about the long-time behavior of these methods and why they are effective when applied with modest ensemble sizes in large dimensional turbulent dynamical systems. By following the basic principles of energy dissipation and controllability of filters, this paper establishes a simple, systematic and rigorous framework for the nonlinear analysis of EnKF and ESRF with arbitrary ensemble size, focusing on the dynamical properties of boundedness and geometric ergodicity. The time uniform boundedness guarantees that the filter estimate will not diverge to machine infinity in finite time, which is a potential threat for EnKF and ESQF known as the catastrophic filter divergence. Geometric ergodicity ensures in addition that the filter has a unique invariant measure and that initialization errors will dissipate exponentially in time. We establish these results by introducing a natural notion of observable energy dissipation. The time uniform bound is achieved through a simple Lyapunov function argument, this result applies to systems with complete observations and strong kinetic energy dissipation, but also to concrete examples with incomplete observations. With the Lyapunov function argument established, the geometric ergodicity is obtained by verifying the controllability of the filter processes; in particular, such analysis for ESQF relies on a careful multivariate perturbation analysis of the covariance eigen-structure.
Progress on perfect lattice actions for QCD

Abstract We describe a number of aspects in our attempt to construct an approximately perfect lattice action for QCD. Free quarks are made optimally local on the whole renormalized trajectory and their couplings are then truncated by imposing 3-periodicity. The spectra of these short ranged fermions are excellent approximations to continuum spectra. The same is true for free gluons. We evaluate the corresponding perfect quark-gluon vertex function, identifying in particular the “perfect clover term”. First simulations for heavy quarks show that the mass is strongly renormalized, but again the renormalized theory agrees very well with continuum physics. Furthermore we describe the multigrid formulation for the non-perturbative perfect action and we present the concept of an exactly (quantum) perfect topological charge on the lattice.
Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $k$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial $\mathrm{Sr}\mathrm{Ti}{\mathrm{O}}_{3}$ growths, heteroepitaxial $\mathrm{Sr}\mathrm{Ti}{\mathrm{O}}_{3}$ grown on scandate substrates, $\mathrm{Ba}\mathrm{Sn}{\mathrm{O}}_{3}$ films grown on $\mathrm{Sr}\mathrm{Ti}{\mathrm{O}}_{3}$ substrates, and ${\mathrm{LaNiO}}_{3}$ films grown on $\mathrm{Sr}\mathrm{Ti}{\mathrm{O}}_{3}$ substrates. This analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities

Humans can ground natural language commands to tasks at both abstract and fine-grained levels of specificity. For instance, a human forklift operator can be instructed to perform a high-level action, like "grab a pallet" or a low-level action like "tilt back a little bit." While robots are also capable of grounding language commands to tasks, previous methods implicitly assume that all commands and tasks reside at a single, fixed level of abstraction. Additionally, methods that do not use multiple levels of abstraction encounter inefficient planning and execution times as they solve tasks at a single level of abstraction with large, intractable state-action spaces closely resembling real world complexity. In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular. We show that the accuracy of the grounding procedure is improved when simultaneously inferring the degree of abstraction in language used to communicate the task. Leveraging hierarchy also improves efficiency: our proposed approach enables a robot to respond to a command within one second on 90% of our tasks, while baselines take over twenty seconds on half the tasks. Finally, we demonstrate that a real, physical robot can ground commands at multiple levels of abstraction allowing it to efficiently plan different subtasks within the same planning hierarchy.
Inversion of adjunction for rational and Du Bois pairs

We prove several results about the behavior of Du Bois singularities and Du Bois pairs in families. Some of these generalize existing statements about Du Bois singularities to the pair setting while others are new even in the non-pair setting. We also prove a new inversion of adjunction result for Du Bois and rational pairs. In the non-pair setting this asserts that if a family over a smooth base has a special fiber X0 with Du Bois singularities and the general fiber has rational singularities, then the total space has rational singularities near X0.
Identification of long-lived charged particles using time-of-flight systems at the upgraded LHC detectors

We study the impact of precision timing detection systems on the LHC experiments’ long-lived particle search program during the HL-LHC era. We develop algorithms that allow us to reconstruct the mass of such charged particles and perform particle identification using the time-of-flight measurement. We investigate the reach for benchmark scenarios as a function of the timing resolution, and find sensitivity improvement of up to a factor of ten over searches that use ionization energy loss information, depending on the particle’s mass.